https://cpw.cvlcollections.org/files/original/e42a9d55906409150beb289db16b230d.pdf f40f1b1bb64e3108228d7a501a3e6a1a PDF Text Text Colorado Division of Wildlife ._,, July 2006 - June 2007 WILDLIFE RESEARCH REPORT State of_ _ _ _ _ _=C=o..,,,lo=r=ad=o"---~---: ""'DC!c.iv!..a!i~s1!-"·o""n'--'o,. , f__,WC!..!.!il., ,_... dli£..,,e'--_ _ _ _ _ _ _ __ Cost Center 3430 : ""'M""a..,mm=""-a:::lsc.. R""e""s:.:::ce::ears.=c""h_ _ __ __ _ _ __ Work Package 3001 : ~D'-=e~er:.....C=o=ns,,__,e=rv..:...;a=t=io=n<----------Task No. 6 : Population Performance of Piceance Basin Mule : Deer in Response to Natural Gas Resource : Extraction and Mitigation Efforts to Address : Human Activity and Habitat Degradation Federal Aid Project: __W-'-'---~18=-=5'-'-R~----Period Covered: July 1, 2006 - June 30, 2007 Authors: C. R. Anderson, and D. J. Freddy Personnel: M. Alldredge, E. Bergman, C. Bishop, R. Kahn, P. Lukacs, T. Remington, M. Schuette; G. White, Colorado State University; H. Sawyer, Western Ecosystems Technology, Inc. ALI information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT A researcher FTE vacancy was filled with a newly hired person in December 2006 who became the project leader for this project. No preliminary field work could be completed in winter-spring 2007 as originally planned for FY2006-07 but assessments of potential study areas, resource inventory maps, and tentative study plan outlines were completed by June 2007. As such, field work for this project will begin winter 2007 and be centered in the Piceance Basin area of northwestern Colorado which is currently undergoing intensive natural gas development in one of the most extensive and important mule deer winter and transition range areas within the state. Our approach will be to experimentally evaluate habitat -treatments that may rehabilitate the landscape to benefit mule deer and to evaluate human-activity management alternatives to reduce the disturbance impacts on mule deer. This project will require a longterm commitment ofat least 10-years from private industry, the BLM, and the CDOW to assess if sustainable mule deer populations can persist within a highly disturbed landscape following implementation of beneficial habitat treatments and development practices. 103 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGREDATION CHARLES R. ANDERSON AND DAVID J. FREDDY P. N. OBJECTIVE To develop approaches to provide for energy extraction in a manner that maintains viable mule deer populations for future recreational and ecological purposes. SEGMENT OBJECTIVES I. Consult with regional personnel to select potential study sites for addressing habitat mitigation and energy development practices that benefit mule deer. 2. Plot historic and current energy development activities to assess potential treatment and control sites for experimental evaluation. 3. Develop draft study proposals for peer review and funiling solicitation. INTRODUCTION Extraction of natural gas from areas throughout western Colorado bas raised concerns among many public stakeholders and the Colorado Division of Wildlife that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Research evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. Our primary goal of this study is to develop approaches to provide for energy extraction in a manner that maintains viable mule deer populations for future recreational and ecological purposes. This may be accomplished by restoring or enhancing habitat conditions on or adjacent to disturbed sites and by modifying development practices. Due to the extensive energy development that is projected to occur over the next 20 years throughout much of the mule deer winter range in the northern Rocky Mountains of the western US, innovative approaches to energy development and mitigation methods are essential to sustain viable mule deer populations in the region. Impacts from development and conversely success of mitigation efforts are often assumed but rarely demonstrated, and these assumptions can only be confirmed by application of well designed research efforts conducted over sufficiently long time periods to measure responses. This project proposes to identify habitat mitigation and energy development approaches that sustain mule deer survival and recruitment during and after habitat disturbance from development activities. This effott will require coordination and cooperation between Colorado Division of Wildlife and the major energy companies. We anticipate this pa1tnersbip will benefit mule deer populations and foster the evolution of wildlife management and energy development practices that are compatible with other wildlife and human values associated with maintaining functional ecosystems over the long term. 104 �STUDY AREA The proposed study sites represent 6 segments of mule deer winter range in the Piceance Basin, southwest of Meeker, Colorado (Figure 1), and the primary energy companies developing these areas include Encana and Exxon-Mobile (Figure 2). Because of the varying levels of development and deer densities relative to differing winter population segments in the Piceance Basin, different experimental units (i.e., mule deer winter ranges) are uniquely suited for addressing different questions. Experimental designs monitoring mule deer responses to treatment (e.g., habitat mitigation) and control areas are necessary to differentiate cause-effect relationships from development versus environmental factors. Suitable control areas require that little or no previous development has occurred and that no development occurs during the experimental time frame. Ideally, both temporal and spatial control areas would be monitored to make valid comparisons to developed and subsequently mitigated sites; temporal controls provide measures of natural variability in mule deer population parameters over time and spatial controls provide measures of variability due to differences in geography. Once spatial and temporal variation is accounted for, inferences can be made relative to development disturbance or mitigation effects on mule deer. The North Ridge, Story/Willow Creek, and Yellow Creek deer population segment areas (Figure 1) currently exhibit little to no development, but it is currently unknown whether or not these areas will be developed in the future; there is potential for future oil shale development in the Story/Willow Creek and Yellow Creek deer areas. North Ridge appears least likely to be developed because it is outside of the current oil shale lease area and only a few wells have historically been drilled on or adjacent to the area, whereas the same cannot be said of the Story/Sprague or Yellow Creek areas. Thus, North ~dge would appear best suited as a temporal control site for comparison to other developed winter ranges within the Piceance Basin and may also serve as a geographic control for the Crooked Wash deer population segment located immediately north and adjacent to the Piceance Basin. The Story/Willow Creek and Yellow Creek deer may provide spatial controls for the Magnolia and Ryan Gulch deer population segments, respectively, but future development potential in these areas is unknown. If these areas become devel~ped in the future (either for oil shale or natural gas), they would provide BACI (Before-AfterControl-Impact) type comparisons strengthening our inference of development impacts on mule deer population performance. Magnolia, Crooked Wash, and Ryan Gulch deer areas have historically received relatively high development activity and currently exhibit moderate-high development, and appear likely to be developed extensively in the future based on the gas development layers currently available (Colorado Oil and Gas Conservation Commission). Pretreatment data in these areas will be represented by parameters associated with developed sites and the measured response will be in the form of habitat treatments and/or differing development practices, which will be measured in comparison to the control sites. We propose including 3 control sites (1 temporal/spatial control and 2 spatial controls) and 3 treatment sites to investigate mule deer response to habitat and/or development treatments (e.g., directional versus non-directional drilling, piping versus trucking condensate, etc.) across a range of deer densities (Table 1). We would strive to split high intensity extraction study sites into 2 halves with one half serving as the 'control' [standard development] and one half serving as the 'treatment' [improved development approach or improved habitat]. The above scenario addresses the potential for establishing control and treatment sites for evaluating mule deer population response to habitat treatments and/or development treatments, and may allow larger scale mule deer responses from energy development to be addressed by comparing control site parameters to developed site parameters; smaller scale inference would require collection of pretreatment data at developed sites (e.g., similar to mitigation treatments in the proposed design) and may not be possible unless the Yellow Creek or Story/Willow Creek areas are developed in the future. Modified versions of the proposed design could be implemented depending on 105 �the level offunding available and the degree to which industry willing to collaborate with this effort. We consider 3 study sites, likely North Ridge, Magnolia, and Crooked Wash, as the minimum number of study sites necessary to adequately address the objectives of this project; the additional proposed study areas will allow increased flexibility in the questions that are addressed and increase our inference relative to mule deer responses to habitat treatments and modifications of development practices. Furthermore, if we are not able to evaluate potential mitigating industrial operation and/or habitat improvements, this study would likely only have the potential to document negative impacts of intense energy extraction practices on mule deer. V RESPONSE VARIABLES To allow for competing hypotheses in regards to potential development and mitigation effects, 4 primary response variables will be measured including (1) overwinter fawn survival, (2) deer density, (3) habitat use patterns, and (4) adult female body condition. (1) To determine if mitigation and/or development treatments elicit a chronic survival response with a long-term population level effect, we will measure over-winter fawn survival in all experimental units. Based on past research (White and Bartmann 1998), treatment effects of 15% change in survival appear biologically significant. (2) To determine if habitat treatments or development practices elicit a brief survival response with a long-term population level effect, we will estimate deer density to determine if there is a difference in carrying capacity between treatment and control experimental units. Because mule deer may respond to development or mitigation at variable rates, we may not be able to detect differences in fawn survival, but estimating deer density will still allow us to determine if development or mitigation efforts have a population level effect. (3) To determine if habitat treatments or development practices elicit a shift in habitat use patterns, we will examine changes in Resource Selection Probability Functions (RSPF; Sawyer et al. 2006) pre- and post-habitat treatments, between areas exhibiting development practices, and compare RSPFs between developed and non-developed sites. We will infer population level impacts if fawn survival and/or deer densities differ relative to changes or differences in habitat use patterns. (4) To determine if adult female mule deer respond positively to habitat treatments and/or· changes in development practices, percent body fat and loin depth will be measured annually during late winter (Bishop et al. 2005, Bergman et al. 2005). We would expect a relatively rapid response in body condition following habitat or development treatments, indicating that treatments are having the intended positive effect. PROJECT EXPENSES Estimating fawn survival, deer density, deer behavioral responses, female body condition, and implementing small scale habitat improvements are costly endeavors involving the purchase of numerous standard VHF radio-collars, specialized GPS radio-collars, helicopter flight hours for deer capture/collaring and aerial surveys, machinery to physically alter the habitat, and personnel to adequately perform day-to-day data collection. If large scale habitat treatments are needed or desired, funding in addition to the estimates below will be required as habitat treatments cost $300 to $1,000/acre depending on the most appropriate treatment for a locale. Minimum cost estimates to design, implement, and evaluate responses of mule deer to habitat mitigation options range form $580,500 to $1,161,00 (most preferred design) per year depending on project design (Table 2). 106 u �LITERATURE CITED BISHOP, C. J., G. C. WHITE, D. J. FREDDY, and B. E. WATKINS. 2005. Effect of nutrition on mule deer recruitment and survival rates. Wildlife Research Report, Colorado Division of Wildlife, Fort Collins. USA. BERGMAN, E. J., C. J. BISHOP, D. J. FREDDY, and G. C. WHITE. 2005. Evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer. Study Plan, Colorado Division of Wildlife, Fort Collins, USA. SAWYER, H., R. M. NIELSON, F. LINDZEY, and L. L. MCDONALD. 2006. Winter habitat selection of mule deer before and during development of a natural gas field. Journal of Wildlife Management 70:396-403. WHITE, G. C., and R. M. BARTMANN. 1998. Effect of density reduction on overwinter survival of freeranging mule deer fawns. Journal of Wildlife Management 62:214-225. Prepared by _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Wildlife Researcher Table 1. Relative density of natural gas wells and mule deer and experimental designation for potential study sites iri the Piceance Basin, Colorado, for addressing mule deer response to natural gas development practices and habitat mitigation. Relative density Experimental Study area Inactive wells Active wells Mule deer designation North Ridge Very low None High Temporal/spatial control Crooked Wash High High High Treatment Story/Willow Creek Low Low Moderate Spatial control Magnolia High High Moderate Treatment Yellow Creek Moderate Low Low Spatial control Ryan Gulch High Moderate Low Treatment ~ 107 �Table 2. Estimated costs for CDOW to conduct desired mule deer research in the Piceance Basin to assess impacts of natural gas extraction on mule deer and evaluate approaches to mitigate habitat impacts. Project should be conducted for 10 years to allow for adequate time to measure biological responses, 2008-2018. Cost Estimates Per Year Per Study Site (2008 dollars) Piceance Basin Mule Deer Research Telemetry collars & equipment $70,000 Helicopter Deer Capture & Surveys $68,500 Other Field Operations & Equipment $15,000 12 months TFTE Personnel {Tech I) $30,000 Vehicle Yearly Lease Plus Mileage (4x4 PU, & 45,000miles) Total cost Per Study Site/Yr Minimum Study One Control Site & Two Treatment Sites Acceptable Two Control Sites & Two Treatment Sites Best Three Control Sites & Three Treatment Sites Cost Per Year (2008 dollars) Cost Per Year (2008 dollars) Cost Per Year (2008 dollars) $580,500 $774,00 $1,161,000 $20,000 $193,500 108 V �Figure l. Proposed mule deer study sites relative to natural gas development in the Piceance Basin, Colorado, July 2007. Piceance Basin Research Areas + Inactive Wells + Drilling Permits • A.:ti,·c \\-d i~ ® ln.:acti,·t \\',:lb D Con1n1I Study - _ .-\n!:a Pmr,rnt<d Smdr .-\n::t.~ 0 ,1 & G,s FielJs C )Oil & Gas &sin, ---===""'""'"' 10 109 �Figure 2. Proposed mule deer study sites relative to the primary energy companies controlling natural gas leases in the Piccance Basin, Colorado, July 2007. Piceance Basin Research Areas 1111 C11,.•s:ipc:ikc Lc:isl.' 1111 Chc\Ton Lca.~e ~ Enc.inn L:nsc 1111 EXXClll 1..i:asc 1111 ~loh1h: lc:alic ~ \\'illi;uns Lc:asc Do,, & °'" Bosms 110 �-. ..J -- -- ...,,I --- ----- _., --- ..,,, . ..,I w -. _'J --_,_, -- Colorado Division of Wildlife July 2007 - June 2008 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No.: Federal Aid Proj ect No. Division of Wildli fe Mammals Research Deer Conservation Population Pe1formance o f Piccance Basin Mule Deer in Response to N atural Gas Resource Extraction and M itigation Efforts to Address 1-luman Activity and Habitat Degradation - Stage 1, Objective 5; Patterns o f Mule Deer Distribution and Movements Colorado 3430 300 1 6 W-1 85-R Pe riod Covered: Jul y I , 2007 - June 30, 2008 Authors: C. R. Anderson and D. J. Freddy Personnel : J. Brodetick. B. de Vergie, D. Finley, L. Gcpfc.rt, C. Harty, K. Kaai, L. Kelly, S. Lockwood, R. Velarde, C DOW; R. Swisher, Quicksil ver Air, Inc. Project support received from Fede ra l Aid in Wildlife Restoration, Colorado M ule Deer Association, Colorado Oi l and Gas Conservation Commission, Williams Production LMT Co., EnCana Corp .. and Shell Petrole um. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR Q UOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. A BSTRACT We propose to experimenta lly eva luate habitat treatments that may rehabilitate the landscape to bene fi t mule deer (Odocoi!eus he111ion11s) and to eva luate human-acti vity management alternati ves to reduce the d isturbance of energy development impacts on mule deer. The Piccancc Basin o f northwestern Colorado was selected as the proj ect area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas within the state. Assessments o f potential study areas, resource inventory maps, and tentati ve study plan outlines were presented to potent ial agency and industry cooperators. Suffic ient fund ing was secured to initiate a pilot study allowing refi nement of study area selection based on distribution of GPS collared deer, address logistics of deer captures and collaring efforts, and begin addressing one of the six proposed objecti ves by monjtoring deer movements from G PS localions in 5 study areas representing varying levels of e nergy development. We attached GPS collars collecting 5 fixes/day lo 75 ad ult female mule deer ( 15/study area) in January, 2008 to document deer movements and habitat use patterns among 5 deer wi nter ranges exposed to varyi ng levels of energy development. Over-winter survival of adult females was 90% (64 of 7 1) and typical for adult female mule deer in the western US. Data analyses of mule deer habitat use patterns will begin once GPS collars arc recovered in February, 2009. These data w ill provide deer beha vior information under ex,isting cond itions and serve as pre-treatment comparisons 10 future 63 �"""" i,,,,,.,I conditions fo llowing habitat treatments and/or improved development practices. Additional funding has become avai lable to initiate the full study proposal (see Appendix [) beginning November 2008, which will provide for evaluation of changes in body condition, fawn sw·vival, and deer densities relative to improved habitat treatments and energy development practices. This project will require additional funding commitments and cooperative agreements beyond spring 20 IOfrom private industry, the BLM, and the CDOW to assess if sustainable mule deer populations can persist within a highly disturbed landscape following implementation of beneficial habitat treatments and development practices. ...... """ \.iii """" "-iii ....., - ._./ \-iii i._..J \..ii 'l....il ....,,; ......, ~ ~ ..., """"" ""-' \=ii 64 ""'"' "--ii �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION ...., STAGE I, OBJECTIVE 5: PATTERNS OF MULE DEER DISTRIBUTION & MOVEMENTS ..,.; CHARLES R. ANDERSON, JR. AND DAVID J. FREDDY ...., ..... -- P. N. OBJECTIVES l. To determine experimentally whether enhancing mule deer habitat conditions on winter and/or transition range elicits behavioral responses, improves body condition, increases overwinter fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, over-winter fawn survival, and winter range mule deer densities. -.I SEGMENT OBJECTIVES 3. Assess the logistics capturing and collaring mule deer via helicopter net-gunning in 5 winter herd segments of the Piceance Basin, Colorado. 4. Improve delineation and identify degree of separation of winter range study sites based on deer distribution and movements from GPS collars collecting 5 fixes/day. 5. Monitor survival of adult female mule deer by daily ground tracking and bi-weekly aerial tracking. 6. Summarize data and present information in an annual Job Progress Report. INTRODUCTION .... .... .... Anderson and Freddy (2007) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance . Much of the rationale for conducting the long-term research is presented in Appendix I. However, this progress report, beginning as of January 2008, focuses only on Objective 5 of the research proposal (Appendix I): monitoring distribution, movements, and habitat selection patterns of adult female mule deer on 5 potential segments of winter range in relation to varying levels of natural gas development, experimental modifications in energy developmental practices, and potential habitat improvement treatments. Long-term funding and support had not been secured to simultaneously address all 6 proposed study objectives on 5 potential winter range segments, but preliminary funding and support had been established to begin to address mule deer movement patterns relative to current natural gas development activities in the Piceance Basin. This initial effort during FY07-08 provided key information to I) document movement patterns and degree of spatial separation of deer among potential experimental control and treatment sites, 2) help refine study area boundaries, 3) begin documenting deer spatial use in proposed experimental control and treatment areas prior to implementing habitat or development improvements, and 4) provide an assessment of deer capture logistics and operational success of improved versions of GPS and VHF radio-telemetry collars. Monitoring spatial use patterns of 65 �deer is planned for at least 5 years as part of the forthcoming major study so that this first year of data acquisition establishes the foundation for long-term data acquisition process. Once longer term financial and administrative commitments have been established, we will incorporate the additional objectives into a revised study plan to achieve our overall goal of developing approaches to provide for energy extraction in a manner that maintains viable mule deer populations for future recreational and ecological purposes. We recently acquired the necessary funding to allow for the complete study proposal to be initiated by fall 2008 and continue through spring 20 I 0. STUDY AREA The Piceance Basin in northwest Colorado was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and also exhibits one of the highest natural gas reserves in North America (Fig. l ). Historically, mule deer numbers on winter range were estimated between 15,000-22,000 (Bartmann 1975), and the current number of well pads (Appendix I: Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 400 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)~Utah juniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1675 m to 2285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry Symphoricarpos oreophilus), and rabbitbrush (Crysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Boute/oua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Unum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture coming from spring snow melt. In our initial proposal, we outlined 6 potential study sites exhibiting varying winter deer densities and varying levels of energy development activity to provide control and treatment experimental units for evaluating improved habitat and development treatments (Appendix I: Table I, Fig. 2). Ultimately, I of the 6 proposed study sites was omitted partly due to funding limitations and ultimately because the omitted area (Crooked Wash) offered limited opportunity to examine habitat improvements due to dry moisture conditions inhibiting success of habitat treatments and future energy development in the area appeared unlikely due to extensive previous development precluding evaluations of improved development practices. The remaining 5 areas were maintained and North Ridge will serve as a temporal control area offering evaluations of annual variation in parameter estimates due to non-development factors from an undeveloped area, and Story/Sprague Gulch (formerly referred to as Story/Willow Creek) and Yellow Creek will serve as spatial control areas to the 2 treatment areas (Magnolia and Ryan Gulch, respectively), providing spatial comparisons from geographically and vegetatively similar areas exposed to minor levels of energy development compared to extensively developed areas receiving improved habitat and/or development treatments. Because the progression and extent of energy development in the future is currently unknown (to CDOW, at least), North Ridge may also serve as a spatial control area to Magnolia or possibly Ryan Gulch should the Story/Sprague Gulch or Yellow Creek study areas become developed in the future. 66 �METHODS -...I --..I -...., - -..I 1,-/ .... Tasks addressed this fiscal year included deer capture and collaring efforts, monitoring adult female mule deer survival, and downloading and plotting GPS location data monthly from a segment of the sample fitted with downloadable GPS collars (24 of 75 deer total). We employed helicopter netgunning techniques (Barrett et al. 1982, van Reenen 1982) to capture 15 adult female mule deer in each of 5 study areas (75 deer total). Once netted, deer were hobbled, blind folded, fitted with GPS collars, and released. Five deer in 4 of the 5 study areas and 4 deer in the Yellow Creek study area were fitted with remotely downloadable GPS collars (GPS-4400S; Lotek Wireless, Newmarket, Ontario, Canada) and the remaining deer in each area were fitted with store-on-board GPS collars (G2 I l OB; Advanced Telemetry Systems, Isanti, MN, USA). To insure GPS fixes for at least l year, both collar types were programmed to attempt a· fix every 5 hours and the fix schedule for store-on-board collars was reduced to attempt a fix every 23 hours July-October. Mule deer mortality monitoring consisted of ground tracking deer daily and aerial monitoring deer approximately every 2 weeks from fixed-wing aircraft. Once a mortality signal was detected, deer were located and necropsied to attempt determination of cause of death. We collected GPS locations from the 24 downloadable collars monthly via ground tracking, if possible, or using fixedwing aircraft. RES ULTS AND DISSCUSSION Deer Captures We captured and GPS collared 4 yearling and 71 adult female mule deer ( 15 deer/study area) from January 10-12, 2008 (Fig. 2). No significant injuries were noted during captures. In planning future capture efforts for adult female mule deer, we will anticipate about 25 captures/day/helicopter. Deer Mortalities We identified l yearling and 7 adult female mule deer mortalities from January-June, 2008 (Table l ). Although winter severity was relatively high this past winter, adult female survival (90%, n = 71) was typical of mule deer populations under normal winter conditions in the western US (Unsworth et al. 1999). Cause of mortality was determined for 4 of the 8 mortalities documented and varied between coyote predation, malnutrition, and vehicle collision (Table l ). Although the other 4 mortalities were undetermined due to timing of carcass inspection, winter severity was likely a factor given 3 of the 4 mortalities occurred during late May (Table I). GPS Data Collection and Deer Distribution GPS data downloads and collars retrieved from mortalities suggested collars were generally functioning as expected, but a few issues were noted that may warrant future attention. GPS location acquisition rates were high (>90%) for all collars except I where intermittent acquisition failures were common (Lotek GPS_4400S; 58% acquisition rate). The single collar exhibiting a low acquisition rate is acceptable relative to the 31 other collars exhibiting high acquisition rates, but the malfunctioning collar will be returned for evaluation once retrieved to potentially enhance collar performance in the future. We noted that false mortality signals (a mortality signal for an active deer) occurred for short durations ( I to a few days) on several occasions during winter monitoring, and we will increase the inactive time period to activate the mortality switch from 4 to 8 hours for future collar orders to try to address this problem. In addition, consultation with collar manufacturers will be conducted in an attempt to address the problem of inactive mortality signals occurring while deer are active. Another, more significant problem was noted when we unsuccessfully attempted to remotely detonate drop-off mechanisms on a few occasions (Lotek collars). The 20 Lotek collars currently in use will require remote detonation for retrieval in February, 2009, but the apparent unreliability of this device may require additional efforts to successfully retrieve the collars. We should consider the feasibility of using helicopter net-gunning to retrieve Lotck collars 67 �during capture e~forts scheduled for late February, 2009, assuming attempts to remotely detonate drop-off mechanisms fail. Monthly downloads and collars retrieved from mortalities yielded GPS movement and distribution data from 32 individuals during winter (Fig. 3), 28 during the spring transition period (Fig. 4), and 24 during early summer (Fig. 5). Observed winter deer distribution (Fig. 3) reasonably followed apriori expectations (Appendix I; Fig. 2) with minor differences in study area boundaries, as defined by deer use, except for the Story/Sprague study area, where wintering deer were distributed farther cast than expected (see Bartmann et al. 1992); this change in distribution may be due to changes in habitat conditions and/or potential increases in other ungulate populations (e.g., elk). Of the 32 deer monitored during winter, no interchange between winter herd segments was noted, but a few individuals traveled beyond areas of interest relative to control and treatment experimental units addressing energy development (Fig. 38). These movements can be addressed by either censoring those data or applying a covariate to the analyses. Based on the winter deer distribution data documented since January and the level of energy development activity present in April, 2008, we provide preliminary study area boundaries (Fig. 3) for future monitoring efforts to address experimental control (North Ridge, Yellow Creek, and Story/Sprague Gulch) and treatment (Ryan Gulch and Magnolia) areas addressing mule deer responses to beneficial habitat treatments and/or development activities. More specific boundaries will be assigned once data are analyzed from the remaining 43 collars scheduled for retrieval in February, 2009. During the spring transition period, deer from North Ridge and the northern half of Magnolia generally moved east, deer from southern Magnolia, Ycllow Creek, and Ryan Gulch moved south, and the Story/Sprague Gulch deer moved relatively short distances south and east (Fig. 4). As expected, summer deer distribution was more widely scattered than during winter with deer distributions radiating from the Piceance Basin to the northeast, east, southeast, and south generally following wintering deer from North Ridge, Magnolia-north, Story/Sprague Gulch, and Magnolia-south, Ryan Gulch, Yellow Creek. _,i FUTURE PLANS Funding has been recently secured to initiate the complete study proposal (Appendix I) beginning fall 2008 and continuing spring 2010. To address the other 5 study objectives outlined in Appendix I, we will attach VHF collars to 50 fawns/study area, increase our GPS sample to 20 GPS collared does/study area, measure body condition of 30 does/study area,- and add IO VHF collared does/study area to enhance mark-resight estimates. The period covered will represent existing development conditions or the pretreatment period and allow estimates of mule deer population parameters relative to current development practices and habitat conditions. Additional funding and cooperative agreements will be necessary to manipulate habitat conditions to benefit mule deer and modify development practices to enhance mule deer condition and survival on winter ranges exposed to energy development. We optimistically anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. ...,, I._; LITERATORE CITED Anderson, C.R., Jr., and D. J. Freddy. 2007. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Draft Study Proposal, Colorado Division of Wildlife, Fort Collins, USA. Bartmann, R. M. 1975. Piccance deer study-population density and structure. Job Progress Report, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51. Colorado Division of Wildlife, Fort Collins, USA. 68 �...., -..,/ Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 121. Barrett, M.W., J.W. Nolan, and L.D. Roy. 1982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin 10: 108-114. Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Picearice Basin, Colorado. Thesis, Colorado State University, Fort Collins, USA. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA . ...,, val Prepared by _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Wildlife Researcher - ...,_ ..__I -.I Table I. Mortalities of GPS collared yearling and adult female mule deer in the Piceance Basin, Colorado, January-June, 2008. Deer ID Study area Mortality date Age class Apparent cause 150.194 150.235 219.159 150.094 219.149 150.275 216.706 217.615 Story/Sprague Gulch Magnolia Ryan Gulch North Ridge Story/Sprague Gulch Ryan Gulch North Ridge Magnolia 1/19/08 4/9/08 4/25/08 5/4/08 5/23/08 5/24/08 5/25/08 5/28/08 Young adult Young adult Yearling Young adult Old adult Young adult Old adult Young adult Undetermined Coyote predation Vehicle collision Coyote predation Malnutrition Undetermined Undetermined Undetermined 69 �- w \iii, Piceance Basin Gas Fields + Gas Basin + Figure I. Piceance Basin project area (dashed line) relative to mule deer winter range, oil and gas fields, and the oil and gas basin. ...., wii/1 Figure 2. Capture locations by study area (solid lines) ofG PS collared adul t fema le mule deer in the Piccance Basin, Colorado, January I 0-12, 2008. 70 - - �--- - -- - --- -- ---- -- -- Winter Mule Deer Locations and Natural Gas Development ,i, Active or Potentially Active Well Pads ' • Figure 3. Mule deer G PS locatio ns by preliminary study area boundary (solid lines) excl uding (lop) and including (bottom) active will pads and e nergy development tacili ties (as of April, 2008) in the Piceance Basin, Colorado. January-A pril , 2008. 71 ....., En ergy Development Facilities Mule cleer locations= circles. stars. triangles. pluses. ancl diamonds �\..,,; - Figure 4. GPS locations of Piccance Bas in mule deer during the spring transition period (April- May, 2008). Capture study site: circles= North Ridge, stars= Magno lia, triangles= Story/Sprague Gulch. diamonds= Ryan Gulch, pluses = Yellow Creek. GPS locations 613 - 7111 108 Deer ID (symbol s= capl u,o sl101 + 11 63_ 06J t 117!t_21 S * • 11 6.i_oH o 11; 0_ ,s~ o 11 n _:.i S; * • 11 65_085 a- 117 1_164 • 11 78_:.SE ,;') ~ 1167_ 10S 9 11 7:!_ 105 -,: 1179_165 • o 1169_1'5 - - "'""' ~ Figure 5. Summer range G PS locations of Piceanee Basin mule deer, June- Jul y, 2008. Capture study site: circles= North Ridge, stars = Magnolia, triangles = Story/Sprague Guk:h, diamonds= Rya n G ulch, pluses= Yellow Creek. 72 - �APPENDIX I PROGRAM NARRATIVE STUDY PLAN FOR MAMMALS RESEARCH FY 2007-08- FY 2012-13 POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION ST AGE I, OBJECTIVE 5: PATTERNS OF MULE DEER DISTRIBUTION & MOVEMENTS A Research Study Plan submitted by: C.R. Anderson, Wildlife Researcher, Mammals Research, Colorado Division of Wildlife D.J. Freddy, Mammals Research Leader, Colorado Division of Wildlife 1,-1 >earl ...., ....,, -..,/ ...., A. Need Extraction of natural gas from areas throughout western Colora4o has raised concerns among many public stakeholders and the Colorado Division of Wildlife that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer by converting native habitat vegetation to drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force populati<;m of the region and the subsequent need for developing additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, incri~ed traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to deer populations. Thus, research documenting these impacts and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado supports one of the largest migratory mule deer populations in North America and also exhibits one of the highest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is projected to be about 15,000 wells, many of which will occur in the Piceance Basin. The Piceance Basin (including the White River gas field immediately to the north) currently supports about 400 active gas well pads, 250 permits for development within the next year, and 200 energy development facilities (Colorado Oil and Gas Conservation Commission; Fig. 1). Wintering mule deer population segments in or immediately adjacent to the Piceance Basin include: Crooked Wash along the White River on the north edge of the Basin, North Ridge between Dry Fork of Piceance Cfeck and the White River in the northeastern portion of the Basin, Yellow Creek along Yellow Creek in the western portion of the Basin, Ryan Gulch between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, Magnolia north and east of Piceance Creek in the central portion of the Basin, and Story/Willow Creek between Willow Creek and Story Gulch in the southern portion of the Basin. Each of these wintering population segments has received varying levels of development, from little-no development in Story/Willow Creek and North Ridge, light development in Yellow Creek, and relatively high development in Ryan Gulch, Crooked Wash, and 73 �Magnolia segments (Fig. 2). Due to advances in resource extraction technology and the increased demand for natural gas, future development and extraction activities will likely focus on natural gas fields previously developed and expand into adjacent areas where previously identified oil shale reserves and natural gas basins provide additional resource extraction opportunities. Because of the variation in the geology relative to gas reserves in the area and the juxtaposition of differing mule deer winter herd segments, several opportunities are available to address different, but related, questions relative to natural gas extraction methods and mitigation efforts relative to mule deer habitat use patterns. Past Research The Piceance Basin has been the location of numerous research investigations conducted by the Colorado Division of Wildlife, Colorado State University, and others which addressed various aspects of mule deer ecology and management beginning in the 1970s and continuing through the mid 1990s. Previous investigations of Piceance Basin mule deer addressed food habits (Hansen and Dearden 1975, Hubbard and Hansen 1976, Gibbs 1978, Bartmann 1983), physiology (Bartmann 1986, Torbit ct al. 1988), development of management techniques (Freddy and Bowden 1983 b, Garrott and White 1984a, Lee et al. 1985, White and Bartmann 1994), efficacy of population sampling methods (Freddy and Bowden 1983a, Bartmann et al. 1986, 1987, White et al. 1989), and population dynamics (White and Bartmann 1983, 1998, Garrott and White 1984b, Lee 1984, Garrott et al. 1987, White ct al. 1987, Bartmann et at. 1992). Previous investigations of mule deer habitat use patterns in the Piceancc Basin (Garrott et al. 1987) suggested fall migration consistently occurred during November, but spring migration varied likely due to winter severity and body condition, where rapid migration was evident when deer were leaving winter range in good condition and delayed migration was indicative of deer transitioning from winter range in relatively poor condition. Garrott et al. ( 1987) also noted strong fidelity to seasonal ranges, that deer shifted from north to south slopes as winter severity increased, and that irrigated and fertilized hay meadows served as important transition areas during fall and spring migration periods. Bartmann et al. ( 1992) manipulated deer densities to demonstrate compensatory mortality in the Piceance Basin mule deer population, where overwinter fawn survival varied inversely with density and adult female survival remained relatively constant; fawn mortality rather than reproduction appeared to be the major process driving the density-dependent mechanism. White and Bartmann ( 1998) reduced deer densities by 75% in their treatment area and reported 16% higher overwinter fawn survival and fawn body mass averaging 0.8 kg higher than the control area, whereas adult female survival was comparable between areas supporting previous findings (Bartmann ct al. 1992). Empirical evidence of mule deer population response to habitat manipulations is currently limited, largely due to the logistical and financial difficulty in conducting long-term research sufficient to address this relationship. Density dependent relationships have been demonstrated (e.g., Bartmann et al. 1992) and habitat quality rather than proximate mortality factors (e.g., predation) appear to be the driving factor (Bartmann et al. 1992, Hurley and Zager 2004, Bishop et al. 2005). Bishop et al. (2005), however, demonstrated enhanced population performance in supplementally fed, free-ranging deer to simulate high quality habitat, and reported 18% higher fawn survival (fetus to yearling; fetus-neonate = 0.127, • overwinter= 0.240) and adult females averaged 5.5% more body fat, but reproduction and adult female survival were similar between treatment and control groups. Bergman et al. (2005, 2006) are currently investigating mule deer population response to habitat treatments in western Colorado, which will likely provide insight into our approach of addressing habitat treatments in response to energy development as this study progresses. Currently, research addressing mule deer activity in response to natural gas development is limited to one study from the Pinedale anticline in Wyoming (Sawyer et al. 2006). Sawyer et al. (2006) examined changes in distribution before and during development of a natural gas field, and observed shifts in mule deer habitat use away from well pads (2.7-3.7 km) within I year of development which continued throughout the study, suggesting indirect habitat loss may be substantially larger than direct 74 i..- �.,_, habitat loss and presumably results in deer using lower quality habitats that may ultimately lead to population decline. Mule deer habitat in Pinedale was much less topographically and vegetatively diverse than the Piceance Basin, however, and mule deer may respond differently where the habitat affords a higher degree of security cover. Mule Deer Response to Habitat Treatments and Changes in Development Practices Our primary goal of this study is to develop approaches to provide for energy extraction in a manner that maintains viable mule deer populations for future recreational and ecological purposes. This may be accomplished by restoring or enhancing habitat conditions on or adjacent to disturbed sites and by modifying development practices. Mitigating developed sites following disturbance requires reseeding or planting native vegetation, control of noxious weeds, and demonstrating success of mitigation efforts. Because mule deer are primarily browsers, shrub establishment will be essential, but shrub establishment is difficult and takes time for reemergence. Mule deer response to winter range mitigation efforts on disturbed sites will require relatively long-term monitoring to determine success of habitat treatments. More rapid habitat, and thus mule deer, responses can be expected from treating mule deer habitat adjacent to developed areas and by irrigating and fertilizing hay meadows adjacent to winter ranges (Garrott et al. 1987). Improving habitat conditions for or reverting succession of shrub communities using roller-chopping, hydro-axing, or fire can improve forage quality, and increasing forage quality and quantity by irrigating and fertilizing hay fields can improve mule deer body condition at critical times when transitioning to and from winter range. In addition to habitat treatments, mule deer may also benefit from modification in development practices that reduce human disturbance. Development practices that concentrate activities and/or minimize human disturbance will most likely minimize detrimental impacts to mule deer populations. Energy development practices that may be informative to investigate include directional versus non-directional drilling, piping versus trucking condensate from well pads, remotely versus directly monitoring gas wells, closing access roads following development, shifting from noisy diesel to quieter natural gas motors, and phased/clustered development where sections of deer winter range are developed while others remain undisturbed until development and mitigation are completed in developed sections. Determining the response of mule deer to specific development practices will require collaboration with the developer, and the specific conditions of the site being developed will dictate which development practices can feasibly be evaluated. Encana and Exxon-Mobile are the primary energy companies controlfing natural gas development in the Piceance Basin (Fig. 3 ). -..I Mule Deer Response to Energy Development Mule deer may negatively respond to energy development from direct reduction in forage availability from development activities, from indirect reduction of forage quality and quantity by shifting their distribution away from development activity to less preferred habitats, from negative physiological responses where deer maintain fidelity in areas exposed to development activities or from a combination of these factors. Depending on the extent and concentration of development, deer may also be able to adjust to development activities without population level impacts, and other factors (e.g., winter severity, drought, habitat succession, predation) also contribute to fluctuations in population performance/trajectory over time. Ultimately, reproduction and survival drive population perfonnance and, based on past research, focusing on fawn survival and recruitment appear to be the most influential parameters given the density dependent nature of these factors versus the apparent density independent nature of adult female survival and reproduction. Documenting proximate factors influencing fawn survival will also be useful and thus changes in distribution, deer density, body condition, and specific mortality factors should also be monitored. Comparing changes in mule deer population parameters relative to energy development will require that undeveloped control areas are monitored and predevelopment data are collected to determine whether or not and to what extent development versus environmental factors may be contributing. This will be challenging given development already in place and the unpredictability of future development that may occur. Large scale impacts from energy development may be detectable by comparing mule deer population parameters from undeveloped sites to ..,./ 75 -..I �developed sites, but natural variation due to geographic differences will be unaccounted for and add error to comparisons. Our ability to examine mule deer response to habitat mitigation and/or beneficial development practices will be better suited for demonstrating cause-effect relationships by allowing controlled experimental designs where habitat manipulation or modifying human behavior (i.e., development practices) provide the treatments for examining positive responses in mule deer population parameters. B. Objective The primary objectives for the long-term research proposal are as follows: I. Determine if winter range and riparian vegetation responds positively to habitat treatments; 2. Determine if fawn and yearling survival is positively influenced by winter range habitat treatments; 3. Determine if fawn and yearling survival is positively influenced by irrigating and fertilizing hay meadows adjacent to winter ranges; 4. Determine if modification of development practices positively influences mule deer population performance; 5. Determine if habitat treatments, changes in development practices, or natural gas development results in distributional shifts on mule deer winter range; 6. Determine if habitat treatments, changes in development practices, or natural gas development results in changing mule deer densities on winter range. The specific objective of this study plan is to address objective 5: Determine if habitat treatments, changes in development practices, or natural gas development result in distributional shifts on mule deer winter range in the Piceance Basin. The primary working hypotheses for the long-term research proposal are as follows: a. Landscape level habitat treatments do not influence forage quantity and quality; b. Fawn and yearling survival are not influenced by winter range habitat treatments; c. Fawn and yearling survival arc not influenced by modification of development practices; d. Mid-winter deer density does not fluctuate in response to habitat treatments, changes in development practices, or natural gas development; e. Mule deer habitat selection does not change in response to habitat treatments, changes in development practices, or natural gas development. The specific working hypothesis of this study plan is: Mule deer habitat selection docs not change in response to habitat treatments, changes in development practices, or natural gas development. C. Expected Results Due to the extensive energy development that is projected to occur over the next 20 years throughout much of the mule deer winter range in the northern Rocky Mountains of the western US, innovative approaches to energy development and mitigation methods are essential to sustain viable mule deer populations in the region. Impacts from development and conversely success of mitigation efforts are often assumed but rarely demonstrated, and these assumptions can only be confirmed by application of well designed research efforts conducted over sufficiently long time periods to measure responses. As a first step toward this effort, we propose to address mule deer habitat selection patterns relative to varying levels natural gas development and associated human activity and ultimately address mule distributional responses to habitat and development modifications anticipated to be beneficial to mule deer. This project will require coordination and cooperation between Colorado Division of Wildlife, land management agencies, and the major energy companies developing the Piceance Basin. We anticipate this partnership will benefit mule deer populations and foster the evolution of wildlife management and 76 -...,, �-energy development practices that are compatible with other wildlife and human values associated with maintaining functional ecosystems over the long term. .-I D. Approach 1. Experimental Approach a. Experimental Units Because of the varying levels of development and deer densities relative to differing winter population segments in the Piceance Basin, different experimental areas (i.e., mule deer winter ranges) arc uniquely suited for addressing mule deer habitat selection patterns relative to varying levels of energy development. Experimental designs monitoring mule deer responses to treatment (e.g., habitat mitigation, modified development practices) and control areas are necessary to differentiate cause-effect relationships from development versus environmental factors. Suitable control areas require that little or no previous development has occurred and that no development occurs during the experimental time frame. Ideally, both temporal and spatial control areas would be monitored to make valid comparisons to developed and subsequently mitigated sites; temporal controls provide measures of natural variability in mule deer population parameters over time and spatial controls provide measures of variability due to differences in geography. Once spatial and temporal variation is accounted for, inferences can be made relative to development disturbance or mitigation effects on mule deer. )'he North Ridge, Story/Willow Creek, and Yellow Creek deer population segment areas (Fig. 2) currently exhibit little to no development, but it is currently unknown whether or not these areas will be developed in the future; there is potential for future oil shale development in the Story/Willow Creek and Yellow Creek deer areas. North Ridge appears least likely to be developed because it is outside of the current oil shale lease area and. only a few natural gas wells have historically been drilled on or adjacent to the area, whereas some development is currently occurring and likely to increase in the Story/Willow Creek and Yell ow Creek areas. Thus, North Ridge would appear best suited as a temporal control site for comparison to other developed winter ranges within the Piceance Basin and may also serve as a geographic control for the Crooked Wash deer population segment located immediately north and adjacent to the Piceance Basin (as of Dec. 2007, the Crooked Was~ site ranks 61" in study priority and will not be sampled in the initial year due to limitedfunding). The Story/Willow Creek and Yellow Creek deer may provide spatial controls for the Magnolia and Ryan Gulch deer population segments, respectively, but future development potential in these areas is unknown. If these areas become developed in the future (either for oil shale or natural gas), they would provide BACI (Before-AfterControl-Impact) type comparisons strengthening our inference of development impacts on mule deer habitat selection patterns. Magnolia, Crooked Wash, and Ryan Gulch deer areas have historically received relatively high development activity and currently exhibit moderate-high development, and appear likely to be developed extensively in the future based on the gas development layers currently available (Colorado Oil and Gas Conservation Commission; Fig. 1). Pretreatment data in these areas will be represented by parameters associated with developed sites and the measured response will be in the form of habitat treatments and/or differing development practices, which will be measured in comparison to the control sites. We propose including 3 control sites ( 1 temporal/spatial control and 2 spatial controls) and 3 treatment sites to investigate mule deer response to habitat and/or development treatments (e.g., directional versus non-directional drilling, piping versus trucking condensate, etc.) across a range of deer densities (Table 1). We would strive to split high intensity extraction study sites into 2 halves with one half serving as the 'control' [standard development] and one half serving as the 'treatment' [improved development approach or improved habitat] (e.g., see Magnolia in Fig. 2). The above scenario addresses the potential for establishing control and treatment sites for evaluating shifts in mule deer habitat use patterns in response to habitat treatments and/or development treatments, and may allow larger scale 77 �comparisons in mule deer habitat use patterns relative to varying levels of energy development to be compared among experimental areas. Modified versions of the proposed design could be implemented depending on the level of funding available and the degree to which industry is willing to collaborate with this effort. We consider 3 study sites, likely North Ridge, Magnolia, and Ryan Gulch, as the minimum number of study sites necessary to adequately address the objectives of this project; the additional proposed study areas will allow increased flexibility in the questions that are addressed and increase our inference relative to mule deer responses to habitat treatments and modifications of development practices. Furthermore, ifwe are not able to evaluate potential for mitigating industrial operation and/or habitat improvements, this study would likely only have the potential to document negative impacts of intense energy extraction practices on mule deer. Table 1. Relative density of natural gas wells and mule deer and experimental designation for potential study sites in the Piceanace Basin, Colorado, for addressing mule deer response to natural gas development practices and habitat mitigation. Relative density Experimental Study area Inactive wells Active wells Mule deer designation North Ridge Very low None High Temporal/spatial control Crooked Washu High High High Treatment Story/Willow Creek Low Low Moderate Spatial control Magnolia High High Moderate Treatment Yellow Creek Moderate Low Low Spatial control Ryan Gulch High Moderate Low Treatment a As of Dec. 2007, for the initial research effort, the Crooked Wash study site ranks 6th in priority and will not be sampled due to limited funding. b. Response Variables To determine if habitat treatments or development practices elicit a shift in habitat use patterns, we will examine changes in Resource Selection Probability Functions (RSPF; Sawyer et al. 2006) preand post-habitat treatments, between areas exhibiting differing development practices, and compare RSPFs between developed and non-developed sites. Population level models for each study area will be compared to assess similarities and differences in habitat selection patterns relative to differing levels of energy development. We suggest relevant habitat attributes associated with mule deer response to habitat treatments and development practices include slope, aspect, elevation, habitat type, road density, distance to well pad, and development activity. Definition for development activity would vary depending on the development treatment investigated. For example, if the development treatment were applied to examine 78 ~ �fluid collection systems, the variable would be coded l or O depending on whether they were present or absent and the RSPF would be estimated relative to this effect. In another example, well pad visitation rate may be the variable of interest and the RSPF would be estimated for a continuous effect of increasing road traffic to well pads. 2. Sample Size I Power Calculations We anticipate 20 GPS collars per experimental area will be sufficient to provide population level inference based on similar studies with ungulates (Millspaugh and Marzluff 200 l) for addressing adult female mule deer habitat selection patterns for each study site. 3. Procedures a). Capture and Handling Methods A total of 120 adult female mule deer will be captured and OPS-collared (20/study area assuming 6 study sites, I00 deer during initial FY07-08 for 5 study sites). Helicopter net-gunning (Barrett et al. 1982, van Reenen 1982) will be used to complete the necessary sample in January 2008 and a combination of helicopter net-gunning and drop netting will be used during March of subsequent years. b). Monitoring Habitat Use Patterns Habitat use patterns on treatment and control sites will be evaluated applying the Resource Selection Probability Function (RSPF) approach of Sawyer et al. (2006), where resource selection is estimated using the relative frequency or absolute probability of use as a function of the predictor variables. This approach will consists of 5 basic steps including (I) estimate the relative frequency of use (an empirical estimate of probability of use) for a large number of sampling units for each GPS collared deer (20/study area), (2) use the relative frequency as the response variable in a multiple regression analysis to model the probability of use for each deer as a function of predictor variables, (3) develop a population level model from the individual deer models for each experimental area, (4) map predictions from each model annually to examine changes in habitat use patterns over time relative to treatment effects, and (5) compare population level model coefficients between treatment and control sites to examine differences in resource selection among non-developed, developed, and mitigated sites. Relative frequency of use for each deer will be estimated by counting the number of deer locations that occur within 100-m radii circular sampling units (representing habitat attributes) systematically sampled throughout each study area; 200-m-wide sample unit should be small enough to detect changes in deer movements and large enough to provide multiple locations for estimating use probability functions. c ). Habitat Manipulations The purpose of habitat manipulation would be 2-fold: 1) replace forage lost directly to surface destmction associated with gas pad/road/infrastructure development through rehabilitation of these areas, and 2) enhance suitable undisturbed vegetation. In both situations, the goal would be to provide habitats/vegetation having enhanced nutritional value to mule deer during fall (pre-winter) and spring (post-winter) migrations and during the critical winter period in order to improve body condition of deer and enhance their probability of survival. Placement of such habitat treatments would need to be evaluated and planned based on identification of priority areas within the Piceance Basin, in general, and specifically within experimental study sites. Opportunities within study sites would, in part, be dependent on cooperation of Energy Corporations, BLM, and private land owners, and site specific potentials that realistically can only be specifically determined after commitments are made in choosing experimental sites. We envision the potential to utilize a full-suite of habitat improvement options. These could include: enhancing existing sagebrush areas using combinations of herbicide, nitrogen fertilizer, chopping-mowing, reseeding with grasses-forbs, and in some cases reseeding with suitable sagebrush species; enhancing mountain brush habitats through burning, hydroaxing, and reseeding; enhancing 79 �~ pinyon-juniper habitats through hydroaxing, burning, and reseeding. Site specific situations could require using advanced mulching, seeding, and irrigation options to effectively rehabilitate sites. In all cases, we would attempt to layout experimental habitat improvements to facilitate evaluation of success both from the standpoint of vegetation rehabilitation and use by mule deer. Past research and monitoring of radio-collared mule deer in the Piceance Basin documented the high use and importance of cultivated hay fields along Piceance Creek. We envision considerable potential to improve management of hayfields to specifically address the needs of deer, especially during post-fall and pre-spring migrations of deer into and out of the Piceance Basin. The potential to manage hayfields for deer will be dependent on options to own or lease fee-title property and water rights. There may be nearly 10.000 acres of suitable hayfields located along Piceance, Ryan, Black Sulphur and Yell ow creeks. In general, we believe that hayfields using more efficient irrigation practices and planted with suitable varieties of alfalfa developed to be grazed more so than for traditional hay production and suitable to alkaline soils would offer high potential to enhance nutrition of deer at key periods of the year. We also could see potential to establish hayfields with appropriate varieties of cool-season grasses (bluegrass for example) that could be managed for high nutritional quality through annual burning, mowing, grazing, and irrigation practices. Such cool season grass fields could provide 'green' forage for deer both during spring "green-up' and fall •re-green' periods, especially if limited irrigation could be applied. The specific design and layout of reformed hayfield management would require considerable planning involving the expertise ofNRCS or University Extension programs and considerable cost (potentially millions of dollars) for fee title ownership of land and water rights, mechanical preparation of hayfields and irrigation systems, and annual management practices once fields were established. l_.i d) Evaluation ~f Development Practices We anticipate options for industry to alter extraction practices that would reduce and/or concentrate human activity and benefit deer by increasing the relative 'security' of existing or improved habitats for deer. Options could include: multi-well versus single-well drilling platforms to reduce well pad density; piping instead of trucking well-condensate; road closures that minimize where traffic occurs; time of day restrictions; remote well-monitoring, or other options that industry may be able to offer. The key to evaluating any of these industrial-human activity options would be to create experimental comparisons using "control' areas [current practices] versus 'treatment' areas [improved practices]. Which alternative practices are tested and in which potential study sites involved will depend upon cooperation from industry. Ideally, energy corporations would cooperate among themselves, the BLM, and with Division of Wildlife to help develop the best possible experimental design among extraction lease areas. l_.i e). Statistical Ana~vses Following Sawyer et al. (2006) for estimating Resource Selection Probability Functions, we will obtain population-level models for each experimental area by first estimating coefficients for each GPScollared deer. A negative binomial distribution will be used to fit the following general linear model (GLM): ln(E[r;]) = ln(total) +/Jo+ P1X1 + ... + /J,,Xp, where r; is the number oflocations for a OPS-collared deer within sampling unit i (i = /, 2, .... r), total is the total number of locations for that deer within each experimental unit, Po is the intercept tenn, P1, .. ./J,, are unknown coefficients for habitat variablesX,, ... ,.,~,. and E[.] denotes the expected value. We will estimate coefficients for the population-level model for each experimental unit following: " 1 11 P1. =- L/Jki' n j=I 80 '-' �-..I where pk.i is the estimate of coefficient k for individual j U = 1, ... ,n) and the variance wi 11 be estimated applying the variation among individual model coefficients. To compare habitat use patterns between areas and over treatment effects, we will map predicted probabilities of use for each study area by season. Differences (P < 0.05) between population level model coefficients will be compared between study areas using a I-test. 4. Project Schedule FY2007-08 FY2008-09 FY2009-I0 FY2010-l I FY201 l-12 FY2012-13 FY2013-14 FY2014-15 FY2015-16 FY2016-l 7 FY2017-18 FY2018-19 ~ -...,,I Pretreatment/Revised Program Narrative Study Plan Pretreatment/Progress Report (PR) Habitat and/or Development Treatments/PR Habitat-and/or Development Treatments/PR Monitor Deer Response/Progress Report Project Status Evaluation Monitor Deer Response/Progress Report Monitor Deer Response/Progress Report Monitor Deer Response/Progress Report Project Status Evaluation Monitor Deer Response/Progress Report Monitor Deer Response/Progress Report Monitor Deer Response/Completion Report Prepare and submit peer-reviewed publications 9/1/2007 8/1/2008 8/1/2009 8/1/2010 8/ 1/20 I I 8/1/2012 8/1/2013 8/1/2014 8/1/2015 8/1/2016 8/1/2017 8/1/2018 5. Annual Cost Estimates Estimating mule deer resource selection probability functions and implementing small scale habitat improvements are costly endeavors involving the purchase of specialized GPS radio-collars, helicopter flight hours for deer capture/collaring, machinery to physically alter the habitat, and personnel to adequately perform day-to-day data collection. If large scale habitat treatments arc needed or desired, funding in addition to the estimates below will be required as habitat treatments cost $300 to $1,000/acre depending on the most appropriate treatment for a locale. Key to evaluating mule deer responses to habitat and/or developmen't treatments will be sufficient and steady funding over a time horizon (minimum of 5-year commitments over the IO year study period) that allows for meaningful biological responses to occur and be measured. Cost estimates per year (2007 dollars for objective #5): GPS Equipment Costs: $200,000 Helicopter Capture Costs: $ 70,000 12 months TFTE: $ 30,000 Vehicle support: $ 20.000 Other field operations and equipment: $ 15,000 Total: $335,000 6. Personnel Charles R. Anderson, Jr., Wildlife Researcher, Project Leader, Colorado Division of Wildlife David J. Freddy, Mammals Research Leader, Colorado Division of Wildlife E. Location of Work The proposed research will take place in or adjacent to the Piceance Basin of northwest Colorado, primarily within Game Management Unit 22 of the White River mule deer DAU D-7, west and southwest of Meeker, Colorado (Fig. 2). 81 �F. Literature Cited Anderson, C.R., Jr., and D. J. Freddy. 2007. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Draft Study Proposal, Colorado Division of Wildlife, Fort Collins, USA. Bartmann, R. M. 1983. Composition and quality of mule deer diets pinyon-juniper winter range, Colorado. Journal of Range Management 36:534-541 Bartmann, R. M. 1986. Growth rates of mule deer fetuses under different winter conditions. Great Basin Naturalist 46:245-248. Bartmann, R. M., L. H. Carpenter, R. A. Garrott, and D. C. Bowden. 1986. Accuracy of helicopter counts of mule deer in pinyon-juniper woodland. Wildlife Society Bulletin 14:356-363. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1987. Aerial mark-recapture estimates of confined mule deer in pinyon-juniper woodland. Journal of Wildlife Management 51 :41-46. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 121. Barrett, M.W., J.W. Nolan, and L.D. Roy. 1982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin I 0: I 08-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, and G. C. White. 2005. Pilot evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer (study plan). Wildlife Research Report July: 23-35. Colorado Division of Wildlife, Fort Collins, USA. Bergman, E. J., C. J. Bishop, D. J. Freddy, and G. C. White. 2006. Evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer (study plan). Wildlife Research Report July: 67-89. Colorado Division of Wildlife, Fort Collins, USA. Bishop, C. J., G. C. White, D. J. Freddy, and B. E. Watkins. 2005. Effect of nutrition on mule deer recruitment and survival rates. Wildlife Research Report July: 37-65. Colorado Division of Wildlife, Fort Collins. USA. Coo~ R.C. 2000. Studies of body condition and reproductive physiology in Rocky Mountain Elk. Thesis, University ofldaho, Moscow, USA Freddy, D. J., and D. C. Bowden. 1983a. Sampling mule deer pellet-group densities injuniper-pinyon woodland. Journal of Wildlife Management 47:476-485. Freddy, D. J., and D. C. Bowden. 1983b. Efficacy of permanent and temporary pellet plots injunipcrpinyon woodland. Journal of Wildlife Management 47:512-516. Freddy, D.J., G.C. White, M.C. Kneeland, R.H. Kahn, J.W. Unsworth, W.J. DeVcrgie, V.K. Graham, J.H. Ellenberger, and C.H. Wagner. 2004. How many mule deer arc there? Challenges of credibility in Colorado. Wildlife Society Bulletin 32:916-927. Garrott, R. A., and G. C. White. 1984a. Evaluation of vaginal implants for mule deer. Journal of Wildlife Management 48:646-648. Garrott, R. A., and G. C. White. 1984b. Methodology for assessing the impacts of oil shale development on the Piceance Basin mule deer herd. Thome Ecological Institute Technical Publication 14:228. 231. Garrott, R. A., G. C. White, R. M. Bartmann, L. H. Carpenter, and A. W. Alldredge. 1987. Movements of female mule deer in northwest Colorado. Journal of Wildlife Management 51 :634-643. Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins, USA. Gill, R.B. 1969. A quadrat count system for estimating game populations. Colorado Division of Game, Fish and Parks, Game Information Leaflet 76. Fort Collins, USA. Hansen, R. M., and B. L. Dearden. 1975. Winter foods of mule deer in the Piceance Basin, Colorado. Journal of Range Management 28:298-300. • Hubbard, R. E., and R. M. Hansen. 1976. Diets of horses, cattle, and mule deer in the Piceance Basin, Colorado. Journal of Range Management 29:389-392. Hurley, M., and P. Zager. 2004. Southeast mule deer ecology - Study I: Influence of predators on mule deer populations. Progress Report, Idaho Department of Fish and Game, Boise, USA. 82 __.i ...,, �..., Kufeld, R.C., J .H. Olterman, and D.C. Bowden. 1980. A helicopter quadrat census for mule deer on Uncompahgre Plateau, Colorado. Journal of Wildlife Management 44:632-639. Lee, J. E. 1984. Mule deer habitat use and movements on Piceance Basin winter range as estimated by radiotelemetry. Thesis, Colorado State University, Fort Collins, USA. Lee, J. E., G. C. White, R. A. Garrott, R. M. Bartmann, and A. W. Alldredge. 1985. Assessing accuracy of a radiotelemetry system for estimating animal locations. Journal of Wildlife Management 49:658-663. Millspuagh, J. J. and J.M. Marzluff. 2001. Radio tracking and animal populations. Academic Press, San Diego, USA. Ramsey, C.W. 1968. A drop-net deer trap. Journal of Wildlife Management 32: 187-190. Sawyer, H., R. M. Nielson, F. Lindzey, and L. L. McDonald. 2006. Winter habitat selection of mule deer before and during development of a natural gas field. Journal of Wildlife Management 70:396403 . Schmidt, R.L., W.H. Rutherford, and F.M. Bodenham. 1978. Colorado bighorn sheep-trapping techniques. Wildlife Society Bulletin 6: 159-163. Stephenshon, T.R., V.C. Bleich, B.M. Pierce, and G.P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. • Stephenshon, T.R., T.R., K.J. Hundertmark, C.C. Schwartz, and V. Van Ballenberghe. 1998. Predicting body fat and body mass in moose with ultrasonography. Canadian Journal of Zoology 76:717722. Torbit, S. C., L. H. Carpenter, R. M. Bartmann, A. W. Alldredge, and G. C. White. 1988. Calibration of carcass fat indicies in wintering mule deer. Journal of Wildlife Management 52:582-588. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Ha.igh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C. 1996. NOREMARK.: Population estimation from mark-resighting surveys. Wildlife Society Bulletin 24:50-52. White, G. C., and R. M. Bartmann. 1983. Estimation of survival rates from band recoveries of mule deer in Colorado. Journal of Wildlife Management 47:506-511. White, G. C., R. A. Garrott, R. M. Bartmann, L. H. Carpenter, and A. W. Alldredge. 1987. Survival of mule deer in northwest Colorado. Journal of Wildlife Management 51 :852-859. White, G. C., R. M. Bartmann, L. H. Carpenter, and R. A. Garrott. 1989. Evaluation of aerial line transects for estimating mule deer densities. Journal of Wildlife Management 53:625-635. White, G. C., and R. M. Bartmann. 1994. Drop nets versus helicopter net guns for capturing mule deer fawns. Wildlife Society Bulletin 22:248-252. White, G. C., and R. M. Bartmann. 1998. Effect of density reduction on overwinter survival of freeranging mule deer fawns. Journal of Wildlife Management 62:214-225. 83 �I ~ r, ' " "f ef; • , V:~-Rla° Bl.A ~~ . r;,. •. Piceance Basin . .:•. •• ~ -~ + Active Wells + Inactive Wells + Permitted Wells -'"""' ,._, ......,/ I.....; - ,L ,. !<hfl'o«&'1 Figure I. atural gas development in the Piceancc Basin, Colorado, July 2007. \,,_; "'-i'I 84 �_,J --...J , !,'' ' ~- ,:I,• Piceance Basin • --t~ell • Research Areas + Inactive Wells - + Drilling Permits -- - - == =· •--·_int4 ..1, ------- I~• Figure 2. Proposed mule deer study sites relative to natural gas development in the Piceance Basin, Colorado, Jul y 2007. 85 �\,...,, I...., Piceance Basin '-"I ~ Research Areas + Gas Lease Ownership 0 1. 1•1111.,J :--111,h .\n:a c::J l'11•j1•"\'1J Slmh \u:.11, ......., ~ 1111 l h\.'":,p,·ll.~• I ,•;i, c fllll'.t.~·, woll.';1,..Fu.::m:i I c.1~< EJ - F\.'\,11il ,·;",: m , 1,,1111( 1\•:i,;,• - \\1lh,111t, l .:.t,'(,: Figure 3. Proposed mule deer study sites relati ve to the primary energy companies controlling natural gas leases in the Piceance Basin, Colorado, July 2007. "- I,,..., 86 �- -- Colorado Division of Wildlife July 2008 - June 2009 WILDLIFE RESEARCH REPORT ...,,/ - _, State of- - - - - - ~ Colorado : Division of Wildlife ~ ~ ~- - - - - =-:...:...:..::-=-=-'-"-'-'-'c..:..:..=:..:..:..:.-=------ - - - - -- - C ost Center 3430 : ::..:.M=a=m'-'-'-'-m=a=ls~R=cs"'"'e""a'-'-rc=h- ' - - - - - - - - - - - - - - Work Package 300 I := D--=c=c"---r--=C--=o~n=se~1_·v~a~ti-=-o~n_ _ _ __ _ _ __ _ _ _ __ Task No.6 : Population Performance of Piccancc Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Fcderal A id Proj ect:_--'-W-'------'1=8-=-5---'-= R ------Period Covered: July 1, 2008 -June 30, 2009 Author: C. R. Anderson Personnel: D. Alkire, E. Bergman, C. Bishop, J. Broderick, 8. dcVergic, D. Fi nley, C. Flickinger, D. Freddy, L. Gepfort, K. Kaai, L. Kelly, T. Knowles, P. Lendrum, P. Lukacs, B. Marsh, M. Reitz, T. Segal, K. Taylor, R. Velarde, CDOW; E. Hollowed, BLM; S. Monsen, Western Ecological Consulting, Inc.; G. White, Colorado State University; R. Swisher, Quicksilver Air, Jnc. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado M ule Deer Foundation, Colorado Oil and Gas Conservation Commission, Colorado State Severance Tax Fun~, EnCana Corp., Shell Petroleum, and Williams Production LMT Co. ----...., - -.J All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT I propose to experimentally evaluate habitat treatments that may improve the landscape to benefit mule deer (Odocoileus hemionus) and evaluate human-activity management alternatives to reduce the disturbance of energy development impacts on mule deer. The Piccance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas within the state. The data presented here represent the first pretreatment year of a long-tenn study addressing habitat modifications and improved energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. I selected 5 winter ra nge study areas representing varying levels of development to serve as treatment (Ryan Guieb and Magnolia) and control (Yellow Creek, Story/Sprague, and North Ridge) sites and recorded habitat use and movement patterns using GPS collars (5 locations/day), estimated overwinter fawn and adult female sw-vival, estimated late winter body condition of adult females using ultrasonography, and estimated abundance using helicopter mark-rcsight surveys. 1 attached 250 VHF collars (SO/study area) to fawns in early December 2008 and 150 VHF ( I 0/study area) and GPS (20/study area) collars to adult female mule deer in late February-early March 2009. In comparing the data among study areas th is first year, Story/Sprague deer appear to be in better phys ical condition than deer from the other winter ranges examined. Migration patterns were similar among 4 of the 5 areas, but Story/Sprague deer traveled shorter distances and spent less time on winter range. Y cllow Creek fawns were lighter than 1I I �~ ~ other study areas and ex hibited the lowest survival o f the a reas investigated. North Ridge deer exhi bited the highest w inter range density and Magnolia and Ryan Gulc h deer exhibited the low.est densities. R easons fo r these differences arc cuITcntly unknown, but could be related to severa l factors including relative habitat conditions, duration on and distance to seasonal ranges, and extent of human activi ty throughout occupied habitats. Meaningful comparisons will be evident once treatments arc implemented a nd comparisons arc possible between areas that arc manipulated (treatment a reas; Ryan Gulch and Magnolia) and those that are not (control areas: Yellow Creek, Story/Sprague, a nd North Ridge). This project w ill require additiona l funding commitments and cooperative agreeme nts beyond spring 20 I0 from private industry, the BLM, and the CDOW to assess if sustai nable mule deer populations can persist w ithin a liighly disturbed landscape following implementation of beneficial habitat treatments a nd development practices. ~ \..,,I ......, '-I -..,..; ~ ..,,,,,,, \..,,I "'""' ;....; """ ......., ,._,. """" 'I;..;, 11 2 �...,/ WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR. P. N. OBJECTIVES 'allll I. 2. To determine experimentally whether enhancing mule deer habitat conditions on winter and/or transition range elicits behavioral responses, improves body condition, increases overwinter fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, over-winter fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars (5 fixes/day) to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 5 study areas experiencing varying levels of energy development of the Piceance Basin, Colorado. 2. Estimate late winter body condition of adult female mule deer in each of the 5 winter herd segments 3. Monitor over-winter survival of fawn and adult female mule deer by daily ground tracking and bi-weekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Summarize data and present information in an annual Job Progress Report. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and the Colorado Division of Wildlife that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to deer populations. Thus, research documenting these impacts and evaluating the most effective strategics for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. .._, 113 �The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us). Anperson and Freddy (2008a) in their long-term research proposal • identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population perfonnance. This progress report describes the first year of addressing mule deer population performance during the pretreatment phase, which includes monitoring habitat selection and behavior patterns of adult female mule deer, overwinter fawn and adult female survival, estimates of adult female body condition during late winter, and abundance estimates on 5 winter range herd segments in relation to varying levels of natural gas development in control and treatment experimental areas prior to proposed experimental modifications in energy developmental practices and potential habitat improvement treatments. STUDY AREAS The Piceance Basin between the cities of Rangely, Meeker, and Rifle in northwest Colorado was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 15,00022,000 (Bartmann 197 5), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utah juniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1675 m to 2285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry Symphoricarpos oreophilus), and rabbitbrush ( Chrysothamnus spp.; Hartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atrip/ex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay fields. Grasses and forbs common to the area consist of wheatgrass (Agropyron spp. ), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Ba/samorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewis ii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture coming from spring snow melt. Wintering mule deer population segments in the Piceance Basin include: North Ridge (57 km2) between Dry Fork of Piceance Creek and the White River in the northeastern portion of the Basin, Yellow Creek (70 km2) along Corral Gulch in the western portion of the Basin, Ryan Gulch (130 km 2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, Magnolia (130 km 2) north and east of Piceancc Creek in the central portion of the Basin, and Story/Sprague Gulch (90 km2) between Story Gulch and Sprague Gulch in the southern portion of the Basin (Fig. l ). Each of these wintering population segments has received varying levels of development, from no development in North Ridge, light development in Story/Sprague Gulch and Yellow Creek, and relatively high development in Ryan Gulch and Magnolia segments (Fig. I). Among the 5 study areas, Yellow Creek and Story/Sprague will serve as spatial controls to Ryan Gulch and Magnolia, respectively, and North Ridge will serve as a temporal control area. Because the progression and extent of energy development in the future is 114 �dynamic and currently unknown, North Ridge may also serve as a spatial control area to Magnolia or possibly Ryan Gulch should the Story/Sprague Gulch or Yellow Creek study areas become developed in the future. METHODS ...., ..,._ ~ Tasks addressed this fiscal year included mule deer capture and collaring efforts, monitoring overwinter fawn and adult female survival, estimating adult female body condition during late winter using ultrasonography, and estimating mule deer abundance applying helicopter mark-resight surveys. employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to capture 50 fawns during early December and 30 adult females during late February-early March in each of the 5 study areas (250 fawns and 150 does total). Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to a handling site for collection of body measurements and were fitted with GPS (20/area; 5 fixes/day; G2 l l OB, Advanced Telemetry Systems, Isanti, MN, USA) or VHF collars (IO/area) and released. Fawn collars were spliced and fitted with 2 lengths of rubber surgical tubing to facilitate collar drop during mid-summer-early autumn, adult VHF collars were attached static, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early April, 20 I0. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). Mule Deer Habitat Use and Movements I downloaded and organized data from GPS collars deployed during the pilot study (January 2008; see Anderson and Freddy 2008b) following collar drop and retrieval late February 2009. GPS collars redeployed late February-early March 2009 maintained the same fix schedule of attempting fixes every 5 hours. All well pads and roads present throughout the 5 study areas in spring 2009 were mapped using hand-held GPS units and data were incorporated into ArcGIS 9.2 for resource selection analyses. I plotted deer locations and recorded timing and distance of spring and fall 2008 migrations for each study area. Mule deer resource selection analyses for the first winter of research (January-May 2008) are pending acquisition of information on timing of road and well pad development and completion. Analyses of data from winter 2008-2009 will be conducted following retrieval of GPS collars in April 20 I0. Over-Winter Survival Mule deer mortality monitoring consisted of daily ground telemetry tracking and aerial monitoring deer approximately every 2 weeks from fixed-wing aircraft. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. I estimated over-winter survival on a weekly basis using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any mortalities occurring within IO days of capture) and collar failures were censored from survival rate estimates. I estimated over-winter survival rates beginning 14 December, 2008-20 June, 2009 for adult females and 14 December, 2008-21 March, 2009 for fawns. Premature failure of surgical tubing integrity beginning late March inhibited my ability to reasonably estimate fawn survival beyond late March. Adult Female Body Measurements I applied ultrasonography techniques described by Stephenson et al. ( 1998, 2002) and Cook et al. (200 I) to measure maximum subcutaneous rump fat (mm) and loin depth (longissimus dorsi muscle, mm). I estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 200 I). combined ultrasound rump fat measurements with BCS to develop an index (rLIVINDEX; Cook et al. 200 I, 2007) of the relative nutritional status of deer from each study area. I examined differences (P < 0.05) in nutritional status among study areas using a two-sample t-test. Other body measurements recorded included pregnancy status (pregnant, barren) via ultrasound, weight (kg), chest girth (cm), and ....,, 115 �hind-foot length (cm). Fetal counts were also recorded in 4 of the 5 study areas to assist a Vaginal Implant Transmitter (VIT) evaluation study (see Bishop 2009). Abundance Estimates . I conducted 4 (Ryan Gulch) or 5 (the remaining study areas) helicopter mark-resight surveys (2 observers and the pilot) during late March-early April, 2009 to estimate deer abundance in each of the 5 study areas. I delineated each study area from GPS locations during the same period the previous year and aerial telemetry locations of radio-collared deer within 2 weeks of the first survey. The survey boundary of each study area was then extended to the nearest section boundary and study areas were divided into 2.6 km2 sampling blocks. Aerial telemetry surveys were conducted during helicopter surveys to determine which marked deer were within each survey area. Initially, I randomly selected IO sampling blocks from each study area (total sampling blocks= 22-50/study area) for each survey and surveyed sampling blocks sequentially to minimize flight time. After the first 2-3 surveys, depending on the area, it became apparent that increasing the number of sampling blocks to improve precision could be accomplished without undue expense, and subsequent surveys included all sampling blocks for the smaller areas (North Ridge, Yellow Creek, Story/Sprague) or 40% of the sampling blocks for the larger areas (Ryan Gulch, Magnolia). I delineated flight paths in ArcGIS 9.2 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500 m spacing throughout selected survey blocks; flight paths during surveys were followed using GPS navigation in the helicopter. All deer observed within and between sampling blocks within the study area were included in abundance estimates. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Manufacturing Co., Brighton, CO USA) were uniquely marked using number, symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. I used program MARK (White and Burnham 1999) applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008) to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, I examined all parameter combinations of varying detection rates with survey occasion or effort (vary P with survey or effort), evaluating population size as equal or varied among surveys (a. = 0 or -=t= 0), and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cl= 0 or -=t= 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISSCUSSION -...I ~ '-' I._ ~ ~ Deer Captures and Survival The capture crew captured 253 fawns in early December 2008 and 150 does in late Februaryearly March 2009. Three fawn and Odoe mortalities occurred during capture and O fawn and 5 doe mortalities occurred during the myopathy period IO days post-capture. Fawn survival during mid-December 2008-late March 2009 varied from 0.688 (Yellow Creek) to 0.925 (Ryan Gulch; Fig 2., Table I). Fawn survival rates differed (P < 0.05) between the Ryan Gulch and Yellow Creek Study areas (Table I). Adult female survival mid-December 2008-late June 2009 varied from 0.762 (North Ridge) to 0.931 (Magnolia; Fig I), but were not different (P > 0.05) among study areas (Table I). Smaller sample sizes for adult females reduced my ability to detect differences relative to fawns, but the apparent lower survival of North Ridge females was partly due to 2 mortalities that occurred during early winter before the March capture effort when only 12-13 marked females were available in each study area. Overall, fawn survival was high during the period examined likely due to the mild winter conditions present through late March, and doe survival was consistent with other mule deer populations experiencing normal winter conditions in the western US (Unsworth et al. 1999). ~ 116 ~ �.__ .... Seasonal Movement Patterns Mule deer migration patterns during 2008 varied among study areas and within the Magnolia study area. North Ridge and north Magnolia deer migrated cast-west typically across US Highway 13; Yellow Creek, Ryan Gulch, and south Magnolia deer migrated south-north summering along the Roan Plateau; and Story/Sprague deer typically migrated relatively short distances south----"north (Fig 3.) . Although summer and winter ranges differed among study areas, distance and timing of migration was similar among 4 of the 5 study areas. Excluding the Story/Sprague study area, median date of spring migration occurred May 17, 2008 (all 4 study areas) and fall migration occurred from October 17-23, 2008; median straight-line migration distances ranged between 30.6 and 39.4 km among the 4 study areas. I noted unique migration patterns among Story/Sprague deer where median spring and fall migration occurred April 29 and December 17, 2008, respectively, and median migration distance was 9.6 km . Story/Sprague deer generally spent less time on winter range and required shorter migration distances to achieve their seasonal metabolic requirements. Mule Deer Body Measurements Body measurements of adult female mule deer recorded 27 February-6 March 2009 were typically highest from the Story/Sprague and North Ridge study areas and lowest from the Yellow Creek and Magnolia study areas (Table 2). Parameters most related to mule deer nutritional status (rLIVINDEX derived from rump fat and BCS; Cook et al. 200 l, 2007) suggested mule deer from the Story/Sprague study area were in the best condition and Yellow Creek deer were in the poorest condition. I observed significantly higher rLIVINDEX values (P < 0.05) among Story/Sprague females than females from the other 4 areas, but differences were not significant (P > 0.05) among the other 4 female groups. Early December fawn weights of males and females averaged 36.4 kg (n = 22, SD = 4.5) and 33.5 kg (n = 27, SD= 3.3) from Ryan Gulch, 33.9 kg (n = 22, SD= 3.6) and 30.5 (n = 28, SD= 4.9) from Yellow Creek, 37.0 kg (n = 24, SD= 3.5) and 33.5 kg (n = 26, SD= 4.0) from Magnolia, 35.8 kg (n = 26, SD= 4.8) and 33.2 kg (n = 24, SD= 3.0) from Story/Sprague, and 35.2 kg (n = 20, SD= 4.3) and 33.9 kg (n = 30, SD= 4.2) from North Ridge. Female fawns from Yellow Creek were significantly lighter (P < 0.05) than female fawns from the other 4 areas and Yellow Creek male fawns were significantly lighter than male fawns from Magnolia (P = 0.0 l 0). Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited constant population size across surveys (i.e., a= 0 suggesting population closure) and homogenous individual sightability (cr2 = 0) for all study areas, and variable sightability (P) across surveys in Ryan Gulch and Magnolia or with survey effort in North Ridge, Story/Sprague, and Ycllow Creek. North Ridge exhibited the highest deer density ( 18.1 /km2) and Ryan Gulch and Magnolia exhibited relatively low deer densities (5.6 and 6.6/km2; Table 3). ..., Abundance estimates were similarly precise from 4 of the 5 study areas (mean CV = 0.16-0.18), with Story/Sprague exhibiting the widest Cls (Table 3; mean CV = 0.29). A relatively small number of marked deer were sighted during surveys (Table 3) suggesting improved precision can be accomplished with increased sample sizes or increasing the number of surveys/study area. Increasing the number of marks/study area by 30 can easily be accomplished by extending GPS drop-off dates beyond the March capture period, which wasn't the case last winter. I also noted that complete coverage of each study area can reasonably be accomplished by increasing flight time by about 20 to 60 minutes/survey depending on the study area and should be more cost effective than increasing number of surveys/area. By increasing the number of marks and complete survey coverage/study area, CV s should improve likely providing detection of <30% change in population size. 117 �SUMMARY AND FUTURE PLANS The goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The infonnation presented here provide data describing mule deer population parameters from the -first pre-treatment year of a long-term study intended to address how mule deer react to landscape scale habitat and human • activity modifications. The pretreatment period is intended to continue I to 2 more winters to provide baseline data to compare against intended improvements in habitat conditions and concentration/reduction in human development activities, which will be maintained for at least 5 years to provide sufficient time to measure how deer respond to these changes. Based on the data collected thus far, Story/Sprague deer appear to be in better physical condition than deer from the other winter ranges examined. Migration patterns were similar among 4 of the 5 areas, but Story/Sprague deer traveled shorter distances and spent less time on winter range. Yellow Creek fawns were lighter than other study areas and exhibited lowest survival of the areas investigated. North Ridge deer exhibited the highest winter range density and Magnolia and Ryan Gulch deer exhibited the lowest densities. Reasons for these differences are currently unknown, but could be related to several factors including relative habitat conditions, duration on and distance to seasonal ranges, and extent of human activity throughout occupied habitats. Meaningful comparisons will be evident once treatments are implemented and comparisons arc possible between areas that are manipulated (treatment areas) and those that are not (control areas). We are currently working towards a habitat improvement plan and identifying beneficial development practices that are both logistically and financially feasible to implement. Investigations of habitat treatment potential arc promising in the Magnolia and Ryan Gulch study areas and we expect positive native plant responses with potential acceleration of response through native seeding. Members of CDOW, BLM, and private consultants will be developing a habitat treatment plan for review and approval by the end of the year. Discussions with Williams Production LMT Co. have produced a clustered development plan to be implemented in the Ryan Gulch study area and new technologies will be implemented to reduce human activity through remote monitoring of well pads and fluid collection systems. I recently contracted with Dr. Terry Bowyer and Patrick Lendrum (MS candidate) of Idaho State University to begin a graduate project addressing mule deer migration and potential influences of human activity along migration routes. I collaborated with Chad Bishop this past winter/spring to test a new VIT design that improves VIT retention (see Bishop 2009) and will improve our ability to address neonate survival (in addition to overwinter survival) and identify fawning habitat on summer range; these factors arc not currently being addressed, but could strengthen our inference about mule deer and energy development if funding and cooperative agreements were developed for this purpose. We are beginning to work collaboratively with ExxonMobile Production Co. and Colorado State University to enhance funding and potentially provide graduate student assistance addressing additional components of mule deer/energy development interactions. Additional funding and cooperative agreements will be necessary to manipulate habitat conditions to benefit mule deer and our current funding sources will need to be maintained to continue monitoring mule deer population parameters at the current level. We optimistically anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. LITERATURE CITED Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008b. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and 118 �...,,_ -...I ...t -...I habitat degradation-Stage I, Objective 5: Patterns o(.mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife, Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piccance deer study-population density and structure. Job Progress Report, Colorado Divison of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements ·of mule deer in the White River. Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 121 . Barrett, M.W., J.W. Nolan, and L.D. Roy. 1982. Evaluation ofa hand-held net-gun to capture large mammals. Wildlife Society Bulletin 10: I 08-114. Bishop, C. J. 2009. Effectiveness of a modified vaginal implant transmitter for capturing mule deer neonates from targeted adult females. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA . Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York, New York, USA. Cook, R. C., J. G. Cook, D. L. Murray, P. Zager, B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65:973-987. Cook, R. C., T. R. Stephenson, W. L. Meyers, J. G. Cook, and L.A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71: 1934-1943. Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins, Colorado, USA. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. McClintock, B. T., G. C. White, K. P. Burnham, and M. A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule der body composition using in vivo and post-mortem indicies of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghc. I 998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Chuck R. Anderson, Wildlife Researcher ...I 119 �Table I. Survival rate estimates (S} of fawn ( 14 Dec. 2008-21 Mar. 2009) and adult female ( 14 Dcc.20 June, 2009) mule deer in 5 winter range study areas of the Piceance in northwest, Colorado. ',.'--'·~ ~ Cohort Study area ~ Initial sample size (n) March doe samplea (n) S(95% CI) ....., ~ ~ Fawns ~ Ryan Gulch 54 0.925 (0.853-1.000) -..., Yellow Creek 43 0.688 (0.546---0.839) ....., Magnolia 50 0.800 (0.688-0.911) Story/Sprague 47 0.823 (0.722-0.937) North Ridge 48 0.833 (0.728-0.939) ~ --,' __, ....., ~ ....., Adult females Ryan Gulch 12 28 0.893 (0. 778-1.000) Yellow Creek 13 28 0.890 (0.737-1.000) Magnolia 12 29 0.931 (0.839-1.000) Story/Sprague 13 29 0.862 (0.737-0.988) North Ridge 13 30 0. 762 (0.536-0.960) 3 Adult female sample size following capture and radio-collaring efforts late February-early March, 2009. 120 ~ �l l l l l f l l l l l l l l l l l l l l l l f l l l l l l l l l l l l l l l l f l l l l ( l t Table 2. Mean body measurements, Body Condition Score (BCS), and an index of relative nutritional status (rLIVINDEX) of adult female mule deer from 5 study areas in the Piceance Basin of northwest Colorado, late February-early March, 2009. Sample sizes= 30/study area and values in parentheses = SD. Study Area Weight (kg) Hind foot length (cm) Chest girth (cm) Loin depth (mm) Rump fat (mm) BCSa rLIVINDEXb Ryan Gulch 52.2 (5.7) 46.9 (1.8) 96.9 (4.1) 40.50 (3.03) 1.73 (1.78) 2.66 (0.55) 2.71 (0.68) Yellow Creek 52.9 (4.8) 47.2 (1.1) 97.4 (4.2) 40.17 (2.95) 1.47 (0.68) 2.50 (0.60) 2.51 (0.63) Story/Sprague 55.6 (5.7) 47.2 (1.2) 96.0 (4.1) 40.70 (3.72) 1.97 (1.00) 3.09 (0.72) 3.12 (0.77) Magnolia 55.3 (5.9) 47.7 (1.5) 87.5 (5.0) 40.53 (3.70) 1.30 (0.79) 2.56 (0.68) 2.57 (0.70) North Ridge 53.3 (5.6) 47.3 (3.3) 97.2 (4.9) 41.13 (2. 70) 1.57 (1.22) 2.60 (0.56) 2.62 (0.60) 11 Body condition score taken from palpations of the rump (Cook et al. 200 I) brLIVEINDEX = (cm rump fat - 0.2) + BCS if rump fat > 2 mm. Otherwise = BCS (Cook et al. 2001, 2007). 121 �Table 3. Mark-resight abunda nce (N) and density estimates of mule deer rrom 5 winter range he rd segments in the Piceance Basin. northwest Colorado, 25 March- 2 April. 2009. Data represent 4 s urveys r·rom Ryan Gulc h and 5 s urveys from the other 4 study areas. Study area Mean No. sighted Mean No. marked N (95°/ti Cl) Dens ity (deer/km") Rya n Gulc h Yellow Creek Magnol ia S tory/Sprague North Ridge 156 138 109 138 238 12 7 6 5 14 727 (626- 854) 720 (605- 870) 854 (7 16- 1,027) I, 125 (853-1,509) 1,028 (874- 1,230) 5.6 10 .3 6.6 12.4 18. 1 - - Well Pads & Study Area Boundaries CJ Ryan Gulch CJ Story_ Spra_gue Gulch Magnolia Nor1h Ridge Yellow Creek ,t Active well pads Figure I. Approxi mate study area boundaries relati ve to active natural gas well pads and ene rgy development fac ilities in the Piceance Bas in of northwest Colorado, spring 2009. 122 - �Fawn Survival, Dec. 2008-Mar. 2009 1.000 -· 1s= - - - . . 0.900 '------~---· .:~~-~~~-~~~~ ~,.,,;;;::- , 0.800 ! 0.700 ~ 0.600 ~ 0.500 ! - - ~ - - - - - - - - - - - - - - - ·; 0.400 ~ 0.300 0.200 0.100 0.000 0 ,-... M 0 r-,. v M r;- ~ M DO ,-... v rl I"') C: N .-t N N N I ~ ~ I 'f"'-1 ""'"4 NI ro ¢ """' 00 L/'I N o:::t co LI) .0 '- 00 u, I """' M '""' -, ""'"4 N .-t N ro '- 'f"'-1 N <l,.I ..0 00 C: u u N (,;J C """' !:: C: u. (l,I .0 .0 ~ ro '- .. ~ I (LI (LI 0 0 I u (LI -Ryan Gulch -story/Sprague Magnolia North Ridge I ~ I -Yellow Creek I I .... ....ro .... .... "O u. !ti (l) u. ~ (LI LL 0 C"J ~ Adult female survival, Dec. 2008-June 2009 a:g~~ f-,.._ _ -----·-=,~.......:·~~~'-====::::!llliilii---......·~·-··=•"··=·":-·. -Yellow c--eek 0.800 ! 0.700 ~ 0.600 ~ 0.500 ·:;; 0.400 ~ 0.300 0.200 0.100 0.000 -Ryan Gulch -story/SpragJe ... , Maenolia North Ridge 0 N M C ':-4 -J .... ro I u 00 a., N 0 u a., Cl r-.. ~ rl rt'l ,-t I LI') ~ N I .-, .-, '1 ~ I w ..0 ~ co -.J N I r'i N .0 M 00 N I rl LI) M N I OJ f'-1 I... N Lf) I.... r.J \ 0 ....re ....re LL CJ ~ ~ < ('J LL I O'I I N') m N I u: (l. (.il > r, C M r3 M '- ~ >· I Q. r3 'I"'" <t ~ ~ C I"- -.J 'I"'" " C: > -= (t ~ -·-~-·-•---·----·~-- Figure 2. Winter survival rates of fawn ( 14 December, 2008-21 March, 2009; top) and adult female ( 14 December-21 June, 2009; bottom) mule deer from 5 study areas in the Piceance Basin of northwest Colorado. Survival rates of Yellow Creek fawns were significantly lower (P < 0.05; Table 1) than survival of Ryan Gulch fawns. Survival rates among other fawn and doe groups were not significantly different (P > 0.05; Table 1). 1 123 �\,;.;;I ~....,,~" ' .. •• ....;..~... -· .,,,:... ~ -!.._t..f · i · .• •~. • l ~I ',·!;'~.('/~~... • ~ .... ~ ~ '. .. ;; -, ,( • - •.,, ,. • ·.:.,,;, . > , ~.--:... •• · '\ ,., - ... "'• <\ I ~ '\-1• :-~ .."'i..,... I' •. '· , ·, ... ...... ........ --., Plus= Yellow Creek Diamond= Ryan Gulch Circle= North Ridge Star= Magnolia Triangle= Stor1/Sprague Figure 3. Mule deer GPS locations from 5 winter range study areas (solid lines; 15 does/study area) in the Piceance Basin of northwest Colorado, January 2008- Fcbruary, 2009. -..... - - 124 �Colorado Division of Wildlife July 2009-June 2010 w .,,_,, - WlLDLIFE RESEARCH REPORT State of_ _ _ _ _ _..:::C=o=lo=r=ad =o"------- : ~D_i_vi_s_io_n_o_f_W_il_d_liii _e_ _ _ _ _ _ _ _ __ Cost Center 3430 : "'"'M""'a=m ==a= m ls~R=e=s=e=a= rc~h~ - - - - - - - - Work Package 3001 : =D~e~e~ r ~C~o~n~se=r_v=at~io~n_ _ _ _ _ _ _ _ __ Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Proj ect: __W-'-'---=18=5~-R=-=-------Period Covered: July I, 2009 - June 30, 20 I 0 Authors: C.R. Anderson and C. J. Bishop 1w - Personnel: E. Bergman, J. Broderick, 8. deVergie, D. Finley, L. Gepfert, M. Grode, K. Kaai, T. Knowles, J. Lewis, P. Lukacs, K. Maysilles, M. Sirnchman, T. Swearingen, R. Velarde, S. Wilson, L. Wolfe, CDOW; E. Hollowed, BLM; S. Monsen, Western Ecological Consulting, Inc.; D. Freddy, Hoch Berg Enterprises; H. Sawyer, Western Ecosystems Technology; P. Lendrum, T Bowyer, Idaho State University; P. Doherty, G. Wittemyer, K. Wi lson, G. Wh ite, Colorado State University; M. Keech, L. Shelton, M. She lton, R. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, lnc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Colorado State Severance Tax Fund, EnCana Corp., Shell Petroleum, and Williams Production LMT Co. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT We propose to experimentally evaluate habitat treatments that may improve the landscape to benefit mule deer (Odocoileus hemionus) and evaluate human-activity management alternatives to reduce the disturbance of energy development impacts on mule deer. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas within the state. The data presented here represent the first 2 pretreatment years of a long-term study addressing habitat modifications and improved energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We modified the previous study design to monitor 4 winter range study areas representing varying levels of development to serve as treatment (Ryan Gu lch, North Magnolia, South Magnolia) and control (North Ridge) sites and recorded habitat use and movement patterns using GPS collars (5 locations/day), estimated overwinter fawn and annual adult female survival, estimated early and late winter body condition of adu lt females using ultrasonography, and estimated abundance using helicopter mark-resight surveys. We attached 250 VHF collars (50- 80/study area) to fawns and 80 VHF collars to does (20/study area) in early December 2009 and I 00 GPS collars (25/study area) to adult fema le mule deer in early March 20 l 0. Based on the data collected thus far, deer from all areas appear to 47 �be in reasonably good condition and are exhibiting high survival rates. Mild winter conditions the past 2 years certainly contributed to the observed mule deer popu lation parameters. lt will be informative to note how the different wintering mule deer herd segments react following a severe winter. Observed differences in winter concentration areas thus far may indicate behavioral modifications to areas of high development activity, but resource selection analyses will be necessary to confinn this supposition. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of the habitat treatments scheduled to begin this fa ll. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less-impacted areas. We may also find that habitat mitigation efforts are not effective in developed areas at all, suggesting that habitat enhancement efforts may be only effective in areas that are not impacted by development. We are also evaluating deer behavioral responses to varying levels of development activity and habitat mitigation treatments. Thjs will allow us to assess the effectiveness of certain Best Management Practices (BMPs) and habitat manipulations for reducing disturbance to deer. The study is slated to run through at least 2015, and preferably 201 8, to adequately measure deer population responses to landscape level manipulations. 48 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR and CHAD J. BISHOP PROJECT NARRITIVE OBJECTIVES l. To determine experimentally whether enhancing mule deer habitat conditions on winter and/or transition range elicits behavioral responses, improves body condition, increases overwinter fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, over-winter fawn survival, and winter range mule deer densities. ""1tsl SEGMENT OBJECTIVES 1. Collect and reattach GPS collars (5 location attempts/day) to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Develop cooperative agreements to initiate habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Summarize data and present information in an annual Job Progress Report. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and the Colorado Division of Wildlife that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape for human housing, supporting businesses, and upgraded 49 '-' �road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to deer populations. Thus, research documenting these impacts and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about I 5,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us). Anderson and Freddy (2008a) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the past 3 years, we have gathered baseline habitat utilization data from OPS-collared deer across the Piceance Basin to allow assessment of mitigation approaches that will be implemented over the next 2-3 years and evaluated for another 5-6 years. We initially selected 5 winter range study areas representing varying levels of development to serve as treatment and control sites. The past 2 years, we also estimated winter fawn survival and annual adult female survival, early and late winter body condition of adult females using ultrasonography, and deer abundance using helicopter mark-resight surveys. We started with 5 study sites to allow flexibility to respond to differences in deer behavior and changing energy development plans, which can directly affect experimental design. During the previous year, we refined our study design using our baseline deer data and current energy development plans of the major companies operating in Piceance Basin. We split I study area (Magnolia split into North and South Magnolia) based on differences in deer movement and behavior patterns from GPS data (Anderson 2009) and eliminated 2 other study sites (Story/Sprague and Yellow Creek) due to incompatible deer behavior patterns to adequately serve as control sites and to reduce the annual project budget to the minimum necessary to meet the original research objectives. This progress report describes the previous 2 years of addressing mule deer population performance during the pretreatment phase, which includes monitoring habitat selection and behavior patterns of adult female mule deer, overwinter fawn and adult female survival, estimates of adult female body condition during early and late winter, and abundance estimates on 4 winter range herd segments in relation to varying levels of natural gas development in control and treatment experimental areas prior to proposed experimental modifications in energy developmental practices and potential habitat improvement treatments. STUDY AREAS The Piceance Basin between the cities of Rangely, Meeker, and Rifle in northwest Colorado was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 15,00022,000 (Bartmann 1975), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utah juniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 167 5 m to 2285 m in elevation (Hartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Hartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay ~l..,I '-" _, "'-' ~ '--' "w,/ \wl ~ '-" l_,i ~ w' "w,/ '-' lwJ 'w _, ~ I._.J w' ~ \._I ~ ~ w' ~ '_,J _,I '-" ~ \.._.,'- 1._.i 50 ~ l,_.i y �fields. Grasses and forbs common to the area consist of wheatgrass (Agropyron spp. ), blue grama (Bouteloua graci/is), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt. 'cl Wintering mule deer population segments we investigated in the Piceance Basin include: North Ridge (57 km2)just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 130 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. I). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.13 pads & facilities/km 2), and relatively high development in the Ryan Gulch (0.64 pads & facilities/km 2) and South Magnolia (0.81 pads & facilities/km 2) segments (Fig. 1). Among the 4 study areas, North Ridge will serve as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (Best Management Practices; BMPs) that may benefit mule deer exposed to energy development, and North and South Magnolia will serve to address the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. METHODS Tasks addressed this fiscal year included mule deer capture and collaring efforts, monitoring overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography, and estimating mule deer abundance applying helicopter markresight surveys. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to capture 50-80 fawns and 20 adult females during early December and 25 adult females during early March in each of the 4 study areas (250 fawns and 180 does total). Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to localized handling sites for collection of body measurements and were fitted with VHF (20/area during December) or OPS collars (25/area during March; 5 fixes/day; 0211 OB, Advanced Telemetry Systems, Isanti, MN, USA) and released. To provide direct measures of decline in overwinter body condition, we attempted to capture the same adult females during the March capture that were captured in December. Fawn collars were spliced and fitted with 2 lengths of rubber surgical tubing to facilitate collar drop during mid-summer-early autumn, adult VHF collars were attached static, and OPS collars were supplied with timed drop-off mechanisms scheduled to release early April, 2011. All radiocollars were equipped with mortality sensing options (i.e., increased pulse rate following 4 hrs of inactivity). Mule Deer Habitat Use and Movements We downloaded and organized data from OPS collars deployed March 2009 following collar drop and retrieval in early April 2010. OPS collars redeployed early March 2010 maintained the same fix schedule of attempting fixes every 5 hours. We plotted deer locations and recorded timing and distance of spring and fall 2009 migrations for each study area. Mule deer winter concentration areas were created using composite OPS data (winter locations since January 2008 from all deer) from each study area and mapped in ArcGIS (ver. 9.3) using Spatial Analyst (kernel probability density functions separated by 51 �~ '-' ~ vi quantiles). Mule deer resource selection analyses are pending completion of high resolution habitat data layers currently being developed by BLM (habitat data layers should be available by 2011 ). Mule Deer Survival Mule deer mortality monitoring consisted of daily ground telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated over-winter survival on a weekly basis using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any mortalities occurring within IO days of capture) and collar failures were censored from survival rate estimates. We estimated survival rates 28 June 2009-26 June 2010 for adult females and 6 December 2009-27 March 2010 for fawns. Premature failure of surgical tubing integrity beginning late March inhibited our ability to reasonably estimate fawn survival beyond March 27, 2010. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. ( 1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm) and loin depth (longissimus dorsi muscle, mm). We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001). We combined ultrasound rump fat measurements with BCS to develop an index (rLIVINDEX; Cook et al. 2001, 2007) of the relative nutritional status of deer from each study area. We examined differences (P < 0.05) in nutritional status among study areas using a two-sample I-test. We considered differences in body condition meaningful when either mean rump fat or rLIVINDEX differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 5 (North Magnolia) or 4 (the remaining study areas) helicopter mark-resight surveys (2 observers and the pilot) during late March, 2010 to estimate deer abundance in each of the 4 study areas. We delineated each study area from GPS locations during the same period the previous year and aerial telemetry locations ofradio-collared deer within I week of the first mark-resight survey. Aerial fixed-wing telemetry surveys were conducted during helicopter surveys to determine which marked deer were within each survey area. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using number, symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. We used program MARK (White and Burnham 1999) applying the mixed logit-normal model (McClintock et al. 2008) to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2 = 0 or* 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). '~ "--'~ ~ i_,i _, '-' l_,.i \wl 'w '-' '-' \wl \wl 'w .._., '-i,I l._i .I '-"~ '-..,I ~ ',wl .._., vi '-i,I \_,I ~ '-' "--' vi vi '-" 1...,1 1...,1 ~ ~~ I._/ 52 ~ 'w ~ �RES UL TS AND DISSCUSSION -' Deer Captures and Survival The helicopter crew captured 253 fawns and 80 does in early December 2009 and 103 does in early March 2010. Seven fawn (ultimate cause= 4 cougar predation, 2 coyote predation, 1 drowning) and 2 doe mortalities (ultimate cause= tangled in fence and coyote predation) occurred within the IO day myopathy period following the December capture and 3 doe mortalities occurred during the March capture (all direct capture myopathy). Fawn survival during early-December 2009-late March 20 IO was similar among study areas (P > 0.05) ranging from 0.872 (Ryan Gulch) to 0.945 (North Magnolia; Table 1, Fig. 2). Although mean fawn survival was higher than last year among 3 of 4 study areas (with the exception of Ryan Gulch; see Anderson 2009), differences were statistically insignificant. Annual adult female survival was also similar among study areas (P > 0.05) ranging from 0.863 (North Ridge) to 0.943 (North Magnolia; Table I, Fig. I) and were comparable to last year (P > 0.05; Anderson 2009). The relatively high fawn survival observed the past 2 winters is likely due to the mild winter conditions present through late March, and doe survival was consistent with other mule deer populations experiencing normal winter conditions in the western US (Unsworth et al. 1999). 'el Seasonal Movement Patterns Migration patterns differed among areas with North Ridge and North Magnolia deer migrating east-west and South Magnolia and Ryan Gulch deer migrating south-north (Fig. 3). Median straight-line migration distances were similar ranging from 32.6 km (Ryan Gulch) to 40.1 km (North Ridge). Similar to seasonal ranges, most deer monitored exhibited strong fidelity to spring and fall migration routes (Fig. 3). Timing of mule deer migration during 2009 was similar among study areas with median spring migration dates occurring between 15 and 20 May and median fall migration dates occurring between 15 and 22 October. Migration dates were later compared to last year (Anderson 2009), occurring 8 to 16 days later in the spring and 11 to 14 days later in the fall. Length of migration was relatively short among areas averaging 5 to IO days in the spring and 4 to 7 days in the fall; these observations were comparable to last year. More detailed analyses of these migration data investigating the influence of human activity are currently being conducted by Patrick Lendrum and Terry Bowyer of Idaho State University. A final report including next year's migration data is scheduled to be completed by spring 2012. Winter concentration areas identified from January 2008-May 2010 (Fig. 4) reasonably followed study area boundaries delineated from deer locations applied the first winter of the project (Anderson and Freddy 2009b). We noted more continuous distributions from Ryan Gulch and North Ridge deer, with South Magnolia deer exhibiting the most fragmented and concentrated distributions, which may be related to relative development densities within each study area. Future resource selection analyses will address these differences relative to habitat attributes within each area. Minor modifications to study area boundaries will be applied in the future to better address winter deer use within each study area (Fig. 4). Mule Deer Body Condition Body condition measurements of adult female mule deer suggested that North and South Magnolia deer returned from summer range (December 2009) in better condition than North Ridge deer (P < 0.05) and condition of Ryan Gulch deer was intermediate and not significantly different (P > 0.05) from the other areas (Table 2). North and South Magnolia deer maintained relatively high body condition over winter, but only North Magnolia deer were in significantly better condition than deer from North Ridge and Ryan Gulch (P < 0.05; March 20 I 0, Table 2) by late winter. Paired comparisons of deer captured during December 2009 and March 20 IO indicted that mean rump fat and % body fat declined 8.3 mm and 6.9% in North Magnolia (n = 15), 8.1 mm and 6.9 % in South Magnolia (n = 16), 3.1 mm and 53 �4.0% in North Ridge (n = 16), and 6.3 mm and 6.6% in Ryan Gulch (n = 19). In comparing late winter body condition from 2009 to 2010, we noted significant improvement from North and South Magnolia deer and similar condition from North Ridge and Ryan Gulch deer. Pregnancy rates were expectedly high ranging from 84% in Ryan Gulch (n = 25) to 100% in South Magnolia (n = 25). Early December fawn weights of males and females averaged 39.5 kg (n = 30, SD= 4.3) and 36.5 kg (n = 30, SD= 3.2) from North Magnolia, 38.5 kg (n = 42, SD= 3.8) and 35. l (n = 18, SD= 4.0) from South Magnolia, 37.5 kg (n = 33, SD= 4.0) and 34.9 kg (n = 50, SD= 4.3) from North Ridge, and 37. l kg (n = 23, SD= 3.3) and 34.5 kg (n = 27, SD= 3.4) from Ryan Gulch. Fawn weights were similar among areas except that male and female fawns from North Ridge were larger than Ryan Gulch fawns (P < 0.05). Because North and South Magnolia study areas were not split until December 2009 and fawn locations were not sufficiently monitored prior to that time, comparisons to 2008 fawn weights were only possible by combining data from North and South Magnolia in 2009. Both males and females from the combined Magnolia area were larger during December 2009 than December 2008. Fawn weights from the other study areas were similar between years expect for males from North Ridge, which were also larger in 2009 (P = 0.047). \..._.,"-' i._.i ~ ~ ~ i_, ~ "-' ~ ~ ~ Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited homogenous individual sightability (cr2 = 0) for all study areas and variable sightability (P) across surveys in 3 of the 4 study areas; sightability was consistent across surveys in North Magnolia. North Ridge exhibited the highest deer density (20.1/knl) and comparably lower deer densities were observed in the other 3 areas (6.9-9.3/km2; Table 3). Abundance estimates were similar to last year (Anderson 2009) except in Ryan Gulch where deer numbers were significantly higher this year. It is unlikely deer abundance increased from 825 (95% CI= 672-1,016) to 1,442 (95% CI= 1112-1878) in 1 year, and we suspect this difference may be partially due differences in sampling approach between years. The abundance estimate from 2009 was derived from subsampling 20 to 40% of the Ryan Gulch study area (Anderson 2009), whereas the 2010 estimate was based on complete sampling of the entire study area. It is plausible that subsampling the study area resulted in a negative bias and we are more comfortable with the 2010 estimate derived from complete coverage of the study area. ~ 'w 'w' ~ I._,.; 41 ~l_,I ~ ~ '._.i Abundance estimates from 2010 were similarly precise from 3 of the 4 study areas (mean CV= 0.16-0.18), with Ryan Gulch exhibiting a relatively wide CI (Table 3; mean CV= 0.27). Number of marked deer was lowest from Ryan Gulch (n = 87) and increasing sample size would improve future estimates, as would increasing the number of mark-resight surveys. Additionally, winter concentration information from the past 3 winters (Fig. 4) can be used to more efficiently focus sampling effort potentially increasing mule deer sightability. Our goal is to achieve CVs of :S0.15 to allow detection of at least 30% population change. We will attempt to improve precision of future mark-resight abundance estimates by increasing sample size using VHF radiocollars and increasing the number of surveys when feasible; simulations suggest CVs can be improved by about 0.02 for each additional mark-resight survey (C. Anderson, unpublished data). ~ 'w vi ~ ~ ~ ~ ~ SUMMARY AND FUTURE PLANS The goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here provide data describing mule deer population parameters from the first 2 years of the pretreatment period of a long-term study intended to address how mule deer react to landscape scale habitat and human activity modifications. The pretreatment period is intended to continue 1 to 2 more winters to provide baseline data to compare against intended improvements in habitat conditions and evaluation of concentration/reduction in human development activities, which will be maintained for at least 5 years to ~ w' _, - 'w ~~ '_,.I 54 '-" ~ '- �-.,I ..._, provide sufficient time to measure how deer respond to these changes. Based on the data collected thus far, deer from all areas appear to be in reasonably good condition and are exhibiting high survival rates. Mild winter conditions the past 2 years certainly contribute to the observed mule deer population parameters. It will be informative to note how the different wintering mule deer herd segments react following a severe winter. Observed differences in winter concentration areas (Fig. 4) may indicate behavioral modifications to areas of high development activity, but resource selection analyses will be necessary to confirm this supposition. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of the habitat treatments. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less-impacted areas. We may also find that habitat mitigation efforts are not effective in developed areas at all, suggesting that habitat enhancement efforts may be only effective in areas that are not impacted by development. In a recent project conducted on the Uncomphahgre Plateau, Bergman et al. (2009) found that habitat treatments implemented in pinyon-juniper habitat in undeveloped areas were effective for deer. We are also evaluating deer behavioral responses to varying levels of development activity and habitat mitigation treatments. This will allow us to assess the effectiveness of certain BMPs and habitat manipulations for reducing disturbance to deer. We recently developed a habitat improvement plan and intend to begin implementation this fall with completion by fall 2011 if feasible or fall 2012 in the Magnolia study areas. In addition, hay field improvements have begun and will continue in the North Magnolia area and we plan to begin discussions addressing hay field improvements in the South Magnolia study area. Recent collaboration agreements with ExxonMobil Development Co. and Colorado State University will provide graduate research opportunities to enhance data collection and inference about mule deer/energy development interactions. Collaboration with Williams Production LMT Co. have produced a clustered development plan to be implemented in the Ryan Gulch study area and new technologies will be implemented to reduce human activity through remote monitoring of well pads and fluid collection systems. We are continuing to work with Dr. Terry Bowyer and Patrick Lendrum (MS candidate) of Idaho State University to address mule deer migration and potential influences of human activity along migration routes. Additional funding and cooperative agreements will be necessary to sustain this project through completion (through at least 2015 and preferably through 2018). We optimistically anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. LITERATURE CITED \sl cl Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008b. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation-Stage I, Objective 5: Patterns of_mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife, Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report, Colorado Divison of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. 55 �'._,I i,_i '-" _.i Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 121. Barrett, M. W., J. W. Nolan, and L. D. Roy. 1982. Evaluation ofa hand-held net-gun to capture large mammals. Wildlife Society Bulletin 10: 108-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, and G. C. White. 2009. Evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, USA. Burnham, K. P., and D.R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York, New York, USA. Cook, R. C., J. G. Cook, D. L. Murray, P. Zager, B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65:973-987. Cook, R. C., T. R. Stephenson, W. L. Meyers, J. G. Cook, and L. A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71 : 1934-1943. Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins, Colorado, USA. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. McClintock, B. T., G. C. White, K. P. Burnham, and M.A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture ofred deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J.C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. \..v',.-1 ~ ~ \._I "-' \wl i_,i 'w ~ _, ~ ~ ~ _, ~ w ~ ~ -~ - '-' ._,, w '._,I ~ '..-' ~ _.I I-, ~ Prepared by _ _ _ _ _ _ _ _ __ Chuck Anderson, Wildlife Researcher ~ ~ ~ '..i ~ '-' '--"~ ~ 56 'w ~ "-' �Table I. Survival rate estimates (S) of fawn (6 Dec. 2009-27 Mar. 2010) and adult female (28 June 2009-26 June 20 l 0) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe samplea (n) S(95% Cl) Fawns Ryan Gulch 47 0.872 (0. 777-0.968) South Magnolia 63 0.937 (0.876-0.997) North Magnolia 55 0.945 (0.884-1.000) North Ridge 80 0.912 (0.849-0.974) Adult females Ryan Gulch 25 47 0.868 (0.757-0.979) South Magnolia 12 38 0.873 (0. 757-0.989) North Magnolia 14 44 0.943 (0.866--1 .000) North Ridge 27 50 0.863 (0.748-0.978) ""-' ., aAdult female sample size following capture and radio-collaring efforts early March, 2010. vu \:sl 57 �Table 2. Mean rump fat (mm), Body Condition Score (BCS)3, and an index of relative nutritional status (rLIVINDEX/ of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December 2009 and March 2010. Values in parentheses= SD. December 2009 March 2009 March 2010 rLIVINDEX Rump fat BCS rLIVINDEX Rump fat 1.73 (1.78) 2.66 (0.55) 2.71 (0.68) 8.35 (6.36) 4.06 (1.13) 4.71 (1.63) 2.31 (1.44) 2.35 (0.48) 2.41 (0.57) South Magnolia 1.47 (0.68) 2.50 (0.60) 2.51 (0.63) 10.05 (6.19) 4.07 (1.21) 4.87 (1.75) 3.12 (2.20) 2.64 (0.59) 2.78 (0.74) North Magnolia 1.30 (0.79) 2.56 (0.68) 2.57 (0.70) 10.20 (5.48) 4.25 (0.96) 5.07 (1.42) 3.15 (2.34) 2.85 (0.53) 2.99 (0.70) North Ridge 1.57 (1.22) 2.60 (0.56) 2.62 (0.60) 5.25 (5.65) 3.63 (1.11) 3.98 (1.59) 1.77 (1.11) 2.42 (0.49) 2.46 (0.54) Study Area Rump fat Ryan Gulch BCS BCS rLIVINDEX aBody condition score taken from palpations of the rump (Cook et al. 2001) brLIVEINDEX = (cm rump fat - 0.2) + BCS if rump fat> 2 mm. Otherwise= BCS (Cook et al. 2001, 2007). 58 ( ( ( ( { ( ( { l { ( ( ( ( { ( ( { ( ( { ( { ( { l ( { { ( ( { ( ( ( C{ ( ( { ( ( l C( { ( �Table 3. Mark-resight abunda nce (N) and density estimates of mule deer from 4 winter range herd segme nts in the Piceance Basin, northwest Colorado, 22- 3 1 March 2009. Data represent 5 resight surveys from North M agno lia and 4 resight surveys from the othe r 3 study areas. Study area Mean No. sighted Mean No. marked N (95% C l) Density (deer/km-) Ryan G ulch 125 II 1,442 (1,112- 1,878) 9.3 South Magnolia 103 18 575 (48 1- 692) 6.9 No1th Magnolia 102 14 595 (498- 715) 7.5 North Ridge 23 1 23 I, 145 (975- 1,348) 20. 1 i.,ftf ..., '-' ..., ...... ...., ..... ..., ! In developrrent J; lnJect1on well South Magnolia .t In production North Ridge G Development facility 2 4 ;,,liles Figure I. Mu le deer winter range study areas re lative lo active natural gas well pads a nd energy development faci lities in the Piceance Basin of north west Colorado, summer 20 l 0. 59 �~ -' ~ '_,i Adult Female Survival, 28 June 2009-26 June 2010 1.000 0.900 0.800 0.700 0.600 - - - - - - - - - - - - - - - - - - - - 0.500 0.400 0.300 - · · t - - - - - - - - - - - - - - - - - - - - 0.200 0.100 0.000 ! l I I I I I I I I I I I I I I Ii;. I I I I I I I I I ! l I I I I ...a. 6 " " N °'r:, °'r:, ~ ~ a. 00 en en <f: > °'c.o ... > .c "ro 2:- <C 6 ... a. u z > a. 2 ro 2 ~ L/".I L/".I N 2::::, :::, --, .-t N .-t I 1.11 N .-t .-t 0 N .-t rtl QJ 0 0 C: ro --, u 0 QJ z 0 1.11 c.o :::, :::, --, 00 I .-t QJ :::, 00 N C: ~ N I .-t 7 L/".I .-t 0 rtl rtl N .-t N C: .-t 'I.. --, LL ~ rtl 00 'I.. 'I.. <C ro '.._I ~ -North Ridge -North Magnolia -south Magnolia -Ryan Gulch :::, ~ ~ ~ ~ _, 6 C: N 7 QJ 0 \_,I C: rtl :::, --, > ro 2 <C _, N QJ ro I '-I ~ L/".I .-t N QJ L/".I .-t N 'I.. I I ~~ ~ 2 ~ \iwl Fawn Survival, 6 December 2009-27 March 2010 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 ~ '-" -- ...-1 \.-,.'..; '-" I I N .-t I ~ u QJ 0 ~ °' r:, 6N .-t .-t u I I N u QJ QJ 0 0 N C: ro 7 "u N QJ 0 I I ~ I °' rtl 6 rtl N --, C: ro --, I C: C'0 .-t .-t C: ro --, r-!. .-t I I 0 rtl ~ N C: ro --, ~ .c QJ LL I I rtl .-t r-!. 0 I ~ "..!t LL LL N N .-t N .c .c QJ ..!t .c QJ QJ LL rtl C: C'0 I ~ 0 .-t N 2 " .-t ro I 00 N .c --, rtl 'I.. • 'I.. C'0 2 ~ -North Magnolia '_,I -south Magnolia ~ ""';J .-t N 'I.. 'I.. 2 2 ro \iwl -Ryan Gulch I I -North Ridge C'0 QJ LL Figure 2. Annual and winter survival rates of adult female (28 June 2009-26 June 201 0; top) and fawn (6 December, 2009-27 March, 201 0; bottom) mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado. Survival rates among fawn and doe groups were statistically similar (P > 0.05; Table 1). _, ~ _, -~ l._.i ~ ~ '-' ~ ~ 'w,I - l,_.J '....,~ ~ 60 \v '-" 1.,.1 �Ry-an Gulch - Diamonds South Magnolia - Stars c::J North Magnolia - Pluses North Ridge - Circles 5 10 20 .,-,____..-.,ill'!!::..::.,i ~!'iiiiiii~!iiiii~~~~~. M,les Figure 3. Mule deer migration routes from 4 winter range study areas in the Piceance Basin of no1ihwest Colorado, spring and fa ll 2009 . .,,.; ._., 6l �Mule deer winter concentration areas Study Area Kernel PDF L..J Ryan Gulch L J 95% D North Magnolia - 92% South Magnolia - 87% North Ridge -78% r ] 0 1.2525 5 - 61% Figure 4. Mule deer winter concentration areas (composite kernel Probability Density Functions; PDF) from 4 study areas in the Piceance Basin of northwest Colorado, December 2008- May 2010. Data from composite GPS locations of adult fema le mule deer by sn1dy area (5 GPS location attempts/day). 62 �- Colorado Division of Parks and Wildlife July I, 2010 - llllle 30, 2011 WILDLIFE RESEARCH REPORT State of_ _ _ _ _ __:C ,c_o""l""oc:.:ra::::d::::o<--_ _ _ _ _ : Division of Parks and Wildlife Cost Center 3430 : ""M""a:::..:mm ==a=ls=-= R=e=se=a=r=ch ' -'-------- - -- - -- Work Package 3001 : ,.D::. !e<>=e"'-r.. ., C<->o'n-'-'--"s"er """""'v'""a""ti' ""'o""n~---- - - - - - - - - Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Miti gation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:._ ____:W . :. . - - -"1=8.:::..5-----"R~ - -- -Period Covered: July I, 2010 - June 30, 2011 Authors: C.R. Anderson and C. J. Bish op ....., ..... ....., - - Personnel: E. Bergman, J. Broderick, P. Damm, B. deVergie, D. Finley, L. Gepfert, M. Grode, C. Ha1ty, K. Kaai, T. Knowles, J. Lewis, P. Lukacs, T. Parks, B. Petch, M. Peterson, R. Velarde, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; S. Monsen, Western Ecological Consulting, Inc.; D. Freddy, Hoch Berg Enterprises; T. Graham, Ranch Advisory Partners; M. Wille, T & M Contractors.; H. Sawyer, Western Ecosystems Technology; P. Lendrum, T Bowyer, Idaho State University; P. Doherty, J. Northrup, G. Wittemyer, K. Wilson, G. White, Colorado State University; M. Keech, L. Shelton, M. She lton, R. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, fnc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co., Marathon Oil Corp., Shell Petroleum, and Williams Production LMT Co. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas within the state. The data presented here represent the first 3 pretreatment years of a long-term shtdy addressing habitat modifications and improved energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored 4 winter range sh1dy areas representing varying levels of development to serve as treatment (Ryan Gulch, North Magnolia, South Magnolia) and control (North Ridge) sites and recorded habitat use and movement patterns using GPS collars (5 location attempts/day), estimated overwinter fawn and annual adult female survival, estimated early and Late winter body condition of adult females using ultrasonography, and estimated abundance using helicopter mark-resight surveys. We targeted 250 fawns (50- 80/study area) and 100 does (20-40/study area) in early December 201 0 for VHF and GPS radiocollar attachment, 51 �respectively, and 80 does in March 2011 (20/study area) for late winter body condition assessment and to increase our GPS radiocollar sample in 3 of the 4 areas ( IO of 20/area excluding Ryan Gulch). Based on the data collected since January 2008, deer from a ll areas appear to be in reasonably good condition and exhibited high survival rates the first 2 years, with lower winter fawn survival through mid-June this past winter in 3 of 4 study areas (excluding North Ridge), and winter range deer densities appear to be stable or increasing. Mild winter conditions the first 2 years fo llowed by more severe winter conditions this year Likely contributed to the observed survival rates and population trends. Observed differences in winter concentration areas thus far may indicate behavioral modifi cations to areas of high development activity, but resource selection analyses will be necessary to confirm this supposition. Pilot habi tat treatments ( 126 acres total) were completed January 20 I I and moist spring weather conditions have resulted in excellent vegetation response thus fa r. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of additional habitat treatments (North and South Magnolia) scheduled for fall/winter 20I 2- 2013 ( 1,200 acres total). This evaluation will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In collaboration with Colorado State University, we are also evaluating deer behavioral responses to vary ing levels of development activ ity in the Ryan Gulch study area. This will allow us to assess the effectiveness of certain Best Management Practices (BMPs) for reducing disturbance to deer. The sh1dy is slated to run through at least 20 L7, and preferably 20 19, to adequately measure mule deer population responses to landscape leve l manipulations. - -- -- --- 52 -- �.._ WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PIC.EANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR and CHAD J. BISHOP PROJECT NARRITIVE OBJECTIVES I. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases overwinter fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, over-winter fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES '-' I. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. '-..I 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Initiate habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. INTRODUCTION '-I Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to mule deer populations. Thus, research documenting these impacts and evaluating the most effective strategies for minimizing and mitigating these activities will 53 �greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us). Anderson and Freddy (2008a) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population perfonnance. During the past 4 years, we have gathered baseline habitat utilization data from OPS-collared deer across the Piceance Basin to allow assessment of mitigation approaches that will be implemented over the next 1-2 years and evaluated for another 4-6 years. We are currently monitoring 1 control area without development (North Ridge), 2 areas with relatively high development activity (0.6-0.8 well pads & facilities/km 2 ; Ryan Gulch, South Magnolia), and another area with relatively minor development activity (0.1 well pads & facilities/km2; North Magnolia). In comparison to the un-manipulated control area (North Ridge), the North and South Magnolia areas will receive similar levels of mechanical habitat treatments to evaluate this mitigation technique in relation to differing development intensities, and deer behavior patterns relative to differing development activities in the Ryan Gulch area will be monitored to identify effective Best Management Practices (BMPs) for future application. This progress report describes the previous 3.5 years (Jan 2008-June 2011) of addressing mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer, overwinter fawn and adult female survival, estimates of adult female body condition during early and late winter, and abundance estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. 1). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utahjuniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1,675 m to 2,285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush ( Ch,ysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixedgrass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinna/a), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt. 54 .,; .,; '-' ~ 'w \_.I ~ \_.I '-' I._.J 'w 1w 'w ~ '---'._ 'w .,; �'cl Wintering mule deer population segments we investigated in the Piceance Basin include: North Ridge (53 krn 2)just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 141 krn2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 krn 2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. I). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.14 pads & facilities/km 2), and relatively high development in the Ryan Gulch (0.60 pads & facilities/km 2) and South Magnolia (0.86 pads & facilities/km2) segments (Fig. 1). Among the 4 study areas, North Ridge will serve as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development, and North and South Magnolia will serve to address the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. METHODS Tasks addressed this period included mule deer capture and collaring efforts, monitoring overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography, estimating mule deer abundance applying helicopter mark-resight surveys, and initiating winter range habitat treatments to benefit mule deer in areas experiencing disturbance from energy development activities. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to capture 50-80 fawns and 20-40 adult females during early December 20 IO and 20 adult females during early March 2011 in each of the 4 study areas. Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to localized handling sites for collection of body measurements and were fitted with GPS collars (20-40/area during December 20 I 0, 0-10/area during March 2011; 5 or 24 fixes/day; G2 l 10B, Advanced Telemetry Systems, Isanti, MN, USA) and released. To provide direct measures of decline in overwinter body condition, 20 does were recaptured in Ryan Gulch and 10 from the other 3 study areas that were captured the previous December; IO uncollared does were also captured in North Ridge, North Magnolia, and South Magnolia to increase sample sizes in those areas. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop during mid-summerearly autumn and GPS collars were supplied with timed drop-off mechanisms scheduled to release early April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 4-8 hrs of inactivity). Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed March 2010 following collar drop and retrieval in early April 2011. GPS collars deployed early December 2010 maintained the same fix schedule of attempting fixes every 5 hours except in Ryan Gulch where fix rates were increased to I/hour to increase resolution of GPS data for evaluation of deer behavior patterns in relation to differing development activities. We plotted deer locations and recorded timing and distance of spring and fall 2010 migrations for each study area. Mule deer winter concentration areas were created using composite GPS data (winter locations March 2010-April 2011 from all deer; 5 location attempts/day) from each study area and mapped in ArcGIS (ver. 9.3) using Spatial Analyst (kernel probability density functions separated by quantiles). Mule deer resource selection analyses are pending completion of high resolution habitat data layers currently being developed by BLM. 55 �Mule Deer Survival Mule deer mortality monitoring consisted of daily ground telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and bi-weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any mortalities occurring within IO days of capture) and collar failures were censored from survival rate estimates. We estimated survival rates 1 July 2010-30 June 2011 for adult females and early December 2010-mid June 2011 for fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. ( 1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007). We examined differences (P < 0.05) in nutritional status among study areas and between years using a two-sample /-test. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, weight (kg), chest girth (cm), and hind-foot length (cm). ._, ._, ._, ~ ._, 'wl 1w Abundance Estimates ~ We conducted 4 (North Ridge) or 5 (the remaining study areas) helicopter mark-resight surveys (2 observers and the pilot) during late March, 2011 to estimate deer abundance in each of the 4 study areas. We delineated each study area from GPS locations collected during winter from previous years (since Jan 2008) and aerial telemetry locations of radio-collared deer within 1 week of the first markresight survey. Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. We used program MARK (White and Burnham 1999) applying the mixed logit-normal model (McClintock et al. 2008) to estimate mule deer abundance and confidence intervals. For mark-resight mode] evaluations, we examined parameter combinations of varying detection rates with survey occasion 2 and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr = Oor * 0). Model selection procedures followed the infonnation-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 264 fawns and I 07 does in December 2010 and 81 does during March 2011. Nine fawn mortalities (ultimate cause= 6 capture myopathy and 3 predation) occurred 56 '--".._ ._, ._, '-' '-' ._, �within the 10 day myopathy period following the December capture and 1 doe mortality each followed the December and March captures (ultimate cause= I capture myopathy and 1 predation). ...,_ Fawn survival from early-December 2010-mid June 2011 was similar (P > 0.05) among 3 of 4 study areas ranging from 0.48 to 0.51, with North Ridge fawns exhibiting marginally higher over-winter survival (0. 70; P < 0.10~ Table 1). In comparison to previous years, North Ridge fawn survival has been consistent since winter 2008/09, but survival in the other 3 areas was lower than last year and lower than the previous 2 years in Ryan Gulch (Fig. 2). Annual adult female survival was similar among study areas (P > 0.05) ranging from 0.77 (North Ridge) to 0.89 (Ryan Gulch; Table l) and was comparable to previous years (P > 0.05; Anderson 2009~ Anderson and Bishop 20 I 0). The relatively lower fawn survival observed this winter (3 of 4 study areas) was likely due to increased winter severity present through mid February, and doe survival was consistent with other mule deer populations experiencing normal winter conditions in the western US (Unsworth et al. 1999). Seasonal Movement Patterns ...., ....,; Migration patterns differed among areas with North Ridge and North Magnolia deer generally migrating east-west and South Magnolia and Ryan Gulch deer migrating south-north (Fig. 3). Median straight-line migration distances were similar ranging from 32.6 km (Ryan Gulch) to 41.3 km (North Magnolia). Similar to seasonal ranges, most deer monitored exhibited strong fidelity to spring and fall migration routes (Fig. 3). Timing of spring migration during 2010 was similar among study areas with median spring migration dates occurring between 8 and 16 May and median fall migration dates occurring between 15 and 23 October. Median migration duration was relatively short among areas ranging from 3 to 8 days in the spring and 2 to 6 days in the fall; these observations were comparable to previous years. More detailed analyses of these migration data investigating the influence of human activity are currently being conducted by Patrick Lendrum and Terry Bowyer ofldaho State University. A final report is scheduled to be completed by spring 2012. Winter concentration areas identified from March 2010-April 2011 (Fig. 4) reasonably followed study area boundaries delineated from previous OPS locations of adult female mule deer (Anderson and Bishop 2010). Winter concentration areas outside study area boundaries primarily resulted from atypical distribution shifts of some North Ridge deer. Within study areas, we noted more continuous distributions from North Magnolia and North Ridge deer, with Ryan Gulch and South Magnolia deer exhibiting more fragmented and concentrated distributions, which may be related to relative development densities and longevity within each study area. Future resource selection analyses will address these differences relative to habitat attributes within each area. Mule Deer Body Condition ~ Body condition measurements of adult female mule deer December 2010 were comparable to 1ast year (Anderson and Bishop 2010) with higher values evident from North and South Magnolia deer, intermediate from Ryan Gulch deer, and lower values from North Ridge deer (Table 2), but differences were only marginal (P < 0.01) between North Ridge and the 2 Magnolia populations (mm rump fat: P = 0.05-0.07). Unlike last year, deer coming into winter range with higher body condition did not maintain improved condition by late winter and all herd segments were similarly low when assessed in March 2011. The similarly low body condition among areas we observed during late winter can likely be attributed to increased winter severity this winter relative to last winter. Overwinter decline in mean% body fat ranged from 3.8% in Ryan Gulch to 4.7% in South Magnolia (Table 2). Pregnancy rates were expectedly high ranging from 95% to 100%/study area (n = 20/area). 57 �Similar to subtle trends in adult female body condition the past 3 years (Table 2), December fawn weights were slightly higher in 2009 than during 2008 and 2010 (Fig. 5). In 2009, male fawns from North and South Magnolia were heavier (P < 0.05) than during 2008 as were Ryan Gulch males when compared to 2010. Similarly, 2009 females were heavier from North Magnolia compared to 2008 and from North Magnolia and Ryan Gulch than during 20 IO (Fig. 5). In comparing fawn weights from December 2010, Ryan Gulch fawns were marginally (P = 0.055; South Magnolia females) or significantly lighter (P < 0.05; both sexes from the other 3 study areas and males from South Magnolia) than other fawns. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited homogenous individual sightability (cr2 = 0) and constant sightability across surveys (P.) for South Magnolia and Ryan Gulch, homogenous individual sightability and variable sightability with survey period for North Ridge, and heterogeneous individual sightability with variable sightability across surveys for North Magnolia. North Ridge exhibited the highest deer density (22.9/km2), followed by North Magnolia ( l l .2/km2), with comparably lower deer densities in South Magnolia and Ryan Gulch (7 .6 and 8. 7/km 2 ; Table 3, Fig. 6). Abundance estimates were similar (P > 0.05) to last year except in North Magnolia where deer numbers increased from 595 to 884. Over the 3 year survey period so far the population trend in North Ridge appears to be increasing with a recent increase in North Magnolia and stability in the other 2 areas (Fig. 6). Abundance estimates from 2011 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.14-0.18. SUMMARY AND FUTURE PLANS The goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The infonnation presented here provide data describing mule deer population parameters from the first 3.5 years of the pre-treatment period of a long-term study intended to address how mule deer react to landscape scale habitat and human activity modifications. The pretreatment period is intended to continue I to 2 more winters to provide baseline data to compare against intended improvements in habitat conditions and evaluation of concentration/reduction in human development activities, which will be maintained for 4--6 years to provide sufficient time to measure how deer respond to these changes. Based on the data collected thus far, deer from all areas appear to be in reasonably good condition and are exhibiting expected survival rates relative to changes in winter severity. Mild winter conditions the first 2 years and more severe winter conditions during the current year likely contributed to the observed mule deer population parameters. Observed differences in winter concentration areas (Fig. 4) may indicate behavioral modifications to areas of prolonged high development activity, but resource selection analyses will be necessary to confinn this supposition. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Bergman et al. (2009) found that habitat treatments implemented in pinyon-juniper habitat in undeveloped areas were effective for deer. We are also evaluating deer behavioral responses to varying levels of development activity. This will allow us to assess the effectiveness of certain BMPs for reducing disturbance to wintering mule deer. We recently implemented a habitat improvement plan and completed our pilot habitat treatments January 201 l (126 acres total) and plan to complete the remaining treatments (~1,080 acres) in the 58 lwl �'-' -cl 'Cl 'Cl '-'~ '411 >.-I ,._,I '4fl -.-1 'Cl ,.,I .._ '-' Magnolia study areas by fall/winter 2012-2013; vegetation response thus far in the pilot treatment sites have been promising, likely due to the moist spring conditions this year. In addition, hay field improvements have been implemented in the North Magnolia area from a collaborative agreement with Williams Production LMT Co. Additional collaboration with Williams Production LMT Co. have produced a clustered development plan to be implemented in the Ryan Gulch study area and new technologies will be implemented to reduce human activity through remote monitoring of well pads and fluid collection systems. Recent collaboration agreements with ExxonMobil Development Co. and Colorado State University have provided graduate research opportunities to enhance data collection and inference about mule deer/energy development interactions. We are continuing to work with Dr. Terry Bowyer and Patrick Lendrum (MS candidate) of Idaho State University to address mule deer migration and potential influences of human activity along migration routes. Additional funding and cooperative agreements will be necessary to sustain this project through completion (through at least 2017 and preferably through 2019). We optimistically anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. \1$1 LITERATORE CITED -.el 'Cl -.I -.e) vs/ 'el 'Cl'._...I -.I -...I -.I -.I -.I "-' 'el 'Ori 'Cl wl \t/lV 'Cl '-' 'Cl ..., ._, - ~ Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Co11ins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008b. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation-Stage I, Objective 5: Patterns of_mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2010. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report, Colorado Divison of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. I 21. Barrett, M. W., J. W. Nolan, and L. D. Roy. I 982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin I 0: 108-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, and G. C. White. 2009. Evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, USA. Burnham, K. P., and D.R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New Yark, New York, USA. Cook, R. C., J. G. Cook, D. L. Murray, P. Zager, B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65:973-987 . Cook, R. C., T. R. Stephenson, W. L. Meyers, J. G. Cook, and L.A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71: 1934-1943. ._ '4,/ .., ._ 59 �Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins, Colorado, USA. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. McClintock, B. T., G. C. White, K. P. Burnham, and M.A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63 :315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46:120-139. White, C. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Chuck R. Anderson, Wildlife Researcher 'w 60 �Table 1. Survival rate estimates (S) of fawn (1 Dec. 2010-18 June 2011) and adult female (1 July 2010-30 June 201 l) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. -.I Cohort ""51 Study area Initial sample size (n) March doe sample11 (n) S(95% CI) Fawns '411 Ryan Gulch 50 0.480 (0.342-0.618) South Magnolia 55 0.508 (0.375-0.640) North Magnolia 60 0.481 (0.351-0.610) North Ridge 77 0.697 (0.594--0.080) Adult females Ryan Gulch 31 51 0.892 (0.800-0.983) South Magnolia 28 53 0.832 (0.708-0.955) North Magnolia 32 54 0. 783 (0.654--0. 912) North Ridge 33 44 0.765 (0.622-0.908) 8 Adult female sample size following capture and radio-collaring efforts March, 2011. '-' 61 \/fSI �Table 2. Mean rump fat (mm), Body Condition Score (BCS3), and % body fat (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2011. Values in parentheses= SD. March 2009 December 2009 %fat Study Area Rump fat Ryan Gulch 1.73 (1.78) 2.66 (0.55) 7.54 (1.80) 8.35 (6.36) 4.06 (1.13) 12.96 (4.53) 2.31 (1.44) 2.35 (0.48) 6.69 (1.58) South Magnolia 1.47 (0.68) 2.50 (0.60) 7.26 (1.82) 10.05 ( 6.19) 4.07 ( 1.21) 13.46 (4.96) 3.12 (2.20) 2.64 (0.59) 7.70 (2.01) North Magnolia 1.30 (0.79) 2.56 (0.68) 6.96 (2.23) 10.67 (5.76) 4.25 (0.96) 13.92 (3.92) 3.15 (2.34) 2.85 (0.53) 8.28 (1.86) North Ridge 1.57 (1.22) 2.60 (0.56) 7.28 (1.66) 5.25 (5.65) 3.63 (1. 11) 11.02 (4.54) 1.77 (1.11) 2.42 (0.49) 6.83 (1.50) BCS Rump fat March 2010 BCS % fat Rump fat BCS %fat Table 2. Continued. December 2010 March 2011 Study Area Rump fat Ryan Gulch 7.75 (6.15) 3.34 (0.98) 10.82 (4.32) 1.55 (0.60) 2.53 (0.42) 7.05 (1.20) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.21 (3.32) 1.65 (0.75) 2.35 (0.50) 6.56 (1.49) North Magnolia 9.55 (6.49) 2.56 (0.68) 11.65 (4.86) 1.65 (0.67) 2.53 (0.49) 7.06 (1.35) BCS %fat Rump fat BCS %fat North Ridge 6.14 (5.29) 3.32 (0.82) I 0.32 (3.39) 1.45 (0.76) 2.24 (0.49) 6.24 (1.45) 0 Body condition score taken from palpations of the rump following Cook et al. (200 I). 62 ( ( ( C( { C{ { { CC{ C( { CCCC{ CC( ( { C( ( { { C{ { ( ( CC( C( ( CCC{ �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 29 March-4 April 2011. Data represent 4 resight surveys from North Ridge and 5 resight surveys from the other 3 study areas. Study area Mean No. sighted Mean No. marked N(95% CI) Density (deer/km2) Ryan Gulch 327 22 1,2 I 9 (1,040--1,431) 8.7 South Magnolia 156 21 630 (542-735) 7.6 North Magnolia 239 22 884 (739-1,060) 11.2 North Ridge 409 30 1,221 ( 1,067-1,399) 22.9 'cl 63 �- Mule Deer Winter Range Study Area Boundaries Study Area Well Pads & Facilities D Rfan Gulch c:J North Magnolia D South Magnolia North Ridge 0 3 J; J; In development or appl,cat,on for dnlhng Producing v.ell [, ll1Ject1on well El Development_fac1l1ties 6 12 Miles Fig ure I. Mule deer winter ra nge study areas relative to active natural gas well pads and energy development faci lities in the Piceance Basin of northwest Colorado, summer 20 11(Accessed http://cogcc.state.co.us/ Aug. 8, 20 I l ). 64 �Ryan Gulch fawn s-2008/09-2010/11 South Magnolia fawn S- 2008/09--2010/11 r===~~~:st~;-;~;;;;~;;;;~~g. ~:~~ 1.00 · 0.90 ····••.• •• 0.80 +--------~-""'°'<;:,----~.....-:-::--'-.....-:-:-=:-:7""""" 0.70 +-----------'-"r,---===--""-::,,---....a...• 0.60 + - - - - - - - - - - = 0.50 + - - - - - - - - - - - - - - - ~ - ~ - - 0.40 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __._ _ . . 3-~::. . . ·-~?=::·...............~.._;::z;-~~:.:~ 0 80 +------=,l.«•41#•1:n·•·•··· o:/U . ••••••••••••••••••• .,.,,...___ TITTTITn'Tff..,-- 0.40 + - - - - - - - - - - - - - - - - - - - - 0.30 · t - - - - - - - - - - - - - - - - - - - 0.20 + - - - - - - - - - - - - - - - - - - - 0.10 + - - - - - - - - - - - - - - - - - - - 0.00 +----r----.---.----.-..---.---.---,-----,.-,--,----,.---,---,----,---, 0.JO - 1 - - - - - - - - - - - - - - - - - - - 0.20 · · - - - - - - - - - - - - - - - - - - 0.10 · - - - - - - - - - - - - - - - - - - - o.oo -1----~--.-------,--,--.-,- . - - - - ~ North Magnolia fawn ~ - 2008/09-2010/11 ~:~~ +---~-;;::::. ::. ' •••. :•• ..--.. 0.60 •••••••••• 0.50 - j - - - - - - - - - - - - - - • • _ • • . . . c . ; • • • : : . £ . ' • ' - T T T I T O " r n n North Ridge fawn S• 2008/09-2010/11 1.UO • -····;•:.•• ~Y..::::·::~~~~~~ ~~ h••>.W!-....W!-.•,..•!-.•t>,- o.so 0.80 ,. • .0.•••..••nTl"o~~0,70 , ,........... ttltttt-0.GO +------------.........._. 0.50 ............ .. 0.40 + - - - - - - - - - - - - - - - - - - - 0.30 + - - - - - - - - - - - - - - - - - - - 0.20 + - - - - - - - - - - - - - - - - - - - 0.10 + - - - - - - - - - - - - - - - - - - - 0.00 -~-.,---------.---.---..---,---,---,---,---, ~.:#i/F_'i":§;~~~,.~.....zt - ...... ~.....-.~..... -•-•-......·nn!llIDI.l ·n·,. .~ 0.80 .................. ::::::au., .... . 0.70 - f - - - - - - - - - - - - - - - = - = = = = u u . . a 0.60 + - - - - - - - - - - - - - - - - - - - - 0.50 + - - - - - - - - - - - - - - - - - - - 0.40 + - - - - - - - - - - - - - - - - - - - 0.30 + - - - - - - - - - - - - - - - - - - - 0.20 + - - - - - - - - - - - - - - - - - - - O.lO + - - - - - - - - - - - - - - - - - - - 0.00 +---.----.---.---.--,--,----.----.---.----,.-,--,--~--.----,-----, 'el Figure 2. Over-winter (Dec-Mar) mule deer fawn survival (.5) from 4 study areas in the Piceance Basin, northwest Colorado, 2008/09 (red lines), 2009/10 (orange lines) and 20010/11 (blue lines). Solid lines= 5 and dashed lines = 95% CI. Comparable data among years December-March due to premature collar drop during 2008 and 2009. 'cl 'SI 65 �D 6'i,:i,;Rl'm,~-';!t~•r\l D North Ridge. Orcles North M agnolla • Pluses South Magnolia• Stars :::J Ryan Gulch. 01arronds 0 5 10 20 ,,~1les Figure 3. Mule deer migration routes from 4 winter range study areas in the Piceance Basin of northwest Colorado, spring and fall 2010. 66 �-- - - Mule Deer Winter Concentration Areas Study Area Kernel PDF O RyanGulch L J 95% North Magnolia -92% 5outh Magnolia - 87% North Ridge - 81% - 70% D D -- 125 25 ~l""""l~-~-~-~~~~IMiles Figure 4. Mule deer wi nter concentration areas (composite kernel Probability Density Functions; PDF) from 4 study areas in the Piceance Basin of no1thwest Colorado, March 20 I 0-April 20 11. Data from composite GPS localions (5 GPS location attempts/day) of adult female mule deer by study area. -...,, - 5 67 �- Male fawn weights 45.0 40.0 I- I 35 .0 tio I - f I I 30.0 ..:t: ... 25.0 .c: Ryan Gulch South Magnolia .!?:> 20.0 (II ~ ■ North Magnolia 15.0 North Ridge 10.0 5.0 0.0 Dec 2008 Dec2009 Dec 2010 Female fawn weights 40.0 35.0 I I I ·I 30.0 tlO 25.0 .... ..c: Ryan Gulch 20.0 South Magnolia 15.0 ■ North Magnolia 10.0 North Ridge .x OJ) 'iii $ 5.0 0.0 Dec 2008 Dec 2009 Dec 2010 Figure 5. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceance Basin, northwest Colorado, December 2008- 20 I 0. 68 �Late winter mule deer density 30 25 N E ~ 20 '.cu 15 - ... - - ... - --- - --- North Ridge ••••• • Ryan Gulch ~ - North Magnolia QJ C -)I@- 10 I South Magnolia 5 0 2009 2011 2010 Year Figure 6. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2011. 'Cl 69 �--- Colorado Division of Parks and Wildlife July 1,201 l - June 30, 2012 WILDLIFE RESEARCH REPORT State of-------=-=-===~----Colorado : Division of Parks and Wildlife Cost Center 3430 : ===-=--===:.=c:'-------------Mammals Research Work Package 3001 : =D'-"e-=-er=--=C-=-o=n=se=rv-'-a=t=io=-=n~-----------Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:_ ~ W~-~18=-=5~-~ R ----Period Covered: July 1, 201 1 - June 30, 2012 Authors: C.R. Anderson and C. J. Bishop """" Personnel: E. Bergman, T. Bryan, A. Burleson, B. deVergie, D. Finley, M. Fisher, L. Gepfert, C. Haity, D. Johnston, A. Jones, T. Knowles, J. Lewis, H. MacIntyre, J. Matijas, B. Panting, T. Parks, B. Petch, J. Rivale, J. Simpson, S. Singleton, M. Trump, B. Tycz, R. Velarde, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; S. Monsen, Western Ecological Consulting, Inc.; D. Freddy, Hoch Berg Enterprises; T. Graham, Ranch Advisory Partners; M. Wille, T & M Contractors.; P. Lendrum, T. Bowyer, ldaho State University; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, G. White, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mu le Deer Association, Colorado Mule Deer Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and Williams Production LMTCo. All information in this report is preliminary and subject to further evaluation. Information MAV NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT - - We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 4 pretreatment years of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored 4 winter range study areas representing varying levels of development to serve as treatment (Ryan Gulch, North Magnolia, South Magnolia) and control (North Ridge) sites and recorded habitat use and movement patterns using GPS collars (2::5 location attempts/day), estimated overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult fema les using ultrasonography, and estimated abundance using helicopter mark-resight surveys. We targeted 260 fawns (60-80/study area) and 140 does (30-40/study area) in early December 201 l for VHF and GPS radiocoUar attachment, respectively, and 140 does in March 201 2 (30-40/study area) for Late winter body condition assessment and to increase our GPS radiocollar sample in l of the 4 areas (24 in Ryan Gulch) to address neonate 48 �survival. Based on the data collected since January 2008, deer from all areas appear to be in reasonably good condition and have exhibited relatively high survival rates 3 of the 4 years (mean fawn S > 0.65) with lower winter fawn survival during 2010/11 in 3 of 4 study areas (mean S = 0.49 excluding North Ridge), and winter range deer densities appear to be stable. More extreme winter conditions during 2010/ 11 likely contributed to the observed decline in fawn survival rates. Pilot habitat treatments in North and South Magnolia (l 16 acres total) were completed January 2011 (Anderson and Bishop 20 11 ), another 54 acres were treated January 2012 to assess mechanical treatment methods (hydro-ax, rollerchop, chain), and all required NEPA surveys were completed this summer for the remaining sites (Fig. 6). The Biological Assessment should be completed during September 2012 allowing the remaining 1,030 acres to be treated using hydro-ax this winter. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of habitat treatments (North and South Magnolia) scheduled for faLl/winter 2012- 2013 (1 ,200 acres total). This evaluation will allow us to determine whether it is possible to effectively mitigate development disturbance in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In collaboration with Colorado State University, we are also evaluating deer behavioral responses to varying levels of development activity in the Ryan Gulch study area and neonate survival in relation to energy development from all study areas. This will allow us to assess the effectiveness of certain Best Management Practices (BMPs) for reducing disturbance to deer and include neonatal data to other demographic parameters for evaluation of mule deer/energy development interactions. The study is slated to run through at least 20 17, and preferably 2019, to adequately measure mule deer population responses to landscape level manipulations. -- - - 49 - �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR and CHAD J. BISHOP PROJECT NARRITIVE OBJECTIVES >cl 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Complete NEPA surveys to allow future habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Initiate neonate survival evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado . Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to mule deer populations. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. 50 �The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. 1). Anderson and Freddy (2008a) in their longterm research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the past 4 years, we have gathered baseline habitat utilization data from OPS-collared deer across the Piceance Basin to allow assessment of mitigation approaches that will be implemented over the next 1-2 years and evaluated for another 4-6 years. We are currently monitoring 1 control area without development (North Ridge), 2 areas with relatively high development activity (0.6-0.9 well pads & facilities/km2; Ryan Gulch and South Magnolia), and another area with relatively minor development activity (0.1 well pads & facilities/km 2; North Magnolia). In comparison to the un-manipulated control area (North Ridge), the North and South Magnolia areas will receive similar levels of mechanical habitat treatments to evaluate this mitigation strategy relative to differing development intensities, and deer behavior patterns relative to differing development activities in the Ryan Gulch area will be monitored to identify effective Best Management Practices (BMPs) for future application. This progress report describes the previous 4.5 years (Jan 2008June 2011) of addressing mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, overwinter fawn and adult female survival; estimates of adult female body condition during early and late winter, and annual late-winter abundance estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. 1). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig.I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edu/is)-Utahjuniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1,675 m to 2,285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atrip/ex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixedgrass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua graci/is), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt. Wintering mule deer population segments we investigated in the Piceance Basin include: North Ridge (53 km2) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch (141 km2) between Ryan Gulch and Dry Gulch in the 51 �southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. 1). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.14 pads & facilities/km2), and relatively high development in the Ryan Gulch (0.60 pads & facilities/km2) and South Magnolia (0.86 pads & facilities/km 2) segments (Fig. I). Among the 4 study areas, North Ridge will serve as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development, and North and South Magnolia will serve to address the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. METHODS 'Cl Tasks addressed this period included mule deer capture and collaring efforts, monitoring overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography, estimating mule deer abundance applying helicopter mark-resight surveys, working with BLM to complete NEPA surveys to proceed with mechanical habitat treatments fall/winter 2012, and initiation of evaluating neonate survival in developed and undeveloped landscapes. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to capture 60--80 fawns and 30--40 adult females during early December 2011 and early March 2012 in each of the 4 study areas. Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (30-40/area during December 2011, primarily recaptures during March 2012; 5 or 24 fixes/day; G2110D, Advanced Telemetry Systems, Isanti, MN, USA) and released. To provide direct measures of decline in overwinter body condition, 30 does were recaptured in each study area that were captured the previous December; 24 uncollared does were also captured in Ryan Gulch to achieve a desired sample size of 30/study area for monitoring neonate survival. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and early autumn, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early in April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 4--8 hrs of inactivity). '151 Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed December 2010 following collar drop and retrieval in early April 2012. GPS collars deployed maintained the same fix schedule of attempting fixes every 5 hours except in Ryan Gulch where fix rates were programmed for I/hour to increase resolution of GPS data for evaluation of deer behavior patterns in relation to differing development activities. We plotted deer locations and recorded timing and distance of spring and fall 2011 migrations for each study area. Mule deer winter concentration areas were created using composite GPS data (March 2010 through April 2011 from all deer; 5 location attempts/day) from each study area and mapped in ArcGIS (ver. 9.3) using Spatial Analyst (kernel probability density functions separated by quantiles). Mule deer resource selection analyses are pending completion of high resolution habitat data layers currently being developed by BLM. 'el Mule Deer Survival Mule deer mortality monitoring consisted of daily ground telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and bi-weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any mortalities occurring 52 �within 10 days of capture) and collar failures were censored from survival rate estimates. We estimated survival rates from 1 July 2011 through 30 June 2012 for adult females and from early December 2011mid June 2012 for fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. ( 1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate % body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years using a two-sample I-test. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 4 (North Ridge, North Magnolia) or 5 (Ryan Gulch, South Magnolia) helicopter mark-resight surveys (2 observers and the pilot) during late March/early April, 2012 to estimate deer abundance in each of the 4 study areas. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011 ). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2012. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys. was recorded as mark ID#, unmarked, or unidentified mark. wl 'wl 'wl ~ We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-nonnal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2- = 0 or* 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 264 fawns and 138 does in Dec and Jan 2011 and 142 does during March 2012. Seventeen fawn mortalities (6.4%; ultimate cause= 6 capture myopathy, 10 predation, 1 vehicle collision) occurred within the 10 day myopathy period. Doe mortalities totaled 5 (3 .1 %; ultimate cause= 4 capture myopathy, 1 vehicle collision) and 7 (4.9%; all capture myopathy) within 10 days of the Dec and Jan and March capture periods, respectively. Mortality rates 10 days post capture have varied between 2-3% for fawns and 0-3% for does since Jan 2008, but were higher this year. Dry conditions and abnormally high dust from pipeline construction relative to previous years may be related. 'wl 'wl 'wl Fawn survival from early December 2010 through mid June 2011 was similar (P > 0.05) among study areas ranging from 0.60 to 0.75 (Table 1; all areas combined= 0.69, 95% CI= 0.63-0.74, n = 247). General comparisons to previous years suggest relatively high fawn survival during winter 2009-2010 and relatively low survival during winter 2010-2011 (Fig. 2), which correlates to some degree to winter 53 'wl I._/ �'SI 'cl 'cl -..I 'Cl severity. Exceptions include North Ridge, which has been stable throughout, and Ryan Gulch where relatively low precision of estimates do not allow statistical discrimination (Fig. 2). Annual adult female survival varied from 0.68 (North Magnolia) to 0.93 (Ryan Gulch; Table 1) this year and was comparable among study areas during 2011/12 and to previous years (P > 0.05) with the exception of North Magnolia deer exhibiting lower survival this year than during 2009/10 (Anderson and Bishop 20 I 0) and lower than Ryan Gulch this year. The relatively low adult female survival from North Magnolia may result in declining population trends if low survival persists. Spring Migration Patterns Collaboration with Idaho State University to direct a graduate student to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has recently been completed. Two manuscripts have been prepared for publication; one is in review and the second has recently been accepted for publication (Lendrum et al. 2012). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 3) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover versus more open areas with increased foraging opportunities through undeveloped landscapes. Migrating deer also selected areas closer to well pads, but avoided roads except in the highest developed areas where road densities may be too high for avoidance without significant deviations from traditional migration routes. These results suggest that deer may avoid disturbance where feasible or increase their rate of travel through highly developed landscapes where the energetic cost of avoidance may be too high. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer December 2011 were higher for deer from Ryan Gulch and North Ridge than previous years (P < 0.05), but were comparable for the North and South Magnolia deer (P > 0.05; Table 2). Comparisons among study areas in December suggested Ryan Gulch deer were in better condition that the other 3 areas. By late winter, however, body condition declined and deer from all study areas exhibited similar condition (Table 2). Improved condition of deer arriving on winter range was expected in December because of improved moisture conditions during spring and summer 2011. We were surprised that condition of North and South Magnolia deer did not mimic deer from the other 2 study areas, especially since there is summer ranges overlap with North Ridge and North Magnolia and Ryan Gulch and South Magnolia, respectively (Fig. 3). It was also surprising that deer from all study areas did not maintain higher condition by late winter given the mild winter conditions that were evident during 2011-2012, as was the case for North and South Magnolia deer during the mild winter of 2009-2010 (Table 2). Slightly higher late winter condition estimates were evident from all areas compared to 2009 and 2011, but these differences were not statistically significant (P > 0.05). December fawn weights were comparable to previous years and among study areas last year, with the exception of Ryan Gulch females which showed improvement over the previous year (Fig. 4). More detailed analyses will be conducted to identify factors potentially attributing to these observations. Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate mule deer survival in developed and undeveloped landscapes (funded by ExxonMobil Production Co.). Mark Peterson (GRA) and Paul Doherty (CSU professor) will be assisting with this research, which began March 2012 and will continue for 3 years. To initiate this component of the study, we targeted 30 adult female mule deer/study area to receive Vaginal Implant Transmitters (VITs) during March 2012. Pregnancy rates during March were normal ranging from 96% to 98%/study area (n = 2846/area). March fetal counts ranged from 1.54 in South Magnolia to 1.92 in North Magnolia. We located 100 does with VITs and 97 neonates at parturition sites, with 85 neonates receiving radiocollars. Neonate 54 �survival will be monitored from June through December each year and compared among study areas relative to energy development activities. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and homogenous individual sightability (cr2 = 0) for North Ridge and South Magnolia deer and variable individual sightability (cr2 if:. 0) for North Magnolia and Ryan Gulch deer. North Ridge exhibited the highest deer density (18.3/km2), with comparably lower deer densities in the other 3 areas (7.4-9.2/krn2; Table 3, Fig. 5). Populations appear stable over the 4 year monitoring period exhibiting annual variation less than the error around point estimates, with the exception of North Magnolia which exhibited a positive increase in 2011 from the previous 2 years (Fig. 5). Abundance estimates from 2012 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.13-0.17. Magnolia Habitat Treatments In proceeding with mule deer habitat improvements in heavy (South Magnolia) and light developed areas (North Magnolia), we completed pilot habitat treatments in January 2011 (116 acres total; Anderson and Bishop 2011) and January 2012 (54 acres) to assess mechanical treatment methods (hydro-ax, roller-chop, chain). All required NEPA surveys were completed this summer for the remaining sites (Fig. 6). The Biological Assessment should be completed by September 2012, allowing the remaining 1,030 acres to be treated using hydro-ax during fall-winter 2012-2013. Vegetation response in the pilot treatment sites was promising by fall 2011 (Fig. 6), likely due to the moist conditions present during the previous spring and summer. Dryer conditions this spring inhibited a similar response, but treatments completed last January exhibited surprisingly good grass and forb growth; shrub response wasn't as vigorous as the previous year. All expenses addressing these habitat treatments will be covered through a Wildlife Management Plan agreement between CPW and ExxonMobil Production/XTO energy. SUMMARY AND COLLABORATIONS The goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here provides data describing mule deer population parameters from the first 4.5 years of the pre-treatment period of a long-term study intended to address how mule deer react to landscape scale habitat and human activity modifications. The pretreatment period will continue through this fall to provide baseline data to compare against intended improvements in habitat conditions and evaluation of concentration and/or reduction in human development activities. Post-treatment monitoring will continue for 4-6 years to provide sufficient time to measure how deer respond to these changes. Based on the data collected thus far, deer from all areas appear to be in reasonably good condition and are exhibiting expected survival rates relative to changes in winter severity. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Bergman et al. (2009) found that habitat treatments implemented in pinyon-juniper habitat in undeveloped areas were effective for deer. We are also evaluating deer behavioral responses to varying levels of development activity. This will allow us to assess the effectiveness of certain BMPs for reducing disturbance to wintering mule deer. Hay field improvements have been completed in the North Magnolia study area by Williams Production LMT Co. to fulfill a Wildlife Management Plan agreement with CPW; elk response has already been evident and mule deer response will continue to be monitored. Additional collaboration 55 'w �'cs/ \/ft!) with Williams Production LMT Co. has produced a clustered development plan recently implemented in the Ryan Gulch study area and new technologies will be implemented to reduce human activity through remote monitoring of well pads and fluid collection systems. Recent collaboration agreements with ExxonMobil Production Co. and Colorado State University have provided graduate research opportunities to enhance data collection and inference about mule deer-energy development interactions. Additional funding and cooperative agreements will be necessary to sustain this project through completion (at least 2017 and preferably through 2019). We pptimistically anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. LITERATURE CITED Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008b. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation-Stage I, Objective 5: Patterns o(mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2010. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2011. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report, Colorado Divison of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 121. Barrett, M. W., J. W. Nolan, and L. D. Roy. 1982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin I 0: 108-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, and G. C. White. 2009. Evaluation of winter range habitat treatments on over-winter survival and body condition of mule deer. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, USA. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York, New York, USA. Cook, R. C., J. G. Cook, D. L. Murray, P. Zager, B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65 :973-987. Cook, R. C., T. R. Stephenson, W. L. Meyers, J. G. Cook, and L.A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71:1934-1943. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Meyers, S. M. McCorquodale, D. J. Vales, L. L. Irwin, P. Briggs Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, P. J. Miller. 2009. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880-896. 56 �Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins, Colorado, USA. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere In press. Mcclintock, B. T., G. C. White, K. P. Burnham, and M.A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63 :315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46:120-139. White, C. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Wildlife Researcher 57 �Table I. Survival rate estimates (S) of fawn (3 Dec. 2011-18 June 2012) and adult female (I July 201130 June 2012) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe sample8 (n) S(95% CI) Fawns Ryan Gulch 57 0.600 (0.466-0.734) South Magnolia 55 0.745 (0.630-0.861) North Magnolia 56 0.721 (0.601-0.842) North Ridge 73 0.681 (0.578-0.784) Adult females Ryan Gulch 44 67 0.927 (0.858-0.997) South Magnolia 30 45 0.903 (0.810-0.997) North Magnolia 31 49 0.683 (0.536-0.830) North Ridge 35 60 0.803 (0.698-0.908) aAdult female sample sizes following capture and radio-collaring efforts March, 2012. 58 �Table 2. Mean rump fat (mm), Body Condition Score (BCS8), and % body fat (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2012. Values in parentheses= SD. December 2009 March2009 % fat BCS Rump fat BCS March 2010 % fat Rump fat BCS %fat Study Area Rump fat Ryan Gulch 1.73 (1.78) 2.66 (0.55) 7.54 (1.80) 8.35 (6.36) 4.06 (1.13) 12.96 (4.53) 2.31 (1.44) 2.35 (0.48) 6.69 (1.58) South Magnolia 1.47 (0.68) 2.50 (0.60) 7.26 (1.82) 10.05 (6.19) 4.07 (1.21) 13.46 (4.96) 3.12 (2.20) 2.64 (0.59) 7.70 (2.01) North Magnolia 1.30 (0.79) 2.56 (0.68) 6.96 (2.23) 10.67 (5.76) 4.25 (0.96) 13.92 (3.92) 3.15 (2.34) 2.85 (0.53) 8.28 (1.86) North Ridge 1.57 (1.22) 2.60 (0.56) 7.28 (1.66) 5.25 (5.65) 3.63 (1.11) 11.02 (4.54) 1.77 (1.11) 2.42 (0.49) 6.83 (1.50) March 2011 December 2011 Table 2. Continued. December 2010 BCS % fat Rump fat BCS % fat Rump fat BCS %fat Study Area Rump fat Ryan Gulch 7.75 (6.15) 3.34 (0.98) 10.82 (4.32) 1.55 (0.60) 2.53 (0.42) 7.05 (1.20) 13.41 (6.93) 4.21 (1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.21 (3.32) 1.65 (0.75) 2.35 (0.50) 6.56 (1.49) 7.53 (4.66) 3.37 (0.76) North Magnolia 9.55 (6.49) 2.56 (0.68) 11.65 (4.86) 1.65 (0.67) 2.53 (0.49) 7.06 (1.35) 9.43 (6.41) 3.79 (0.93) 11.15 (3.57) North Ridge 6.14 (5.29) 3.32 (0.82) 10.32 (3.39) 1.45 (0.76) 2.24 (0.49) 6.24 (1.45) 9.81 (5.81) 3.62 (1.00) 11.22 (3.38) 9.95 (2.73) 59 ( CCC( ( { ( C{ ( C< ( ( ( CC( CCCC{ { { CC( C( CCC{ { ( CC( C{ C{ CC �Table 2. Continued. March 2012 Study Area Rump fat Ryan Gulch 2.15 (1.44) 2.74 (0.44) 7.22 (1.16) South Magnolia 1.71 (0.76) 2.58 (0.36) 6.97 (1.12) North Magnolia 1.87 (0.78) 2.85 (0.33) 7.65 (0.94) North Ridge 2.24 (1.58) 2.70 (0.35) 7.26 (1.05) 8 BCS % fat Body condition score taken from palpations of the rump following Cook et al. (2009). 60 �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 27 March-4 April 2012. Data represent 4 helicopter resight surveys from North Ridge and North Magnolia and 5 resight surveys from Ryan Gulch and South Magnolia. Study area Mean No. sighted Mean No. marked N(95% CI) Density (deer/km2} Ryan Gulch 268 24 1,048 (897-1,243) 7.4 South Magnolia 161 25 630 (556-724) 7.6 North Magnolia 267 32 727 (648-840) 9.2 North Ridge 319 34 972 (862-1, 113) 18.3 ~ 61 �-- ...., Mule Deer VVinter Range Study Area Boundaries Well pads and facilities D Ryan Gulch 0 3 [iJ Development faci lities 6 12 Miles Figure l. Mule deer winter range study areas relative to acti ve natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado, summer 20 12 (Accessed http://cogcc.state.co.us/ Aug. 8, 20 12). - - Nor1h Magnolia ! In deve lopment or application for drilling ! Prod ucin g well [ l South Magnolia J; Injection well - -- Norlh Ridge 62 �Ryan Gulch fawn S- 2008/09-2010/11 South Magnolia fawns - 2008/09-2010/11 1.00 T -~~~~~~;;;;;;;==-=-------------- 1.00 -----· O.CJO i:E r 0.80 •·---·--·--·•··•----·-·---·0.70--0.60 ; 0.50 T:--------'-'-....-;;:-~......,:.,r::::='miimi,iiimiimir •••••••••••••••••••••••• . ::::::~.:.:;;;:::::~::·== 060 ~ ...... o:so : 1 - - - - - - - •••• •········ •• ••. 0.40 ~:_ _ _ _ _ _ ____;=---•• -•••- ••-•••- ••- •• -• ••- ••-•••- ••-.. -• ••- ..-•• -. . 1- - - - - - - - - 0.30 +------- 0.40 ~-_ _ _ _ _ _ _ _ _ _ _ _ _..:....,..,,..-. ..-..-..-. . 0.30 .;....;- - - - - - - - - - - - - - - - - 0.20 I 0,10 I 0.00 ! 0.20 ~:- - - - - - - - - - 0.10 + 0.00 : ! I ; i ; ~ ~ ~ ~ to ~ to ~ ~ ~ ~ ~ ~ II, ~ "'.., ~ ~'>j #li'Ji J> ~~ .....~ ,? w"' 'ti... ~-,; """"t ~"' ~ ~... _,I,, ,,,,~ \'Ii ~.... ~fc _:I)... ~~- i't ~~ " ,_"l) ..,,, -A... »~ ..e.""t ~ b ~ to-:. ~? ~,, , ~ to ~ .J'to ~ ~'J, ~ ~ ~~ tc~... ,_'ti ~"'... t/' l? ''Ii ,f.... ~,,,-.. '"' 'c~ ~ ~ ~ ~ ~ ~,, 'ti...!lo ~~ to.., ~'J,, ~!lo ~... ~l I .,il) ~~ ~,..., ~ol l•.., ~"' lfJ'lfJ' ~ '"'l ~ ~ '\)., ~"' 'tXr:/l t1-·~~,.,~ ~ ~~- ~ w' North Magnolia fawn s-2008/09-2010/11 North Ridge fawns - 2008/09-2010/11 1.00 . ,.... 0.90 •·-··------.!,..!ll.. 0.80 r--~·-··--.. --.......,._,.,......- !.!;:;~;_:~:~,:~'"• • ~:: r·······- ·····-·-·•··· .... 0 so .I I • ••••• •h ~:::•. ~-;.-;,~-;.-.-;....._ _ _ _ _ • - •••••••• ·• •••••• ·---·-···--· o:40 ; 0.30 ! 0.20 1 0.10 i 0.00 l r I ~to ~ :'> to ,.,:; to ~ ~ II, ,.'o ~ .., II, 'o 'd.., ~ ~~ #Ii~ i ';,:~ "',,, to... ~., 'd.., ~"" ,,,, ~... _,J.,~ ,--. ~.... ~<,, e... ~ ~... ~~ _A, _A,"l) ~ .,J,..,. -:,~· ,,... (1'" (1'" ,~ ~,,, '< ~ ~"' ~'I§ '1""" 'ti' ~ ~"O' ~ ~ ~ e ~., -°"' '"' <l ~'b-\ ~ Figure 2. Over-winter (Dec-Mar & June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin, northwest Colorado, 2008/09 (red lines), 2009/10 (orange lines), 2010/11 (blue lines), and 2011/12 (black lines). Solid lines= Sand dashed lines= 95% CI. Comparable data among years DecemberMarch 2008-2009 and 2009-2010 due to premature collar drop and December-mid-June 2010-2011 and 2011-2012. 63 �- - North Ridge and North Magnolia Summer Range -- Colorado - 'W Figure 3. Mu le deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult female mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state, county, and natural-gas; white lines) from May 2009 (Lower right; from Lendrum et al. 20 12). - 64 �Male fawn weights 42.0 40.0 _ 38.0 C Ryan Gulch 0.0 ..::,:: ... ]> 36.0 C South Magnolia Q) II North Magnolia 5 34.0 C North Ridge 32.0 - 30.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Female fawn weights _ 38 .0 - i - - - - - - - -- -- - - - -- - 0.0 ..::,:: ... ]> 36.0 C Ryan Gulch C South Magnolia cu ■ North Magnolia 5 34.0 CNorth Ridge 32.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Figure 4. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceance Basin, northwest Colorado, December 2008- 201 1. 65 - �Late winter mule deer density 30.0 25.0 .... E 20.0 --- --- - t - - - - - + - - - - - - - , . - - · - - -- ~ '41111 "'cu;::- ts.o --- --- - North Ridge ••••••• Ryan Gulch cu C 10.0 - • North Magnolia - South Magnolia 5.0 0.0 2009 2010 2011 2012 Year Figure 5. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2012. 66 �- North Magnolia treatement sites (587 acres) LJ BearSet_l 5_35b_ andG i BearSet_ l _8andA_E - LJ BearSet_36_54andJ •• '] GreasewoodSet_g16_g29 GreasewoodSet_g1_g 15 [ J GreasewoodSet_g30_g4 2 LeeOversights_a_fand 16_ 17 Mechanical treatment comparison (54 acres) North Hatch PIiot Treatments ( 116 acres) Mule Deer Study Areas North Magnolia Figure 6. Habitat treatment site delineations in 2 mule deer study areas (600 acres each) of the Piceance Basin, northwest Colorado (Top; cyan and yellow polygons have been completed and remaining sites are scheduled for treatment fall/winter 201 2/ I 3). January 2011 hydro-ax treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 20 LI (Lower right, ground view). 67 - - �Colorado Parks and Wildlife July I, 2012 - June 30, 2013 WILDLIFE RESEARCH REPORT State of_ _ _ _ ______;:C::;..;o=lo.;;;.;r=a=d=-o_ _ _ _ _ : ""'"P=ar=k=-s-=an=d=---W____i=ld=h--·fe____________ Cost Center 3430 : =M=amm==al=s-=R=e=s=ear=c=h_ _ _ _ _ _ _ _ _ _ __ Work Package 3001 : =D__e__ er__C=on=s;;;..;;e.....rv.....a__t__ io__n_______________ Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:__W.. . . . ,;-___ 1 8__5__ -R_ _ _ __ Period Covered: July 1, 2012 - June 30, 2013 \e,I 'el Author: C. R. Anderson, Jr. Personnel: N. Bellerose, E. Bergman, C. Bishop, E. Cato, A. Collier, D. Collins, B. deVergie, S. Eno, D. Finley, M. Fisher, B. Frankland, L. Gepfert, T. Gettelman, M. Grode, T. Jenkins, D. Johnston, T. Knowles, M. Melham, J. Matijas, S. Nagy, B. Petch, J. Rivale, R. Schilowsky, R. Velarde, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; D. Freddy, Hoch Berg Enterprises; T. Graham, Ranch Advisory Partners; M. Wille, T & M Contractors.; P. Lendrwn, T. Bowyer, Idaho State University; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT 'Cl .., We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 5 pretreatment years of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia, South Magnolia) and control (North Ridge, Ryan Gulch) sites and recorded habitat use and movement patterns using GPS collars (~5 location attempts/day), estimated overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult females using ultrasonography, and estimated abundance using helicopter mark-resight surveys. During this research segment, we targeted 280 fawns (60-80/study area) and 170 does (30-70/study area) in early December 29 �2012 for VHF and GPS radiocollar attachment, respectively, and 140 does in March 2013 (30-40/study area) for late winter body condition assessment. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of the 2 treatment areas were completed April 2013. Post-treatment monitoring will continue for 4-6 years to provide sufficient time to measure how deer respond to these changes. Based on data collected during the pretreatment phase: ( 1) annual adult survival was consistent among areas averaging 80-84% annually, but overwinter fawn survival was more variable ranging from 48% to 85% within study areas, with annual and study area differences primarily due to annual weather conditions and in some cases density dependent influences; (2) migratory mule deer selected increased cover and increased their rate of travel through developed areas, but did not avoid development structures and avoided negative influences through behavioral shifts in timing and rate of migration; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, which likely relate to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer densities appeared to increase in 3 of 4 areas, with a recent decline in North Ridge, but the most recent North Ridge density was comparable to the first 2 years of the study. Detailed habitat use analyses are still pending for the pretreatment period. We will continue to collect population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In collaboration with Colorado State University, we are also evaluating deer behavioral responses to varying levels of development activity in the Ryan Gulch study area and neonate survival in relation to energy development from all study areas. This will allow us to assess the effectiveness of certain Best Management Practices (BMPs) for reducing disturbance to deer and include neonatal data to other demographic parameters for evaluation of mule deer/energy development interactions. The study is slated to run through at least 2017, but extending the study through 2019 is preferable to adequately measure mule deer population responses to landscape level manipulations. w 1w w 30 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTMTY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Complete habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Continue neonate survival evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the 31 �need for additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to mule deer populations. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. 1). Anderson and Freddy (2008a) in their longterm research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the past 5 years, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We are currently monitoring 2 control areas: 1 with development (0.6 pads & facilities/km 2; Ryan Gulch) and 1 without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km 2 and North Magnolia, 0.1 well pads & facilities/km 2), that also recently received habitat improvements (604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated over the next 4-6 years to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavior patterns in relation to energy development activities in the Ryan Gulch area are being monitored to identify effective Best Management Practices (BMPs) for future energy development planning. This progress report describes the previous 5.5 years (Jan 2008-June 2013) of mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter, and annual late-winter abundance/density estimates. ,._, 1w' STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. 1). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig.1) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utahjuniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1,675 m to 2,285 min elevation (Bartmann and Steinert 1981). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush ( Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixedgrass hay fields. Grasses and forbs common to the area consist of wheatgrass (Agropyron spp. ), blue grama (Boute/oua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Ba/samorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astraga/us spp.), Lewis flax (Linum /ewisii), evening 32 -' �".-I primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.}, Indian paintbrush (Castilleja spp.}, and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt. Wintering mule deer population segments we investigated include: North Ridge (53 km2) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 141 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the northcentral portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. I). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.14 pads & facilities/km 2), and relatively high development in the Ryan Gulch (0.60 pads & facilities/km 2) and South Magnolia (0.86 pads & facilities/km2) segments (Fig. I). Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address humanactivity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. 'Cl METHODS Tasks addressed this period included mule deer capture and collaring efforts, monitoring neonate and overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography, estimating mule deer abundance applying helicopter markresight surveys, and working with BLM and the contractor to complete mechanical habitat treatments by spring 2013. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to target 280 fawns in December 2012/January 2013, 170 adult females during early December 2012, and 140 adult females (mostly recaptures) during early March 2013. Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (5 or 48 fixes/day; G211 OD, Advanced Telemetry Systems, Isanti, MN, USA) and released. To provide direct measures of decline in overwinter body condition, we targeted 30adult females in each study area that were captured the previous December; Vaginal Implant Transmitters (VITs) were also inserted to assist with neonate capture and collaring efforts spring 2013. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and early autumn, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early in April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 4-8 hrs of inactivity). Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed from December 2011 through April 2013. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except in Ryan Gulch where location rates were programmed for every 30 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. We plotted deer locations and recorded timing and distance of spring and fall 2012 migrations for each study area. Mule deer winter concentration areas were created using composite GPS data (March 2010 through April 2011 from all deer; 5 location attempts/day) from each study area and mapped in ArcGIS (ver. 9.3) using Spatial Analyst (kernel probability density functions separated by quantiles). Mule deer 33 �resource selection analyses are pending completion of high resolution habitat data layers currently being developed by BLM. Mule Deer Survival Mule deer mortality monitoring consisted of daily ground telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and bi-weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture) and collar failures were censored from survival rate estimates. We estimated survival rates from 1 July 2012 through 30 June 2013 for adult females, from birth to mid December for neonates, and from early December 2012-mid June 2013 for fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years using a two-sample t-test. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg), chest girth ( cm), and hind-foot length (cm). Abundance Estimates We conducted 4 (North Ridge, North Magnolia, South Magnolia) or 5 (Ryan Gulch) helicopter mark-resight surveys (2 observers and the pilot) during early April to estimate deer abundance in each of the 4 study areas. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2013. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2 = 0 or -:t:- 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 277 fawns in Dec 2012-Jan 2013, 165 does in Dec 2012, and 138 does during March 2013. Eight fawn mortalities (2.9%; ultimate cause= 3 capture myopathy, 5 predation) occurred within the 10 day censorship period. Doe mortalities totaled 4 (2.5%; all capture 34 1w �.._ myopathy) and 4 (2.9%; 3 capture myopathy, 1 predation) within 10 days of the December and March capture periods, respectively. Mortality rates, 10 days post capture, have varied between 2-3% for fawns and 0--3% for does since Jan 2008, except during the 2011-2012 capture season where myopathy rates were higher (3-6%) due to dry, warm conditions (Anderson and Bishop 2012). Fawn survival from early December 2012 through mid June 2013 was similar (P > 0.05) among 3 study areas ranging from 0.75 to 0.85, but was lower in North Ridge (0.53; Table 1). General comparisons to previous years suggest relatively high fawn survival occurred during winters 2009-2010 and 2012-2013, and relatively low survival during winter 2010--2011 (Fig. 2), which correlates to some degree to winter severity. North Ridge exhibited lower survival during 2012-2013 (Fig. 2), which appeared to be driven by density dependent rather than climatic factors. Annual adult female survival varied from 0.73 (North Ridge) to 0.86 (North Magnolia; Table 1) during 2012-2013 and was comparable among study areas during 2012-2013 and to previous years (P > 0.05), with the exception oflower survival in North Magnolia during 2011-2012 (S = 0.68, Anderson and Bishop 2012). Sample sizes for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g., >15-20%). Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has recently been completed. Two manuscripts have been accepted for publication (Lendrum et al. 2012, Lendrum et al. 2013; Appendix A). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 3) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript (Lendrum et al. 2013), we addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures, snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, averaging 4-8 days among the 4 areas (straight line distance between seasonal ranges averaged 33 - 45 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in long-term fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer from North Ridge and Ryan Gulch were lower than from deer from North Magnolia (P < 0.05), but were comparable otherwise (P > 0.05, Fig. 4, Table 2). By late winter, however, body condition declined and deer from all study areas exhibited similar condition (Fig. 4, Table 2). These observations have been generally consistent throughout the study, where early winter condition is variable between study areas and typically follows 35 �the pattern of better condition in North and South Magnolia deer, respectively poorer condition in Ryan Gulch and North Ridge, and poor condition in all areas by late winter. Exceptions occurred during late winter 2010 and early winter 2011, where North and South Magnolia and Ryan Gulch and North Ridge deer, respectively, exhibited improved condition than during other time periods (Fig. 4). December fawn weights by study area were higher in Ryan Gulch during 2012-2013, but were lower and have declined recently in the other 3 study areas (Fig. 5). In general, seasonal moisture conditions appear to be driving differences in annual body condition within study areas, but other factors appear related to differences among study areas. We suspect density dependent factors (forage capacity relative to deer density) are related to observed differences in early winter body condition among study areas. More detailed analyses will be conducted to identify factors attributing to these observations. Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate survival in developed and undeveloped landscapes (funded by ExxonMobil Production Co.) beginning spring 2012. Mark Peterson (ORA) and Paul Doherty (CSU professor) are assisting with this research, which will continue through 2014. Neonate capture and collaring efforts totaled 85 during spring 2012 and 67 during spring 2013. Estimated neonate survival through midDecember 2012 was 0.39 (95% CI= 0.28-0.50). Factors influencing neonate mule deer survival from developed and undeveloped landscapes will be addressed by late 2014. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and homogenous individual sightability ( cr2 = 0) for South Magnolia deer and variable individual sightability ( cr2 -;p 0) for the other 3 areas. North Ridge exhibited the highest deer density (16.1/km2), with comparably lower deer densities in the other 3 areas (8.9-10.4/km2; Table 3, Fig. 6). Populations appeared to increase over the 5 year monitoring period in 3 of the study areas (from 6.S/km2 to 10.1/km2), with a recent decline in North Ridge since 2011 (from 22.8/km2 to 16.1/km2); the current North Ridge density is comparable to the first 2 years of the study (Fig. 6). The recent North Ridge decline was likely related to density dependent factors, which were also evident in lower early winter body condition (Fig. 4) and a recent increase in malnutrition mortalities of adult females (from O in 2010-2011 to 7 in 2012-13). Abundance estimates from 2013 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.15-0.16. Magnolia Habitat Treatments We proceeded with habitat improvements in high (South Magnolia) and low development areas (North Magnolia) during 2012-2013. We completed pilot habitat treatments in January 2011 (116 acres total; Anderson and Bishop 2011; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA), mechanical treatment method comparison treatments (hydro-ax, roller-chop, chain) in January 2012 (54 acres), and hydro-axe habitat treatments in April 2013 (434 acres; Determination of NEPA Adequacy: DOI-BLM-CO-110-2012-0134-DNA), totaling 604 treated acres in each study area (Fig. 7). Vegetation response in the pilot treatment sites was visually evident by fall 2011 (Fig. 7), likely due to the moist conditions during the previous spring and summer. Early spring 2013 moisture has resulted in good vegetative responses from the most recently treated sites. Vegetation and mule deer responses will be documented for the next 4-6 years to assess the utility of this mitigation approach in benefiting mule deer exposed to energy development disturbance. All expenses addressing these habitat treatments will be covered through a Wildlife Management Plan agreement between CPW and ExxonMobil Production/XTO energy. 36 '._I �SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the first 5.5 years of the pre-treatment period. The pretreatment period was completed during spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and reduction in human development activities. Winter range habitat improvements resulting in 604 acres of mechanically treated pinionjuniper/mountain shrub habitats in each of 2 study areas were completed April 2013. Post-treatment monitoring will continue for 4-6 years to provide sufficient time to measure how deer respond to these changes. Based on data collected prior to habitat improvements (i.e., pretreatment phase): (1) annual adult survival was consistent among areas averaging 80-84% annually, but overwinter fawn survival was variable, ranging from 48% to 85% within study areas, with annual and study area differences primarily due to annual weather conditions and in some cases density dependent influences; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was consistent within areas, with higher variability among study areas early winter, which was likely related to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer densities appear to be increasing in 3 of 4 areas, with a recent decline in North Ridge, but the current North Ridge density is comparable the first 2 years of the study. Detailed habitat use analyses are pending for the pretreatment period. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Bergman et al. (2009) found that habitat treatments implemented in pinion-juniper habitat in undeveloped areas increased overwinter survival of fawns by a magnitude of 1.15. We are also evaluating deer behavioral responses to varying levels of development activity. This will allow us to assess the effectiveness of certain BMPs for reducing disturbance to wintering mule deer. ..._ -.I Hay field improvements have been completed in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; elk (Cervus e/aphus) response has been evident but mule deer response has been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Additional collaboration with WPX Energy has resulted in a clustered development plan in the Ryan Gulch study area and new technologies will be implemented to further reduce human activity through remote monitoring of well pads and fluid collection systems. Collaborative research with Idaho State University and Colorado State University/ExxonMobil Production has produced 3 peer-reviewed publications addressing mule deer migration (Lendrum et al. 2012, 2013) and improved approaches to address animal habitat use patterns (Northrup et al. 2013 ); these publications are summarized in Appendix A. Additional funding and cooperative agreements will be necessary to sustain this project to completion (preferably through 2019). We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. 37 �LITERATORE CITED Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. 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Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309 . Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader 'Cl "el .,,,., 39 �Table 1. Survival rate estimates (S) of fawn (2 Dec. 2011-15 June 2013) and adult female (1 July 201230 June 2013) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe sample8 (n) S(95% en Fawns Ryan Gulch 78 0.752 (0.655-0.849) South Magnolia 54 0. 778 (0.667-0.889) North Magnolia 61 0.852 (0.763-0.941) North Ridge 76 0.529 (0.416-0.643) Adult females Ryan Gulch 32 57 0.799 (0.671-0.926) South Magnolia 39 51 0.801 (0.675-0.927) North Magnolia 37 60 0.863 (0.752-0.975) North Ridge 42 58 0. 726 (0.595-0.857) 8 Adult female sample sizes following capture and radio-collaring efforts March, 2012. 40 �Table 2. Mean rump fat (mm), Body Condition Score (BCS8), and% body fat (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2013. Values in parentheses= SD. March 2009 December 2009 March 2010 Study Area Rump fat Ryan Gulch 1.73 (1.78) 2.66 (0.55) 7.54 (1.80) 8.35 (6.36) 4.06 (1.13) 12.96 (4.53) 2.31 (1.44) 2.35 (0.48) 6.69 (1.58) South Magnolia 1.47 (0.68) 2.50 (0.60) 7.26 (1.82) 10.05 (6.19) 4.07 (1.21) 13.46 (4.96) 3.12 (2.20) 2.64 (0.59) 7.70 (2.01) North Magnolia 1.30 (0.79) 2.56 (0.68) 6.96 (2.23) 10.67 (5. 76) 4.25 (0.96) 13.92 (3.92) 3.15 (2.34) 2.85 (0.53) 8.28 (1.86) North Ridge 1.57 (1.22) 2.60 (0.56) 7.28 (1.66) 5.25 (5.65) 3.63 (I.I 1) 11.02 (4.54) 1.77 (1.11) 2.42 (0.49) 6.83 (1.50) March 2011 December 2011 BCS % fat Rump fat BCS % fat Rump fat BCS % fat Table 2. Continued. December 2010 Study Area Rump fat Ryan Gulch 7.75 (6.15) 3.34 (0.98) 10.82 (4.32) 1.55 (0.60) 2.53 (0.42) 7.05 (1.20) 13.41 (6.93) 4.21 (1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.21 (3.32) 1.65 (0.75) 2.35 (0.50) 6.56 (1.49) 7.53 (4.66) 3.37 (0.76) North Magnolia 9.55 (6.49) 2.56 (0.68) 11.65 (4.86) 1.65 (0.67) 2.53 (0.49) 7.06 (1.35) 9.43 (6.41) 3.79 (0.93) 11.15 (3.57) North Ridge 6.14 (5.29) 3.32 (0.82) 10.32 (3.39) 1.45 (0.76) 2.24 (0.49) 6.24 (1.45) 9.81 (5.81) 3.62 (1.00) 11.22 (3.38) BCS % fat Rump fat 41 BCS %fat Rump fat BCS % fat 9.95 (2.73) �Table 2. Continued. March 2012 December 2012 Study Area Rump fat Ryan Gulch 2.15 (1.44) 2.74 (0.44) 7.22 (1.16) 6.34 (4.35) 3.30 (0.77) 9.34 (2.43) 1.87 (0.90) 2.65 (0.37) 6.90 (1.59) South Magnolia 1.71 (0.76) 2.58 (0.36) 6.97 (1.12) 8.13 (5.71) 3.41 (1.04) 10.22 (3.23) 2.03 (0.78) 2.62 (0.26) 7.17 (0.68) North Magnolia 1.87 (0.78) 2.85 (0.33) 7.65 (0.94) 9.80 (6.35) 3.89 (1.17) 11.25 (3.60) 1.81 (0.91) 2.16 (0.41) 6.91 (1.08) North Ridge 2.24 (1.58) 2.70 (0.35) 7.26 (1.05) 5.76 (4.10) 3.32 (0.82) 1.87 (0.73) 2.48 (0.34) 6.70 (1.12) 8 BCS %fat Rump fat BCS March 2013 % fat 9.06 (2.31) Rump fat BCS %fat Body condition score taken from palpations of the rump following Cook et al. (2009). ( ( ( C C C ( ( f C C C C { C C C C C C C ( C ( { ( C C C C C C C { C C C C C ( C l C { C { �'Cl Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 1--6 April 2013. Data represent 4 helicopter resight surveys from North Ridge, North Magnolia, and South Magnolia and 5 resight surveys from Ryan Gulch. Study area Mean No. sighted Mean No. marked N(95% Cl) Density (deer/km2) 'cl Ryan Gulch 245 27 1,309 (1,131-1,530) 9.3 South Magnolia 182 24 743 (644-875) 8.9 North Magnolia 261 29 950 (824--1,111) 10.4 North Ridge 314 31 858 (753-1,006) 16.1 43 �Mule Deer Winter Range Study Areas Mule deer study areas Well Pads & Facilities D North Magnolia J; 1 South Magnolia .!. b North Ridge Ryan Gulch 0 2.5 □ 5 In developmenl/ application for drilling Injection well Producing well Development facilities 10 ~ ~ ~ ~ ~ ; ; ~ ~ ~ ~ - - - - - •Miles Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado, swnmer 20 I 3 (Accessed http://cogcc.state.eo.us/ Aug. 19, 2013). 44 �Ryan Gulch fawn S South Magnolia fawn S 2008/09-2012/13 2008/09-2012/13 0.80 0.70 t===::~~~~~;;~;:~=:~~~~~~~~~~ 0.60 +----~~~-.:...:3111....._.............. 0.50 t-----__:~~...,l":":"'llliiiiiiiiiiiiiijiiii'" 0.40 - + - - - - - - - - Z - - •.- ... --..- ... -- ... --..-.-. 0.30 - + - - - - - - - - - - - - - 0.20 - - - - - - - - - - - - 0.10 - - - - - - - - - - - - 0.00 -+-r--.-.-..-.-..-.-.--.-.---.-.---.-.-..-.-..-.-.--.-.--------....-.-. 1.00 . - ~ T ' I ' " : . " ' ! ~ 0.90 0.80 0.70 +----&r--=~,......,.........-~~/!Nw.,.,.,,-0.60 -I-----~~~!!!!!!!!!!!!!~.....~-=-=-.:!,!_ 0.50 +----------=:.........,.......,.....,,...--~o.4o - + - - - - - - - - - - - - - - - ' ' " s - = - , - ~ 0.30 - + - - - - - - - - - - - - - 0.20 - + - - - - - - - - - - - - - 0.10 - - - - - - - - - - - - 0.00 -+-,-......,.........-,,....,....,...,....,....,....,......,.........,........,--.,-,....,....,...,....,........,..-.-,-, North Magnolia fawn S North Ridge fawn S 2008/09-2012/13 2008/09-2012/13 1.00 • 0.90 0.80 0.70 0.60 +-----__::_a.--...:::~~--......~............... 0.50 -t------~r;-.:: ..-..-.-.-=---llliiiiiiiiiiiiiiiiiiiii"" 0.40 - - - - - - - - ~ - - . •.-..-...-...-. 0.30 - - - - - - - - - - - - 0.20 - + - - - - - - - - - - - - - 0.10 - - - - - - - - - - - - 0.00 -+-,-.,......,-......-,,....,....,-r-r--.--r-"'T""T"".,......,-......-,,....,....,--.--,--r-,-....-.---, 1.00 0.90 0.80 0.70 0.60 +---------...-::-.::.a,-...c.:~:;:;===0.50 4 - - - - - - - - - - - 1 + , , . , - r - e , - - _ _ _ ; ; = . 0.40 - - - - - - - - - - - - - - - - " ~ 0.30 - - - - - - - - - - - - 0.20 - + - - - - - - - - - - - - - 0.10 - - - - - - - - - - - - 0.00 -+-,--.---,-......-,,....,....,-r-r-,-,-"'T""T"".,......,-......-,,....,....,-..--.--r-,-....-.-, ,-::~~~~~~=== 1.00 -1-0.90 'Cl Figure 2. Over-winter (Dec-Mar & June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin, northwest Colorado, 2008/09 (red lines), 2009/10 (orange lines), 2010/11 (blue lines), 2011/12 (black lines), and 2012/13 (purple lines). Solid lines= 5 and dashed lines= 95% Cl. Comparable data among years December-March 2008-2009 and 2009-2010 due to premature collar drop and Decembermid-June 2010-2011, 2011-2012, and 2012-2013. 45 �lXJ North Ridge and North Magnolia Summer Range ..... Gulch ands Summer Figure 3. Mule deer study a reas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adul t female mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state, county, and natural-gas; white lines) from May 2009 (Lower right; from Lendrum et al . 20 12). 46 - �Early winter rump fat 15 13 Ell .. J! 9 a. E ::, a:: 7 -North Ridge .§. -=-North Magnolia --Ryan Gulch -south Magnolia 5 "Cr/ 3 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Late winter rump fat 4 3.5 e 3 -====- North Ridge .§. ,! 2.5 -North Magnolia a. E i ,==..-Ryan Gulch 2 -south Magnolia 1.5 1 Mar 2009 Mar 2010 Mar 2011 Mar 2012 Mar 2013 Figure 4. Mean early (early Dec., Top) and late winter (early Mar, Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorodo, March 2009-March 2013. Error bars= 95% CI. 47 �Male fawn weights 42.0 T 40.0 38.0 .E 36.0 QI) 'iii ~ rl] ,_ n .~ tttt - ~~ 1T ]~ 1rr 34.0 t- ,- -n - 32.0 t- ,- - - 1, fl i l - DRyan Gulch Ii r~ 1 ' DSouth Magnolia D North Magnolia D North Ridge - - 30.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Female fawn weights 42.0 . . . . . - - - - - - - - - - - - - - - - - - - 40.0 + - - - - - - - - - - - - - - - - - - - - 38.0 DRyan Gulch tiii =.E 36.0 DSouth Magnolia QI) 'iii ~ D North Magnolia 34.0 D North Ridge 32.0 30.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Figure 5. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceance Basin, northwest Colorado, December 2008- 2012. 48 �Piceance Basin late winter mule deer density 30.0 25.0 20.0 -- -- -- N j ~ 15.0 cu cu - - North Ridge •••••• Ryan Gulch Q - 10.0 • North Magnolia - s o u t h Magnolia 5.0 0.0 2009 2010 2011 2012 2013 Year Figure 6. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2013. 'Cl 'Cl 49 �North Magnolia 1reatement siles (587 acres) LJ BearSet_ l 5_35b_andG BearSet_ I _BandA_E LJ BearSe1_36_54andJ GreasewoodSet_g16_g29 GreasewoodSet_g I _g 15 D Greasewooc1Set_g30_g42 LeeOversigh1s_a_tand I B_ 17 Mechanical treatm,nt comparison (54 acres) North Hatch Pilot Treatments (1 I 6 acres) - Mule Deer Study Areas North Magnolia South Magnolia 2 4 8 Figure 7. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan. 2011, yellow polygons completed Jan. 2012, and remaining polygons completed April 2013). January 2011 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 2011 (Lower right, ground view). 50 ...... �.._, -.cl ._ 'cl '-''-..I Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. 'cl Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development 'WI 'Cl ._ ._ 1 1 PATRICK E. LENDRUM1. CHARLES R. ANDERSON, JR. 2, RYAN A. LoNG ,JoHN G. KIE .AND R. TERRY BoWYER1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2Colorado Division of Parks and Wildlife, Grand Junction, Colorado 81505 USA 'Cl ..., Citation: Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82. http://dx.doi.org/10. 1890/ES12-00165.I -.I Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoi/eus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 20 IO in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 'di -..I ,c;I -..I 'Cl ... 'Cl .._, '-'1'-s,I' Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes 'Cl .._ ......, Patrick E. Lendrum 1, Charles R. Anderson Jr. 2, Kevin L. Monteith 1•3, Jonathan A. Jenks4, R. Terry Bowyer1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA -- Citation: Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi:10.1371/journal.pone.0064548 .,,_ Abstract 'Cl "Cl le/ -..I 'el .... ._ --._ ... - ~ Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-20 IO. Conclusions/Significance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. , ._J 'ell ""'1:11 51 �Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTI-IRUP 1, MEVIN 8. HOOTEN 1.u, CHARLES R. ANDERSON, JR."1, AND GEORGE WIITEMYER 1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J.M., M. 8. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framewor~ whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 52 1w �\._.,I Colorado Parks and Wildlife July 1, 2013 - June 30, 2014 WILDLIFE RESEARCH REPORT State of._ _ _ _ _ _C __o__l=o=ra=d=o_ _ _ _ _ : =-P=ar=k=s..;;;;an=d=-..;.W.;..:i=ld=l=if=◄ e_ _ _ _ _ _ _ _ __ Cost Center 3430 : =M=amm==a=l=-s=R=e=se=ar=c=h;:....__ _ _ _ _ _ _ _ _ __ Work Package 3001 : =D=e=er:....;C=o=n=s=erv:..:..:a=tio=n=--------------Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:_ _ W_-__ 18;a...5_-R __________ Period Covered: July 1, 2013 - June 30, 2014 Author: C.R. Anderson, Jr. Personnel: N. Bellerose, E. Bergman, C. Bishop, E. Cato, A. Collier, D. Collins, B. deVergie, S. Eno, D. Finley, M. Fisher, B. Frankland, L. Gepfert, T. Gettelman, M. Grode, T. Jenkins, D. Johnston, T. Knowles, M. Melham, J. Matijas, S. Nagy, B. Petch, J. Rivale, R. Schilowsky, K. Stonehouse, R. Velarde, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; D. Freddy, Hoch Berg Enterprises; T. Graham, Ranch Advisory Partners; M. Wille, T & M Contractors.; P. Lendrum, T. Bowyer, Idaho State University; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Colorado State Severance Tax Fund, EnCana Corp., Exxoru\fobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT We propose to experimentally evaluate winter range habitat treatments and human-activity management altemati ves intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 5 pretreatment years and l year post treatment of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia, South Magnolia) and control (North Ridge, Ryan Gulch) sites and recorded habitat use and movement patterns using GPS collars (2::5 location attempts/day), estimated overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult females using ultrasonography, and estimated abundance using helicopter mark-resight surveys. During this research segment, we targeted 240 fawns (60/study area) and 170 does (30-70/study area) in early I 1liiil~Iffll1rnm BDOW028744 �December 2013 for VHF and GPS rad.iocollar attachment, respectively, and 120 does in March 2013 (30/study area) for late winter body condition assessment. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of the 2 treatment areas with minor and extensive energy development, respectively. Post-treatment monitoring will continue for 4 years to provide sufficient time to measure how vegetation and deer respond to these changes. Based on data collected during the 5-year pretreatment phase and 1 year posttreatment: (1) annual adult survival was consistent among areas averaging 80-84% annually, but overwinter fawn survival was more variable ranging from 48% to 95% within study areas, with annual and study area differences primarily due to annual weather conditions and in some cases density dependent influences; (2) migratory mule deer selected increased cover and increased their rate of travel through developed areas, but did not avoid development structures and avoided negative influences through behavioral shifts in timing and rate of migration; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, which likely relate to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer densities have increased in 3 of 4 areas, with fluctuating and recently increasing deer densities evident in the 4 t11 area; (5) post treatment vegetation responses have been promising with evidence of improved forage conditions, but longer tenn monitoring will be required to address the full potential of habitat mitigation efforts. Detailed habitat use analyses are still pending for the pretreatment period. We will continue to collect population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to detennine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation efforts toward less or non-impacted areas. In collaboration with Colorado State University, we are also evaluating deer behavioral responses to varying levels of development activity in the Ryan Gulch study area and neonate survival in relation to energy development from all study areas. This will allow us to assess the effectiveness of certain Best Management Practices (BMPs) for reducing disturbance to deer and include neonatal data to other demographic parameters for evaluation of mule deer/energy development interactions. The study is slated to run through 2018 to allow sufficient time for measuring mule deer population responses to landscape level manipulations. 2 -.., '-6,/ �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fa'Ml survival, or ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Monitor habitat treatment response for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Continue neonate survival evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or noxious weeds, by fragmenting habitat because of drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the 3 �need for additional landscape for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to mule deer populations. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. I). Anderson and Freddy (2008a) in their longterm research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the past 5 years, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We are currently monitoring 2 control areas: I with development (0.6 pads & facilities/km1 ; Ryan Gulch) and l without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km1 and North Magnolia, 0.1 well pads & facilities/km\ that also recently received habitat improvements ( 604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated over the next 3-5 years to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavior patterns in relation to energy development activities in the Ryan Gulch area are being monitored to identify effective Best Management Practices (BMPs) for future energy development planning. This progress report describes the previous 6.5 years (Jan 2008-June 2014) of mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, oveJWinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter; and annual late-winter abundance/density estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edu/is)-Utahjuniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from 1,675 m to 2,285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanclzier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixedgrass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comara), Indian rice grass (O,yzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarotlzreae), pinnate tansymustard (Descurainia pinnata ), milkvetch (Astragalus spp. ), Lewis tlax (Linum lewisii), evening 4 V -\ U �primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rain storms. 2 Wintering mule deer population segments we investigated include: North Ridge (53 km ) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 14 l km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km 2) between the Dry Fork of Piceance Creek and Lee Gulch in the northcentral portion of the Basin. and South Magnolia (83 krn 2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. 1). Each of these wintering population segments has received varying levels of nanrral gas development: no development in North Ridge, light development in North Magnolia (0.1 l pads & facilities/km 2), and relatively high development in the Ryan Gulch (0.46 pads & facilities/km2) and South Magnolia (0.67 pads & facilities/knl) segments (Fig. l). Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address humanactivity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and No11h and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. METHODS Tasks addressed this period included mule deer capnrre and collaring efforts, monitoring neonate and overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography, estimating mule deer abundance applying helicopter markresight surveys, and monitoring vegetation responses to habitat treatments completed spring 2013. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to target 240 fawns and 170 adult females during early December 2013, and 120 adult females (primarily recaptures) during early March 2014. Once netted, all deer were hobbled and blind folded. Fawns were weighed, radiocollared and released on site, and adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (5 or 48 fix attempts/day; G2110D, Advanced Telemetry Systems, Isanti, MN, USA) and released. To provide direct measures of decline in overwinter body condition, we targeted 30 adult females in each study area that were captured the previous December; Vaginal Implant Transmitters (VITs) were also inserted to assist with neonate capture and collaring efforts spring 2014. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and autumn, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early in April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed and recovered since 2008. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and l 0 control deer from North Ridge where location rates were programmed for every 30 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) is currently analyzing resource selection data relative to energy development activity and should have results finalized for inclusion in next year's annual report. Mule deer resource selection analyses to address success of habitat improvements are pending until vegetation responses are fully realized, which are anticipated by fall 2018. 5 �Mule Deer Survival V Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected. deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated survival rates from I July 2013 through 30 June 2014 for adult females, from birth to mid December for neonates, and from early December 2013-mid June 2014 for fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years using a two-sample t-test. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 3 helicopter mark-resight surveys (2 observers and the pilot) during late March to estimate deer abundance in each of the 4 study areas; l to 2 additional surveys/study area were scheduled to achieve increased precision, but were not possible due to helicopter mechanical issues. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011 ). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2014. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID. Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (a:!= 0 or ;t:. 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 242 fawns and 167 does during Dec 2013 and 118 does during March 2014. Four fawn monalities (I. 7%; ultimate cause= 2 capture myopathy, I predation, 1 vehicle 6 -.,, V �collision) occurred within the 10 day censorship period. Doe mortalities totaled 4 (2.4%; capture myopathy) and 2 (1.7%; capture myopathy) within 10 days of the December and March capture periods, respectively. Mortality rates, 10 days post capture, have varied between 2-3% for fawns and 0-3% for does since Jan 2008, except during the 2011-2012 capture season where myopathy rates were higher (36%) due to dry. warm conditions (Anderson and Bishop 2012). Fawn survival from early December 2013 through mid June 2014 was similar (P> 0.05) among study areas ranging from 0.84 to 0.95 (Table 1). General comparisons to previous years suggest relatively high fawn survival occurred during winters 2009-2010 and 2013-2014: and relatively low survival during winter 2010-2011 (Fig. 2), which correlates to summer forage condition evident from December fawn weights (Fig. 3) and winter severity. Annual adult female survival varied from 0.76 (North Magnolia) to 0.90 (South Magnolia; Table 1) during 2013-2014, but was comparable among study areas during 2013-2014 and to previous years (P > 0.05), with the exception of lower survival in North Magnolia during 2011-2012 (S= 0.68, Anderson and Bishop 2012). Sample sizes for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g., >15-20%). Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has recently been completed. Three manuscripts have been accepted for publication (Lendrum et al. 2012, Lendrum et al. 2013; Lendrum et al. 2014; Appendix A). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 4) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript (Lendrum et al. 2013), we addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures. snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, with median migration durations of 3-8 days among the 4 areas (straight line distance between seasonal ranges averaged 32--40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in long-term fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication (Lendrum et al. 2014 ), we monitored migratory mule deer in the Piceance Basin to examined the relationship between the Normalized Difference Vegetation Index (NDVI). which is a course-scale measure of forage quality using a GIS assessment of vegetation greenness, and fecal nitrogen to assess the assumption that forage quality and deer diets can be reasonably linked to address 7 �deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were informative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NOVI values, and progressed to summer range as vegetation green-up and nitrogen levels increased, but ahead of peak vegetation green-up on summer range. I suspect this rapid migration strategy is evident for deer in relatively good condition and allows for early arrival on summer range to take advantage optimal forage conditions prior to parturition. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer were comparable among study areas during December 2013 (P > 0.05, Fig. 5, Table 2). Early winter condition this year was comparable to previous years with exception of relatively low condition expressed by North Ridge deer during 2009 and by North Ridge and Ryan Gulch deer last year; Ryan Gulch exhibited generally improved condition during December 2011 (Fig. 5). With the exception of Ryan Gulch does exhibiting relatively poor condition during 2014, late winter body condition was higher this year when compared to 2009 and 2011, but lower than North and South Magnolia does during 2010 (Fig. 5, Table 2). These observations appear more related to seasonal moisture conditions and winter severity than development intensity thus far. December 2013 fawn weights by study area were higher in all comparisons with the exception of Ryan Gulch females (Fig. 3). In general, seasonal moisture conditions appear to be driving differences in annual body condition within study areas, but other factors appear related to differences among study areas. We suspect density dependent factors (forage capacity relative to deer density) are related to observed differences in early winter body condition among study areas. More detailed analyses will be conducted to identify factors attributing to these observations. Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate survival in developed and undeveloped landscapes (funded by ExxonMobil Production Co.) beginning spring 2012. Mark Peterson (Graduate Research Assistant) and Paul Doherty (CSU professor) are assisting with this research, which will continue through 2014. Neonate capture and collaring efforts totaled 85 during spring 2012, 67 during spring 2013, and 54 during spring 2014. Estimated neonate survival through mid-December 2012 was 0.39 (95% CI= 0.28-0.50) and 0.37 (95% CI= 0.25--0.48) from birth to mid-December 2013; neonate survival estimates for 2014 are pending. Factors influencing neonate mule deer survival from developed and undeveloped landscapes will be addressed by mid 2015. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and homogenous individual sightability (cr2 = 0) for South Magnolia deer and variable individual sightability (cr2 -:j:. 0) for the other 3 areas. North Ridge exhibited the highest deer density (22.2/km\ with comparably lower deer densities in the other 3 areas (9.7-10.9/km2; Table 3, Fig. 6). Populations have increased over the 6 year monitoring period in 3 of the study areas (from 6.5/km2 to l 0.4/km 2), with a fluctuating population in North Ridge (from 14.4/km2 to 22.8/km2); the current North Ridge density is comparable to the peak population estimate from 2011 (Fig. 6). The previous North Ridge decline was likely related to density dependent factors, evident in lower early winter body condition (Table 2) and an increase in malnutrition mortalities of radio-collared adult females during the decline (from O in 2010-2011 to 7 in 2012-13). 8 U �Abundance estimates from 2013 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.15--0.17. Magnolia Habitat Treatments We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 201 I; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA}, 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop, chain) in January 2012 (Stephens 2014), and 1,038 acres of hydro-ax treatments in April 2013 (Determination of NEPA Adequacy: DOI-BLM-CO-I 10-2012-0134DNA), totaling 604 treated acres in each study area (Fig. 7). Vegetation response in the pilot treatment sites was visually evident by fall 20 l l (Fig. 7), and resulted in statistically significant (P < 0.05) increases in native grass cover and apparent (>50% increase) but not yet significant increases in native forb cover by spring 2013 (2014 results are still pending). Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3 fold increase in grasses, forbs, and shrubs combined after 2 growing seasons (versus control sites), but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following l growing season, but statistical comparisons are pre-mature. As anticipated, grass and forb responses should be evident 2 to 3 year post-treatment, with longer term response expected (3-5 years) from palatable shrubs. Of note, relatively high moisture conditions experienced during spring 2014 resulted in higher than normal prevalence of cheatgrass (Bromus tectorum); cheatgrass invasion has previously been minor to non-existent. Cheatgrass invasion, however, does not appear directly related to treatment sites because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. GPS data addressing deer use of treatment sites is just becoming available and will be analyzed as additional data are collected and vegetation responses progress. Vegetation and mule deer responses will be documented for the next 4 years to assess the utility of this mitigation approach in benefiting mule deer exposed to energy development disturbance. All expenses addressing these habitat treatments will be covered through a Wildlife Management Plan agreement between CPW and ExxonMobil Production/XTO energy. SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 5-year pre-treatment period and I year post-treatment. The pretreatment period was completed during spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of2 study areas were completed April 2013, and preliminary vegetation responses appear promising. Post-treatment monitoring will continue for 4 years to provide sufficient time to measure how deer respond to these changes. Based on data collected prior to habitat improvements (i.e., pretreatment phase): (l) annual adult survival was consistent among areas averaging 80-84% annually, but overwinter fawn survival was variable, ranging from 48% to 85% within study areas, with annual and study area differences primarily due to annual weather conditions and in some cases density dependent influences; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid 9 �development structures; (3) mule deer body condition early and late winter was consistent within areas, with higher variability among study areas early winter, which was likely related to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer densities appear to be increasing in 3 of 4 areas, with midterm decline and recent increase in North Ridge. Detailed habitat use analyses are pending for the pretreatment period. We will continue to collect the various population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to detennine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Colorado, Bergman et al. (2014) found that habitat treatments implemented in pinion-juniper habitat in undeveloped areas increased oveiwinter survival of fawns by a magnitude of I . 15. We are also evaluating deer behavioral responses to varying levels of development activity. This will allow us to assess the effectiveness of certain BMPs for reducing disturbance to wintering mule deer. Hay field improvements have been completed in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; elk (Cervus elaphus) response has been evident but mule deer response has thus far been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Additional collaboration with WPX Energy has resulted in a clustered development plan in the Ryan Gulch study area and new technologies will be implemented to further reduce human activity through remote monitoring of well pads and fluid collection systems. Collaborative research with Idaho State University, Colorado State University/ExxonMobil Production, and Utah State University/Utah Division of Wildlife Resources has produced 6 peer-reviewed publications addressing mule deer migration (Lendrum et al. 2012, 2013, 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013 ), mule deer response to helicopter capture and handling (Northrup et al. 2014 ), and potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014); these publications are summarized in Appendix A. Additional funding and cooperative agreements will be necessary to sustain this project to completion (preferably through 2018). We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. LITERATURE CITED Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr.. and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and D. J. Freddy. 2008b. Population perfonnance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation-Stage I, Objective 5: Patterns of_mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2010. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and C. J. Bishop. 2011. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. V �Anderson, C.R., Jr., and C. J. Bishop. 2012. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Parks and Wildlife, Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report, Colorado Divison of Wildlife. Fort Collins, Colorado. USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality in a Colorado mule deer population. Wildlife Monograph No. 12 l. Barrett, M. W., J. W. Nolan, and L. D. Roy. 1982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin I 0: 108-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, G. C. White. and PF. Doherty, Jr. 2014. Habitat management influences over-winter survival of mule deer fawns in Colorado. Journal of Wildlife Management 78(3):448-455; DOI: 10.1002/jwmg.683 Burnham, K. P., and D.R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New Yor~ New York, USA. Cook, R. C., J. G. Cook, D. L. Murray, P. Zager, B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65 :973-987. Cook, R. C., T. R. Stephenson, W. L. Meyers, J. G. Cook, and L.A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71: 1934-1943. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Meyers, S. M. McCorquodale, D. J. Vales, L. L. Irwin, P. Briggs Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, P. J. Miller. 2009. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880-896. Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson, Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: l 0.1002/wsb.450 Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University, Fort Collins. Colorado, USA. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.org/l 0. Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: 10.13 7 l/joumal.pone.0064548 Lendrum, P. E., C.R. Anderson, Jr .. K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/10. l016/j.mambio.20l4.05.005 McClintock, B. T., G. C. White, K. P. Burnham, and M.A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Northrup, J.M.~ M. B. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7):1456-1463. 11 �Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg. 705 Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Stephens, G. J. 2014. Understory responses to mechanical removal ofpinyon-juniper overstory. MS Thesis, Colorado State University, Ft. Collins USA. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Barnnann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture ofred deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-42 l in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society. Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. White, G. C., and 8. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. u Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader --, 12 �Table 1. Survival rate estimates (S) of fawn (2 Dec. 2013-15 June 2014) and adult female (1 July 201330 June 2014) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe samplea (n) S (95% Cl) Fawns Ryan Gulch 60 0.950 (0.895-1.000) South Magnolia 61 0.868 (0. 783--0.953) North Magnolia 60 0.883 (0.802--0.965) North Ridge 56 0.838 (0.741--0.935) Adult females Ryan Gulch 36 62 0.846 (0. 745--0.94 7) South Magnolia 31 55 0.901 (0.806--0.997) North Magnolia 33 55 0. 764 (0.637--0.890) North Ridge 29 51 0. 792 (0.666--0.918) aAdult female sample sizes following capture and radio-collaring efforts March, 2014. 13 �) ) () Table 2. Mean rump fat (mm). Body Condition Score (BCS'\ and% body fat(% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2014. Values in parentheses= SD. March 2009 March 2010 December 2009 Study Arca Rump fat Ryan Gulch 1.73 ( 1.78) 2.66 (0.55) 7.08 ( 1.27) 8.35(6.36) 4.06(1.13) I0.54 (3.72) 2.31 ( 1.44) 2.35 (0.48) 6.37 ( 1.41) South Magnolia 1.29 (0.47) 2.51 (0.66) 6.74 (2.27) I 0.05 (6.19) 4.07 ( 1.21) 11.44 (3.50) 3.12 (2.20) 2.64 (0.59) 7.11 (l.69) North Magnolia 1.31 ( 1.01) 2.66 (0.68) 7.15 (1.63) 10.67 (5.76) 4.25 (0.96) 11.94 (3.39) 3.15 (2.34) 2.85 (0.53) 7.54 (1.53) North Ridge 1.57 ( 1.22) 2.60 (0.56) 6.81 ( 1.68) 5.25 (5.65) 3.63 (I.II) 9.37 (3.08) 1.77 ( 1.11) 2.42 (0.49) 6.39 ( I .45) March 2011 December 20 I I BCS % fat Rump fat BCS % fat Rump fat BCS % fat Table 2. Continued. December 20 l 0 % fat BCS % fat Ryan Gulch 7.26 (6.36) 3.24 (0.96) 9.69 (3.56) 1.55 (0.60) 2.53 (0.42) 6.72 (1.37) 13.41 (6.39) 4.21 ( 1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.27 (3.75) 1.65 (0.75) 2.35 (0.50) 6.15 (1.75) 8.19 (5.54) 3.41 (0.82) 10.34 (3.28) North Magnolia 9.55 (6.49) 3.46 ( 1.16) I0. 79 (4.26) 1.65 (0.67) 2.53 (0.49) 6.79 (1.47) 8.73 (5.77) 3.74 (0.91) I 0.73 (3.14) North Ridge 7.25 (5.41) 3.47 (0.86) 9.85 (3.02) 1.45 (0.76) 2.24 (0.49) 5.78 ( 1.79) 8.86 (5.37) 3.51 (0.99) 10.77 (3.33) 14 % fat BCS Rump fat Rump fat BCS Rump fat Study Area �Table 2. Continued. March 2012 December 2012 % fat BCS Rump fat BCS March 2013 BCS % fat Rump fat 9.34 (2.43) 1.87 (0.90) 2.65 (0.37) 6.90 ( 1.59) 7.00 (1.13) 8.30 (5.71) 3.46 (1.07) 10.32 (3.23) 2.06 (0.77) 2.65 (0.26) 7.19 (0.66) 1.90 (0.76) 2.84 (0.34) 7.62 (0.95) 9.66 (6.41) 3.84 (1.16) 11.18(3.64) 1.76 (0.91) 2.59 (0.41) 6.87 (1.11) 2.24 ( 1.58) 2.70 (0.35) 7.26 ( 1.05) 5.76 (4.10) 3.32 (0.82) 1.87 (0.73) 2.48 (0.34) 6.70(1.12) Study Arca Rump fat Ryan Gulch 2.15 ( 1.44) 2.74 (0.44) 7.22 ( 1.16) 6.34 (4.35) 3.30 (0.77) South Magnolia 1.68 (0.77) 2.59 (0.36) North Magnolia North Ridge 9.06 (2.31) % fat Table 2. Continued. December 2013 BCS March 2014 % fat Rump fat BCS % fat Study Arca Rump fat Ryan Gulch 9.27 (6.29) 3.47 (0.87) 10.61 (3.76) 1.69 (0.85) 2.68 (0.39) 7.03 (0.99) South Magnolia 11.27 (8.40) 3.99 (1.04) 11.40(4.16) 2.57 ( 1.61) 2.96 (0.30) 7.75 (0.68) North Magnolia 9.00 (6.15) 3.44 (0.78) 10.48 (3.25) 2.33 (2.12) North Ridge 11.17 (5.28) 3.85 (0.72) 1 1.66 (2.69) 2.38 ( 1.52) 2.68 (0.39) 2.80 (0.49) 7.31 (1.43) 7.16(1.14) aBody condition score taken from palpations of the rump following Cook ct al. (2009). 15 ) ) ) �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 24-28 March 2014. Data represent 3 helicopter resight surveys from each study area. Study area Mean No. sighted Mean No. marked N(95% CI) Density (deer/km~) Ryan Gulch 328 _.,, - 1,498 (l,270-1.791) 10.6 South Magnolia 208 30 77 l (664-909) 9.7 North Magnolia 261 32 862(748-1,011) 10.9 North Ridge 413 33 1.183 (1.029-1,388) 22.~ u --....., 16 �Mule Deer Winter Range Study Areas Mul e deer study areas Zll!'-.:MP'3t:I D Nor1h Magnolia Soutn Magnolia l l0'1h Ridge ; yan Gulcn 25 ! In de\'elopme nt l Proaucing ,,,en Developmem rac •1nies 1 5 10 r-.111es Figure 1. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado, winter 20 13/ 14 (Accessed http://cogcc.state.eo.us/ Dec. 3 1.2013). 17 I Well Pads & Facilities �Ryan Gulch fawn S South Magnolia fawn S 2008/09-2013/14 2008/09-2013/14 1.00 0.90 0.80 t===:~:!~;i.;~~~~~~~~~f!~~~~ 0.70 -t0.60 +----......1.,~nllili~..;..:.;:-............... 0.50 0.40 0.30 •••••••••••••••• 0.20 - - - - - - - - - - - - - 0.10 0.00 North Magnolia fawn S North Ridge fawn S 2008/09-2012/13 2008/09-2013/14 1.00 T"ilir.-~ 0.90 ~•.....-1111,,ii;:: 0.80 0.70 0.60 4 -_ _ __.;;.......,_-311i._;:..u...-~............i~ 0.50 -t-1 -------S,,e;-:-:--==--a.iiiiiiiiiiiiiiiiiiiiiii" 0.40 - - - - - - - - - - - - - - - 0.30 - i . - - - - - - - - - - - - - 0.20 - + - - - - - - - - - - - - - - 0.10 - - - - - - - - - - - - - - - - 0.00 i I l 1 i l I i i I I I , I I i l i I I I I I I I . I t l 1.00 0.90 0.80 0.70 0.60 0.50 +--------___,,+ro....---,__;;;=0.40 0.30 0.20 . . . . . . - - - - - - - - - - - - - - 0.10 + - - - - - - - - - - - - - 0.00 I I I . I i I I I I I I i I I I I i I I I I I I I I i JI u Figure 2. Over-winter (Dec-Mar & June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin, northwest Colorado, 2008/09 (red lines), 2009/10 (orange lines). 2010/11 (blue lines), 2011/12 (black lines), 2012/13 (purple lines), and 2013/14 (cyan lines). Solid lines= .Sand dashed lines= 95% Cl. Comparable data among years: December-March 2008-2010 due to premature collar drop and December-mid-June 2010-2014. ---. 18 �Male fawn weights 42.0 40.0 I 38.0 fa 36.0 ·a; tt I 0 s 34.0 rt ~ j Ii [ ~ I t f-- J ,_a ,- , 1 1, T ,_ - ,_ y 1:: 30.0 . C South Magnolia DNorth Magnolia I- C North Ridge 1: 32.0 Cl Ryan Gulch - - 1: - - Il l 11 - ' ~ =- Dec2008 Dec2009 Dec2010 Dec2011 Dec2012 Dec2013 Female fawn weights 42.0 40.0 bO 38.0 D Ryan Gulch -" -fa 36.0 s 34.0 T ~ i~f j .T It f n "ai l T j J 32.0 ' ,_: 30.0 !J ~ j IJ ,- I• 11 ,, ~ ,_ I t ~ f ttJ l a ~ DSouth Magnolia D North Magnolia DN orth Ri dge - Dec2008 Dec2009 Dec2010 Dec2011 Dec2012 Dec 2013 Figure 3. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceancc Basin. northwest Colorado, December :2008-20 13. 19 �r I- /*-- _ _ 1 / i \\. l.itah ;· . ·yomrng lf--I l Colorado r I .,__ r-- Figure 4. \fole deer study areas in the Piceance Basin of northwestern Colorado, USA (Top). spring 2009 migrati on routes of adu lt female mule deer (11 = 52: Lower left). and active natural-gas well pads (black dots) and roads (state. county, and natural-gas: white lines) from May 2009 (Lower 1ight; from Lendrum et al. 20 12). 20 �Early winter rump fat (mm) 16 14 12 1 i c==aNorth Ridge 10 -==-North Magnolia 8 a. ---m... E 6 = = Ryan Gulch ==-South Magnolia 4 2 0 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Late winter rump fat (mm) 4.00 3.50 3.00 e .§. 2.50 ==- North Ridge = ~ 2.00 North Magnolia =~Ryan Gulch C. E 1.50 == c==:aSouth Magnolia 1.00 0.50 0.00 Mar 2009 Mar 2010 Mar 2011 Mar 2012 Mar 2013 Mar 2014 Figure 5. Mean early (early Dec., Top) and late winter (early Mar., Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado, March 2009-March 2014. Error bars = 95% Cl. 21 �Piceance Basin late winter mule deer density 30.00 25.00 e 20.00 ~ North Ridge QI Ryan Gulch ":::15.00 QI Q North Magnolia 10.00 - s o u t h Magnolia 5.00 0.00 2009 2010 2011 2012 2013 2014 Year Figure 6. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2014. u 22 V �\/orlh M ag~olla treatemem sites (58; ac,es) I = 3ear:':et_ • 5_35b_andC: 3ear5et_ • _aanctA _E 3ear:3:t_36_54andJ GreasewoodSet_ g I 6_g29 GreasewooctSet_QI.Jl 15 C Greasewooose,_g30_g4~ Lee0vers,gnts_a_land 16_ 17 Medlanrc31:re:=trret"\t companson (54 acres) Nor111 Hatch Pilot Treatments ( • • 6 acres; Mule Deer Study Areas Nor111 f,1agnoha Figure 7. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Bas in, northwest Colorado (Top; cyan polygons completed Jan. 2011 using hydro-axe; ye llow polygons completed Jan.2012 us ing hydro-axe. roller-chop. and chaining; and remaining polygons completed Ap1il 20 I 3 usi ng hydro-axe). January 2011 hydro-axe treatment-s ite phocos from North Hatch Gulch du1ing Apri l (Lower left, aeria l view) and October. 2011 (Lower right. grnund view). 23 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development 1 PATRICKE. LENDRUM 1, CHARLES R. ANDERSON. JR.~. RYAN A. LoNo • Jo11"1'\ G. KrE',AND R. TERRY BOWYER' 1 Department ofBiological Sciences, Idaho State University. Pocatello. Idaho 83209 USA Colorado Division of Parks and Wildlife, Grand Junction, Colorado 81505 USA 2 Citation: Lendrum, P. E.. C.R. Anderson. Jr.. R. A. Long, J. G. Kie. and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landsc:ipe strucrure and natural-gas development. Ecosphere 3(9):82 http:.idx.doi.org/10.1890/ES 12-00165.1 Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species. and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoileus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns ofresource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (11 = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 20 IO in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally. deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas. deer may simply increase their rate of travel along established migration routes. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum'. Charles R. Anderson Jr.!. Kevin L Monteith'·J. Jonathan A. Jenks\ R. Terry Bowyer' Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA,! Colorado Division of Parks and Wildlife, Grand Jwiction, Colorado, USA. 3 Wyoming Cooperative Fish and Wildlife Research Unit. University of Wyoming, Laramie. Wyoming, USA.4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA 1 Citation: Lendrum, P. E.. C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks. R. T. Bowyer. :?013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: I0.1371/joumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sutliciently plastic to compensate for such changes. warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoi/eus hemio11us) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-2010. Co11c/uslo11s/Signijica11ce: Timing of spring migration was related to winter weather (particularly snow depth) :111d access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was intluenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult temales. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure. but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of aiTival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. 24 �~ Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M.1'ORTIIRUP MEVIN B. HOOTEN ·! CHARLES R. ANDERSON. JR.". A.'ID GEORGE WITTEMYER' 'Department offish, Wildlite. and Conservation Biology. Colorado State University. 1474 Campus Delivery, Fort Collins, Colorado 80.523 USA =u.s. Geological Survey. Colorado Cooperative Fish and Wildlite Research Unit. 1474 Campus Delivery, Fon Collins, Colorado 80523 USA ;Colorado State University. Depanment of Statistics, Colorado State University, 1474 Campus Delivery. Fort Collins, Colorado 80523 USA J.'.\-tammals Research Section Colorado Parks and Wildlife. 71 l Independent Avenue. Grand Junction. Colorado 81505 USA 1 1 • 3 , Citation: 1':onhrup. J.M., M. B. Hooten. C.R. Anderson. Jr.. and G. Wittemyer. .:?013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. http://dx.doi.org/ I0.1890/ I.:?- I688. I Abstract. Habitat selection is a fundamental aspect of animal ecology. the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework. whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models~ they often are fit using logistic regression. This framework otlers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates. which is common for landscape characteristics. exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely. take care with interpretations and use cross validation to assess robustness. Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. 1'ORTHRUP 1• CHARLES R. A:"l"DERSON. JR;_ A.'\ID GEORGE W1TTEMYER 1 1 Depanment offish, Wildlife, and Conservation Biology. Colorado State Uni\'ersity. 1474 Campus Delivery, Fort Collins. Colorado 805.23 USA ~Mammals Research Section Colorado Parks and Wildlife. 71 l lndependent Avenue. Grand Junction, Colorado 81505 USA Citation: Nonhrup, J. M.. C. R. Anderson. Jr.. and G. Wittemyer. 2014. Etlects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife :Management 78(4):731-738: DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology. and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals. and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition. we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement. but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours. with 71 % of deer returning in the first day, and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due co the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported. and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. ,~: 2014 The Wildlite Society. 25 �Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns offorage quality V Patrick E. Lendrum\ Charles R. Anderson Jr.\ Kevin L. Monteith\ Jonathan A. knks'1. R. Terry Bowyer• • Depanment of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA h Mammals Research Section Colorado Parks and Wildlite, 711 Independent Avenue. GrJnd Junction 81505, USA "Wyoming Cooperative Fish and Wildlife Research Unit. Depanment of Zoology and Physiology, University of Wyoming. 3166, IO00 East Vniversity Avenue. Laramie 82071, USA J Department of Natural Resource Management, South Dakota State t.:niversity. Box ~1408, Brookings 57007. USA Citation: Lendrum, P. E.. C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement ofa rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biolo!:,ry: hnp: .. Jx.doi.,m.!il o. IO I6:j.mambio..:?O I4.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, otien along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Normalized Difference Vegetation Index; NDVI) in spring 2010 in the Piceance Basin of northwestern Colorado, t;SA. NDVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NDVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet. which was evident in patterns of NOVI and fecal nitrogen. Moreover, these deer 'jumped" rather than ··surted" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation. and malnutrition during migration. Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN1, RAi'\IDY T. LARSEN1, :MARKE. PETERSON\ CHARLES R. A..'\IDERSON, JR.3, KENT R. HERSEY 4, AND BROCK R. McMILLAN 1 1 Department of Plant and Wildlife Sciences, Brigham Young t.:niversity, 27 5 WIDB, Provo, UT 84602, USA :: Department of Fish, Wildlite. and Conservation Biology. Colorado Scace University, 1474 Campus Delivery, Fort Collins, CO 80523. USA 3 Colorado Parks and Wildlife, 711 Independent Avenue. Grand Junction. CO 8I505, USA 4 Utah Division of Wildlife Resources, 1594 W North Temple, Salt Lake City, UT 8-1114. USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson. Jr.. K. R. Hersey. and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing ofpanurition. Wildlite Society Bulletin; DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices intluence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer ( Odocoileus hcmio11us), changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing ofparrurition. and synchrony ofpamnition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fa,vns, and a less synchronous panurition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males,100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= l.258; SE= 1.672; r= 0.752; P = 0.454), or synchrony of parturition (F = l .0i3; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/temale ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected. e.1014 The Wildlite Society. --- 26 �" \..,I Colorado Parks and Wildlife July 1, 2014-June 30. 2015 WILDLIFE RESEARCH REPORT State of________ C___ o__ lo.....ra___d.. .,_o_ _ _ _ _ : .;;...Pa=r=ks=-=an____d----a,.W__i__ ld__li.....fe____________ Cost Center 3430 : ta__m ____m . . . . a. . ___ls___R ___e_____s.....e__a ___ rc__h______________ Work Package 3001 : =D__e___e__ r ___ C___o_____n___ se____r_v____ at__io__n_______________ Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Miti2ation Efforts to Address Human Activitv and Habitat Degradation Federal Aid Project:_ ___,;_W;._-.;;.. 18;;;..;;5;._-=R;.. .__ _ __ .;;.;rv__ Period Covered: July 1. :2014 - June 30. 2015 Author: C. R. Anderson, Jr. Personnel: N. Bellerose, E. Bergman, T. Blecha. D. Collins, J. Decoste, 8. deVergie. D. Finley. M. Fisher, L. Gepfert, D. Johnston, T. Knowles. D. Lewis, J. Matijas. N. Mesce, B. Petch, J. Rivale, G. Sanchez, R. Schilowsky, G. Smith, K. Stonehouse. R. Velarde. L. Wolfe. CPW; E. Hollowed, L. Belmonte, BLM; D. Freddy, Hoch Berg Enterprises; T. Graham, Ranch Advisory Partners; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson. Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.: L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation. Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum. and \\'PX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT ~ We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 5 pretreatment years and 3 years post treatment of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia. South Magnolia) and control (North Ridge, Ryan Gulch) sites and recorded habitat use and movement patterns using GPS collars (?:5 location attempts/day), estimated neonatal, overwinter fawn and annual adult female survival. estimated early and late winter body condition of adult females using ultrasonography. and estimated abundance using helicopter mark-resight surveys. During this research segment, we targeted 240 fawns (60/study area) and 120 does (30/study area) in early December 2014 for VHF and GPS radiocollar attachment, respectively, and adult female ~COLO DIV WILDLIFE RESEARCH CTR LIB I~ Illffll ll~I IIIIII II ll~l lllll lllll ll~I IIII 11111111 BDOW029160 �body condition assessment. We attempted recapture of 120 does in March 2015 (30/study area) for late winter body condition assessment. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of the 2 treatment areas with minor and extensive energy development respectively. Post-treatment monitoring will continue for another 3 years to provide sufficient time to measure how vegetation and deer respond to these changes. Based on data collected during the 5-year pretreatment phase and 3 years post-treatment: (1) annual adult survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable. ranging from 48% to 95% within study areas~ with annual and study area differences primarily related to annual weather conditions; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas. and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was consistent within areas~ with higher variability among study areas early winter, which was likely related to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and intensity of development activity, which may provide for several options in future development planning; (5) mule deer densities appear to be increasing in 3 of 4 areas, with a stable population in North Ridge; and (6) post treatment vegetation responses have been promising with evidence of improved forage conditions, but longer term monitoring will be required to address the full potential of habitat mitigation efforts. Detailed habitat use analyses are still pending for the pretreatment period. We will continue to collect population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation efforts toward less or non-impacted areas. In collaboration with Colorado State University. we are also monitoring neonate survival in relation to energy development from all study areas. This will allow us to include neonatal data to other demographic parameters for evaluation of mule deer/energy development interactions. The study is slated to run through 2018 to allow sufficient time for measuring mule deer population responses to landscape level manipulations. 2 V V �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses. improves body condition. increases fawn survival. or ultimately. population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition. fawn survival. and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavior patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and biweekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate mule deer abundance in each study area. 5. Monitor habitat treatment response for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Continue neonate survival evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION ~\._./ Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads. or noxious weeds, by fragmenting habitat because of drill pads and roads. by increasing noise levels via compressor stations and vehicle traffic. and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the .,.., �need for additional landscape for human housing. supporting businesses, and upgraded road/transportation infrastructure. Additional1y, increased traffic on rural roads will raise the potential for vehicle-animal collisions and additive direct mortality to mule deer populations. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing an~ mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also exhibits some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. I). Anderson and Freddy (2008a) in their longterm research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the past 7 years. we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We are currently monitoring 2 control areas: 1 with development (0.6 pads & facilities/km 2; Ryan Gulch) and I without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km 2 and North Magnolia, 0.1 well pads & facilities/km\ that also recently received habitat improvements (604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated until spring 2018 to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavior patterns in relation to energy development activities in the Ryan Gulch area are being monitored to identify effective Best Management Practices (BrvtPs) for future energy development planning. This progress report describes the previous 7.5 years (Jan 2008-June 2015) of mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter; and annual late-winter abundance/density estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely. Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utahjuniper (Juniperus osteosperma; pinion-juniper) shrubland complex ranging from I ~6i5 m to 2,285 m in elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (.A.melanchier utahensis). mountain mahogany (Cercocarpus montanus). bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii). mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush. salt bush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixedgrass hay fields. Grasses and forbs common to the area consist of wheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis). needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides). arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata). milkvetch (Astragalus spp.), Lewis flax (Limon lewisii), evening 4 V �~ primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata). buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.). and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by wann dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rain storms. Wintering mule deer population segments we investigated include: North Ridge (53 km 1) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch (141 kni) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin. North Magnolia (79 km 2) between the Dry Fork of Piceance Creek and Lee Gulch in the northcentral portion of the Basin. and South Magnolia (83 km 2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. l ). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge. light development in North Magnolia (0.1 pads & facilities/km 1 ). and relatively high development in the Ryan Gulch (0.6 pads & facilities/krn 2) and South Magnolia (0.9 pads & facilities/km 1) segments (Fig. 1). Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address humanactivity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and heavy (South Magnolia) energy development activities. METHODS --.., '--" Tasks addressed this period included mule deer capture and collaring efforts. monitoring neonate. overwinter fawn and annual adult female survival. estimating adult female body condition during early and late winter using ultrasonography, estimating mule deer abundance applying helicopter mark-resight surveys, and monitoring vegetation responses to habitat treatments completed spring 2013. We employed helicopter net-gunning techniques (Barrett et al. 1982. van Reenen 1982) to target 240 fawns and 120 adult females during early December 2014. and 120 adult females (primarily recaptures) during early March 2015. Once netted, all deer were hobbled and blind folded. Fawns were weighed, radio-collared and released on site, and adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (5 fix attempts/day; G2 I 1OD. Advanced Telemetry Systems, Isanti, MN. USA) and released. To provide direct measures of decline in overwinter body condition. we targeted 30 adult females in each study area that were captured the previous December; Vaginal Implant Transmitters (VITs) were also inserted to assist with neonate capture and colJaring efforts spring 2015. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between midsummer and autumn, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early in April of the year following deployment: All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). Mule Deer Habitat Use and Movements --., We downloaded and summarized data from GPS collars deployed and recovered since 2008. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and 10 control deer from North Ridge where location rates were programmed for every 30-60 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) recently analyzed resource selection data relative to energy development and those results are addressed below. Mule deer resource selection analyses to address success of habitat improvements are pending until vegetation responses are fully realized, which are anticipated by fall 2018. 5 �Mule Deer Survival Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958. Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within IO days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated survival rates from I July 2014 through 30 June 2015 for adult females, from birth to mid December for neonates. and from early December 2014-mid June 2015 for fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. ( 1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001~ 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years using a two-sample t-test. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg). chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 4 helicopter mark-resight surveys (2 observers and the pilot) during late March to estimate deer abundance in each of the 4 study areas. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2015. We delineated flight paths in ArcGIS 9.3 prior to surveys following topographic contours (e.g.. drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two approximately 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#. unmarked, or unidentified mark. We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-nonnal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr~ = 0 or~ 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 242 fawns and 113 does during Dec 2014 and 120 does during March 2015. Eight fawn mortalities (3.3%: proximate cause= 4 capture myopathy, 4 predation) occurred within the 10 day censorship period. Doe monalities totaled I (0.9%; cougar predation) and 7 (5.8%; 6 6 �capture myopathy: 1 cougar predation) within 10 days of the December and March capture periods. respectively. Mortality rates. 10 days post capture. have varied between 2-3% for fawns and 0--3% for does since Jan 2008. except during the 2011-2012 capture season where myopathy rates were higher (36%) due to dry. warm conditions (Anderson and Bishop :W 12). and during the March 2015 doe captures. Nothing abnonnal occurred during March 20 I 5 capture efforts and reasons for the higher. than normal myopathy rate are unclear. Fawn survival from early December 2014 through mid June 20 I 5 was similar (P > 0.05) among study areas ranging from 0.86 to 0.95 (Table 1). General comparisons to previous years suggest relatively high fawn survival occurred during winters 2009-2010, 2013-2014. and 2014-2015 and relatively low survival during winter 20I0-2011 (Fig. 2). which correlates to summer forage condition evident from December fawn weights (Fig. 3) and winter severity. Annual adult female survival varied from 0.81 (Ryan Gulch) to 0.95 (North Magnolia; Table I) during 2014-2015~ but was comparable among study areas during 2014-2015 and to previous years (P > 0.05). with the exception of lower survival in North Magnolia during 2011-2012 (S= 0.68, Anderson and Bishop 2012). Sample sizes for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g.. >15-20%). Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has recently been completed. Three manuscripts from this effort have been published (Lendrum et al. 20 I 2, Lendrum et al. :2013; Lendrum et al. 2014; Appendix A). ·'--' In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 4) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads: except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript (Lendrum et al. 2013). we addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall. spring migration was influenced by snow depth, temperature. and green-up on winter and summer range; increasing temperatures. snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short. with median migration durations of 3-8 days among the 4 areas (straight line distance between seasonal ranges averaged 32-40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures~ but departed later. arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival~ the relatively minor shift we observed should not result in long-term fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication (Lendrum et al. 2014)~ we monitored migratory mule deer in the Piceance Basin to examined the relationship between the Normalized Difference Vegetation Index (NOVI), which is a course-scale measure of forage quality using a G IS assessment of vegetation greenness, and fecal 7 �nitrogen to assess the assumption that forage quality and deer diets can be reasonably linked to address deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were informative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NOVI values. and progressed to summer range as vegetation green-up and nitrogen levels increased~ but ahead of peak vegetation green-up on summer range. I suspect this rapid migration strategy is evident for deer in relatively good condition and allows for early arrival on summer range to take advantage optimal forage conditions prior to parturition. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer were comparable among study areas during December 2014 (P > 0.05) with the exception of South Magnolia does exhibiting greater rump fat than North Ridge does (P < 0.05, Fig. 5, Table 2). Early winter condition this year was comparable to previous years with exception of relatively low condition expressed by North Ridge deer during 2009 and 2012; Ryan Gulch exhibited generally improved condition during December 2011 (Fig. 5). With the exception of Ryan Gulch does exhibiting relatively poor condition during 2014, late winter body condition was higher this year when compared to 2009 and 2011, but lower than North and South Magnolia does during W 10 (Fig. 5~ Table 2), and may be trending upward due to relatively mild winters since 201 1. These observations appear more related to seasonal moisture conditions, relative deer densities (Fig. 6), and winter severity than development intensity thus far. December 2014 fawn weights by study area were comparable with the exception of heavier males from North Ridge in comparison to South Magnolia and heavier females in comparison to Ryan Gulch and North Magnolia (Fig. 3). OveralL fawn weights were relatively low in comparison to previous years (Fig. 3), and December fawn condition has been correlated with winter survival (Fig. 2), but high fawn survival persisted this year likely due to the mild winter and good spring/summer moisture improving forage conditions. More detailed analyses will be conducted to identify factors attributing to these observations. Mule Deer Behavioral Response to Energy Development We recently completed evaluations of deer behavior patterns in relation to energy development activities (Northrup et al. 2015). We found diurnal responses to development activity, where deer used timbered areas away from development activity while bedded during the day and moved into more open areas generally closer to developed areas while foraging at night. Avoidance of producing pads and roads declined from 400 m to 200 m and about 140 m to 60 m from daytime to nighttime, respectively, but increased from 600 m to 800 m for nighttime drilling pad activity. We suspect deer behaviorally respond to fluctuations in development activity, where road traffic and producing well pad activity decline at night. but drilling pad disturbance may increase from compressors and lights used to facilitate nighttime drilling activity. These evaluations were applied during an active drilling phase in the Piceance Basin and deer behavior was compromised in 25% (nighttime) to 50% (day time) of critical winter range during that period. However~ deer densities have increased (Fig. 6) and this suggests that deer can behaviorally mediate development disturbance at some level by taking advantage of fluctuations in development a~tivity to address their nutritional requirements. Given the plasticity in deer behavior, a number of potential options for future development planning exits including drilling schedule modifications (seasonal and/or diurnal). concentrated/staged development~ reducing road traffic~ and using light/noise barriers around drill rigs. It will be interesting to determine if habitat improvements will further reduce development disturbance and increase management options for future development planning. 8 V �'--1' Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate survival in developed and undeveloped landscapes (funded by ExxonMobil Production Co.) beginning spring 2012. Mark Peterson (Graduate Research Assistant) and Paul Doherty (CSU professor) are assisting with this research. which was completed December 2014. Neonate capture and collaring efforts totaled 85 during spring 2012. 67 during spring 2013. and 54 during spring 2014. Estimated neonate survival through mid-December 2012 was 0.39 (95% Cl= 0.28-0.50). 0.37 (95% CI= 0.25-0.48) from birth to mid-December 20 l 3, and 0.57 (95% Cl= 0.44 - 0. 70) from binh to midDecember 2014. Data are currently being analyzed to address factors potentially influencing neonate mule deer survival from developed and undeveloped landscapes and should be finalized for next year's annual report. Mule Deer Population Estimates " '--1" Mark-resight models that best predicted abundance estimates (lowest AICi:; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and homogenous individual sightability (cr2 = 0) for South Magnolia deer and variable individual sightability (cr2 -:f; 0) for the other 3 areas. North Ridge exhibited the highest deer density ( 17.5/km\ with comparably lower deer densities in the other 3 areas (8.4-12.0/km 2; Table 3, Fig. 6). Populations have increased over the 7 year monitoring period in 3 of the study areas (from 6.5/km 1 to I0.31km2). with fluctuating population estimates from North Ridge (from 14.3/km 2 to 22.8/km 2); the current North Ridge density estimate is comparable to the mean population estimate of 18.8 deer/km 1 from the past 7 years and generally represents a stable population with the exception of 2013 when the lowest estimate occurred (Fig. 6). The fluctuating population estimates from North Ridge may be somewhat related to a permeable boundary in northwest portion of the study are~ where deer commonly cross the study area boundary. Assuming some of this variability is due to lack of closure. estimation methods will be investigated to attempt to reduce this variation. Abundance estimates from 20 I 4 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (C ICY) ranging from 0. 13-0. 14. Magnolia Habitat Treatments We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 2011; Environmental Assessment: 001-BLM-CO- l 10-:20 I l-004-EA). 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop. chain) in January 2012 (Stephens 2014), and 1.038 acres of hydro-ax treatments in April 2013 (Determination of NEPA Adequacy: DOI-BLM-CO-110-2012-0134DNA)~ totaling 604 treated acres in each study area (Fig. 7). Vegetation response in the pilot treatment sites was visually evident by fall 2011 (Fig. 7). and resulted in statistically significant (P < 0.05) increases in native grass and forb cover by the 2014 growing season. 2015 results are pending, but shrub responses appear promising from visual inspection this spring. Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3 fold increase in grasses. forbs, and shrubs combined after 2 growing seasons (versus control sites). but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following 1 growing season. but statistical comparisons are pending. As anticipated, grass and forb responses should be evident 2 to 3 year post-treatment. with longer term response expected (3-5 years) from palatable shrubs. Of note, relatively high moisture conditions experienced during spring 2014 and 2015 resulted in higher than nonnal prevalence of cheatgrass (Bromus tectorum); cheatgrass invasion has previously been minor to non-existent. Cheatgrass invasion, however. does not appear directly related to treatment sites 9 �because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. u GPS data addressing deer use of treatment sites is just becoming available and will be analyzed as additional data are collected and vegetation responses progress. Vegetation and mule deer responses will be documented for the next 3 years to assess the utility of this mitigation approach in benefiting mule deer exposed to energy development disturbance. SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 5-year pre-treatment period and 3 years post-treatment. The pretreatment period was completed by spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 study areas were completed April 2013. and preliminary vegetation responses appear promising. Post-treatment monitoring will continue for 3 additional years to provide sufficient time to measure how deer respond to these changes. Based on data collected prior to habitat improvements (i.e., pretreatment phase): (1) annual adult survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 48% to 95% within study areas. with annual and study area differences primarily due to annual weather conditions; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, which was likely related to seasonal moisture within areas and relative forage capacity among areas; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (5) mule deer densities appear to be increasing in 3 of 4 areas, with a stable population in North Ridge. Detailed habitat use analyses are pending for the pre and post-habitat treatment period. We will continue to collect the various population and habitat use data across study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Colorado, Bergman et al. (2014) found that habitat treatments implemented in pinion-juniper habitat in undeveloped areas increased overwinter survival of fawns by a magnitude of 1.15. Hay field improvements have been completed in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (VlwlP) agreement with CPW; elk (Cervus elaphus) response has been evident, but mule deer response has thus far been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Collaborative research with agency biologists, graduate students. and university professors has produced 10 peer-reviewed publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011 ), mule deer migration (Lendrum et al. :2012, 2013~ 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013). mule deer response to helicopter capture and handling (Northrup et al. 2014a), potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014), mule deer genetics in relation to body condition and migration (Northrup et al. 2014b), spatial and temporal factors influencing 10 u u �~ auditory vigilance in mule deer (Lynch et al. 20 I 4). and the relationship of plant phenology with mule deer body condition (Seral et al. 2015); these publications are summarized in Appendix A. Additional funding and cooperative agreements will be necessary to sustain this project to completion (preferably through 2018). We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. LITERATURE CITED ~~ Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins. CO. USA. Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan. Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008b. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation-Stage I. Objective 5: Patterns o(mule deer distribution & movements. Pilot Study, Colorado Division of Wildlife. Ft. Collins. CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2010. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report. Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and C. J. Bishop. 2011. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and C. J. Bishop. 2012. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Parks and Wildlife. Ft. Collins, CO, USA. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report. Colorado Divison of Wildlife, Fort Collins, Colorado. USA. Bartmann, R. B., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51. Colorado Division of Wildlife. Fort Collins, Colorado, USA. Bartmann, R. M., G. C. White, and L. H. Carpenter. 1992. Compensatory mortality.in a Colorado mule deer population. Wildlife Monograph No. 121. .--., Barrett, M. W., J. W. Nolan. and L. D. Roy. 1982. Evaluation of a hand-held net-gun to capture large mammals. Wildlife Society Bulletin I 0: I 08-114. Bergman, E. J., C. J. Bishop, D. J. Freddy, G. C. White. and PF. Doherty, Jr. 2014. Habitat management influences over-winter survival of mule deer fawns in Colorado. Journal of Wildlife Management 78(3):448-455; DOI: 10.1002/jwmg.683 Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York. New York. USA. Cook, R. C., J. G. Cook. D. L. Murray. P. Zager. B. K. Johnson, and M. W. Gratson. 2001. Development of predictive models of nutritional condition for rocky mountain elk. Journal of Wildlife Management 65 :973-987. Cook, R. C.. T. R. Stephenson, W. L. Meyers, J. G. Cook. and L.A. Shipley. 2007. Validating predictive models of nutritional condition for mule deer. Journal of Wildlife Management 71: 1934-1943. Cook. R. C., J. G. Cook. T. R. Stephenson, W. L. Meyers, S. M. McCorquodale. D. J. Vales. L. L. Irwin. P. Briggs Hall, R. D. Spencer, S. L. Murphie. K. A. Schoenecker, P. J. Miller. 2009. Revisions of rump fat and body scoring indices for deer. elk. and moose. Journal of Wildlife Management 74:880-896. II �Freeman. E. D.. R. T. Larsen, M. E. Peterson, C.R. Anderson, Jr., K. R. Hersey. and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 Gibbs, H. D. 1978. Nutritional quality of mule deer foods, Piceance Basin, Colorado. Thesis, Colorado State University. Fort Collins. Colorado, USA. Kaplan. E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. Lendrum, P. LC. R. Anderson, Jr., R. A. Long, J. K. Kie. and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.orn/10. Lendrum, P. LC. R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: I 0.137 l/journal.pone.0064548 Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith. J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: htto://dx.doi.onz/10.1016/j.mambio.2014.05.005 Lynch, E.. J.M. Northrup. M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: 10.1093/beheco/aru 158. Mcclintock. B. T., G. C. White, K. P. Burnham, and M. A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York. USA. Northrup, J.M., M. B. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78( 4):731-738; DOI: 10.1002/jwmg. 705 Northrup, J.M., A. B. Shafer. C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014b. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Northrup. J.M., C. R. Anderson, Jr.. and G. Winemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology In press. Pollock. K. H.. S. R. Winterstein. C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Searle, K. R., M. B. Rice. C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 00298549: DOI 10.1007/s00442-015-3348-9 Stephens, G. J. 2014. Understory responses to mechanical removal of pinyon-juniper overstory. MS Thesis~ Colorado State University, Ft. Collins USA. Stephenson. T. R., V. C. Bleich, B. M. Pierce. and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. • Stephenson. T. R.. K. J. Hundertmark. C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth. J. W.. D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado. Idaho, and Montana. Journal of Wildlife Management 63 :315-326. 12 V �'-,/ Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-4:! 1 in L. Nielsen. J. C. Haigh. and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society. Milwaukee, USA. White. G. C .. and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. White, G. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson. Jr .. Mammals Research Leader 13 �Table 1. Survival rate estimates (S) of fawn (1 Dec. 2014-15 June 2015) and adult female (1 July 201430 June 2015) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe sample3 (n) S (95% Cl) Fawns Ryan Gulch 59 0.932 (0.867-0.996) South Magnolia 58 0.931 (0.865-0.996) North Magnolia 58 0.948 (0.891-1.000) North Ridge 58 0.860 (0.770-0.950) Adult females Ryan Gulch 26 52 0.806 (0.671-0.941) South Magnolia 26 54 0.879 (0.764-0.994) North Magnolia 29 56 0.948 (0.875-1.000) North Ridge 27 49 0.851 (0. 737-0.966) 3 Adult female sample sizes following capture and radio-collaring efforts March~ 2015. 14 �( (/ ) Table 2. Mean rump fat (mm), Body Condition Score (BCS 0), and% body fat(% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2015. Values in parentheses= SD. March 2010 December 2009 March 2009 Rump fat BCS % fat 8.35(6.36) 4.06(1.13) I 0.54 (3.72) 2.31 (1.44) 2.35 (0.48) 6.37 (1.41) 6.74 (2.27) 10.05 (6.19) 4.07 ( 1.21) 11.44 (3.50) 3.12 (2.20) 2.64 (0.59) 7.11 (1.69) North Magnolia 1.3 I ( 1.01) 2.66 (0.68) 7.15 ( 1.63) 10.67 (5.76) 4.25 (0.96) 11.94 (3.39) 3.15 (2.34) 2.85 (0.53) 7.54 ( 1.53) North Ridge 1.57 ( 1.22) 5.25 (5.65) 3.63 ( 1.11) 1.77 ( 1.1 I) 2.42 (0.4()) 6.39 ( I .45) Study Area Rump fat BCS % fat Ryan Gulch 1.73 (1.78) 2.66 (0.55) 7.08 ( 1.27) South Magnolia 1.29 (0.47) 2.51 (0.66) 2.60 (0.56) 6.81 ( 1.68) Rump fat BCS % fat 9.37 (3.08) Table 2. Continued. Decem her 20 I 1 March 201 I December 20 I 0 BCS % fat Study Area Rump fat RCS % fat Rump fat BCS % fat Ryan Gulch 7.26 (6.36) 3.24 (0.96) 9.69 (3.56) 1.55 (0.60) 2.53 (0.42) 6.72 ( I .37) 13.4 I (6.39) 4.21 ( 1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.27 (3.75) 1.65 (0.75) 2.35 (0.50) 6.15 (1.75) 8.18 (5.45) 3.41 (0.82) 10.34 (3.28) North Magnolia 9.55 (6.49) 3.46(1.16) 10. 79 ( 4.26) 1.65 (0.67) 2.53 (0.49) 6.79 (1.47) 8.76 (5.77) 3.74 (0.91) 10.73 (3.14) North Ridge 7.25 (5.4 I) 3.47 (0.86) 9.85 (3.02) 1.45 (0.76) 2.24 (0.49) 6.30 ( 1.65) 8.86 (5.37) 3.51 (0.99) I 0.77 (3.33) 15 Rump fat �Table 2. Continued. December 2012 March 2012 March '.2013 % fat Rump fat BCS % fat Rump fat BCS '½, fat 2.15 ( 1.44) 2.74 (0.44) 7.22 ( 1.16) 6.34 (4.35) 3.30 (0.77) 9.34 (2.43) 1.87 (0.90) 2.65 (0.37) 7.14 (0.89) South Magnolia 1.66 (0.77) 2.59 (0.36) 7.03 ( 1.13) 8.30 (5.71) 3.46 ( 1.07) I 0.32 (3.23) 2.06 (0.77) 2.65 (0.26) 7.19 (0.66) North Magnolia 1.90 (0.76) 2.84 (0.34) 7.61 (0.96) 9.66 (6.41) 3.84(1.16) 11.18 (3.64) 1.76 (0.91) 2.59 (0.4 I) 6.87 ( 1.11) North Riclge 2.24 ( 1.58) 2.70 (0.35) 7.26 ( 1.05) 5.76 (4.10) 3.32 (0.82) 9.06 (2.31) 1.87 (0.73) 2.48 (0.34) 6.70 ( 1.12) Study Area Rump fat Ryan Gulch BCS Table~- Continued. March 2014 December 2013 December 2014 % fat Rump fat 9.27 (6.29) 3.47 (0.87) I 0.61 (3.76) 1.69 (0.85) South Magnolia 11.27 (8.40) 3.99 ( 1.04) 11.40 (4.16) 2.57 ( 1.61) 2.96 (0.30) 7.75 (0.68) North Magnolia 9.00 (6.15) 3.44 (0.78) 10.48 (3.25) 2.33 (2.12) 2.80 (0.49) 7.31 (1.43) 9.52 (5.83) 3.83 ( 1.04) 11.18 (3.32) North Ridge 11.17 (5.28) 3.85 (0.72) I 1.66 (2.69) 2.38 ( 1.52) 2.68 (0.39) 7.16 (1.14) 7.93 (5.50) 3.74 (0.76) I 0.20 (3.0 I) Study Area Rump tat Ryan Gulch BCS BCS % fat 2.68 (0.39) 7.03 (0.99) Rump fat 8.50 (6.76) BCS % fat 3.69 ( 1.03) 10.56 (3.70) I 0.96 (6.82) 4.08 ( 1.06) 11.98 (3.81) 16 ( C ( �() Table 2. Continued. March 2015 Study Area Rump fat Ryan Gulch 2.62 (0.95) 2.89 (0.40) 7.44 (0.53) South Magno Iia 2.66 ( 1.36) 2.97 (0.55) 7.62 (0.74) North Magnolia 2.25 (0.97) :!.90 (0.42) 7.49 (0.90) North Ridge 2.28 ( 1.37) 2.92 (0.46) 7.43 ( 1.05) 11 BCS %fat Body condition score taken from palpations of the rump following Cook et al. (2009). 17 �Table 3. Mark-resight abundance (lv) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado. 23-27 March 2015. Data represent 4 helicopter resight surveys from each study area. Study area Mean No. sighted Mean No. marked N(95% CI) Density ( deer/km:) Ryan Gulch 309 31 1A11 (1,231-1.637) 10.0 South Magnolia 202 32 698 (618-800) 8.4 North Magnolia 288 35 950 (841-1,092) 12.0 North Ridge 285 29 931 (820-1.075) 17.5 u 18 �ule deer study areas ri or1n ~.1agno11a Soutn r.1agno1ta Well Pads & Facilities : 1n csevelopment :_ Prooucing \\ ell _ D eYt:IOpm t:nl f3t. lhttes rionh 'l.1age 2$ 10 ~~~~~iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii.lM1les Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado. winter 20 13/ 14 (Accessed http://cogcc.state.co.us/ Dec. 31, 20 13). 19 �Ryan Gulch fawn S South Magnolia fawn S 2008/09-2014/15 2008/09-2014/15 1.00 0.90 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 ........... .,_,_. o.80 . _;.;~.;..~; ;f:.;.-.,;.~ . 0.70 t==~~.::_.~s~;;t:.~J:~:1~-,.;~ + 0.60 + - - - -- - - ·~ ~ __:__:_;~ ,;.:,a.1,1,,1,jl,l,l,j~ 0.50 +-- - - -~ r:--=:3--r:-:,,....--..0.40 + - -- - -- - - - - ''++-r- - - - -0.30 + - - - - - -- -- - - - - 0.20 - + - - - - - - - - - - - - - - 0.10 + - - - - - - -- -- - - - 0. 00 +-r--r--r-.,-,--r-r-r.-rr-rr,-,-y-,-,-..-.-r-r-r.,--,.-, North Magnolia fawn 5 North Ridge fawn 5 2008/09-2014/15 2008/09-2014/15 1.00 T + ~•~ ==! 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 I i I I I I I '" 1.00 ,-.,.~ !!II! 0.90 +-____e......u.._: 0.80 0.70 t====~~=~~;;;: 0.60 0.50 0.40 + - - -- - - -- - - - - ~~ 0.30 - + - - - - -- - - - -- - - - 0 . 20 + - - - - - - - - - - - - - 0.10 + - - - - - - - - - - - - - o. 00 +-,-~.,-,-.,-,-.,-,-.,-,-.,-,-.,-,-,-,-,-,-,-,-,..,....,--.-,r r , ........... I I I I I I I I I I I I I I I Figure 2. Over-winter (Dec-Mar & June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin, northwest Colorado, 2008/09 (red lines), 2009/10 (orange lines), 20 10/ 11 (blue lines), 20 11 /12 (black lines), 2012/ 13 (purple lines), 2013/ 14 (cyan lines), and 2014/ 15 (brown lines). Solid lines = 5 and dashed lines = 95% Cl. Comparable data among years: December-March 2008-20 10 due to premature collar drop and December-mid-June 20I0-2015. 20 �Male fawn weights 42.0 40.0 32.0 30.0 .. DRyan Gulch I- I- I- ,_ I- I- ,_ ,_ I- I- ,_ I- 1- I- ,_ I- I- .... Dec 2008 ti: -.. 1-- .i.. _ I., l - - Dec 2010 Dec 2009 DSouth Magnolia ,.., 6. - Dec 2011 !::: _ Dec 2012 b, tm D North Ridge 6. l - - 1.. Dec 2013 ■ North Magnolia - I- Dec 2014 Female fawn weights 42.0 40.0 38.0 DRyan Gulch DSouth Magnolia 34.0 ~ 32.0 30.0 ~ I- .... - a~ I- .. - .. .. .. .. ,_ LI 0North Ridge T I- lei. ■ North Magnolia J I- ~ =. . ,. == .. .. = .u. ~ .. Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Figure 3. Mean male and female fawn weights and 95% Cl (error bars) from 4 mule deer study areas in the Piceance Basin, northwest Colorado. December 2008-201 4. 21 �Gulch and South Mag Summer Range Figure 4. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adu lt female mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state. county, and natural-gas; white lines) from May 2009 (Lower right; from Lendrum et al. 201 2). 22 �Early winter rump fat 16 14 l 12 ~ 10 ... .!!! 8 a. E ::J a: 6 - North Ridge - North Magnolia Ryan Gulch 4 - south Magnolia ,E 2.0 - North Ridge 0. - North Magnolia 2 0 <) :-,_'\- :-,,Cl R:,°> ~ ~<., '\.,C) ~<., <) <) '\.,C) '\.,C) ~<., ~<., <) ~ 0 :-,_'\.- '\.,C) ~<, <) '\.,C) ~<., <) Late winter rump fat 4.0 3.5 3.0 E .§. 2.5 E 1.5 ::J a: Ryan Gulch 1.0 - South Magnolia 0.5 0.0 Figure 5. Mean early (early Dec., Top) and late winter (early Mar., Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado, March 2009-March 20 15. Error bars = 95% Cl. 23 �Piceance Basin late winter mule deer density u 30.00 25.00 20.00 N E .:ii= ":::- 15.00 - Cl.I Cl.I - North Ridge • • •• • • Ryan Gulch C 10.00 - • North Magnolia - South Magnolia 5.00 0.00 2009 2010 2011 2013 2012 2014 2015 Year Figure 6. Mule deer density estimates and 95% Cl (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2015. u 24 �Nonh Magn,Jl1a ueaten,ent sues (587 acres) Bea,Set_ l 5_~5b_andG Bear5et_ I _8ana;._E [ J 6eJr$et_3 6_5-landJ C,re asewoodSet_g I 6_g29 1?,n~a~ewoodSet_1J 1_g 15 Gre3~ewood5et_ g30_g4 2 LeeOvers,ghts_a_f and I 6_ 17 MethJntc Jl tr~atmer.t compa11$on 154 .1cres) - - ~Jorth HJtc.h PIiot Treatments ( 116 ac,es) - $oulh MJgnolia ue atment :ates (4-10 acres) 5011th MJgnolt7t :! A Figure 7. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan. 20 I 1 using hydro-axe; yellow polygons completed Jan. 20 12 using hydro-axe, roller-chop, and chaining; and remaining polygons completed April 2013 using hydro-axe). January 20 11 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 2011 (Lower ri ght. ground view). 25 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP', CHARLES R. ANDERSON Jr. ', DANIEL P. WALSH', ERIC J. BERGMAN', PETER KUECHLE2, and JOHN ROTH 2 'Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA ' Advanced Telemetry Systems, Isanti, Minnesota 55040 USA Citation: Bishop. C. J .. C.R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011 . Effectiveness ofa redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8): 1797-1806: DOI: I0. 1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer ( Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable te.chnique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VlTs). To date. VJTs have a llowed research opportunities that we re not previously possible: however. VITs are often expelled from adult females prepartum. which limits the ir effectiveness. We redesigned an ex isting VlT manufactured by Advanced Te lemetry Syste ms (A TS: Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. O ur objective was to increase VlT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly des igned VlTs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawn s. We also developed an equation for determining VlT sample size necessary to achieve a specified sample size of neonates. The probability of a VIT being retained until parturition was 0 .766 (SE 1/4 0.0605) and the probability ofa VIT being retained to within 3 days of parturition was 0.894 (SE ¼ 0.044 1). In a similar study using the original VlT wings (Bishop et al. 2007), the probability ofa VIT being retained until parturition was 0.447 (SE¼ 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE¼ 0.0456). Thus. o ur design modification increased V IT retention to parturition by 0.3 19 (SE '/4 0.0765) and VIT retention to within 3 days of parturition by 0.27 1 (SE ¼ 0 .0634). Considering dams that retained V lTs to within 3 days of parturition, the probability of detecting at least I neonate was 0.952 (SE 1/4 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE ¼ 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future studies. we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring s urvival in mule deer. © 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM' , CHARLES R. ANDERSON JR.1 , RYAN A. LONG', JOHN G. KIE', AND R. TERRY BOWYER' 'Department of Biological Sciences, Idaho State University. Pocatello, Idaho 83209 USA 'Colorado Parks and Wildliti:, Grand Junction, Colorado 8 1505 USA Citation: Lendrum, P. E., C. R. Anderson Jr., R. A. Long. J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: efli:cts of landscape structure and natural-gas development. Ecosphere 3(9):82 hnp.//dx doo.org/10 t890/ESl2-00165. t Abstract. The disruption of traditional m igratory routes by anthropogenic disturbances has shifted patterns of resource selection by man)' species. and in some instances has caused populations to decline. Moreover. in recent decades populations of mule deer (Odocoileus hemionus) have declined throughout much of their historic range in the western United States. We used reso urceselection functions to determine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado. USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally. deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover. whereas deer from the least developed areas te nded to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in a ll instances except along the most highly deve loped migratory ro utes. where road densities may have been too high for deer to avo id roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fide lity ungulates demonstrate towards migration routes. If avoidance is feasible. then deer may select areas fu rther from development. whereas in highly developed areas. deer may simply increase their rate of travel a long establ ished migration routes. 26 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum\ Charles R. Anderson Jr.:, Kevin L ~lonteith 1.J, Jonathan A. Jenks\ R. Terrv Bon-ver 1 • Depanment of Biological Sciences. Idaho State University. Pocatello, Idaho. L'S..\. : Colorado D1visio~ of P:i;ks and Wildlife. Gr.ind Junction, Colorado, USA. 3 Wyoming Cooperative Fish and Wildlife Research Unit, L1niversity of Wyoming. Laramie. Wyoming, USA/ Depanment of Natural Resource Management. South Dakota State lJniversity. Brookings. South Dakota. CSA Citation: Lendrum, P. E., C.R. Anderson Jr.. K. L. :\-lomeith. J. A Jenks. R. T. Bowyer. 2013. Migrating :\-lule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: I0.1371,joumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes. many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of panicular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sutliciently plastic to compensate for such changes. warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of depanure from winter range and arrival to summer range of female mule deer (Odocoileus hemionus) in northwestern Colorado. USA. which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics. patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer. equipped them with GPS collars. and observed patterns of spring migration during 2008--2010. Conclusions/Significance: Timing of spring migration was related 10 winter weather (particularly snow depth) and access to emerging vegetation. which varied among years, but was highly synchronous across study areas within years. Additionally. timing of migration was influenced by the collective effects of anthropogenic disturbance. rate of travel. distance traveled. and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure. but early arrival tbr females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas. especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP', MEVJN 8. HOOTEN 1.!..1, CHARLES R. ANDERSON JR.\ AND GEORGE WITTEMYER' 1 Department offish, Wildlife, and Conservation Biology, Colorado State University. 1474 Campus Delivery. Fort Collins, Colorado 80523 USA U.S. Gc:ological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 1 Colorado State University, Department of Statistics, Colorado State University. 1474 Campus Delivery. Fort Collins. Colorado 80523 USA ~Mammals Research Section Colorado Parks and Wildlire, 711 Independent Avenue. Gr.ind Junction. Colorado 81505 USA 2 Citation: Northrup. J. M., M. B. Hooten, C. R. Anderson Jr.. and G. Winemyer. 2013. Practical guidance on char.icterizing availability in resomce selection functions under a use-ava1lability design. Ecology <J4( 7): I 45o- l 4o3. hup:1/ux.do1.org/ I0.1890/12-1688. I Abstract. Habitat selection is a fundamental aspect of animal ecology. the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework. whereby animal locations are contrasted with random locations {the availability sample). Although most use-availability methods are in fact spatial point process models. they often are fit using logistic regression. This framework offers numerous methodological challenges. for which the literature provides little guidance. Specifically. the size and spatial extent of the availability sample influences coetlicienc estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates. which is common for landscape characteristics. exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data. which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and. where bias is likely. take care with interpretations and use cross validation to assess robustness. 27 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP 1, CHARLES R. ANDERSON JR\ AND GEORGE WITTEMYER 1 1 Department of Fish. Wildlite. and Conservation Biology, Colorado State University. 1474 Campus Delivery, Fort Collins, Colorado 80523 USA :Mammals Research Section Colorado Parks and Wildlife. 711 Independent Avenue. Grand Junction, Colorado 81505 USA Citation: Northrup. J.M .. C.R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738: DOI: 10.1002/j,,mg.705 ABSTRACT Research on wildlife movement. physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus). a focal species for research in North America we investigated pre- and post-recapture movements of collared individuals. and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition. we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture. we found no differences in displacement. but movement rates demonstrated seasonal effects. with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements. recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71 % of deer returning in the first day. and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures. likely due to the deer returning to their home ranges. followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supponed, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. 2014 The Wildlife Society. e Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendrum■• Charles R. Anderson Jr.\ Kevin L. Monteitbc, Jonathan A. Jen~\ R. Terry Bowyer" • Department ofBiological Sciences. Idaho State University. 921 South 8th Avenue. Stop 8007, Pocatello 83209. USA h Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81505, USA • Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, 3166, 1000 East University Avenue, Laramie 82071, USA .i Department of Natural Resource Management, South Dakota State University, Box 2140B. Brookings 57007, USA Citation: Lendrum. P. E.. C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement ofa rapidly migrating ungulate to spatiotcmporal patterns of forage quality. Mammalian Biology: http://dx.doi.or!?./I0.1016/j.mambio.:?014.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn. which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality. as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Nonnalized Difference Vegetation Index: NDVI) in spring 2010 in the Piceance Basin of northwestern Colorado. USA. NOVI increased throughout spring, and was affected primarily by snow depth when snow was present. and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration. increased rapidly to an asymptote during migration. and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NDVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet. which was evident in patterns of NDVI and focal nitrogen. Moreover, these deer ..jumped" rather than '"surfed" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition. and to minimize detrimental factors such as predation. and malnutrition during migration. 28 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEM..\.N 1, RANDY T. LARSEN 1, ~URK E. PETERSON!, CHARLES R. ANDERSOi'i JR.J, KENT R. HERSEY\ A~D BROCK R. :\'lc~OLLA.N' : Department of Plant and Wildlife Sciences. Brigham Young University. 275 WIDB. Provo. UT 84602, USA : Department of Fish. Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins. CO 80523. USA ; Colorado Parks and Wildlite. 71 I Independent Avenue. Grand Junction. CO 81505. USA 'Utah Division ofWildlite Resources. 1594 W North Temple. Salt Lake City. UT 84114. US.A Citation: Freeman. E. D., R. T. Larsen. M. E. Peterson, C.R. Anderson Jr.. K. R. Hersev. and B. R'.'McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy. synchrony. and timing of parturition. Wildlife Society Bulletin~ DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices intluence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoi/eus hemionus). changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing of parturition. and synchrony of parturition if inadequate numbers of mules are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are intluenced by decreased male/female ratios, recruitment may be reduced (e.g.. tewer births. later parturition resulting in lower survival of fawns. and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy. synchrony of parturition. and timing ofparrurition between exploited mule deer populations with a relatively high (Piceance. CO. USA: 26 males/100 temales) and a relatively low (Monroe, UT. USA: 14 males/100 females) male/female ratio. We detennined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%: z = 0.821: P = 0.794), timing of parturition (estimate= 1.258: SE= 1.672: t = 0.752: P = 0.454), or synchrony of parturition (F= 1.073: P = 0.859) between Monroe Mountain and Piceance Basin. respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruinnent remains unaffected. Q 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph .\I. Northrup, 1 Aaron B. A. Shafer/ Charles R. Anderson Jr/ Da,·id W. Coltman" and George Wittemyer 1 I Depanment of Fish. Wildlite, and Conservation Biology, Colorado State University. Fon Collins. CO, USA 2 Department of Evolutionary Biology. Evolutionary Biology Centre, Uppsala University, Uppsala. Sweden 3 Mammals Research s~ction, Colorado Parks and Wildlife. Grand Junction, CO. USA 4 Department of Biological Sciences, University of Alberta. Edmonton, AB. Canada. Citation: Northrup. J.M .. A. B. Shater, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. :?OJ.i. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1152-4571: doi: I 0. 1111 /eva. 12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoi/eus hemionus). we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing. (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic ditlerentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns). one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species. these findings revealed a link between genetic variation and imponant phenotypes at a tine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or ditlerential mitochondrial hap lo types intluencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management. even in widely distributed panmictic species. 29 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch: Joseph M. Northrup,b Megan F. McKenna,c Charles R. Anderson Jr/ Lisa Angeloni,a.c and George Wittemyera." •Graduate Degree Program in Ecology. Colorado State University. 1474 Campus Delivery. Fort Collins. CO 80523. USA hDcpartment offish. Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery. Fort Collins, CO 80523. USA ~atural Sounds and Night Skies Division. National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA. dMammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road. Fort Collins. CO 80526, L:SA ~cpartment of Biology, Colorado State University, 1878 Campus Delivery. Fort Collins. CO 80523. USA Citation: Lynch. E., J.M. Northrup. M. F. McKenna. C. R Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology: doi: l0.10931beheco/arul58. While visual forms of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance. auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic narure of auditory vigilance makes it difficult to study. but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active narural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic teatures were less clear. Our results suggest that pauses during rumination represent a fonn of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can tacilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle 1 • }lindy 8. Rice 2 • Charles R. Anderson 2 • Chad Bishop= • N. T. Hobbs3 1 NERC Centre for Ecology and Hydrology, Bush Estate. Penicuik EH26 0QB, UK : Colorado Parks and Wildlife, 317 W. Prospect Road. Fort Collins, CO 80526, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University. Fort Collins 80524, CO, USA Citation: Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. :o 15. Asynchronous vegetation phenology enhances winter body condition ofa large mobile herbivore. Oecologia ISSN 0029-8549: DOI l0.1007/s00442-015-3348-9 Abstract Understanding how spatial and cemporal heterogeneity influence ecological processes tbrms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population managemenL Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoi/eus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially. temporal patterns of vegetation phenology were linked with differences in body condition. with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. 30 V �, Colorado Parks and Wildlife July 1, 2015 -June 30, 2016 ~ State of Cost Center Work Package Task No. Colorado 3430 3001 6 WILDLIFE RESEARCH REPORT : =-Pa=r.:::ks::a.. a=n=d;__W.:. .:. :.::il=dl=::aifi=-e_ _ _ _ _ _ _ _ _ _ __ :=M=a=mm=a=l=s~R=e=se=a=rc=h=--------------: =D-=e-=er~C-==o=ns=e=rv..;...a=t=io=n=--------------: Population Perfonnance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project: _____W___-.. .;;;1-=8=-5--=R___-___15_______ Period Covered: July 1, 2015 -June 30, 2016 Author: C.R. Anderson, Jr. --...,_ "-,,I Personnel: T. Asnicar, E. Bergman, K. Bond, E. Cardenas, D. Collins, 8. deVergie, D. Finley, M. Fisher, L. Gepfert, J. Hudson, D. Johnston, T. Knowles, D. Lewis, T. Mullins, J. Pelham, 8. Petch, J. Rivale, R. Schilowsky, G. Smith, D. Thibodeau, R. Velarde, T. Verzuh, S. Williams, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; T. Graham, Ranch Advisory Partners; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAV NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT ---.. ~ We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 9 years of data (5 years of pretreatment, 4 years post treatment) of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored deer on 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia, South Magnolia) and control (North Ridge, Ryan Gulch) sites. We recorded habitat use and movement patterns, estimated neonatal, overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult females, and estimated abundance. During this research segment, we targeted 240 fawns (60/study area) and 120 does (30/study area) in early December 2015 for VHF and GPS radiocollar attachment, respectively, and adult female body condition assessment. We attempted recapture of 120 does (30/study area) and 40 fawns (20 in 2 study areas) in March 2016 for late winter body condition assessment. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinionI 1iiililliii BDOW029663 �juniper/mountain shrub habitats in each of the 2 treatment areas with minor and extensive energy development, respectively. Post-treatment monitoring will continue for 2 additional years to provide sufficient time to measure how vegetation and deer respond to these changes. Based on data collected prior to habitat improvements (i.e., pretreatment phase): ( 1) annual adult survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 48% to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition and annual weather conditions; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition appeared related to seasonal moisture and winter severity; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; (5) late winter mule deer densities have increased in all study areas, averaging up to 6% annual growth rates since 2008; and (6) post treatment vegetation responses have provided evidence of improved forage conditions, but longer term monitoring will be required to address the full potential of habitat mitigation efforts. Detailed habitat use analyses are still pending for the pretreatment period. We will continue to collect population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development disturbances in highly developed areas, or whether it is better to allocate mitigation efforts toward less or non-impacted areas. In collaboration with Colorado State University, we monitored neonate survival in relation to energy development on all study areas. This wilJ allow us to include neonatal and parturition data with other demographic parameters to evaluate mule deer/energy development interactions. This study is slated to continue through 2018 to allow sufficient time for measuring mule deer population responses to landscape level manipulations. 2 '"'-,,,) �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABIT AT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES ", 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavioral patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. ~ 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. Estimate early and late winter fawn weights in areas with and without habitat treatments to assess winter fawn condition relative to habitat improvements. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and bi- weekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate late winter mule deer abundance and density in each study area. 5. Monitor habitat treatment response for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Continue neonate survival and adult female parturition evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION ..-..._, '--" Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the 3 �primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or introduction of noxious weeds, by fragmenting habitat with drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape conversion for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management U efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also covers some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. 1). Anderson and Freddy (2008a) in their long- term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the first 5 years of this study, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We are currently monitoring 2 control areas: 1 with development (0.6 pads & facilities/km 2 ; Ryan Gulch) and I without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km 2 and North Magnolia, 0.1 well pads & facilities/km2), that also recently received habitat improvements {604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated until spring 2018 to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavioral patterns in relation to energy development activities in the Ryan Gulch area are being monitored to identify effective Best Management Practices {BMPs) for future energy development planning. This progress report describes the previous 8.5 years (Jan 2008-June 2015) of mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter; and annual late-winter abundance/density estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as home to one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig.I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utah juniper (Jwziperus osteosperma; pinionjuniper) shrubland complex ranging from 1,675 m to 2,285 min elevation (Bartmann and Steinert 1981). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany ( Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands ofbig 4 U �sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed- grass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis lzymenoides), arrowleafbalsamroot (Balsamorhi=a sagittata), broom snakeweed (Gutierrezia sarotlzreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by wam1 dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rain storms. -..., \._.I Wintering mule deer population segments we are investigating include: North Ridge (53 km2) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 141 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the north- central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin ( Fig. 1). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.1 pads & facilities/km\ and relatively high development in the Ryan Gulch (0.6 pads & facilities/km2) and South Magnolia (0.9 pads & facilities/km 2) segments (Fig. 1). Development activity was high through 2011 and has declined substantially since natural gas prices began to decline in 2012. Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and relatively heavy (South Magnolia) energy development activities. METHODS Tasks addressed this period included mule deer capture and collaring, monitoring neonate, overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography and winter fawn condition using early and late winter fawn weights, estimating mule deer abundance applying helicopter mark-resight surveys, and monitoring vegetation responses to habitat treatments completed spring 2013. We employed helicopter net- gunning techniques (Barrett et al. 1982, van Reen en 1982) to target 240 fawns and 120 adult females during early December 2015, and 120 adult females and 40 fawns (primarily recaptures) during early March 2016. Once netted, all deer were hobbled and blind folded. Fawns were weighed and radiocollared, and sex was recorded prior to release at the capture site. Adult females were transported to localized handling sites for recording body measurements and fitted with OPS collars (5 fix attempts/day; G2110D, Advanced Telemetry Systems, Isanti, MN, USA) prior to release. To provide direct measures of decline in overwinter body condition, we targeted 30 adult females in each study area that were captured the previous December; Vaginal Implant Transmitters (VITs) were also inserted in does on the Ryan Gulch and South Magnolia study areas to assist with neonate capture and collaring efforts spring 2016. During March, 20 fawns were recaptured, weighed and released in South Magnolia (in the habitat treatment areas) and Ryan Gulch (control area) to quantify overwinter declines in fawn body condition. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and autumn for winter fawns and during winter for neonates, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). 5 �Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed and recovered since 2008. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and IO control deer from North Ridge where location rates were programmed for every 30-60 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) recently analyzed resource selection data relative to energy development (Northrup 2015) and those results are addressed below. Mule deer resource selection analyses to address success of habitat improvements are pending until vegetation responses are fully realized, which are anticipated by fall 2018. U Mule Deer Survival Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death. We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated survival rates from 1 July 2015 through 30 June 2016 for adult females, from birth to mid December for neonates, and from early December 2015-mid June 2016 for winter fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years evident in non-overlapping 95% confidence intervals. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 4 helicopter mark-resight surveys (2 observers and the pilot) during late March to estimate deer abundance in 3 of the 4 study areas and conducted a 5th survey in South Magnolia. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011 ). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2016. We delineated flight paths in ArcGIS 10.0 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. 6 U �We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2 = 0 or-:;; 0). Model selection procedures followed the infom1ation-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 242 fawns and 123 does during Dec 2015 and 115 does and 40 fawns during March 2016. Five fawn mortalities (2.1 %; proximate cause = 4 capture myopathy, I predation) occurred within the IO day censorship period during December and 4 fawn mortalities ( 10.0%; 3 capture myopathy, 1 predation) occurred during the March capture. Doe mortalities totaled 2 (1.6%; capture myopathy) and 4 (3.5%; capture myopathy) within 10 days of the December and March capture periods, respectively. Mortality rates 10 days post capture have typically varied between 2.5-3.5% for fawns and does since Jan 2008, except during the 2011-2012 capture season where myopathy rates were higher (3-6%) due to dry, warm conditions (Anderson and Bishop 2012). Excluding March fawn captures, myopathy rates were below expected levels during December and within normal levels during March. The relatively high myopathy rates for late winter fawn captures were likely linked to the severe winter conditions evident through January 2016. Fawn survival from early December 2015 through mid June 2016 was similar (P > 0.05) among study areas ranging from 0.74 to 0.84, with the exception of North Ridge where survival was lower (49%; Table I). A higher rate of malnutrition/disease (30%) was documented for North Ridge fawns during the 2015-16 winter than in previous years and other study areas (typically <5%). Premature collar drop during 2008-09 and 2009-l O did not allow for winter fawn survival estimates past late March, but survival rates among study areas were similar (P < 0.05) each year and comparable to 20·1 l-12 and 2012-13 (excluding North Ridge) during 2008-09 and to the higher survival rates from 2013-14 and 2014-15 during 2009-10 (Fig. 2). General comparisons to previous years suggest moderate to high fawn survival occurred during most winters and study areas with the exception of winter 2010-2011 for 3 of the 4 study areas and for North Ridge during winter 2012-13 and 2015-16 (Fig. 2). Low winter fawn survival (Fig. 2) appears to correlate with summer forage condition evident from lower December fawn weights (Fig. 3 ). Annual adult female survival varied from 0.72 (South Magnolia) to 0.89 (North Magnolia; Table I) during 2015-16, but was comparable among study areas during 2015-16 and to previous years (P > 0.05), with the exception oflower survival in North Magnolia during 2011-12 (S= 0.68, Anderson and Bishop 2012). Relatively low sample sizes per study area for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g., >15-20%). Estimates below 80% are biologically concerning if these values represent the respective population, but low statistical power precludes confirmation within study areas. When combined among study areas, annual survival estimates have varied from 79% in 2012-13 to 86% in 2014-15. Lower combined survival estimates are consistent with extreme environmental conditions consisting of dryer moisture conditions during late winter/spring (2012-13) or cold temperatures with heavy snow during early winter (2015-16). Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in 7 �developed and undeveloped landscapes (funded from energy company contributions) has recently been completed. Four manuscripts from this effort have been published (Lendrum et al. 2012, Lendrum et al. 2013, Lendrum et al. 2014, Anderson and Bishop; Appendix A). u In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 4) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript Lendrum et al. (2013) addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures, snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, with median migration durations of 3-8 days among the 4 areas (straight line distance between seasonal ranges averaged 32-40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in long-term fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication Lendrum et al. (2014), monitored migratory mule deer in the Piceance Basin to examined the relationship between the Normalized Difference Vegetation Index (NOVI), which is a course-scale measure of forage quality using a GIS assessment of vegetation greenness, and fecal nitrogen to assess the assumption that forage quality and deer diets can be reasonably iinked to address deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were informative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NOVI values, and progressed to summer range as vegetation green-up and nitrogen levels increased, but ahead of peak vegetation green-up on summer range. I suspect this rapid migration strategy is evident for deer in relatively good condition and allows for early arrival on summer range to take advantage optimal forage conditions prior to parturition. Anderson and Bishop (2014) summarized results from Lendrum et al. (2012, 2013) and Sawyer et al. (2012) addressing migratory mule deer and energy development in northwest Colorado and south-central Wyoming, respectively. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/km2 ) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on 8 u �winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer during December 2015 were relatively high from Ryan Gulch does (P > 0.05) and relatively low from North Ridge does (P < 0.05, Fig. 5, Table 2). Early winter- condition this year was comparable to previous years with the exception ofrelatively low condition expressed by North Ridge deer during 2009, 2012 and 2015 and relatively high condition from Ryan Gulch does during December 2011 and 2015 (Fig. 5). Adult female body condition during early winter appears primarily related to the proportion of lactating does identified during December captures, where higher condition correlates with lower lactation rates. With the exception of Ryan Gulch and North Ridge does exhibiting relatively poor condition during 2014 and 2016, respectively, late winter body condition recently trended upward, but either stabilized or declined this past winter (North Ridge; Fig. 5, Table 2). The recent increase was likely related to relatively mild winters since 2011 and the downturn this past winter is likely related to the relatively severe winter conditions evident through January consisting oflow temperatures and deep snow on winter range; temperatures increased during February resulting in rapid snow-melt, which likely improved late winter condition above what might have resulted if severe winter conditions continued. Adult female body condition thus far appears more related to early winter lactation rates, seasonal moisture conditions, relative deer densities (Fig. 6), and winter severity than observed development intensity thus far. December 2015 fawn weights by study area were comparable with the exception of generally lighter fawns from North Ridge (Fig. 3). Overall, excluding North Ridge, fawn weights were moderate in comparison to previous years (Fig. 3 ), and December fawn condition has been correlated with winter fawn survival (Fig. 2), which was consistent with the low winter survival of North Ridge fawns this past winter. Because adult female body condition has been largely uninformative in regards to habitat treatment responses (pending further analyses), we began late winter fawn recaptures in South Magnolia (habitat treatment area) and Ryan Gulch (control area) to assess changes in over-winter condition. Weight loss was significantly less (P < 0.001) for fawns from the area receiving habitat treatments than for fawns from the untreated area. We will continue monitoring winter weight loss for fawns from the treatment and control area to evaluate over-winter fawn condition in areas with and without habitat improvements. Mule Deer Behavioral Response to Energy Development We recently completed evaluations of deer behavior patterns in relation to energy development activities (Northrup et al. 2015). We found diurnal responses to development activity, where deer used timbered areas away from development activity while bedded during the day and moved into more open areas generally closer to developed areas while foraging at night. Disturbance distances from producing pads and roads declined from 600 m to 200 m and about 140 m to 60 m from daytime to nighttime, respectively, but increased from 600 m to 800 m for nighttime drilling pad activity. We 9 �suspect deer behaviorally respond to fluctuations in development activity, where road traffic and producing well pad activity decline at night, but drilling pad disturbance may increase from compressors and lights used to facilitate nighttime drilling activity. These evaluations were applied during an active drilling phase in the Piceance Basin and deer use was influenced by development activity in 25% (nighttime) to 50% (day time) of critical winter range during that period. However, deer densities have comparably increased among developed and undeveloped study areas (Fig. 6) suggesting that deer can behaviorally mediate development disturbance under observed development and deer densities by taking advantage of fluctuations in development activity to address their nutritional requirements. Given the plasticity in deer behavior, a number of potential options for future development planning exits including drilling sc~edule modifications (seasonal and/or diurnal), concentrated/staged development, reducing road traffic, and using light/noise barriers around drill rigs. It will be interesting to determine if habitat improvements will further reduce development disturbance and increase management options for future development planning. Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate survival and adult female parturition in developed and undeveloped landscapes (funded by ExxonMobil Production Co.) beginning spring 2012. Mark Peterson (Graduate Research Assistant) and Paul Doherty (CSU professor) assisted with this research, which was completed December 2014, and continued by CPW during 2015. Neonate capture and collaring efforts totaled 85 during spring 2012, 67 during spring 2013, 54 during spring 2014, and 59 during spring 2015. Estimated neonate survival through mid-December was 0.39 (95% CI= 0.28-0.50) during 2012, 0.37 (95% CI= 0.25-0.48) during 2013, 0.57 (95% CI= 0.44-0.70) during 2014, and 0.36 (95% CI= 0.23-0.49) during 2015. Through December 2015, predation was the highest mortality factor (averaging about 50% annually), with relatively low incidents of starvation/disease directly influencing spring/summer fawn survival (mean= 3.2%). Manuscripts addressing neonate fawn survival and adult female parturition in relation to energy development are currently in preparation and review for publication. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and variable individual sightability (cr2 = 0) for North Magnolia deer and homogenous sightability (cr2 -;/:. 0) for the other 3 areas. North Ridge exhibited the highest deer density (26.0/km2), with comparable but lower deer densities in the other 3 areas (10.7-13.2/km2; Table 3, Fig. 6). Densities increased over the 8 year monitoring period in all study areas ranging from an estimated 50% increase in North Ridge to a 103% increase in North Magnolia (mean estimated increase across study areas= 78%); North Ridge deer appeared to decline during 2012 and 2013, but have increased recently, while the other 3 areas have exhibited consistent and similar rates of increase (Fig. 6). Abundance estimates from 2016 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of V ariati on (CICV) ranging from 0.14-0.15. Magnolia Habitat Treatments We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 2011; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA), 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop, chain) in January 2012 (Stephens 2014), and 1,038 acres of hydro-ax treatments in April 2013 (Determination of NEPA Adequacy: DOI-BLM-CO-110-2012-0134- DNA), totaling 604 treated acres in each study area (Fig. 7). IO u �·,'. -....,_; Vegetation response in the pilot treatment sites was visually evident by fall 2011 (Fig. 7), and resulted in statistically significant (P < 0.05) increases in native grass and forb cover by the 2014 growing season. 2016 results are pending, but shrub responses appear promising from data collected this spring. Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3 fold increase in grasses, forbs, and shrubs combined after 2 growing seasons (versus control sites), but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following I growing season and shrub responses have been notable during the 4 th growing season, but statistical comparisons are still pending. As anticipated, grass and forb responses were evident 2 to 3 years post-treatment, with longer term response expected (3-5 years) for palatable shrubs. Of note, relatively high moisture conditions experienced during spring 2014 and 2015 resulted in higher than normal prevalence of cheatgrass (Bromus tectontm); cheatgrass invasion has previously been minor to non-existent. Cheatgrass invasion, however, does not appear directly related to treatment sites because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. GPS data addressing deer use of treatment sites is becoming available and will be analyzed as additional data are collected and vegetation responses progress. We observed improved fawn condition (P < 0.00 I) in South Magnolia following the 4 th growing season of habitat treatments when compared to fawn condition in the Ryan Gulch control area. Vegetation and mule deer responses will continue to be documented for the next 2 years to assess the utility of this mitigation approach in benefiting mule deer exposed to energy development disturbance. SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 5-year pretreatment period and 4 years post-treatment. The pretreatment period was completed by spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 study areas were completed by April of 2013, and subsequent vegetation responses have met or exceeded expectations. Post-treatment monitoring will continue for 2 additional years to provide sufficient time to measure how deer respond to these changes. Based on data collected through year 8 of this IO-year project: (1) annual adult female survival was consistent among areas averaging 7987% annually, but overwinter fawn survival was variable, ranging from 48% to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition and annual weather conditions; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (5) late winter mule deer densities have increased in all study areas, ranging from 50% in North Ridge to 103% in North Magnolia. Detailed habitat use analyses are pending for the pre and post-habitat treatment period. We will continue to collect the various population and habitat use data across II �study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a recent project conducted on the Uncomphahgre Plateau, Colorado, Bergman et al. (2014) found that habitat treatments implemented in pinion-juniper habitat in undeveloped areas increased overwinter survival of fawns by a magnitude of 1.15. Hay field improvements have been completed in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; elk (Cervus elaphus) response has been evident, but mule deer response has thus far been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW a11owed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Collaborative research with agency biologists, graduate students, and university professors has produced 12 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011), mule deer migration (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013), mule deer response to helicopter capture and handling (Northrup et al. 2014a), potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014), mule deer genetics in relation to body condition and migration (Northrup et al. 2014b ), spatial and temporal factors influencing auditory vigilance in mule deer (Lynch et al. 2014), the relationship of plant phenology with mule deer body condition (Seral et al. 2015), and mule deer responses to differing energy development activities to inform future development planning (Northrup et al. 2015); these publications are summarized in Appendix A. Additional funding and cooperative agreements will be necessary to sustain this project to completion (preferably through 2018). We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. 12 �LITERATURE CITED Anderson, C.R., Jr. 2009. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. Freddy. 2008a. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and D. J. 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Oecologia ISSN 0029- 8549; DOI I 0.1007/s00442-015-3348-9 Stephens, G. J. 2014. Understory responses to mechanical removal ofpinyon-juniper overstory. MS Thesis, Colorado State University, Ft. Collins USA. 14 ~ �~ Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J.C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. White, G. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader ............ 15 �Table 1. Survival rate estimates (S) of fawn (1 Dec. 2015-15 June 2016) and adult female (1 July 201530 June 2016) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe sample8 (n) S(95% CI) Fawns Ryan Gulch 60 0. 744 (0.632-0.856) South Magnolia 59 0.844 (0.751-0.938) North Magnolia 59 0.811 (0.711-0.912) North Ridge 59 0.492 (0.364-0.619) Adult females Ryan Gulch 30 56 0.837 (0.714-0.960) South Magnolia 33 53 0. 723 (0.580-0.867) North Magnolia 28 56 0.886 (0.778-0.994) North Ridge 23 50 0.752 (0.602-0.902) 8 Adult female sample sizes following capture and radio-collaring efforts March, 2016. 16 u �( ) () ( Table 2. Mean rump fat (mm), Body Condition Score (BCS8), and % body fat (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2016. Values in parentheses= SD. March 2009 March 2010 December 2009 %fat Rump fat Ryan Gulch 1.73 ( 1.78) 2.66 (0.55) 7.08 ( 1.27) 8.35 (6.36) 4.06 (1.13) 10.54 (3.72) 2.31 ( 1.44) 2.35 (0.48) 6.37 (1.41) South Magnolia 1.29 (0.47) 2.51 (0.66) 6.74 (2.27) 10.05 (6.19) 4.07 ( 1.21) 11.44 (3.50) 3.12 (2.20) 2.64 (0.59) 7.11 (1.69) North Magnolia 1.3 I ( 1.01) 2.66 (0.68) 7.15 (l.63) 10.67 (5.76) 4.25 (0.96) 11.94 (3.39) 3.15 (2.34) 2.85 (0.53) 7.54 (1.53) North Ridge 1.57 (1.22) 2.60 (0.56) 6.81 ( 1.68) 5.25 (5.65) 3.63 ( 1.11) 9.37 (3.08) 1. 77 ( 1.11) 2.42 (0.49) 6.39 (1.45) March 2011 December 2011 BCS Rump fat BCS %fat Rump fat BCS % fat Study Area Table 2. Continued. December 2010 %fat Rump fat BCS %fat Rump fat BCS %fat Ryan Gulch 7.26 (6.36) 3.24 (0.96) 9.69 (3.56) 1.55 (0.60) 2.53 (0.42) 6.72 (1.37) 13.41 (6.39) 4.21 (1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.27 (3.75) 1.65 (0.75) 2.35 (0.50) 6.15 ( I. 75) 8.18 (5.45) 3.41 (0.82) 10.34 (3.28) North Magnolia 9.55 (6.49) 3.46 ( 1.16) 10.79 (4.26) 1.65 (0.67) 2.53 (0.49) 6.79 (1.47) 8.76 (5.77) 3.74 (0.91) 10.73 (3.14) North Ridge 7.25 (5.41) 3.47 (0.86) 9.85 (3.02) 1.45 (0.76) 6.30 ( 1.65) 8.86 (5.37) 3.51 (0.99) 10.77 (3.33) 15 2.24 (0.49) Rump fat BCS Study Area �Table 2. Continued. December 2012 March 2012 March 2013 % fat Rump fat BCS % fat Rump fat 2.15 (1.44) 2.74 (0.44) 7.22 (1.16) 6.34 (4.35) 3.30 (0.77) 9.34 (2.43) 1.87 (0.90) South Magnolia 1.66 (0.77) 2.59 (0.36) 7.03 (1. 13) 8.30 (5.71) 3.46 ( 1.07) 10.32 (3.23) 2.06 (0.77) 2.65 (0.26) 7.19 (0.66) North Magno Iia 1.90 (0.76) 2.84 (0.34) 7.61 (0.96) 9.66 (6.41) 3.84 ( 1.16) 11.18 (3.64) 1.76 (0.91) 2.59 (0.41) 6.87 ( 1.11) North Ridge 2.24 (1.58) 2.70 (0.35) 7.26 (1.05) 5.76 (4.10) 3.32 (0.82) 1.87 (0.73) 2.48 (0.34) Study Area Rump fat Ryan Gulch BCS 9.06 (2.31) BCS % fat 2.65 (0.37) 7.14 (0.89) 6.70 ( 1.12) Table 2. Continued. March 2014 December 2013 BCS % fat Rump fat BCS December 2014 % fat Rump fat Study Area Rump fat Ryan Gulch 9.27 (6.29) 3.47 (0.87) 10.61 (3.76) 1.69 (0.85) 2.68 (0.39) 7.03 (0.99) 8.50 (6.76) 3.69 (1.03) 10.56 (3.70) South Magnolia 11.27 (8.40) 3.99 ( 1.04) 11.40 (4.16) 2.57 (l.61) 2.96 (0.30) 7.75 (0.68) 10.96 (6.82) 4.08 (1.06) 11.98 (3.81) North Magnolia 9.00 (6.15) 3.44 (0.78) 10.48 (3.25) 2.33 (2.12) 2.80 (0.49) 7.31 (1.43) 9.52 (5.83) 3.83 (1.04) 11.18 (3.32) North Ridge 11.17 (5.28) 3.85 (0.72) 11.66 (2.69) 2.38 ( 1.52) 2.68 (0.39) 7.16 (1.14) 7.93 (5.50) 3.74 (0.76) 10.20 (3.01) BCS %fat 16 C ( C �( ( ) ( Table 2. Continued. March 2015 December 2015 Rump fat BCS % fat 12.80 (6.83) 4.24 (0.88) 12.89 (3.72) 2.29 (0.64) 2.81 (0.37) 7.29 (0.52) 7.62 (0.74) 6.93 (4.83) 3.83 (0.89) 9.83 (2.69) 2.07 (1.39) 2.78 (0.39) 7.46 (0.93) 2.90 (0.42) 7.49 (0.90) 8.79 (6.01) 3.79 (0.85) 10.81 (3.54) 2.43(1.01) 2.71 (0.39) 7.17 (0.87) 2.92 (0.46) 7.43 (1.05) 5.47 (5.49) 3.25 (0.66) 1.58 (0.70) 2.51 (0.41) 6.73 (1.26) Study Area Rump fat BCS % fat Ryan Gulch 2.62 (0.95) 2.89 (0.40) 7.44 (0.53) South Magnolia 2.66 (1.36) 2.97 (0.55) North Magnolia 2.25 (0.97) North Ridge 2.28 (1.37) 3 March 2016 Rump fat BCS 8ody condition score taken from palpations of the rump following Cook et al. (2009). 17 % fat 9.35 (2.75) �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 21 March-2 April 2016. Data represent 4 helicopter resight surveys from 3 study areas and 5 resight surveys from South Magnolia. Study area Mean No. sighted Mean No. marked N(95% en Density (deer/kni2) Ryan Gulch 441 32 1,754 (1,534-2,025) 12.5 South Magnolia 199 23 888 (780-I,027) 10.7 North Magnolia 310 29 1,045 (910-1,226) 13.2 North Ridge 489 26 1,381 ( 1,204-1,609) 26.0 18 �Well P1ad.s 6 Faci lities Sovci \I ilgnolla ~yan GLHh 2, - O t:\••d O JJffl t::N f .!l, 10 •,fll~~ Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado, winter 20 I 3/14 (Accessed http://cogcc.state.eo.us/ Dec. 3 1, 2013). Development activity has subsided with no additional drilling since 2013. 19 w,e~ �Winter fawn survival 2010-11 - 2015-16 1.00 frl 0.90 0.80 -- 0.60 - 0.50 - 0.40 0.30 0.20 0.10 0.00 - --- - ~-- 1--- 0.70 - 1--- - - - - - - 2011-12 2012-13 ,- - 1--- ,- ,- - - D Ryan Gulch - D South Magnolia - D North Ridge ■ North Magnolia - --~ 2010-11 1--- ,- - - ~ - - -- 2013-14 2014-15 2015-16 Figure 2. Over-winter (Dec-June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin, northwest Colorado. Error bars= 95% Cl. 20 �Male fawn weights 42.0 40.0 iii 38.0 =-fa 36.0 0Ryan Gulch DSouth Magnolia 'cii 3: 34.0 ~ ~ II North Magnolia ' ', l d 32.0 30.0 ii j l H D North Ridge p Dec Dec Dec Dec Dec Dec Dec Dec 2008 2009 2010 2011 2012 2013 2014 2015 Female fawn weights 42.0 40.0 -;:a 38.0 = fD URyan Gulch 36.0 DSouth Magnolia ~ 34.0 ■ North Magnolia aNorth Ridge 32.0 30.0 Dec Dec Dec Dec Dec Dec Dec Dec 2008 2009 2010 2011 2012 2013 2014 2015 Figure 3. Mean male and female fawn weights and 95% CI ( error bars} from 4 mule deer study areas in the Piceance Basin, northwest Colorado, December 2008-2015. 21 �Colorado Figure 4. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult female mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state, county, and natural-gas; white lines) from May 2009 (Lower right; from Lendrum et al. 2012). 22 �Early winter rump fat 16 14 _ 12 E .§. 10 -North Ridge J! -North Magnolia .. 8 ~ E 6 a: 4 ::::s ----~ Ryan Gulch -south Magnolia 2 0 Dec Dec Dec Dec Dec Dec Dec 2009 2010 2011 2012 2013 2014 2015 Late winter rump fat 4.00 - , - - - - - - - - - - - - - - - - - - - - - - - - - - 3.50 - t - - - - - - - - - - - - - - - - - - - - - - - - 3.00 - t - - - - - - - - - - - - - - - - - - - - - - - - - - t--7:;t-~--~~-=-:;~~;;z~~~1.50 t=l~~;~t;~~i~~;;E~~:!~~~~t~~=~~~~St= 2.50 2 00 • 1.00 - - - - - - - - - - - - - - - - ~ - - - - - - - - - - -North Ridge -North Magnolia -Ryan Gulch -south Magnolia 0.50 - - - - - - - - - - - - - - - - - - - - - - 0.00 Mar Mar Mar Mar Mar Mar Mar Mar 2009 2010 2011 2012 2013 2014 2015 2016 Figure 5. Mean early {early Dec., Top) and late winter (early Mar., Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado, March 2009-March 2016. Error bars = 95% CI. ........._ 23 �Piceance Basin late winter mule deer density 35.00 30.00 25.00 N j 20.00 - ~ t 15.00 - North Ridge • • • • • • Ryan Gulch Q - 10.00 • North Magnolia - s o u t h Magnolia 5.00 0.00 2009 2010 2011 2012 2013 2014 2015 2016 Year Figure 6. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2016. Estimates for North Ridge 2014 and 2015 and for North Magnolia 2015 were adjusted upward (using GPS migration data) to account for early migration from winter range prior to and during surveys. V 24 �M :iilnrl• 1 t -~.Jt?n".,, :""t ':I~('$ t[•67 ,:'\(T'f." >I t:t:.:r::~t_ 1 : _ Ji;L_ .inJ 1., - -: : ~•. t- l'\1JI f17:,-t_f1_y1 ~ :;r:: .t::..1: •"11,;U': r"l_~ Jrl_g-1 :i - ~'!C'\E·r;1gt.:.. a_ J f"trll [ 4 1: Mule Deer Study Areas Figure 7. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan. 2011 using hydro-axe; yellow polygons completed Jan. 20 I 2 using hydro-axe, roller-chop, and chaining; and remaining polygons completed April 20 I 3 using hydro-axe). January 20 11 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 2011 (Lower right, ground view). 25 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. U Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP 1, CHARLES R. ANDERSON Jr. 1, DAi"llEL P. WALSH 1, ERIC J. BERGMAN 1, PETER KUECHLE 2, and JOHN ROTH 2 'Colorado Parks and Wildlife, Fort Collins. Colorado 80526 USA 2 Advanced Telemetry Systems. Isanti, Minnesota 55040 USA Citation: Bishop, C. J., C.R. Anderson Jr., D. P. Walsh. E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness ofa redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8):1797-1806; DOI: 10.1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expeJled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability of a VIT being retained until parturition was 0. 766 (SE =0.0605) and the probability of a VIT being retained to within 3 days of parturition was 0.894 (SE = 0.0441 ). In a similar study using the original VIT wings (Bishop et al. 2007), the probability of a VIT being retained until parturition was 0.447 (SE= 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE= 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE= 0.0765) and VIT retention to within 3 days of parturition by 0.271 (SE= 0.0634). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least I neonate was 0.952 (SE = 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers plaMing future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer.© 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICKE. LENDRUM 1• CHARLES R. ANDERSON JR. 2, RYAi"l A. LONG1.JOHN G. KIE 1, A."lD R. TERRY BOWYER1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho 83209 USA :?Colorado Parks and Wildlife, Grand Junction, Colorado 81505 USA Citation: Lendrum, P. E., C.R. Anderson Jr., R. A. Long, J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http://dx.doi.org/l 0.1890/ES 12-00165. l Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoi/eus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns ofresource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees ofnatural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas. deer may simply increase their rate of travel along established migration routes. 26 ·v �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum 1, Charles R. Anderson Jr.2, Kevin L. Monteith 1"', Jonathan A. Jenks'. R. Terry Bowyer' 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings. South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith. J. A. Jenks. R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogcnically Altered Landscapes. PloS ONE 8(5): c64548. DOI: 1O. l 371/joumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodolog)'IPrincipal Findi11gs: We studied timing and synchrony of departure from winter range and arrival to summer range offemale mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-20 l 0. Condusions/Signijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. \.,,_I Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP 1, !\ilEVIN B. HOOTEN 1.l.3, CHARLES R. ANDERSON JR.'. AND GEORGE WITTEMYER1 'Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J.M., M. B. Hooten, C.R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7):1456-1463. http://dx.doi.org/10.1890/12-1688.I Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing inteipretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness . ..-....., 27 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR2, AND GEORGE WITIEMYER1 1 Dcpanment of Fish, Wildlife, and Conservation Biology, Colorado State University. 1474 Campus Delivery, Fort Collins, Colorado 80523 USA Mammals Research Section Colorado Parks and Wildlife. 711 Independent Avenue, Grand Junction, Colorado 81505 USA 2 Citation: Nonhrup, J.M .. C.R. Anderson Jr., and G. Wittcmyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (mihr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71 % of deer returning in the first day, and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker hut non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 2014 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendrum•. Charles R. Anderson Jr.\ Kevin L. Monteith\ Jonathan A. Jenksd, R. Terry Bowyer• ~ Department ofBiological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA h Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81505, USA c Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming. 3166. 1000 East University Avenue, Laramie 82071, USA d Department of Natural Resource Management, South Dakota State University, Box 2140B, Brookings 57007, USA Citation: Lendrum, P. E., C.R. Andenmn Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement ofa rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http:·'/dx.doi.org/10. l 0 16/j.mambio.20 I4.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoi/eus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Normalized Difference Vegetation Index; NDVI) in spring 2010 in the Piceance Basin of northwestern Colorado, USA. NDVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NOVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NOVI and fecal nitrogen. Moreover. these deer "jumped" rather than "surfed" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition. and to minimize detrimental factors such as predation. and malnutrition during migration. 28 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN 1, RANDY T. LARSEN 1, MARK E. PETERSON2, CHARLES R. ANDERSON JR.3, KENT R. HERSEY', AND BROCK R. McMILLAN 1 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fon Collins, CO 80523, USA Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81505, USA 4 Utah Division of Wildlife Resources, 1594 W Nonh Temple, Salt Lake City, UT 84114, USA 2 3 Citation: Freeman, E. D., R. T. Larsen. M. E. Peterson. C.R. Anderson Jr.. K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced ( e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males/100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= 1.258; SE= l.672; t = 0.752; P = 0.454), or synchrony of parturition (F = l.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected. @ 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Nortbrup, 1 Aaron B. A. Shafer,2 Charles R. Anderson Jr,3 David W. Coltman" and George Wittemyer 1 I Dcpanment of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Dcpanment of Evolutionary Biology, Evolutionary Biology Centre. Uppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Department of Biological Sciences, University of Alhena, Edmonton, AB, Canada. Citation: Northrup, J.M., AB. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer ( Odocoilcus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management, even in widely distributed panmictic species. 29 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch,■ Joseph M. Northrup,b Megan F. McKenna,C Charles R. Anderson Jr,d Lisa Angeloni,... and George Wittemyera.b aGraduatc Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA bDcpartmcnt offish. Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA ~atural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, dMammals Research Section, Colorado Parks and Wildlife. 317 W. Prospect Road, Fort Collins, CO 80526. USA COepartmcnt of Biology, Colorado State University. 1878 Campus Delivery, Fort Collins, CO 80523, USA Citation: Lynch, E.. J.M. Northrup, M. F. McKenna. C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology: doi: 10.1093/beheco/aru 158. While visual forms of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a form of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road, Fort Collins, CO 80526. USA Citation: Anderson. C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79lh North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee. eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular imponance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule de~r (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/km2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible. habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available. we encourage agencies to incorporate such migration corridors into land-use plans (e.g .. resource management plans) and National Environmental Policy Act documents. 30 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle1 • Mindy 8. Rice 1 • Charles R. Anderson1 • Chad Bishop 1 • N. T. Hobbs3 1 NERC Centre for Ecology and Hydrology, Bush Estate, Pcnicuik EH26 0QB, UK 2 Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 80526, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University. Fon Collins 80524, CO, USA Citation: Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phcnology enhances winter body condition of a large mobile herbivore. Occologia ISSN 00:?9-8549; DOI 10.1007ls00442-0 l 5-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus /Jemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR .2 and GEORGE WITTEMYER 1• 3 --...., '-.,,/ 'Department of Fish, Wildlife and Conservation Biology, Colorado State University. Fort Collins, CO, USA ?Mammals Research Section. Colorado Parks and Wildlife, Fort Collins, CO. USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J.M., C.R. Anderson. Jr., and G. Winemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: IO. l l I llgcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over SO% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers. mangers, and industry when attempting to mitigate habitat loss due to energy development. 31 �Colorado Parks and Wildlife July 1, 2016 June 30, 2017 WILDLIFE RESEARCH REPORT : Parks and Wildlife : Mammals Research : Deer Conservation : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation State of Cost Center Work Package Task No. Colorado 3430 3001 6 Federal Aid Project: W-243-R2 : Period Covered: July 1, 2016 June 30, 2017 Author: C. R. Anderson, Jr. Personnel: E. Bergman, E. Cardenas, D. Collins, B. deVergie, D. Finley, M. Fisher, L. Gepfert, J. Hudson, D. Johnston, T. Knowles, D. Lewis, T. Mullins, J. Pelham, B. Petch, J. Rivale, R. Schilowsky, G. Smith, R. Velarde, T. Verzuh, S. Williams, L. Wolfe, CPW; E. Hollowed, L. Belmonte, BLM; T. Graham, Ranch Advisory Partners; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; B. Mallow, Kiwi Air, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent the first 9 years of data (5 years of pretreatment, 4 years post treatment) of a long-term study addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored deer on 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia, South Magnolia) and control (North Ridge, Ryan Gulch) sites. We recorded habitat use and movement patterns, estimated neonatal, overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult females, and estimated late winter annual abundance/density. During this research segment, we targeted 240 fawns (60/study area) and 120 does (30/study area) in early December 2016 for VHF and GPS radiocollar attachment, respectively, and adult female body condition assessment. We attempted recapture of 120 does (30/study area) and 40 fawns (20 in 2 study areas) in March 2017 for late winter body condition assessment. Winter range habitat improvements completed spring 2013 resulted in 604 acres of 1 �mechanically treated pinion-juniper/mountain shrub habitats in each of the 2 treatment areas with relatively minor and extensive energy development, respectively. Post-treatment monitoring will continue for another year to provide sufficient time to measure how vegetation and deer respond to these changes. Based on data collected through year 9 of this 10-year project: (1) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31% to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and winter conditions potentially enhancing predation success; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; (5) late winter mule deer densities have consistently increased in 3 of 4 study areas, averaging about +6% annually, with the North Ridge study area exhibiting erratic population changes that may be an artifact of periodic migration behavior prior to survey timing; and (6) post treatment vegetation responses have provided evidence of improved forage conditions, but longer term monitoring will be required to address the full potential of habitat mitigation efforts. Detailed habitat use analyses are still pending for the pre and post-treatment periods. We will continue to collect population and habitat use data across all study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development disturbances in highly developed areas, or whether it is better to allocate mitigation efforts toward less or non-impacted areas. In collaboration with Colorado State University, we monitored neonate survival in relation to energy development on all study areas since 2012. This will allow us to include neonatal and parturition data with other demographic parameters to evaluate mule deer/energy development interactions. This study is slated to continue through 2018 to allow sufficient time for measuring mule deer population responses to landscape level manipulations. 2 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Collect and reattach GPS collars to maintain sample sizes for addressing mule deer habitat use and behavioral patterns in 4 study areas experiencing varying levels of energy development of the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. Estimate early and late winter fawn weights in areas with and without habitat treatments to assess winter fawn condition relative to habitat improvements. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and bi-weekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate late winter mule deer abundance and density in each study area. 5. Monitor habitat treatment response for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Continue neonate survival and adult female parturition evaluations to complete demographic parameters for assessing mule deer/energy development interactions. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the 3 �primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or introduction of noxious weeds, by fragmenting habitat with drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape conversion for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also covers some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. 1). Anderson and Freddy (2008) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the first 5 years of this study, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We are currently monitoring 2 control areas: 1 with development (0.6 pads & facilities/km2; Ryan Gulch) and 1 without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km2 and North Magnolia, 0.1 well pads & facilities/km2), that also received habitat improvements from 2011– 2013 (604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated until 2018 to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavioral patterns in relation to energy development activities in the area are being monitored to identify effective Best Management Practices (BMPs) for future energy development planning. This progress report describes the previous 9.5 years (Jan 2008–June 2017) of mule deer population performance during the pretreatment phase on 4 winter range herd segments, which includes monitoring habitat selection and behavior patterns of adult female mule deer; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition and fawn weights during early and late winter; and annual late-winter abundance/density estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as home to one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. 1). Historically, mule deer numbers on winter range were estimated between 20,000–30,000 (White and Lubow 2002), and the current number of well pads (Fig.1) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utah juniper (Juniperus osteosperma; pinionjuniper) shrubland complex ranging from 1,675 m to 2,285 m in elevation (Bartmann and Steinert 1981). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble’s oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big 4 �sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay fields. Grasses and forbs common to the area consist of wheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleaf balsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rain storms. Wintering mule deer population segments we are investigating include: North Ridge (53 km2) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch (141 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. 1). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.1 pads & facilities/km2), and relatively high development in the Ryan Gulch (0.6 pads & facilities/km2) and South Magnolia (0.9 pads & facilities/km2) segments (Fig. 1). Development activity was high through 2011 and has declined substantially since natural gas prices began to decline in 2012. Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and relatively heavy (South Magnolia) energy development activities. METHODS Tasks addressed this period included mule deer capture and collaring, monitoring neonate, overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography and winter fawn condition measuring early and late winter fawn weights, estimating mule deer abundance applying helicopter mark-resight surveys, and monitoring vegetation responses to habitat treatments completed spring 2013. We employed helicopter netgunning techniques (Barrett et al. 1982, van Reenen 1982) to target 240 fawns and 120 adult females during early December 2016, and 120 adult females and 40 fawns (primarily recaptures) during early March 2017. Once netted, all deer were hobbled and blind folded. Fawns were weighed and radiocollared, and sex was recorded prior to release at the capture site. Adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (5 fix attempts/day; G2110D, Advanced Telemetry Systems, Isanti, MN, USA) prior to release. To provide direct measures of decline in overwinter body condition, we targeted 30 adult females in each study area that were captured the previous December. During March, 20 fawns were recaptured, weighed and released in South Magnolia (within the habitat treatment areas) and Ryan Gulch (control area) to quantify overwinter declines in fawn body condition. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and autumn for winter fawns and during winter for neonates, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). 5 �Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed and recovered since 2008. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and 10 control deer from North Ridge where location rates were programmed for every 30-60 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) recently analyzed resource selection data relative to energy development (Northrup 2015) and those results are addressed below. Mule deer resource selection analyses to address success of habitat improvements are pending until vegetation responses are fully realized, which will begin by fall 2018. Mule Deer Survival Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death (Stonehouse et al., 2016). We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated survival rates from 1 July 2016 through 30 June 2017 for adult females, from birth to mid December for neonates, and from early December 2016–mid June 2017 for winter fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate % ingesta-free body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years evident in non-overlapping 95% confidence intervals. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 4 helicopter mark-resight surveys (2 observers and the pilot) during late March/early April to estimate deer abundance in all 4 study areas. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2017. We delineated flight paths in ArcGIS 10.0 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. 6 �We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (2 = 0 or 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 242 fawns and 121 does during Dec 2016 and 122 does and 41 fawns during March 2017. Sixteen fawn mortalities (6.6%; proximate cause = 5 capture myopathy, 11 predation) occurred within the 10 day censorship period during December and 2 fawn mortalities (4.9%; 1 capture myopathy, 1 predation) occurred during the March capture. Doe mortalities totaled 2 (1.7%; capture myopathy) and 4 (3.3%; capture myopathy) within 10 days of the December and March capture periods, respectively. Mortality rates 10 days post capture have typically varied between 2.5– 3.5% for fawns and does since Jan 2008, except during the 2011–2012 capture season where myopathy rates were higher (3–6%) due to dry, warm conditions (Anderson and Bishop 2012). Excluding December fawn captures, myopathy rates were comparable to expected levels when compared to previous years. The relatively high myopathy rates (including ongoing predation that occurred within the 10-day censorship period) for early winter fawn captures were likely linked to the relatively severe winter conditions evident through January 2017, resulting in crusted snow conditions, and unusually high levels of coyote (Canis latrans) predation; crusted snow conditions may have enhanced predation by coyotes by enabling them to stay above the snow while fleeing deer were inhibited by breaking through the crust. Fawn survival estimates from early December 2016 through mid June 2017 were similar (overlapping 95% CIs) among 3 study areas ranging from 0.51 to 0.67, with a lower survival estimate from South Magnolia fawns (0.31; Table 1, Fig. 1). In comparison to previous years, coyote predation was much more common in all areas this winter and the dominant proximate cause of mortality in South Magnolia. This increase in coyote predation may be related to multiple factors including increasing coyote numbers in response to high rabbit densities the past few years following by a recent drop in the rabbit population in combination with crusted snow conditions enhancing coyote predation success on mule deer fawns. General comparisons to previous years suggest relatively low fawn survival this winter, which was comparable to the lower survival rates observed during 2010-11 (Fig. 2). Both low survival winters coincided with harsher weather conditions with an added influence of predation coupled with crusted snow this past winter. Winter fawn survival (Fig. 2) also appears to correlate with summer forage conditions as suggested from relative December fawn weights (Fig. 3). Annual adult female survival varied from 0.73 (Ryan Gulch) to 0.91 (North Magnolia; Table 1) during 2016–17, but was comparable among study areas during 2016–17 and to previous years (P > 0.05), with the exception of lower survival in North Magnolia during 2011–12 (Ŝ = 0.68, Anderson and Bishop 2012). Relatively low sample sizes per study area for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g., >1520%). Estimates below 80% are biologically concerning if these values represent the respective population, but low statistical power precludes confirmation within study areas. When combined among study areas, annual survival estimates have varied from 79% in 2012-13 to 86% in 2014-15 and was 83% this year, which is comparable to the long term average. Lower combined survival estimates are consistent with extreme environmental conditions consisting of dryer moisture conditions during late winter/spring (2012-13) and/or cold temperatures with heavy snow during early winter (2015-16). 7 �Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has been completed. Four manuscripts from this effort have been published (Lendrum et al. 2012, Lendrum et al. 2013, Lendrum et al. 2014, Anderson and Bishop 2014; Appendix A). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 4) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript Lendrum et al. (2013) addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures, snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, with median migration durations of 3–8 days among the 4 areas (straight line distance between seasonal ranges averaged 32–40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status (Table 2). Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in longterm fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication Lendrum et al. (2014), monitored migratory mule deer in the Piceance Basin to examined the relationship between the Normalized Difference Vegetation Index (NDVI), which is a course-scale measure of forage quality using a GIS assessment of vegetation greenness, and fecal nitrogen to assess the assumption that forage quality and deer diets can be reasonably linked to address deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were informative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NDVI values, and progressed to summer range as vegetation green-up and nitrogen levels increased, but ahead of peak vegetation green-up on summer range. It is plausible that this rapid migration strategy is evident for deer in relatively good condition and allows for early arrival on summer range to take advantage of optimal forage conditions prior to parturition. Anderson and Bishop (2014) summarized results from Lendrum et al. (2012, 2013) and Sawyer et al. (2012) addressing migratory mule deer and energy development in northwest Colorado and south-central Wyoming, respectively. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/km2) and minimizing 8 �disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no or minimal surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer during December 2016 were similar among study areas (P < 0.05, Fig. 5, Table 2). Early winter condition this year was moderate to low compared to previous years with notably high condition exhibited from Ryan Gulch does during December 2011 and 2015 (Fig. 5). Adult female body condition during early winter appears primarily related to the proportion of lactating does identified during December captures, where higher condition correlates with lower lactation rates. Since 2011, excluding Ryan Gulch during 2014, North Ridge during 2016 and North Magnolia this year, late winter body condition initially trended upward and appears to be stabilizing recently (Fig. 5, Table 2). The observed increase was likely related to relatively mild winters from 2012 - 2015 and the generally stabilizing condition may be related to more severe conditions during early winter the past 2 years; temperatures increased during mid-winter resulting in snow-melt, which likely improved late winter condition above what might have resulted if severe winter conditions continued. Adult female body condition thus far appears more related to early winter lactation rates, seasonal moisture conditions, relative deer densities (Fig. 6), and winter severity than observed development intensity thus far. December 2016 fawn weights were generally lower than observed during previous years (about 3.6 kg below average; Fig. 3). December fawn condition has been correlated with winter fawn survival (Fig. 2), which was consistent with the relatively low winter fawn survival observed this past winter. Because adult female body condition has been largely uninformative in regards to habitat treatment responses (pending further analyses), we began late winter fawn recaptures in South Magnolia (habitat treatment area) and Ryan Gulch (control area) to assess changes in over-winter body condition the past 2 years. Fawns from both areas exhibited significant weight loss (P < 0.05) during 2015-16, with fawns from the treatment area exhibiting significantly less weight loss (P < 0.001; -3.1 kg) than fawns from the untreated area (-5.6 kg). Results from winter 2016-17 differed in that fawns from both areas exhibited similarly reduced body condition early winter (about 4 kg lighter on average) and maintained their weights into late winter (negligible weight differences from Dec to Mar). These results are conflicting with respect to habitat treatment effects, but will require more detailed analyses to address other factors that may influence nutritional benefits of habitat improvements on winter range. We will continue monitoring over-winter condition of fawns from the treatment and control areas to evaluate over-winter fawn condition in areas with and without habitat improvements. Mule Deer Behavioral Response to Energy Development We recently completed evaluations of deer behavior patterns in relation to energy development activities (Northrup et al. 2015, 2016a, 2016b). We found diurnal responses to development activity, where deer used timbered areas away from development activity while bedded during the day and 9 �moved into more open areas generally closer to developed areas while foraging at night. Disturbance distances from producing pads and roads declined from 600 m to 200 m and about 140 m to 60 m from daytime to nighttime, respectively, but increased from 600 m to 800 m for nighttime drilling pad activity. We suspect deer behaviorally respond to fluctuations in development activity, where road traffic and producing well pad activity decline at night, but drilling pad disturbance may increase from compressors and lights used to facilitate nighttime drilling activity. These evaluations were applied during an active drilling phase in the Piceance Basin and deer use was influenced by development activity in 25% (nighttime) to 50% (day time) of critical winter range during that period. However, deer densities have comparably increased among developed and undeveloped study areas (Fig. 6) suggesting that deer can behaviorally mediate development disturbance under observed development and deer densities by taking advantage of fluctuations in development activity to address their nutritional requirements. Given the plasticity in deer behavior, a number of potential options for future development planning exits including drilling schedule modifications (seasonal and/or diurnal), concentrated/staged development, reducing road traffic, and using light/noise barriers around drill rigs. It will be informative to determine if habitat improvements will further reduce development disturbance and increase management options for future development planning. Neonate Survival To complete demographic parameters addressing mule deer–energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate neonate survival and adult female parturition in developed and undeveloped landscapes (funded by ExxonMobil Production Co.) beginning spring 2012. Mark Peterson (Graduate Research Assistant) and Paul Doherty (CSU professor) assisted with this research, which was completed December 2014, and continued by CPW during 2015 and 2016. Neonate capture and collaring efforts totaled 85 during spring 2012, 67 during spring 2013, 54 during spring 2014, 59 during spring 2015, and 58 during spring 2016. Overall, estimated neonate survival through mid-December was 0.39 (95% CI = 0.28–0.50) during 2012, 0.37 (95% CI = 0.25–0.48) during 2013, 0.57 (95% CI = 0.44 – 0.70) during 2014, 0.36 (95% CI = 0.23–0.49) during 2015, and 0.32 (95% CI = 0.19–0.44) during 2016. Through December 2016, predation was the highest proximate mortality factor (averaging about 50% annually), with relatively low incidents of starvation/disease directly influencing spring/summer fawn survival (mean < 4%). Manuscripts addressing neonate fawn survival and adult female parturition in relation to energy development are currently in preparation and review for publication. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (Pt) for all study areas, and variable individual sightability (2 = 0) for North Magnolia deer and homogenous sightability (2 ≠ 0) for the other 3 areas. North Ridge exhibited the highest deer density (15.8/km2), with comparable but lower deer densities in the other 3 areas (10.3–11.9/km2; Table 3, Fig. 6). Densities similarly increased over the 9 year monitoring period in 3 of the 4 study areas averaging about 6% annual increases from North Magnolia, South Magnolia and Ryan Gulch. Mule deer density estimates from North Ridge have been erratic with a significant decline this past year (Fig. 6). Biological support for the recent decline is lacking based on similar demographic parameter estimates when compared to the other study areas showing population growth (Tables 1 and 2, Figs. 2, 3 and 5). Reasons for the recent decline in North Ridge are unclear, but lack of closure partially due to early migration has been an issue in the past and may have artificially reduced the population estimate this year. Active GPS collars addressing last year’s movement patterns will be collected April 2018 and movement data will be assessed to address the potential closure issue. 10 �Abundance estimates from 2017 were similarly precise from all 4 study areas, but error of estimates was greater (mean CICVs ranged from 0.19 – 0.22) than past surveys (typically ~0.15). Increased error was likely associated with reduced sightability from a different helicopter (Hughes MD 500) used than during previous years (Bell 47). We shifted to the Hughes MD 500 helicopter to increase power and consistently allow for 3 people in the helicopter to assist with deer surveys. The Bell 47 is less powerful and, depending on weather conditions, may only provide for 2 people in the helicopter, but provides for increased visibility. Based on estimate comparisons using the 2 different helicopters, the Bell 47 provides improved population estimates even with the potential limitation of survey personnel and will be used for future surveys. Magnolia Habitat Treatments We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 2011; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA), 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop, chaining) in January 2012 (Stephens 2014), and 1,038 acres of hydro-ax treatments in April 2013 (Determination of NEPA Adequacy: DOIBLM-CO-110-2012-0134- DNA), totaling 604 treated acres in each study area (Fig. 7). Vegetation response in the pilot treatment sites was visually evident by fall 2011 (Fig. 7), and resulted in statistically significant (P < 0.05) increases in native grass and forb cover by the 2014 growing season. 2016 results are still pending, but shrub responses appear promising from data collected last fall. Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3 fold increase in grasses, forbs, and shrubs combined after 2 growing seasons (versus control sites), but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following 1 growing season and shrub responses have been notable since the 4th growing season, but statistical comparisons are still pending. As anticipated, grass and forb responses were evident 2 to 3 years post-treatment, with longer term response expected (3-5 years) for palatable shrubs. Of note, relatively high moisture conditions experienced during spring 2014 and 2015 resulted in higher than normal prevalence of cheatgrass (Bromus tectorum); cheatgrass invasion has previously been minor to non-existent. Cheatgrass invasion, however, does not appear directly related to treatment sites because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. GPS data addressing deer use of treatment sites is becoming available and will be analyzed as additional data are collected and vegetation responses progress. We observed improved fawn condition (P < 0.001) in South Magnolia following the 4 th growing season of habitat treatments when compared to fawn condition in the Ryan Gulch control area. Vegetation and mule deer responses will continue to be documented for the next year to assess the utility of this mitigation approach in benefiting mule deer exposed to energy development disturbance. SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 4-year pretreatment period and 5 years post-treatment. The pretreatment period was completed by spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 11 �study areas were completed by April of 2013, and subsequent vegetation responses have met or exceeded expectations. Post-treatment monitoring will continue for another year to provide sufficient time to measure how deer respond to these changes. Based on data collected through year 9 of this 10-year project: (1) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31% to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and winter conditions potentially enhancing predation success; (2) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (3) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (4) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (5) late winter mule deer densities have consistently increased in 3 of 4 study areas, averaging about +6% annually, with the North Ridge study area exhibiting erratic population changes that may be an artifact of periodic migration behavior prior to survey timing. Detailed habitat use analyses are pending for the pre and post-habitat treatment periods. We will continue to collect the various population and habitat use data across study sites to evaluate the effectiveness of habitat improvements on winter range. This approach will allow us to determine whether it is possible to effectively mitigate development impacts in highly developed areas, or whether it is better to allocate mitigation dollars toward less or non-impacted areas. In a previous project conducted on the Uncomphahgre Plateau, Colorado, Bergman et al. (2014) found that habitat treatments implemented in pinion-juniper habitat in undeveloped areas increased overwinter survival of fawns by a magnitude of 1.15. Hay field improvements have been completed in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; elk (Cervus elaphus) response has been evident, but mule deer response has thus far been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Collaborative research with agency biologists, graduate students, and university professors has produced 15 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011), approaches to address proximate mortality factors from field necropsies of mule deer (Stonehouse et al. 2016), mule deer migration (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013), mule deer response to helicopter capture and handling (Northrup et al. 2014a), potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014), mule deer genetics in relation to body condition and migration (Northrup et al. 2014b), spatial and temporal factors influencing auditory vigilance in mule deer (Lynch et al. 2014), the relationship of plant phenology with mule deer body condition (Seral et al. 2015), and mule deer responses to differing energy development activities to inform future development planning (Northrup et al. 2015, Northrup et al. 2016a, Northrup et al. 2016b); these publications are summarized in Appendix A. Ongoing cooperative agreements will be necessary to sustain this project to completion (through 2018). We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation’s energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. 12 �LITERATURE CITED Anderson, C. R., Jr., and D. J. Freddy. 2008. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and C. J. Bishop. 2011. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C. R., Jr., and C. J. 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Shafer, C. R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037. Northrup, J. M., C. R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016a. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:27462757 Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2016b. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Sawyer, H., M. J. Kauffman, A. D. Middleton, T. A. Morrison, R. M. Nielson, and T. B. Wycoff. 2012. A framework for understanding semi-permeable barrier effects on migratory ungulates. Journal of Applied Ecology 50:68-78. Searle, K. R., M. B. Rice, C. R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549; DOI 10.1007/s00442-015-3348-9 Stephens, G. J. 2014. Understory responses to mechanical removal of pinyon-juniper overstory. MS Thesis, Colorado State University, Ft. Collins USA. Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. 14 �Stonehouse, K. F., C. R. Anderson Jr., M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J. C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46:120-139. White, G. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by Charles R. Anderson, Jr., Mammals Research Leader 15 �Table 1. Survival rate estimates (Ŝ) of fawn (1 Dec. 2016–15 June 2017) and adult female (1 July 2016– 30 June 2017) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe samplea (n) Ŝ (95% CI) Fawns Ryan Gulch 61 0.506 (0.381–0.632) South Magnolia 53 0.312 (0.183–0.441) North Magnolia 54 0.655 (0.525–0.785) North Ridge 60 0.665 (0.546–0.785) Adult females Ryan Gulch 28 50 0.726 (0.570–0.882) South Magnolia 23 46 0.849 (0.720–0.997) North Magnolia 29 57 0.914 (0.829–0.999) North Ridge 19 47 0.845 (0.721–0.968) a Adult female sample sizes following capture and radio-collaring efforts March, 2017. 16 �Table 2. Mean rump fat (mm), Body Condition Score (BCSa), and % ingesta-free body fat (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009–2017. Values in parentheses = SD. March 2009 December 2009 March 2010 Study Area Rump fat Ryan Gulch 1.73 (1.78) 2.66 (0.55) 7.08 (1.27) 8.35 (6.36) 4.06 (1.13) 10.54 (3.72) 2.31 (1.44) 2.35 (0.48) 6.37 (1.41) South Magnolia 1.29 (0.47) 2.51 (0.66) 6.74 (2.27) 10.05 (6.19) 4.07 (1.21) 11.44 (3.50) 3.12 (2.20) 2.64 (0.59) 7.11 (1.69) North Magnolia 1.31 (1.01) 2.66 (0.68) 7.15 (1.63) 10.67 (5.76) 4.25 (0.96) 11.94 (3.39) 3.15 (2.34) 2.85 (0.53) 7.54 (1.53) North Ridge 1.57 (1.22) 2.60 (0.56) 6.81 (1.68) 5.25 (5.65) 3.63 (1.11) 9.37 (3.08) 1.77 (1.11) 2.42 (0.49) 6.39 (1.45) March 2011 December 2011 BCS % fat Rump fat BCS % fat Rump fat BCS % fat Table 2. Continued. December 2010 Study Area Rump fat BCS % fat Rump fat Ryan Gulch 7.26 (6.36) 3.24 (0.96) 9.69 (3.56) 1.55 (0.60) 2.53 (0.42) 6.72 (1.37) 13.41 (6.39) 4.21 (1.17) 13.17 (3.64) South Magnolia 9.85 (6.78) 3.30 (0.61) 11.27 (3.75) 1.65 (0.75) 2.35 (0.50) 6.15 (1.75) 8.18 (5.45) 3.41 (0.82) 10.34 (3.28) North Magnolia 9.55 (6.49) 3.46 (1.16) 10.79 (4.26) 1.65 (0.67) 2.53 (0.49) 6.79 (1.47) 8.76 (5.77) 3.74 (0.91) 10.73 (3.14) North Ridge 7.25 (5.41) 3.47 (0.86) 9.85 (3.02) 1.45 (0.76) 2.24 (0.49) 6.30 (1.65) 8.86 (5.37) 3.51 (0.99) 10.77 (3.33) 15 BCS % fat Rump fat BCS % fat �Table 2. Continued. March 2012 December 2012 BCS % fat Rump fat BCS March 2013 Study Area Rump fat % fat Ryan Gulch 2.15 (1.44) 2.74 (0.44) 7.22 (1.16) 6.34 (4.35) 3.30 (0.77) 9.34 (2.43) 1.87 (0.90) 2.65 (0.37) 7.14 (0.89) South Magnolia 1.66 (0.77) 2.59 (0.36) 7.03 (1.13) 8.30 (5.71) 3.46 (1.07) 10.32 (3.23) 2.06 (0.77) 2.65 (0.26) 7.19 (0.66) North Magnolia 1.90 (0.76) 2.84 (0.34) 7.61 (0.96) 9.66 (6.41) 3.84 (1.16) 11.18 (3.64) 1.76 (0.91) 2.59 (0.41) 6.87 (1.11) North Ridge 2.24 (1.58) 2.70 (0.35) 7.26 (1.05) 5.76 (4.10) 3.32 (0.82) 1.87 (0.73) 2.48 (0.34) 6.70 (1.12) 9.06 (2.31) Rump fat BCS % fat Table 2. Continued. December 2013 BCS March 2014 % fat Rump fat Study Area Rump fat Ryan Gulch 9.27 (6.29) 3.47 (0.87) 10.61 (3.76) 1.69 (0.85) 2.68 (0.39) 7.03 (0.99) 8.50 (6.76) 3.69 (1.03) 10.56 (3.70) South Magnolia 11.27 (8.40) 3.99 (1.04) 11.40 (4.16) 2.57 (1.61) 2.96 (0.30) 7.75 (0.68) 10.96 (6.82) 4.08 (1.06) 11.98 (3.81) North Magnolia 9.00 (6.15) 3.44 (0.78) 10.48 (3.25) 2.33 (2.12) 2.80 (0.49) 7.31 (1.43) 9.52 (5.83) 3.83 (1.04) 11.18 (3.32) North Ridge 11.17 (5.28) 3.85 (0.72) 11.66 (2.69) 2.38 (1.52) 2.68 (0.39) 7.16 (1.14) 7.93 (5.50) 3.74 (0.76) 10.20 (3.01) 16 BCS December 2014 % fat Rump fat BCS % fat �Table 2. Continued. March 2015 December 2015 BCS % fat Rump fat BCS March 2016 Study Area Rump fat % fat Ryan Gulch 2.62 (0.95) 2.89 (0.40) 7.44 (0.53) 12.80 (6.83) 4.24 (0.88) 12.89 (3.72) 2.29 (0.64) 2.81 (0.37) 7.29 (0.52) South Magnolia 2.66 (1.36) 2.97 (0.55) 7.62 (0.74) 6.93 (4.83) 3.83 (0.89) 9.83 (2.69) 2.07 (1.39) 2.78 (0.39) 7.46 (0.93) North Magnolia 2.25 (0.97) 2.90 (0.42) 7.49 (0.90) 8.79 (6.01) 3.79 (0.85) 10.81 (3.54) 2.43 (1.01) 2.71 (0.39) 7.17 (0.87) North Ridge 2.28 (1.37) 2.92 (0.46) 7.43 (1.05) 5.47 (5.49) 3.25 (0.66) 1.58 (0.70) 2.51 (0.41) 6.73 (1.26) 9.35 (2.75) Table 2. Continued. December 2016 Study Area Rump fat Ryan Gulch 8.20 (4.90) 3.94 (0.97) 10.46 (2.70) 2.39 (0.74) 2.49 (0.38) 6.78 (0.97) South Magnolia 6.27 (4.62) 3.54 (0.88) 9.37 (2.53) 2.48 (0.77) 2.57 (0.35) 7.09 (0.63) North Magnolia 7.90 (5.52) 3.86 (1.01) 10.34 (3.14) 1.82 (0.72) 2.53 (0.27) 7.05 (0.58) North Ridge 7.74 (5.48) 3.85 (0.95) 10.01 (3.09) 2.30 (1.61) 2.74 (0.45) 7.23 (1.21) a BCS March 2017 % fat Rump fat BCS Body condition score taken from palpations of the rump following Cook et al. (2009). 17 % fat Rump fat BCS % fat �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 27 March–4 April 2017. Data represent 4 helicopter resight surveys from all 4 study areas. Study area Mean No. sighted Mean No. marked N (95% CI) Density (deer/km2) Ryan Gulch 284 16 1,446 (1,184–1,792) 10.3 South Magnolia 171 14 991 (803–1,246) 11.9 North Magnolia 188 18 904 (757–1,098) 11.4 North Ridge 182 15 840 (695–1,040) 15.8 18 �deer study reas Well Pad & F•cllllles f 1n oeve10pmem 7 oe.ie10pm en1 · 1es 10 IIH Figure 1. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado, winter 2013/14 (Accessed http://cogcc.state.co.us/ Dec. 31, 2013). Development activity has subsided with no additional drilling since 2013. 19 �Winter fawn survival 2010-11 – 2016-17 1.00 fir 0.90 0.80 -- 0.70 0.60 0.50 .._ 0.40 0.30 0.20 0.10 --- --- I- I- 1 ,- - f 1 T T -- 1- i --I- 0.00 - f- - f- f- - □ Ryan Gulch □ South Magnolia • North Magnolia □ North Ridge f- -f- 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 Figure 2. Over-winter (Dec–June) mule deer fawn survival (Ŝ) from 4 study areas in the Piceance Basin, northwest Colorado. Error bars = 95% CI. Winter survival rates from 2009-10 and 2010-11 are unavailable due to pre-mature collar drop, but 2009-10 and 2010-11 survival mirrored rates observed during 2011-12 and 2012-13 (excluding North Ridge), respectively, until collars began dropping during mid – late March of those years. 20 �Male fawn weights 42.0 Weight (kg) 40.0 38.0 ~ - 34.0 32.0 30.0 a Ryan Gulch - 36.0 28.0 - - - - = - == Dec 2008 Dec 2009 - - - - - - - = - == Dec 2010 Dec 2011 Dec 2012 a South Magnolia • North Magnolia a North Ridge =- Dec 2013 Dec 2014 Dec 2015 Dec 2016 Female fawn weights 42.0 Weight (kg) 40.0 38.0 a Ryan Gulch a South Magnolia 36.0 34.0 30.0 28.0 - - 32.0 == - - - - = , == Dec 2008 Dec 2009 Dec 2010 Dec 2011 - = , == = , == Dec 2012 Dec 2013 • - - North Magnolia - = Dec 2014 , == rtP Dec 2015 Dec 2016 I r a North Ridge Figure 3. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceance Basin, northwest Colorado, December 2008–2016. 21 �North Rief!,~ and ~Orth 1'fo(!!'Olia Summet f1"1g~ Ryan Gulc!i imd Sou\b M Swl1ther Ran_ge Figure 4. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult female mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state, county, and natural-gas; white lines) from May 2009 (Lower right; from Lendrum et al. 2012). 22 �Early winter rump fat 16 Rump fat (mm) 14 12 10 North Ridge 8 North Magnolia 6 Ryan Gulch 4 South Magnolia 2 0 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2016 Late winter rump fat 4.0 Rump fat (mm) 3.5 3.0 2.5 North Ridge 2.0 North Magnolia 1.5 Ryan Gulch 1.0 South Magnolia 0.5 0.0 Mar 2009 Mar 2010 Mar 2011 Mar 2012 Mar 2013 Mar 2014 Mar 2015 Mar 2016 Mar 2017 Figure 5. Mean early (early Dec., Top) and late winter (early Mar., Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado, March 2009–March 2017. Error bars = 95% CI. 23 �Piceance Basin late winter mule deer density 35.00 30.00 Deer/km2 25.00 20.00 North Ridge 15.00 Ryan Gulch North Magnolia 10.00 South Magnolia 5.00 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year Figure 6. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009–2017. Estimates for North Ridge 2014 and 2015 and for North Magnolia 2015 were adjusted upward (using GPS migration data) to account for early migration from winter range prior to and during surveys. 24 �eear5et_15_J b_Jndl.l aA_E C.... llearSet_36_ ., dSP't_.1!11l_gi9 er •l H • h Pilot T~ tmerm; 11 H 11Cresl ae sI Study Ar s Figure 7. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan. 2011 using hydro-axe; yellow polygons completed Jan. 2012 using hydro-axe, roller-chop, and chaining; and remaining polygons completed April 2013 using hydro-axe). January 2011 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 2011 (Lower right, ground view). 25 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP1, CHARLES R. ANDERSON Jr. 1, DANIEL P. WALSH1, ERIC J. BERGMAN1, PETER KUECHLE2, and JOHN ROTH2 1 Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA 2 Advanced Telemetry Systems, Isanti, Minnesota 55040 USA Citation: Bishop, C. J., C. R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8):1797-1806; DOI: 10.1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expelled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability of a VIT being retained until parturition was 0.766 (SE = 0.0605) and the probability of a VIT being retained to within 3 days of parturition was 0.894 (SE = 0.0441). In a similar study using the original VIT wings (Bishop et al. 2007), the probability of a VIT being retained until parturition was 0.447 (SE = 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE = 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE = 0.0765) and VIT retention to within 3 days of parturition by 0.271 (SE = 0.0634). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least 1 neonate was 0.952 (SE = 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer. © 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM1, CHARLES R. ANDERSON JR.2, RYAN A. LONG1, JOHN G. KIE1, AND R. TERRY BOWYER1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2 Colorado Parks and Wildlife, Grand Junction, Colorado 81505 USA Citation: Lendrum, P. E., C. R. Anderson Jr., R. A. Long, J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http://dx.doi.org/10.1890/ES12-00165.1 Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoileus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 26 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum1, Charles R. Anderson Jr.2, Kevin L. Monteith1,3, Jonathan A. Jenks4, R. Terry Bowyer1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C. R. Anderson Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: 10.1371/journal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008–2010. Conclusions/Significance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use–availability design JOSEPH M. NORTHRUP1, MEVIN B. HOOTEN1,2,3, CHARLES R. ANDERSON JR.4, AND GEORGE WITTEMYER1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., M. B. Hooten, C. R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a useavailability design. Ecology 94(7):1456-1463. http://dx.doi.org/10.1890/12-1688.1 Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use–availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use–availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 27 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP1, CHARLES R. ANDERSON JR2, AND GEORGE WITTEMYER1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., C. R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71% of deer returning in the first day, and 91% returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 2014 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendruma, Charles R. Anderson Jr.b, Kevin L. Monteithc, Jonathan A. Jenksd, R. Terry Bowyera a Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA b Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81505, USA c Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, 3166, 1000 East University Avenue, Laramie 82071, USA d Department of Natural Resource Management, South Dakota State University, Box 2140B, Brookings 57007, USA Citation: Lendrum, P. E., C. R. Anderson Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/10.1016/j.mambio.2014.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Normalized Difference Vegetation Index; NDVI) in spring 2010 in the Piceance Basin of northwestern Colorado, USA. NDVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NDVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NDVI and fecal nitrogen. Moreover, these deer “jumped” rather than “surfed” the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation, and malnutrition during migration. 28 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN1, RANDY T. LARSEN1, MARK E. PETERSON2, CHARLES R. ANDERSON JR.3, KENT R. HERSEY4, AND BROCK R. McMILLAN1 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA 2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81505, USA 4 Utah Division of Wildlife Resources, 1594 W North Temple, Salt Lake City, UT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C. R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males/100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate = 1.258; SE = 1.672; t = 0.752; P = 0.454), or synchrony of parturition (F = 1.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected. © 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup,1 Aaron B. A. Shafer,2 Charles R. Anderson Jr,3 David W. Coltman4 and George Wittemyer1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J. M., A. B. Shafer, C. R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management, even in widely distributed panmictic species. 29 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch,a Joseph M. Northrup,b Megan F. McKenna,c Charles R. Anderson Jr,d Lisa Angeloni,a,e and George Wittemyera,b Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA b Department of Fish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA c Natural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, d Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA e Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA a Citation: Lynch, E., J. M. Northrup, M. F. McKenna, C. R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi:10.1093/beheco/aru158. While visual forms of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a form of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA Citation: Anderson, C. R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79th North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/km2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 30 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle1 · Mindy B. Rice2 · Charles R. Anderson2 · Chad Bishop2 · N. T. Hobbs3 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UK 2 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80524, CO, USA 1 Citation: Searle, K. R., M. B. Rice, C. R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029-8549; DOI 10.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP1 , CHARLES R. ANDERSON JR .2 and GEORGE WITTEMYER1 , 3 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers, mangers, and industry when attempting to mitigate habitat loss due to energy development. 31 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup1, Charles R. Anderson Jr2 and George Wittemyer1,3 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8–78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup,1 Charles R. Anderson Jr.,2 Mevin B. Hooten,3 and George Wittemyer4 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA 3 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 Department of Fish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Northrup, J. M., C. R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife–human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado, USA, an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 32 �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse, ,1,2 Charles R. Anderson Jr.,1 Mark E. Peterson,1,2 and David R. Collins1 1 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Stonehouse, K. F., C. R. Anderson Jr., M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly, these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The information presented here is based primarily on field experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/learn/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality; efforts discerning ultimate cause will require specific tissue sample collections, where possible, submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document, be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. 33 �Colorado Parks and Wildlife July 1, 2017 - June 30, 2018 WILDLIFE RESEARCH REPORT State of __________ C__o=Io__ra=d=o________ : .....P=ar:.:.:ks:-=a=nd=-a..Wa...::i=ld=li=fe;a....__ _ _ _ _ _ _ _ _ _ __ Cost Center 3430 : __M=a__m__m=a=I__s __R=e__ se=ar__c__h______________ Work Package 3001 : =D.....ee.....r__C __o__n=s__erva.. . .,.; .;a=ti__ on_ _ _ _ _ _ _ _ _ _ _ __ Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project: ________ W___-2___4___3___ -R3 __________ Period Covered: July 1, 2017 - June 30, 2018 Author: C. R. Anderson, Jr. Personnel: E. Bergman, D. Collins, B. deVergie, D. Finley, M. Fisher, L. Gepfert, D. Johnston, T. Knowles, B. Petch, J. Rivale, G. Samsill, E. Sawa, G. Smith, R. Velarde, S. Williams, L. Wolfe, CPW; L. Belmonte, BLM; T. Graham, Ranch Advisory Partners; P. Doherty, J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent preliminary and final results of a 10-year research project addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored deer on 4 winter range study areas representing varying levels of development to serve as treatment (North Magnolia, South Magnolia) and control (North Ridge, Ryan Gulch) sites. We recorded habitat use and movement patterns, estimated neonatal, overwinter fawn and annual adult female survival, estimated early and late winter body condition of adult females, and estimated annual abundance among study areas. During this research segment, we targeted 240 fawns (60/study area) and 120 does (30/study area) in early December 2017 for VHF and GPS radiocollar attachment, respectively, and adult female body condition assessment. We attempted recapture of 120 does (30/study area) and 40 fawns (20 in 2 study areas) in March 2018 for late winter body condition assessment. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinionjuniper/mountain shrub habitats in each of the 2 treatment areas with minor and extensive energy I �development, respectively. Based on final (migration, mule deer behavioral responses, reproductive success and neonate survival) and preliminary data analyses for this 10-year project: ( 1) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition (Dec fawn mass), annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from a 50% increase in North Ridge to a 103% increase in North Magnolia, however densities have stabilized recently in 3 of the 4 study areas and a recent decline in density was evident in North Ridge; (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (5) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) when drought conditions persisted during the third trimester of doe parturition. Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning, and final results addressing the interaction of mule deer behavioral and demographic factors associated with energy development activity have recently been submitted for scientific review and publication. Completion of this project, including final data collection, analyses and interpretation of results, is anticipated by fall/winter 2020. u 2 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTMTY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES I. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES I. Collect and reattach OPS collars to maintain sample sizes for addressing mule deer habitat use and behavioral patterns in 4 study areas experiencing varying levels of energy development in the Piceance Basin, northwest Colorado. 2. Estimate early and late winter body condition of adult female mule deer in each of the 4 winter herd segments using ultrasound techniques. Estimate early and late winter fawn weights in areas with and without habitat treatments to assess winter fawn condition relative to habitat improvements. 3. Monitor over-winter fawn and annual adult female mule deer survival by daily ground tracking and bi-weekly aerial tracking. 4. Conduct Mark-Resight helicopter surveys to estimate late winter mule deer abundance and density in each study area. 5. Monitor habitat treatment response for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 6. Complete investigations of neonate fawn survival and adult female parturition relationships relative to mule deer/energy development interactions. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the 3 �primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or introduction of noxious weeds, by fragmenting habitat with drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape conversion for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also covers some of the largest natural gas reserves in North America Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. I). Anderson and Freddy (2008) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the first 5 years of this study, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We recently completed monitoring 2 control areas: I with development (0.6 pads & facilities/km 2; Ryan Gulch) and I without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similar development intensities (South Magnolia, 0.9 well pads & facilities/km 2 and North Magnolia, 0.1 well pads & facilities/km 2), that also received habitat improvements (604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated through spring 2018 to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavioral patterns in relation to energy development activities in the area are being monitored to identify effective Best Management Practices (BMPs) for future energy development planning. This progress report describes the previous IO years (Jan 2008-June 2018) of mule deer population performance during the pre and post-treatment phases on 4 winter range herd segments, which includes monitoring habitat selection, migration and behavior patterns of adult female mule deer; parturition success; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter; and annual late-winter abundance/density estimates. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as home to one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edulis)-Utahjuniper (Juniperus osteosperma; pinionjuniper) shrubland complex ranging from 1,675 m to 2,285 m in elevation (Bartmann and Steinert 1981). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big 4 u �sagebrush, saltbush (Atriplex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum /ewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gilia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rain storms. Wintering mule deer population segments we are investigating include: North Ridge (53 km 2) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 141 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. 1). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.1 pads & facilities/knl), and relatively high development in the Ryan Gulch (0.6 pads & facilities/km 2) and South Magnolia (0.9 pads & facilities/km 2) segments (Fig. I). Development activity was high through 2011 and has declined substantially since natural gas prices began to decline in 2012. Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and relatively heavy (South Magnolia) energy development activities. METHODS Tasks addressed this period included mule deer capture and collaring, monitoring neonate, overwinter fawn and annual adult female survival, estimating adult female body condition during early and late winter using ultrasonography and winter fawn condition measuring early and late winter fawn weights, estimating mule deer abundance applying helicopter mark-resight surveys, and monitoring vegetation responses to habitat treatments completed spring 2013. We employed helicopter netgunning techniques (Barrett et al. 1982, van Reenen 1982) to target 240 fawns and 120 adult females during early December 2016, and 120 adult females and 40 fawns (primarily recaptures) during early March 2017. Once netted, all deer were hobbled and blind folded. Fawns were weighed and radiocollared, and sex was recorded prior to release at the capture site. Adult females were transported to localized handling sites for recording body measurements and fitted with GPS collars (5 fix attempts/day; 0211 OD, Advanced Telemetry Systems, Isanti, MN, USA) prior to release. To provide direct measures of decline in overwinter body condition, we targeted 30 adult females in each study area that were captured the previous December. During March, 20 fawns were recaptured, weighed and released in South Magnolia (in the habitat treatment areas) and Ryan Gulch (control area) to quantify overwinter declines in fawn body condition. Fawn collars were spliced and fitted with rubber surgical tubing to facilitate collar drop between mid-summer and autumn for winter fawns and during winter for neonates, and GPS collars were supplied with timed drop-off mechanisms scheduled to release early April of the year following deployment. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). 5 �Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed and recovered since 2008. GPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and IO control deer from North Ridge where location rates were programmed for every 30-60 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) recently analyzed resource selection data relative to energy development (Northrup 2015) and those results are addressed below. Mule deer resource selection analyses to address success of habitat improvements are pending until vegetation responses are fully realized, which will begin by fall 2018. Mule Deer Survival Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death (Stonehouse et al. 2016). We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier 1958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated survival rates from 1 July 2016 through 30 June 2017 for adult females, from birth to mid-December for neonates, and from early December 2016-mid June 2017 for winter fawns. Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (2001) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate% ingesta-free body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years evident in non-overlapping 95% confidence intervals. We considered differences in body condition meaningful when mean rump fat or % body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via blood samples, fetal counts using ultrasonagraphy, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 4 helicopter mark-resight surveys (2 observers and the pilot) during late March/early April to estimate deer abundance in all 4 study areas. We delineated each study area from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 2011 ). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confirmed adult female locations during surveys from GPS data acquired April 2017. We delineated flight paths in ArcGIS 10.0 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance mark-resight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. 6 �We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-normal model (McClintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2 = 0 or :;c 0). Model selection procedures followed the information-theoretic approach of Burnham and Anderson (2002). RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 237 fawns and 121 does during Dec 2017 and 103 does and 40 fawns during March 2018. Fifteen fawn mortalities (6.3%; proximate cause= 4 capture myopathy, 10 predation, 1 vehicle collision) occurred within the 10-day censorship period during December and 3 fawn mortalities (7.5%; 1 capture myopathy, I predation, 1 fence entanglement) occurred during the March capture. Doe mortalities totaled 2 (1.7%; capture myopathy) and 4 (3.9%; 3 capture myopathy, 1 predation) within IO days of the December and March capture periods, respectively. Mortality rates within 10 days post capture have typically varied between 2.5-3.5% for fawns and does since Jan 2008, except during the 2011-2012 capture season where myopathy rates were higher (3--6%) due to dry, warm conditions (Anderson and Bishop 2012) and 2016-17 (6.6% for Dec fawns) when reduced fawn condition may have enhanced coyote predation success. The relatively high myopathy rates (including ongoing predation that occurred within the 10-day censorship period) for early winter fawn captures during 2017-2018 were similar to last year with lighter December fawn weights and high coyote predation. Relatively higher fawn myopathy rates were expected during late winter captures and have ranged from 4.9 to 10.0% the past 3 years. Fawn survival from early December 2017 through mid-June 2018 was more variable among study areas than in past years ranging from 0.34 to 0. 76 (Table l, Fig. 2). Based on CI overlap, North Ridge and North Magnolia fawns exhibited higher survival than Ryan Gulch fawns, and North Ridge fawn survival was also higher than in South Magnolia (Table 1). The range in winter fawn survival was unusual in comparison to previous years (Fig. 2), but correlated closely with the variability observed in December fawn weights (Fig. 3); early winter fawn condition (as reflected in Dec fawn mass) likely contributes to over-winter survival potential, and reduced weights the past 2 years is likely related to the lower survival rates observed recently from Ryan Gulch and South Magnolia (Fig. 2, Fig. 3). Premature collar drop during 2008-09 and 2009- IO did not allow for winter fawn survival estimates past March, but survival rates among study areas were similar (P < 0.05) each year and comparable to 2011-12 and 2012-13 (excluding North Ridge) during 2008-09 and to the higher survival rates from 2013-14 and 2014-15 during 2009-10 (Fig. 2). General comparisons to previous years suggest moderate to high fawn survival occurred during most winters and study areas with the exception of winter 2010-2011 for 3 of the 4 study areas, North Ridge during winter 2012-13 and 2015-16, and Ryan Gulch and South Magnolia the past 2 years (Fig. 2). Low winter fawn survival (Fig. 2) may to correlate with summer forage condition assuming lower December fawn weights (Fig. 3) represent an index of summer fall forage conditions; severe winter conditions can also strongly influence winter fawn survival, but winter conditions during this study have been mild to moderate with the exception of winter 2010-11, which may have more strongly influenced winter fawn survival that year. Annual adult female survival varied from 0.73 (North Ridge) to 0.87 (South Magnolia; Table 1) during 2017-18, but was comparable among study areas and to previous years (P > 0.05), with the exception oflower survival in North Magnolia during 2011-12 (S = 0.68, Anderson and Bishop 2012). Relatively low sample sizes per study area for adult female survival do not allow statistical 7 �discrimination among years unless large differences are evident (e.g.,> 15-20%). Estimates below 80% are biologically concerning if these values represent the respective population, but low statistical power precludes confirmation within study areas. When combined among study areas, annual survival estimates have varied from 79% in 2012-13 to 86% in 2014-15, but consistent CI overlap including large sample sizes (exceeding 100 in July and 200 in Mar annually) supports consistent annual doe survival during this study. Adult female mule deer exhibit consistently high survival rates unless extreme weather events and/or habitat degradation persists, which has not been evident since 2008. Mule Deer Body Condition Early-winter body condition measurements of adult female mule deer during December 2017 were relatively low among study areas compared to previous years and Ryan Gulch does exhibited the lowest condition estimates among study areas (P < 0.05; Fig. 4, Table 2). Although fall body condition is likely related to spring/summer forage conditions, doe condition is also influenced by energy expended for fawn rearing, and appears to be strongly influenced by lactation status; I observed a strong correlation between December lactation rate and body condition (mm rump fat; P = 0.004, r = 0.62, n = 20). Thus, while fall body condition represents an index of nutritional status entering winter, it also appears to be a useful metric to assess fall reproductive status, where low fat levels represent high fall fawning rates; the low December fat levels observed from Ryan Gulch does during 2017 was associated with highest fall lactation rate (0.63) recorded during the study. In contrast, late winter condition appears more strongly related to winter severity and winter range forage conditions and low fat levels observed during December do not necessarily manifest into poor late winter condition (Fig. 4, Table 2). Late winter doe condition among study areas this past winter was comparable to the long term average (Fig. 4), and was reflective of mild to moderate winter conditions consisting of infrequent snow storms and minimal snow pack on winter range. December 2017 fawn weights by study area represented a gradient in fawn condition ranging from low to high for Ryan Gulch and North Ridge, respectively (Fig. 3), which corresponded to winter fawn survival (Fig. 2). Overall fawn condition for 3 of 4 study areas (excluding North Ridge) has declined the past 2 years (Fig. 3) and may be related to changes in summer forage conditions (further analyses pending). Because adult female body condition has been largely uninformative in regards to habitat treatment responses (pending further analyses), we began late winter fawn recaptures in South Magnolia (habitat treatment area) and Ryan Gulch (reference area) to assess changes in over-winter condition. Weight loss during winter 2015-16 was significantly less (P < 0.001) for fawns from the area receiving habitat treatments than for fawns from the untreated area, but no net weight loss was detected during the following 2 winters for either study area (P ~ 0.396), suggesting strong annual effects. Vegetation measurements from treatment and control sites indicate recent summer/fall use of shrubs potentially negating forage benefits on winter range. Additional investigations to address this issue will be conducted summer/fall 2018 to confirm summer/fall use of treatment sites and whether or not intended forage benefits on habitat treatment sites persist on winter range. Mule Deer Population Estimates Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and variable individual sightability (cr2 = 0) for North Magnolia deer and homogenous sightability (cr2 f. 0) for the other 3 areas. During 2018 North Ridge exhibited the highest deer density ( l 5.8/km2), with comparable but lower deer densities in the other 3 areas (9.2-l l .3/k.m2; Table 3, Fig. 5). Abundance estimates from 2018 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.12-0.17 (Table 3). Densities increased over the first 8- 8 u �year monitoring period in all study areas ranging from an estimated 50% increase in North Ridge to a 103% increase in North Magnolia (mean estimated increase across study areas= 78%); North Ridge deer appeared to decline during 2012 and 2013, but subsequently increased, while the other 3 areas exhibited consistent and similar rates ofincrease from 2009-2016 (mean annual increase= 0.064; Fig. 5). Excluding the North Ridge study area, late winter mule deer densities have apparently stabilized since 2016 (Fig. 5). The reason for decline since 2016 for the North Ridge deer population is unclear and not completely explained by demographic parameters monitored during the study. Erratic population estimates observed from North Ridge may be partially attributed to lack of geographic closure more commonly associated with this study area (primarily from earlier spring migration timing). Population vital rates will be analyzed and compared to abundance estimates to assess factors contributing to population change by study area. Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has been completed. Four manuscripts from this effort have been published (Lendrum et al. 2012, Lendrum et al. 2013, Lendrum et al. 2014, Anderson and Bishop 2014; Appendix A). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 6) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript Lendrum et al. (2013) addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures, snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, with median migration durations of 3-8 days among the 4 areas (straight line distance between seasonal ranges averaged 32-40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in long-term fitness consequences. Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication Lendrum et al. (2014), monitored migratory mule deer in the Piceance Basin to examined the relationship between the Normalized Difference Vegetation Index (NOVI), which is a course-scale measure of forage quality using a GIS assessment of vegetation greenness, and fecal nitrogen to assess the assumption that forage quality and deer diets can be reasonably linked to address deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were informative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NDVI values, and progressed to summer range as vegetation green-up and nitrogen levels increased, but ahead of peak vegetation green-up on summer range. I suspect this rapid migration strategy is evident for deer in relatively good condition and 9 �allows for early arrival on summer range to take advantage optimal forage conditions prior to parturition. Anderson and Bishop (2014) summarized results from Lendrum et al. (2012, 2013) and Sawyer et al. (2012) addressing migratory mule deer and energy development in northwest Colorado and south-central Wyoming, respectively. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (>2 well pads/km2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by increasing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. Mule Deer Behavioral Response to Energy Development u We completed evaluations of deer behavior patterns in relation to energy development activities (Northrup et al. 2015). We found diurnal responses to development activity, where deer used timbered areas away from development activity while bedded during the day and moved into more open areas generally closer to developed areas while foraging at night. Disturbance distances from producing pads and roads declined from 600 m to 200 m and about 140 m to 60 m from daytime to nighttime, respectively, but increased from 600 m to 800 m for nighttime drilling pad activity (pad response depicted in Fig. 7). We suspect deer behaviorally respond to fluctuations in development activity, where road traffic and producing well pad activity decline at night, but drilling pad disturbance may increase from compressors and lights used to facilitate nighttime drilling activity. These evaluations were applied during an active drilling phase in the Piceance Basin and deer use was influenced by development activity in 25% (nighttime) to 50% (day time) of critical winter range during that period. However, deer densities have comparably increased among developed and undeveloped study areas (excluding North Ridge; Fig. 5) suggesting that deer can behaviorally mediate development disturbance under observed development and deer densities by taking advantage of fluctuations in development activity to address their nutritional requirements. Given the plasticity in deer behavior, a number of potential options for future development planning exits including drilling schedule modifications (seasonal and/or diurnal), concentrated/staged development, reducing road traffic, and using light/noise barriers around drill rigs. It will be interesting to determine if habitat improvements will further reduce development disturbance and increase management options for future development planning. Reproductive Success and Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement 10 V �to investigate reproductive success (Peterson et al. 2017), including pregnancy rates (early Mar) and fetal survival (Mar until birth), and early fawn survival (0- 6 months; Peterson et al. 2018) in developed and relatively undeveloped landscapes beginning spring 2012 and continuing through Dec 2014. We applied statistical models to address reproductive success under contrasting energy development scenarios and noted that pregnancy and in utero fetal rates (early Mar; n = 346) were high (0.948, SE= 0.012 and 1.877, SE = 0.029, respectively) and statistically indistinguishable between study areas. Fetal survival (n = 383), however, was lower (P < 0.05) in the developed study area during 1 of 3 years (2012; Fig. 8) when drought conditions were present, suggesting the combination of severe weather conditions and development activity under observed conditions may influence fetal survival. There was no apparent influence from energy development in 0-6 month fawn survival (n = 184) based on similar mortality rates between study areas; mean daily mortality probabilities from predation, malnutrition and unknown causes were nearly identical (Fig. 9). These results suggest that natural gas development did not exert measureable influence on mule deer pregnancy rates, fetal rates or early fawn survival, but may have negatively influenced fetal survival during 2012 when does were exposed to drought conditions during the third trimester. These findings are consistent with developed areas in a production phase (little to no drilling activity) exhibiting moderate pad densities (0.4--0.9 pads/km2), and relationships may differ in areas of higher pad densities and/or drilling activity. Magnolia Habitat Treatments We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 2011; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA), 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop, chain) in January 2012 (Stephens 2014), and 1,038 acres of hydro-ax treatments in April 2013 (Determination ofNEPA Adequacy: DOI-BLM-CO-110-2012-0134- DNA), totaling 604 treated acres in each study area (Fig. 10). Vegetation response in the pilot treatment sites was visually evident by fall 201 I (Fig. 10), and resulted in statistically significant (P < 0.05) increases in native grass and forb cover by the 2014 growing season. Final results are pending, but shrub responses appear promising from data collected through spring 2018. Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3-fold increase in grasses, forbs, and shrubs combined after 2 growing seasons (versus control sites), but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following I growing season and shrub responses have been notable during the 4th growing season, but statistical comparisons are still pending. As anticipated, grass and forb responses were evident 2 to 3 years posttreatment, with longer tenn response expected (3-5 years) for palatable shrubs. Ofnote, relatively high moisture conditions experienced during spring 2014 and 2015 resulted in higher than normal prevalence of cheatgrass (Bromus tectorum); cheatgrass invasion has previously been minor to non-existent in this area. Cheatgrass invasion, however, does not appear directly related to treatment sites because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. GPS data addressing deer use of treatment sites has been collected through April 2018, with collars from the Dec 2107 - May 2018 sample (n = I 06) still on deer in the field. The remaining collars will be collected during the final capture effort in March 2019. The final spring vegetation response measurements for habitat treatment and control areas were collected the past spring and final shrub response data will be collected Sep. 20 I 8. Final data analyses will be initiated once OPS collars are collected in March. Thus far, we observed improved fawn condition (P < 0.00 I) in South Magnolia following the 4th growing season of habitat treatments when compared to fawn 11 �condition in the Ryan Gulch control area, but we did not detect a response the following 2 winters. Ongoing data analyses suggests that fall shrub condition appears to have declined recently indicating that summer/fall shrub use may be increasing and potentially inhibiting the intended benefit of habitat treatments on winter range. We deployed remote cameras on treatment and control areas this summer and will continue monitoring into the fall to further address summer/fall use and identify species utilizing treatment sites on mule deer winter range. Although results are preliminary, vegetation responses through the first 4 years post treatment provided the intended forage benefit and there is some evidence that fawn condition improved as a result. Recent changes in habitat use by multiple species (potentially including wild horses and livestock) may have reduced winter forage benefits recently, but additional data collection and analyses will be necessary for confirmation. Analyses of doe use of treatment sites throughout the study are still pending and will provide information addressing the utility of habitat improvement projects as a mitigation technique to offset energy development disturbance on mule deer winter range. V SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 10-year study period, with the final year of data collection for some parameters (i.e., habitat treatment response and adult female habitat use) remaining. The pretreatment period was completed by spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of2 study areas were completed by April of 2013, and subsequent vegetation responses have met or exceeded expectations through 2016. The post-treatment monitoring period was completed June 2018, with the final year of habitat use and habitat treatment response data collection still pending. Based on final (migration, mule deer behavioral responses, reproductive success and neonate survival) and preliminary data analyses for this 10-year project: ( 1) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to 103% in North Magnolia, but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge; (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (5) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) when drought conditions persisted during the third trimester of doe parturition. Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning, and final results addressing the interaction of mule deer behavioral and demographic factors associated with energy development activity have recently been submitted for scientific review and publication. Completion of this project, including final data collection, analyses and interpretation of results, is anticipated by fall/winter 2020. 12 u �Hay field improvements were completed during 2012 in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; rapid and continued elk (Cervus elaphus) use of these areas was evident, but mule deer response has been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Collaborative research with agency biologists, graduate students, and university professors has produced I 8 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011), mule deer migration (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013), mule deer response to helicopter capture and handling (Northrup et al. 2014a), potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014), mule deer genetics in relation to body condition and migration (Northrup et al. 2014b), spatial and temporal factors influencing auditory vigilance in mule deer (Lynch et al. 2014), the relationship of plant phenology with mule deer body condition (Seral et al. 2015), the influence ofindividual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman et at. 20 I 8), and mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 20 I 5, 2016a, 2016b, Peterson et al. 2017, 2018); these publications are summarized in Appendix A. We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. 13 �LITERATURE CITED Anderson, C.R., Jr., and D. J. Freddy. 2008. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2011. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2012. 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Thesis, Colorado State University, Fort Collins, Colorado, USA. 14 �Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.org/ I0. Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: IO. l 371/journal.pone.0064548 Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/10.1016/j.mambio.2014.05.005 Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angelor:ii, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: 10.1093/beheco/aru 158. McClintock, B. T., G. C. White, K. P. Burnham, and M. A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271- 289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Northrup, J. M. 2015. Behavioral response of mule deer to natural gas development in the Piceance Basin. Dissertation, Colorado State University, Fort Collins, USA. Northrup, J.M., M. 8. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7):1456-1463. Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731738; DOI: IO. I 002/jwmg.705 Northrup, J.M., A. B. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer.2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037. Northrup, J.M., C.R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:2746-2757 Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: IO.Ill I/ddi.12417 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: I0.1111/wlb.00341 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82: 1135-1148, DOI: 10.1002/jwmg.21476 Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Sawyer, H., M. J. Kauffman, A. D. Middleton, T. A. Morrison, R. M. Nielson, and T. B. Wycoff. 2012. A framework for understanding semi-permeable barrier effects on migratory ungulates. Journal of Applied Ecology 50:68-78. Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549; DOI 10.1007/s00442-015-3348-9 Stephens, G. J. 2014. Understory responses to mechanical removal of pinyon-juniper overstory. MS Thesis, Colorado State University, Ft. Collins USA. 15 �Stephenson, T. R., V. C. Bleich, B. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J.C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. White, G. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader 16 �Table 1. Survival rate estimates (S) of fawn (1 Dec. 2017-15 June 2018) and adult female (1 July 201730 June 2018) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe sample8 (n) S (95% Cl) Fawns Ryan Gulch 53 0.335 (0.207-0.464) South Magnolia 53 0.479 (0.343-0.616) North Magnolia 57 0.665 (0.543-0.788) North Ridge 58 0.757 (0.645-0.868) Adult females Ryan Gulch 27 52 0.832 (0.722-0.942) South Magnolia 32 56 0.869 (0. 757-0.980) North Magnolia 27 49 0.781 (0.650---0.913) North Ridge 24 45 0. 731 (0.583-0.878) aAdult female sample sizes following capture and radio-collaring efforts March, 2017. 17 �Table 2. Mean rump fat (mm) and % ingesta-free body fat' (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2018. Values in parentheses= SD. March 2009 Study Area Rump fat Ryan Gulch %fat December 2009 March 2010 December 20 I0 Rump fat %fat Rump fat % fat Rump fat %fat 1.73 (1.78) 7.08 (1.27) 8.35 (6.36) 10.54 (3.72) 2.31 (1.44) 6.37 (1.41) 7.26 (6.36) 9.69 (3.56) South Magnolia 1.29 (0.47) 6.74 (2.27) IO.OS (6.19) 11.44 (3.50) 3.12 (2.20) 7.11 (1.69) 9.85 (6.78) 11.27 (3.75) North Magnolia 1.31 (1.01) 7.15 (1.63) 10.67 (5.76) 11.94 (3.39) 3.15 (2.34) 7.54 (1.53) 9.55 (6.49) 10. 79 (4.26) North Ridge 1.57 (1.22) 6.81 (1.68) 5.25 (5.65) 1.77 (1.11) 6.39 (1.45) 7.25 (5.41) 9.85 (3.02) 9.37 (3.08) Table 2. Continued. March 2011 Study Area Rump fat %fat Ryan Gulch 1.55 (0.60) 6.72 (1.37) South Magnolia December 2011 Rump fat %fat March 2012 December 2012 Rump fat %fat Rump fat %fat 13.41 (6.39) 13.17 (3.64) 2.15 (1.44) 7.22 (1.16) 6.34 (4.35) 9.34 (2.43) 1.65 (0.75) 6.15 (1.75) 8.18 (5.45) 10.34 (3.28) 1.66 (0.77) 7.03 (1.13) 8.30 (5.71) I 0.32 (3.23) North Magnolia 1.65 (0.67) 6.79 (1.47) 8.76 (5.76) 10. 73 (3. I4) 1.90 (0.76) 7.61 (0.96) 9.66 (6.41) I 1.18 (3.64) North Ridge 1.45 (0.76) 6.30 (1.65) 8.86 (5.65) 10. 77 (3.33) 2.24 (1.58) 7.26 (1.05) 5.76 (4.10) 9.06 (2.31) 15 C ( C �( ( (_ Table 2. Continued. March 2013 December 2013 March 2014 December 2014 %fat 9.27 (6.29) 10.61 (3.76) 1.69 (0.85) 7.03 (0.99) 8.50 (6.76) 10.56 (3.70) 2.06 (0.77) 7.19 (0.66) 11.27 (8.40) 11.40 (4.16) 2.57 (1.61) 7.75 (0.68) 10.96 (6.82) 11.98 (3.8 I) North Magnolia 1.76 (0.91) 6.87 (1.11) 9.00 (6.15) 10.48 (3.25) 2.33 (2.12) 7.31 (1.43) 9.52 (5.83) 11.18 (3.32) North Ridge 1.87 (0.73) 6.70 (1.12) 11.17 (5.28) 11.66 (2.69) 2.38 (1.52) 7. 16 (1.14) 7.93 (5.50) Rump fat %fat Ryan Gulch 1.87 (0.90) 7.14 (0.89) South Magnolia Rump fat % fat Rump fat %fat Rump fat Study Area 10.20 (3.01) Table 2. Continued. March 2015 December 2015 March 2016 December 2016 Rump fat %fat Rump fat %fat 12.80 (6.83) 12.89 (3.72) 2.29 (0.64) 7.29 (0.52) 8.20 (4.90) 10.46 (2. 70) 9.83 (2.69) 2.07 (1.39) 7.46 (0.93) 6.27 (4.62) 9.37 (2.53) 2.25 (0.97) 7.49 (0.90) 8.79 (6.01) 10.81 (3.54) 2.43 (1.01) 7.17 (0.87) 7.90 (5.52) 10.34 (3.14) 2.28 (1.37) 7.43 (1.05) 5.47 (5.49) 1.58 (0.70) 6.73 (1.26) 7.74 (5.48) 10.01 (3.09) Study Area Rump fat %fat Rump fat Ryan Gulch 2.62 (0.95) 7.44 (0.53) South Magnolia 2.66 (1.36) 7.62 (0.74) 6.93 (4.83) North Magnolia North Ridge %fat 9.35 (2.75) 16 �Table 2. Continued. March 2017 December 2017 March 2018 Study Area Rump fat % fat Rump fat %fat Rump fat % fat Ryan Gulch 2.39 (0.74) 6.78 (0.97) 4.47 (3.57) 8.62 (1.80) 2.13 (0.76) 7.40 (0.50) South Magnolia 2.48 (0.77) 7.09 (0.63) 6.67 (5.23) 9.56 (2.73) 2.19 (1.18) 7.40 (0.72) North Magnolia 1.82 (0.72) 7.05 (0.58) 6.16 (4.32) 9.23 (2.47) 1.87 (0.63) 7.15(1.11) North Ridge 2.30 (1.37) 7.23 (1.21) 6.60 (4.29) 9.38 (2.35) 2.35 (0.80) 7.73 (1.03) 8 lngesta-free body fat calculated following Cook et al. (2009). 17 ( ( C �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado. 26-31 March 2018. Data represent 4 helicopter resight surveys from 3 of 4 study areas. with South Magnolia receiving 5 surveys. Study area Mean No. sighted Mean No. marked N(95% Cl) Density (deer/km 2 ) Ryan Gulch 418 19 1.397 ( 1.186-1.674) 9.9 South Magnolia 239 26 764 (678-874) 8.1 North Magnolia 319 27 899 (774-1.070) 9.8 North Ridge 305 32 838 (748-954) 15.8 18 �,,. Mule Deer Winter Range Study Areas Mule deer study areas ' D r1onh l.!agnol1a Well Pads & Facilities .: Jn development ; PrOductng well _ Deve1opment Jae111J,es Sou1n Magnolia 25 10 ,.111.i:s Figure I. Mule deer winter range study areas relative to active natural gas wel l pads and energy development facilities in the Piceance Basin of northwest Colorado. winter 2013/ 14 (Accessed http://cogcc.state.eo.us/ Dec.31.2013). Development activity has subsided with minimal drilling activity since 2013. 19 �Winter fawn survival 2010-11 - 2017-18 1.00 tr 0.90 1 0.80 - 1-- - 0.40 - 0.30 - - 0.20 - - 0.10 - - - 0.70 0.60 0.50 - - - a T 1 T T i-- t-- i - - -- - - - - ,_ - - - - - - - - - - □ Ryan Gulch □ South Magnolia El North Magnoha 0.00 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Figure 2. Over-winter (Dec-June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin. no11hwest Colorado. 20 I0-11 - 20 17-18. E1rnr bars = 95% Cl. Fa,vn survival estimates past late March unavailable for winter 2008-09 and 2009-10 due to premature collar drop. but survival estimates were comparable to 20 11-1 2 and 20 12- 13 (excluding 2012- 13 North Ridge) during 2008-09 and to the higher survival rates from 20 13-1 4 and 2014- 15 during 2009-10. 20 �Male faw n w eights 42.0 40.0 r - 30.0 - - - '- - - - '- - - - - '- - - '- - -, DRvan Gulch D South Magno!ii) - c......: - ■North MagnoliJ '- - D Nort h Ridge '- 28.0 Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Female fawn weights 42.0 40.0 co 38.0 D Rvan Gulch ... :::. 36.0 ..c: D South M agnolia -~ 34.0 3: ■North Magnolia 32.0 D NcrthRidge 30.0 28.0 Dec 2008 Dec 2009 Dec 2010 Dec Dec Dec Dec Dec Dec Dec 2011 2012 2013 2014 2015 2016 2017 Figure 3. Mean male and female fawn weights and 95% C I (error bars) from 4 mule deer study areas in the Piceance Basin. 11011hwest Colorado. December 2008- 20 I 7. 21 �Early winter rump fat (mm) 16 I 14 12 10 8 6 I 4 2 0 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2016 Dec 2017 ■ North Ridge ■ North Magnolia Ryan Gu lch ■ South Magnolia Late winter rump fat (mm) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Mar 2009 Mar 2010 Mar 2011 Mar 2012 Mar 2013 Mar 2014 Mar 2015 Mar 2016 Mar 2017 Mar 2018 ■ North Ridge ■ North M agnolia Ryan Gu lch ■ South Magnolia Figure 4. M ean early (early Dec .. Top) and late winter (early Mar.. Bollom) body conditi on (mm rump fat) of adult female mule deer from 4 w inter range study areas in the Piceance Basin of northwest Colorado. March 2009- March 20 18. E1Tor bars = 95% CI. �Piceance Basin late winter mule deer density 35.00 30.00 25.00 l":::- 20.00 - t 15.00 C - North Ridge • • • ••• Ryan Gulch - • North Magnolia - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year ---• - ---•···-- -------------------------' Figure 5. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2018. Estimates for were adjusted upward (using GPS migration data) to account for early migration from winter range prior to and during surveys during years when early migration biased estimates low (North Ridge 2014-2017, North Magnolia 2015 and 2017, Ryan Gulch 2017). u 24 ------------ �Colorado Figure 6. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adul t female mule deer (n = 52; Lower left), and acti ve natural-gas well pads (black dots) and roads (state, county, and natural-gas; white lines) from May 2009 ( Lower right; from Lendrum et al. 20 12). 25 �I ::·+----~ ---+----+ --- -f - - - r---+----+ 8 'a' C") I I I I I I I I I Prod 400 Prod 600 Prod 800 Prod 1000 Drlll 400 Drlll 600 Drill 800 Drlll 1000 Covariates j +---+----+----+---- ------ -----t----+ O i, 1J e 8 'a' C") I Prod 400 Prod 600 Prod 800 Drlll 400 Covariates Prod 1000 Drill 600 Drill 800 Drill 1000 Figure 7. Posterior distributions of population-level coefficients related to natural gas development for RSF models during the (a) day and (b) night for 53 adult female mule deer in the Piceance Basin, Northwest Colorado. Dashed line indicates 0 selection or avoidance of the habitat features. 'Drill' and 'Prod' refer to well pads where there was active drilling or producing pads, respectively. The numbers following 'Drill' or 'Prod' represent the concentric buffer over which the number of well pads was calculated (e.g., 'Drill 600' is the number of well pads with active drilling between 400-600 m from the deer location; from Northup et al. 20 I 5). 26 �,......_ 1.00 Q) - - - '-- 0.80 rt iii .... ro> 0.60 -~ :l Cl) roQ) 0.40 LL 0.20 0.00 2012 2013 2014 Year o High development o Low development I Figure 8. Model-averaged estimates of fetal survival (± 95% Cl) of mule deer fetuses from early March until birth (late May- June) in high and low energy development study areas of the Piceance Basin. northwest Colorado. 2012-20 14 ( from Peterson et al. 2017). ?;' 0.016 iii .:: 0 E 0 0.012 &' 1i -gro 0.008 ls. .z:. ·a; 0 0.004 High development Low development development Low development development Low development 2012 2012 2013 2013 2014 2014 High High Study area □ Predation □ Malnutrition ■ Unknown mortality Figure 9. Mean daily probability of death by predation. malnutrition. or unknown mortality(± 95% Cl) of mule deer fawns (0 to 6 months old) in high and lovv energy development study areas of the Piceance Basin Colorado. 2012-20 14 (from Peterson et al. 2018). 27 �North M agnoha treal~ment sites (587 ac,esl Bear~et_ 15_35b_JndG Bea,~ t_ I _Band~ _E Bear5et_36_54ana., G 1 ~ J $~\l't00dSet_ g 16_g19 •~1easewoocSe1_g I _g :s G,ea~ewoodSe:_g30_g4 2 LaeOvers,ryhts_a_rand I~- 17 Mecriamc31tre 1trrent compari son t5J acres.) Figure I 0. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin. northwest Colorado (Top: cyan polygons completed Jan. 20 I I using hydro-axe: yellow pol ygons completed Jan. 20 l 2 using hydro-axe. roller-chop. and chaining: and remaining polygons completed April 2013 using hydro-axe). January 20 11 hydro-axe treatment-site photos from N011h Hatch Gulch during April (Lower left. aerial view) and October. 201 1 (Lower ri ght. ground view). 25 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP CHARLES R. ANDERSON Jr. 1, DANIEL P. WALSH ERIC J. BERGMAN 1, PETER KUECHLE and JOHN ROTH2 1 Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA 2 Advanced Telemetry Systems, Isanti, Minnesota 55040 USA 2 1 , 1, , Citation: Bishop, C. J., C. R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8):1797-1806; DOI: 10.1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expelled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability ofa VIT being retained until parturition was 0.766 (SE= 0.0605) and the probability of a VIT being retained to within 3 days of parturition was 0.894 (SE = 0.0441 ). In a similar study using the original VIT wings (Bishop et al. 2007), the probability of a VIT being retained until parturition was 0.447 (SE= 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE= 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE== 0.0765) and VlT retention to within 3 days of parturition by 0.271 (SE== 0.0634). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least l neonate was 0.952 (SE= 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer.© 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM 1, CHARLES R. ANDERSON JR. 2, RYAN A. LONG 1, JOHN G. KIE 1, AND R. TERRY BOWYER 1 1 Department ofBiological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2colorado Parks and Wildlife, Grand Junction, Colorado 81505 USA Citation: Lendrum, P. E., C.R. Anderson Jr., R. A. Long, J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http://dx.doi.org/10.1890/ES 12-00165.1 Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoi/eus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to detennine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 26 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum1, Charles R. Anderson Jr. 2, Kevin L Monteith 1.l, Jonathan A. Jenks.., R. Terry Bowyer 1 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: 10.l371~oumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoi/eus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-2010. Condusions/Signijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP 1, MEVIN B. HOOTEN 1.J.J, CHARLES R. ANDERSON JR..., AND GEORGE WITIEMYER 1 1 Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., M. B. Hooten, C. R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7):1456-1463. http:l/dx.doi.org/l0.1890/12-1688.1 Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a us~availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most us~availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 27 u �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR2, AND GEORGE WITTEMYER 1 1Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J.M., C.R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal ofWildlife Management 78(4):731-738~ DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71 % of deer returning in the first day, and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 2014 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns offorage quality Patrick E. Lendrum•, Charles R. Anderson Jr. h, Kevin L Monteithr, Jonathan A. Jenbd, R. Terry Bowyer• • Department ofBiological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA b Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81 SOS, USA c Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, 3166, 1000 East University Avenue, Laramie 82071, USA d Department of Natural Resource Management, South Dakota State University, Box 2140B, Brookings 57007, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement ofa rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/lO. l016/j.mambio.2014.0S.00S ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Nonnalized Difference Vegetation Index; NOVI) in spring 2010 in the Piceance Basin of northwestern Colorado, USA. NOVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NDVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NOVI and fecal nitrogen. Moreover, these deer "jumped" rather than "surfed" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation, and malnutrition during migration. 28 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition V ERIC D. FREEMAN', RANDY T. LARSEN', MARKE. PETERSON1, CHARLES R. ANDERSON JR.3, KENT R. HERSEY', AND BROCK R. McMILLAN' 1 Depanment of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA 2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81505, USA 4 Utah Division of Wildlife Resources, 1594 W North Temple, Salt Lake City, UT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing ofpanurition. Wildlife Society Bulletin~ DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males/100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6%and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= 1.258; SE= 1.672; 1 = 0.752; P = 0.454), or synchrony of parturition (F = 1.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected. © 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup,' Aaron B. A. Shafer,1 Charles R. Anderson Jr,3 David W. Coltman" and George Wittemyer 1 I Department offish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Depanment of Biological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J.M., A. B. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571 ~ doi: 10.1111/eva.12 l 89 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management, even in widely distributed panmictic species. 29 V �,~ Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch: Joseph M. Northrup,b Megan F. McKenna,r Charles R. Anderson Jr,d Lisa Angeloni,~ and George Wittemye.-a.b 'Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA ti>epartment of Fish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA ~atural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, dMammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA COepartment of Biology, Colorado State University. 1878 Campus Delivery, Fort Collins, CO 80523, USA Citation: Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology~ doi: I0.1093/beheco/aru 158. While visual forms of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a form of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA Citation: Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79lh North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importWlce for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (>2 well pads/km 2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 30 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle 1 • Mindy B. Rice2 • Charles R. Anderson 2 • Chad Bishop2 • N. T. Hobbs3 1 NERC Centre for Ecology and Hydrology. Bush Estate, Penicuik EH26 0QB. UK Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins. CO 80526, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University. Fort Collins 80524, CO, USA 2 Citation: Searle. K. R.. M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition ofa large mobile herbivore. Oecologia ISSN 0029-8549; OOl 10.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR.,2 and GEORGE WITTEMYER 1 • 3 1Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2Mammals Research Section. Colorado Parks and Wildlife, Fort Collins, CO, USA 3Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup. J.M., C.R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers, mangers, and industry when attempting to mitigate habitat loss due to energy development. 31 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup•, Charles R. Anderson Jr and George Wittemyer1.l 1Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J.M., C. R Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: I0. I I I l/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8-78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup, 1 Charles R. Anderson Jr.,2 Mevin 8. Hooten,3 and George Wittemyel" 'Department ofFish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA 3 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department ofFish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 0epartment ofFish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Northrup, J.M., C.R. Anderson, Jr., M. 8. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado, USA, an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 32 �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse,•.z Charles R. Anderson Jr., 1 Mark E. Peterson, 1.2 and David R. Collins1 'Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly, these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The information presented here is based primarily on field experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/leam/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality; efforts discerning ultimate cause will require specific tissue sample collections, where possible, submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document, be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson,' Charles R. Anderson Jr.,2 Joseph M. Northrup•, and Paul F. Doherty Jr. 1 1Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: IO. Ill l/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat, particularly for ungulate species. Understanding how this development impacts reproductive success metrics that are influential for ungulate population dynamics is important to guide management of ungulates. However, the influences of natural gas development on reproductive success metrics of mule deer Odocoileus hemionus have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceance Basin, northwest Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utero fetal rates were high and statistically indistinguishable between study areas. Fetal survival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival, particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning, such as implementing habitat treatments to improve forage availability and quality, minimizing disturbance to hiding and foraging habitat particularly during parturition, and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 33 �Colorado Parks and Wildlife July I, 2018-June 30, 2019 WILDLIFE RESEARCH REPORT State of ______C=o=l=o=ra=d=o_ _ _ _ _: !..P.!!!ar~ks~an~d~W~il~d~li~~e:..,___ _ _ _ _ _ _ _ _ __ Cost Center 3430 : .uM.!!am~m.:.!:a~l~s..!.:R~e~se~ar:::..:c=:.:.h:.. .__ _ _ _ _ _ _ _ _ __ Work Package 3001 : .::::D:..:::ee:::r:..-C~o=n~s~e::..;rv~a=ti=on=-=--------------Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:. ____W__-=-24"""""3'---"--"R3=-----Period Covered: July I, 2018-June 30, 2019 Author: C. R. Anderson, Jr. Personnel: E. Bergman, D. Bilyeu-Johnston, G. Cahal, D. Collins, B. deVergie, D. Finley, M. Fisher, L. Gepfert, T. Knowles, B. Petch, J. Rivale, E. Sawa, Z. Swennes, M. Way, L. Wolfe, CPW; L. Belmonte, BLM; T. Graham, Ranch Advisory Partners; P. Doherty Jr., J. Northrup, M. Peterson, G. Wittemyer, K. Wilson, Colorado State University; R. Swisher, S. Swisher, Quicksilver Air, Inc.; D. Felix, Olathe Spray Service, Inc.; L. Coulter, Coulter Aviation; H. Sawyer, Western Ecosystems Technology, Inc. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the authors. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT ~- We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent preliminary and final results of a I0-year research project addressing habitat improvements and evaluation of energy development practices intended to improve mule deer fitness in areas exposed to extensive energy development. We monitored deer on 4 winter range study areas representing relatively high (Ryan Gulch, South Magnolia) and low (North Magnolia, North Ridge) levels of development activity to address factors influencing deer behavior and demographics and to evaluate success of habitat treatments as a mitigation option. We recorded habitat use and movement patterns, estimated annual neonatal, overwinter fawn and annual adult female survival, estimated annual early and late winter body condition of adult females, and estimated annual abundance among study areas. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 treatment areas with minor (North Magnolia) and extensive (South Magnolia) energy development, respectively. During this research segment, we removed store-on-board GPS collars from adult female mule deer, addressed �mule deer winter concentration areas during a post-drilling production phase, measured vegetation response of habitat treatment sites and established camera grids to address summer/fall use of habitat treatments. Based on final (migration, mule deer behavioral responses, reproductive success and neonate survival) and preliminary data analyses for this IO-year project: (I) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to I03% in North Magnolia, but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge; (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (5) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) when drought conditions persisted during the third trimester of doe parturition. Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning, and final results addressing the interaction of mule deer behavioral and demographic factors associated with energy development activity have recently been submitted for scientific peer-review and publication. Final data collection addressing GPS collar recovery and summer/fall use of habitat treatment sites will be completed by December 2019. Completion of this project, including data analyses and interpretation of results, is anticipated by fall/winter 2020-21. 2 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTMTY AND HABITAT DEGRADATION CHARLESR.ANDERSON,JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES I. Recover remaining GPS collars to address the final year of adult female mule deer habitat use and behavioral patterns in 4 study areas experiencing varying levels of energy development and response to habitat treatments as mitigation in the Piceance Basin, northwest Colorado. 2. Develop winter utilization distributions using mule deer GPS data to inform future development planning for the Piceance Basin mule deer winter range. 3. Monitor vegetation responses from habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 4. Continue to evaluate large herbivore use of habitat treatments during summer/fall using remote camera sampling. INTRODUCTION Extraction of natural gas from areas throughout western Colorado has raised concerns among many public stakeholders and Colorado Parks and Wildlife (CPW) that the cumulative impacts associated with this intense industrialization will dramatically and negatively affect the wildlife resources of the region. Concern is especially high for mule deer due to their recreational and economic importance as a principal game species and their ecological importance as one of the primary herbivores of the Colorado Plateau Ecoregion. Extraction of natural gas will directly affect the potential suitability of the landscape used by mule deer through conversion of native habitat vegetation with drill pads, roads, or introduction of noxious weeds, by fragmenting habitat with drill pads and roads, by increasing noise levels via compressor stations and vehicle traffic, and by increasing the year-round presence of human activities. Extraction will indirectly affect deer by increasing the human work-force population of the region resulting in the need for additional landscape conversion for human housing, supporting businesses, and upgraded road/transportation infrastructure. Additionally, increased traffic on rural roads will raise the potential for vehicle-animal collisions. Thus, research documenting these relationships and evaluating the most effective strategies for minimizing and mitigating these activities will greatly enhance future management efforts to sustain mule deer populations for future recreational and ecological values. 3 �The Piceance Basin in northwest Colorado contains one of the largest migratory mule deer populations in North America and also covers some of the largest natural gas reserves in North America. Projected energy development throughout northwest Colorado within the next 20 years is expected to reach about 15,000 wells, many of which will occur in the Piceance Basin, which currently supports over 250 active gas well pads (http://cogcc.state.co.us; Fig. I). Anderson and Freddy (2008) in their long-term research proposal identified 6 primary study objectives to assess measures to offset impacts of energy extraction on mule deer population performance. During the first 5 years of this study, we gathered baseline habitat utilization and demographic data from radiocollared deer across the Piceance Basin to allow assessment of habitat mitigation approaches that were completed April 2013. We recently completed monitoring 2 control areas: I with development (0.6 pads & facilities/km 2; Ryan Gulch) and I without (North Ridge). The control areas will be compared with 2 treatment areas experiencing similarly contrasting development intensities (South Magnolia, 0.9 well pads & facilities/km 2 and North Magnolia, 0.1 well pads & facilities/km 2), that also received habitat improvements (604 acres each). Habitat and mule deer responses to mechanical habitat treatments will be evaluated through spring 2019 to assess the success of this habitat mitigation strategy to benefit mule deer exposed to energy development disturbance. In addition, mule deer behavioral patterns in relation to energy development activities in the area have been monitored to identify effective Best Management Practices (BMPs) for future energy development planning. This progress report describes the previous IO years (Jan 2008-June 2018) of mule deer population performance during the pre and post-treatment phases on 4 winter range herd segments. This includes monitoring habitat selection, migration and behavior patterns of adult female mule deer; parturition success; spring/summer neonate, overwinter fawn and annual adult female survival; estimates of adult female body condition during early and late winter; and annual late-winter abundance/density estimates. The final year of GPS data collection from existing mule deer collars, vegetation response to habitat treatments and summer/fall use of treatment sites is ongoing and anticipated for completion by December 2019. STUDY AREAS The Piceance Basin, located between the cities of Rangely, Meeker, and Rifle in northwest Colorado, was selected as the project area due to its ecological importance as home to one of the largest migratory mule deer populations in North America and because it exhibits one of the highest natural gas reserves in North America (Fig. I). Historically, mule deer numbers on winter range were estimated between 20,000-30,000 (White and Lubow 2002), and the current number of well pads (Fig. I) and projected number of gas wells in the Piceance Basin over the next 20 years is about 250 and 15,000, respectively. Mule deer winter range in the Piceance Basin is predominantly characterized as a topographically diverse pinion pine (Pinus edu/is)-Utahjuniper (Juniperus osteosperma; pinionjuniper) shrubland complex ranging from 1,675 m to 2,285 min elevation (Bartmann and Steinert 1981 ). Pinion-juniper are the dominant overstory species and major shrub species include Utah serviceberry (Amelanchier utahensis), mountain mahogany (Cercocarpus montanus), bitterbrush (Purshia tridentata), big sagebrush (Artemisia tridentata), Gamble's oak (Quercus gambelii), mountain snowberry (Symphoricarpos oreophilus), and rabbitbrush (Chrysothamnus spp.; Bartmann et al. 1992). The Piceance Basin is segmented by numerous drainages characterized by stands of big sagebrush, saltbush (Atrip/ex spp.), and black greasewood (Sarcobatus vermiculatus), with the majority of the primary drainages having been converted to mixed-grass hay fields. Grasses and forbs common to the area consist ofwheatgrass (Agropyron spp.), blue grama (Bouteloua gracilis), needle and thread (Stipa comata), Indian rice grass (Oryzopsis hymenoides), arrowleafbalsamroot (Balsamorhiza sagittata), broom snakeweed (Gutierrezia sarothreae), pinnate tansymustard (Descurainia pinnata), milkvetch (Astragalus spp.), Lewis flax (Linum lewisii), evening primrose (Oenothera spp.), skyrocket gilia (Gi/ia aggregata), buckwheat (Erigonum spp.), Indian paintbrush (Castilleja spp.), and penstemon (Penstemon spp.; Gibbs 1978). The climate of the Piceance Basin is characterized by warm 4 �dry summers and cold winters with most of the annual moisture resulting from spring snow melt and brief summer monsoonal rainstorms. 2 Wintering mule deer population segments we are investigating include: North Ridge (53 km ) just north of the Dry Fork of Piceance Creek including the White River in the northeastern portion of the Basin, Ryan Gulch ( 141 km2) between Ryan Gulch and Dry Gulch in the southwestern portion of the Basin, North Magnolia (79 km 2) between the Dry Fork of Piceance Creek and Lee Gulch in the north-central portion of the Basin, and South Magnolia (83 km2) between Lee Gulch and Piceance Creek in the south-central portion of the Basin (Fig. 1). Each of these wintering population segments has received varying levels of natural gas development: no development in North Ridge, light development in North Magnolia (0.1 pads & facilities/km 2), and relatively high development in the Ryan Gulch (0.6 pads & facilities/km 2) and South Magnolia (0.9 pads & facilities/km 2) segments (Fig. 1). Development activity was high through 2011 and into 2012, but has declined substantially since natural gas prices began to decline by fall 2012. Among the 4 study areas, North Ridge has served as an unmanipulated control site, Ryan Gulch will serve to address human-activity management alternatives (BMPs) that benefit mule deer exposed to energy development and as a developed control area for comparison to the developed treatment area receiving habitat improvements (South Magnolia), and North and South Magnolia will allow us to assess the utility of habitat treatments intended to enhance mule deer population performance in areas exposed to light (North Magnolia) and relatively high (South Magnolia) energy development activities. METHODS Tasks addressed this period include adult female mule deer captures to remove store-on-board GPS collars, delineate winter mule deer concentration areas using GPS locations during a non-drilling, production phase (2012 - 2018), continue spring and fall vegetation measurements to address vegetation response to habitat treatments completed spring 2013, and document summer/fall use of habitat treatment sites using remote camera sampling. We employed helicopter net-gunning techniques (Barrett et al. 1982, van Reenen 1982) to capture and remove GPS collars from adult females during early March 2018. Once netted, all deer were hobbled and blind folded. Adult females were transported to localized handling sites for recording body measurements and remove GPS collars (5 fix attempts/day; 021 lOD, Advanced Telemetry Systems, Isanti, MN, USA) prior to release. GPS collars were supplied with timed drop-off mechanisms scheduled to release July 2019 for recovery from adult females that were not captured during March capture efforts. All radio-collars were equipped with mortality sensing options (i.e., increased pulse rate following 8 hrs of inactivity). Mule Deer Habitat Use and Movements We downloaded and summarized data from GPS collars deployed and recovered since 2008. OPS collars maintained the same schedule of attempting to collect locations every 5 hours, except for 40 does in Ryan Gulch and 10 control deer from North Ridge where location rates were programmed for every 30-60 minutes to increase resolution of movement data for evaluation of deer behavior patterns in relation to differing development activities. Joe Northrup (CSU PhD Candidate) analyzed resource selection data relative to energy development (Northrup 2015) and those results are addressed below. We contracted with Western Ecosystems Technology, Inc. (Laramie, WY USA) to develop winter utilization distributions from adult female mule deer GPS locations collected during a post-drilling production phase (2012-2018) in the Piceance Basin, using the Brownian bridge movement modeling approach described by Home et al. (2007). 5 �Mule Deer Survival Mule deer mortality monitoring consisted of daily ground-telemetry tracking and aerial monitoring approximately every 2 weeks from fixed-wing aircraft on winter range and weekly aerial monitoring on summer range. Once a mortality signal was detected, deer were located and necropsied to assess cause of death (Stonehouse et al. 2016). We estimated weekly survival using the staggered entry Kaplan-Meier procedure (Kaplan and Meier I 958, Pollock et al. 1989). Capture-related mortalities (any doe/fawn mortalities occurring within 10 days of capture; excluding neonates) and collar failures were censored from survival rate estimates. We estimated annual survival rates from 1 July through 30 June for adult females, from birth to mid-December for neonates, and from early December-mid June for winter fawns. V Adult Female Body Measurements We applied ultrasonography techniques described by Stephenson et al. (1998, 2002) and Cook et al. (200 I) to measure maximum subcutaneous rump fat (mm), loin depth (longissimus dorsi muscle, mm), and to estimate % ingesta-free body fat. We estimated a body condition score (BCS) for each deer by palpating the rump (Cook et al. 2001, 2007, 2009). We examined differences (P < 0.05) in nutritional status among study areas and between years evident in non-overlapping 95% confidence intervals. We considered differences in body condition meaningful when mean rump fat or% body fat differed statistically between comparisons. Other body measurements recorded included pregnancy status (pregnant, barren) via ultrasonography and blood samples, fetal counts using ultrasonography, weight (kg), chest girth (cm), and hind-foot length (cm). Abundance Estimates We conducted 3-5 helicopter mark-resight surveys (2 observers and the pilot) during late March/early April to annually estimate deer abundance in all 4 study areas. We delineated study area boundaries from GPS locations collected on winter range during the first 3 years of the study (Jan 2008 through April 20 I I). Two aerial fixed-wing telemetry surveys/study area were conducted during helicopter mark-resight surveys to determine which marked deer were within each survey area, and we confinned adult female locations during surveys from GPS data acquired the following April each year. We delineated flight paths in ArcGIS 10.0 prior to surveys following topographic contours (e.g., drainages, ridges) and approximating 500-600 m spacing throughout each study area; flight paths during surveys were followed using GPS navigation in the helicopter. Two 12 x 12 cm pieces of Ritchey livestock banding material (Ritchey Livestock ID, Brighton, CO USA) were uniquely marked using color, number, and symbol combinations and attached to each radio-collar to enhance markresight estimates. Each deer observed during surveys was recorded as mark ID#, unmarked, or unidentified mark. We used program MARK (White and Burnham 1999), applying the immigration-emigration mixed logit-nonnal model (Mcclintock et al. 2008), to estimate mule deer abundance and confidence intervals. For mark-resight model evaluations, we examined parameter combinations of varying detection rates with survey occasion and whether individual sighting probabilities (i.e., individual heterogeneity) were constant or varied (cr2 = 0 or* 0). Model selection procedures followed the infonnation-theoretic approach of Burnham and Anderson (2002). 6 V �RESULTS AND DISCUSSION Deer Captures and Survival The helicopter crew captured 56 does during March 2019 to remove store-on-board GPS collars. No recapture myopathies occurred during March capture efforts. Forty-four does remain collared with GPS collars programmed to drop during July 2019. The remaining collars will be located and recovered during summer/fat I 2019. Data collection for estimating deer survival rates ended July 2018. Survival results for the duration of the study are reported in the next 2 paragraphs. Fawn survival from early December 2017 through mid-June 2018 was more variable than in past years ranging from 0.34 to 0. 76 (Table 1, Fig. 2). Based on CI overlap, North Ridge and North Magnolia fawns exhibited higher survival than Ryan Gulch fawns and North Ridge fawn survival was also higher than in South Magnolia {Table 1). The range in winter fawn survival was unusual in comparison to previous years (Fig. 2), but correlated with similar variability observed in December fawn weights (Fig. 3); early winter fawn condition likely contributes to over-winter survival potential, and reduced condition the past 2 years is likely related to the lower survival rates observed recently from Ryan Gulch and South Magnolia (Fig. 2, Fig. 3). Premature collar drop during 2008-09 and 2009-10 did not allow for winter fawn survival estimates past late March, but survival rates among study areas were similar (P < 0.05) each year and comparable to 20 I 1-12 and 2012-13 (excluding North Ridge) during 2008-09 and to the higher survival rates from 2013-14 and 2014-15 during 200910 (Fig. 2). General comparisons to previous years suggest moderate to high fawn survival occurred during most winters and study areas with the exception of winter 2010-2011 for 3 of the 4 study areas, North Ridge during winter 2012-13 and 2015-16, and Ryan Gulch and South Magnolia the past 2 years (Fig. 2). Low winter fawn survival (Fig. 2) appears to correlate with summer forage condition evident from lower December fawn weights (Fig. 3); severe winter conditions can also strongly influence winter fawn survival, but winter conditions during this study have been mild to moderate with the exception of winter 2010-11, which may have more strongly influenced winter fawn survival that year. Annual adult female survival varied from 0.73 (North Ridge) to 0.87 (South Magnolia; Table 1) during 2017-18, but was comparable among study areas and to previous years (P > 0.05), with the exception of lower survival in North Magnolia during 2011-12 (S = 0.68, Anderson and Bishop 2012). Relatively low sample sizes per study area for adult female survival do not allow statistical discrimination among years unless large differences are evident (e.g.,> 15-20%). Estimates below 80% are biologically concerning if these values represent the respective population, but low statistical power precludes confinnation within study areas. When combined among study areas, annual survival estimates have varied from 79% in 2012-13 to 86% in 2014-15, but consistent CI overlap including large sample sizes (exceeding 100 in July and 200 in Mar annually) supports consistent annual doe survival during this study. Adult female mule deer exhibit consistently high survival rates unless extreme weather events and/or habitat degradation persists, which has not been evident since 2008. Mule Deer Body Condition Data collection to address mule deer body condition ended March 2018. Body condition results for the duration of the study are reported in the next 3 paragraphs. Early-winter body condition measurements of adult female mule deer during December 2017 were collectively relatively low among study areas compared to previous years and Ryan Gulch does 7 �exhibited the lowest condition estimates among study areas (P < 0.05; Fig. 4, Table 2). Although fall body condition is likely related to spring/summer forage conditions, doe condition is also influenced by energy expended for fawn rearing, and appears to be strongly influenced by lactation status; I observed a strong correlation between December lactation rate and body condition (mm rump fat; P = 0.004, r = 0.62, n = 20). Thus, while fall body condition represents an index of nutritional status entering winter, it also appears to be a useful metric to assess fall reproductive status, where low fat levels represent high fall fawning rates; the low December fat levels observed from Ryan Gulch does during 2017 was associated with highest fall lactation rate (0.63) recorded during the study. In contrast, late winter condition appears more strongly related to winter severity and winter-range forage conditions and low fat levels observed during December do not necessarily manifest into poor late winter condition (Fig. 4, Table 2). Late winter doe condition among study areas this past winter was comparable to the long-term average (Fig. 4), and was reflective of mild to moderate winter conditions consisting of infrequent snowstorms and minimal snow pack on winter range. December 2017 fawn weights by study area represented a gradient in fawn condition ranging from low to high for Ryan Gulch and North Ridge, respectively (Fig. 3), which corresponded to winter fawn survival (Fig. 2). Overall fawn condition for 3 of 4 study areas (excluding North Ridge) has declined the past 2 years (Fig. 3) and may be related to changes in summer forage conditions (further analyses pending). Because adult female body condition has been largely uninformative in regards to habitat treatment responses (pending further analyses), we began late winter fawn recaptures in South Magnolia (habitat treatment area) and Ryan Gulch (reference area) to assess changes in over-winter condition. Weight loss during winter 2015-16 was significantly less (P < 0.001) for fawns from the area receiving habitat treatments than for fawns from the untreated area, but no net weight loss was detected during the following 2 winters for either study area (P ~ 0.396). Vegetation measurements from treatment and control sites indicate recent summer/fall use of shrubs potentially negating forage benefits on winter range. Additional investigations to address this issue will be conducted summer/fall 2018 and 2019 to confirm summer/fall use of treatment sites and whether or not intended forage benefits on habitat treatment sites persist on winter range. V Mule Deer Population Estimates Data collection to address annual mule deer abundance/density estimates ended March 2018. Population size/density results for the duration of the study are reported in the paragraph below. Mark-resight models that best predicted abundance estimates (lowest AICc; Burnham and Anderson 2002) exhibited variable sightability across surveys (P,) for all study areas and variable individual sightability (cr2 = 0) for North Magnolia deer and homogenous sightability (cr2 ;/; 0) for the other 3 areas. During 2018, North Ridge exhibited the highest deer density (15.8/km2), with comparable but lower deer densities in the other 3 areas (9.2-l l .3/km2 ; Table 3, Fig. 5). Abundance estimates from 2018 were similarly precise from all 4 study areas with the mean Confidence Interval Coefficient of Variation (CICV) ranging from 0.12--0.17 (Table 3). Densities increased over the first 8year monitoring period in all study areas ranging from an estimated 50% increase in North Ridge to a 103% increase in North Magnolia (mean estimated increase across study areas= 78%); North Ridge deer appeared to decline during 2012 and 2013, but subsequently increased, while the other 3 areas exhibited consistent and similar rates ofincrease from 2009-2016 (mean annual increase= 0.064; Fig. 5). Excluding the North Ridge study area, late winter mule deer densities have apparently stabilized since 2016 (Fig. 5). The reason for decline since 2016 for the North Ridge deer population is unclear and not completely explained by demographic parameters monitored during the study. Erratic population estimates observed from North Ridge may be partially attributed to lack of geographic closure more commonly associated with this study area (primarily from earlier spring migration 8 V �timing). Population vital rates will be analyzed and compared to abundance estimates to assess factors contributing to population change by study area. Spring Migration Patterns Collaboration with Idaho State University to address mule deer migration patterns in developed and undeveloped landscapes (funded from energy company contributions) has been completed. Four manuscripts from this effort have been published (Lendrum et al. 2012, Lendrum et al. 2013, Lendrum et al. 2014, Anderson and Bishop 2014; Appendix A). In addressing habitat selection during spring migration, Lendrum et al. (2012; Fig. 6) noted that mule deer migrating through the most developed landscapes exhibited longer step lengths (straight line distance between GPS locations) and selected habitats providing greater security cover than deer in undeveloped landscapes that migrated through more open areas that provided increased foraging opportunities. Migrating deer also selected areas closer to well pads, but avoided roads, except in the highest developed areas where road densities were likely too high for avoidance without significant deviations from traditional migration routes. In the second manuscript, Lendrum et al. (2013) addressed biological and environmental factors influencing spring migration and assessed how energy development influenced migratory behavior. Overall, spring migration was influenced by snow depth, temperature, and green-up on winter and summer range; increasing temperatures, snow melt and emerging vegetation dictated timing of winter range departure and summer range arrival. Duration of Piceance Basin mule deer migration was short, with median migration durations of 3-8 days among the 4 areas (straight-line distance between seasonal ranges averaged 32-40 km). Deer in poor condition migrated later than deer in good condition, but condition was similar among areas regardless of development status. Migrating deer from developed study areas did not avoid development structures, but departed later, arrived earlier and migrated more quickly than deer from undeveloped areas. While large changes in timing of migration could have nutritional consequences and negatively influence reproduction and neonate survival, the relatively minor shift we observed should not result in long-tenn fitness consequences, which was supported by Peterson et al. (2018; see Reproductive Success and Neonate Survival below). Migratory deer in the Piceance Basin appear to avoid negative effects of energy development through behavioral shifts in timing and rate of migration. In the third publication Lendrum et al. (2014) monitored migratory mule deer in the Piceance Basin to examined the relationship between the Nonnalized Difference Vegetation Index (NOVI), which is a course-scale measure of forage quality using a G IS assessment of vegetation greenness, and fecal nitrogen to assess the assumption that forage quality and deer diets can be reasonably linked to address deer habitat use patterns from remotely sensed data. We found that diet quality evident from fecal nitrogen and course measures of vegetation green-up were infonnative, and that Piceance Basin mule deer exhibited rapid migration (3 to 8 days depending on study area), left winter range following snow melt with lowest fecal N and NOVI values, and progressed to summer range as vegetation green-up and nitrogen levels increased, but ahead of peak vegetation green-up on summer range. I suspect this rapid migration strategy is evident for deer in relatively good condition and allows for early arrival on summer range to take advantage optimal forage conditions prior to parturition. Anderson and Bishop (2014) summarized results from Lendrum et al. (2012, 2013) and Sawyer et al. (2012) addressing migratory mule deer and energy development in northwest Colorado and south-central Wyoming, respectively. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may 9 �alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/km 2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by increasing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. V Mule Deer Behavioral Response to Energy Development We completed evaluations of deer behavior patterns in relation to energy development activities (Northrup et al. 2015). We found diurnal responses to development activity, where deer used timbered areas away from development activity while bedded during the day and moved into more open areas generally closer to developed areas while foraging at night. Disturbance distances from producing pads and roads declined from 600 m to 200 m and about 140 m to 60 m from daytime to nighttime, respectively, but increased from 600 m to 800 m for nighttime drilling pad activity (pad response depicted in Fig. 7). We suspect deer behaviorally respond to fluctuations in development activity, where road traffic and producing well pad activity decline at night, but drilling pad disturbance may increase from compressors and lights used to facilitate nighttime drilling activity. These evaluations were applied during an active drilling phase in the Piceance Basin and deer use was influenced by development activity in 25% (nighttime) to 50% (daytime) of critical winter range during that period. However, deer densities have comparably increased among developed and undeveloped study areas (excluding North Ridge; Fig. 5) suggesting that deer can behaviorally mediate development disturbance under observed development and deer densities by taking advantage of fluctuations in development activity to address their nutritional requirements. Given the plasticity in deer behavior, a number of potential options for future development planning ex.its including drilling schedule modifications (seasonal and/or diurnal), concentrated/staged development, reducing road traffic, and using light/noise barriers around drill rigs. It will be interesting to determine if habitat improvements will further reduce development disturbance and increase management options for future development planning. Reproductive Success and Neonate Survival To complete demographic parameters addressing mule deer-energy development interactions, CPW, Colorado State University, and ExxonMobil Production entered into a collaborative agreement to investigate reproductive success (Peterson et al. 2017), including pregnancy rates (early Mar) and fetal survival (Mar until birth), and early fawn survival (0 - 6 months; Peterson et al. 2018) in developed and relatively undeveloped landscapes beginning spring 2012 and continuing through Dec 2014. We applied statistical models to address reproductive success under contrasting energy development scenarios and noted that pregnancy and in utero fetal rates (early Mar; n = 346) were high (0.948, SE= 0.012 and 1.877, SE= 0.029, respectively) and statistically indistinguishable between study areas. Fetal survival (n = 383), however, was lower (P < 0.05) in the developed study IO V �area during 1 of 3 years (2012; Fig. 8) when drought conditions were present, suggesting the combination of severe weather conditions and development activity under observed conditions may influence fetal survival. There was no apparent influence from energy development in 0--6 month fawn survival (n = 184) based on similar mortality rates between study areas; mean daily mortality probabilities from predation, malnutrition and unknown causes were nearly identical (Fig. 9). These results suggest that natural gas development did not exert measureable influence on mule deer pregnancy rates, fetal rates or early fawn survival, but may have negatively influenced fetal survival during 2012 when does were exposed to drought conditions during the third trimester. These findings are consistent with developed areas in a production phase (little to no drilling activity) exhibiting moderate pad densities (0.4-0.9 pads/km2), and relationships may differ in areas of higher pad densities and/or drilling activity. Winter Range Habitat Treatments and Habitat Utilization Distributions We completed 116 acres of pilot habitat treatments in January 2011 (Anderson and Bishop 2011; Environmental Assessment: DOI-BLM-CO-110-2011-004-EA), 54 acres of mechanical treatment method comparison treatments (hydro-ax, roller-chop, chain) in January 2012 (Stephens 2014), and 1,038 acres of hydro-ax treatments in April 2013 (Determination ofNEPA Adequacy: DO1-BLM-CO-I I 0-2012-0134-DNA), totaling 604 treated acres in each study area (Fig. 10). Vegetation response in the pilot treatment sites was visually evident by fall 2011 (Fig. 10), and resulted in statistically significant (P < 0.05) increases in native grass and forb cover by the 2014 growing season. Final results are pending, but shrub responses appear promising from data collected through spring 2019. Stephens (2014) reported that all 3 mechanical treatment methods compared resulted in roughly a 3-fold increase in grasses, forbs, and shrubs combined after 2 growing seasons (versus control sites), but cautioned that rollerchop treatments may be more vulnerable to invasive species response. Vegetative responses from 2013 hydro-ax treatments were visually evident following 1 growing season and shrub responses have been notable during the 4 th growing season, but statistical comparisons are still pending. As anticipated, grass and forb responses were evident 2 to 3 years posttreatment, with longer term response expected (3-5 years) for palatable shrubs. Of note, relatively high moisture conditions experienced during spring 2014 and 2015 resulted in higher than normal prevalence of cheatgrass (Bromus tectorum); cheatgrass invasion has previously been minor to non-existent in this area. Cheatgrass invasion, however, does not appear directly related to treatment sites because occurrence is evident in both treatment and control areas. We anticipate this outbreak will subside based on past competitive advantage of native species to dominate, but we will continue to monitor species composition and address cheatgrass persistence in treatment and control sites. OPS data addressing deer use of treatment sites has been collected through March 2019, with remaining collars from the Dec 2017 sample (n = 44) still on deer in the field. The remaining collars will be collected during summer/fall 2019 (collars were programmed to drop July 2019). The final spring vegetation response measurements for habitat treatment and control areas were collected th is past spring (final data analyses pending) and final shrub response data will be collected Sep. 2019. Final data analyses will be initiated once OPS collars are collected this summer/fall. Thus far, we observed improved fawn condition (P < 0.001) in South Magnolia following the 4 th growing season of habitat treatments when compared to fawn condition in the Ryan Gulch control area, but we did not detect a response the following 2 winters. Ongoing data analyses suggests that fall shrub condition appears to have declined recently indicating that summer/fall shrub use may be increasing and potentially inhibiting the intended benefit of habitat treatments on winter range. We deployed remote cameras on treatment and control areas July 2018 and 2019 to further address summer/fall use and identify species utilizing treatment sites on mule deer winter range. Although results are 11 �preliminary, vegetation responses through the first 4 years post treatment provided the intended forage benefit and there is some evidence that fawn condition improved as a result. Recent changes in habitat use by multiple species (potentially including wild horses and livestock) may have reduced winter forage benefits recently, but additional data collection and analyses will be necessary for confirmation. Analyses of doe use of treatment sites throughout the study are still pending and will provide information addressing the utility of habitat improvement projects as a mitigation technique to offset energy development disturbance on mule deer winter range. We delineated mule deer winter concentration areas for each study area from 2012 - 2018, which represents a non-drilling (drilling activity had ended by fall 2012) production phase (active well pads producing natural gas) in the Piceance Basin. Results exhibiting high use areas during winter are reported in Appendix B. This information is intended to provide guidance for site selection in future development planning (i.e., placement of well pads, facilities and roads). Directional drilling technology should provide options for development activity to occur in areas of relatively low use on mule deer winter range. Ultimately, we will expand our analyses to address mule deer winter concentration areas throughout the entire Piceance Basin (and possibly other mule deer pinion-juniper winter ranges), but final results will not be available until 2021. SUMMARY AND COLLABORATIONS The long-term goal of this study is to investigate habitat treatments and energy development practices that enhance mule deer populations exposed to extensive energy development activity. The information presented here summarizes mule deer population parameters from the 10-year study period, with the final year of data collection for some parameters (i.e., habitat treatment response and adult female habitat use) remaining. The pretreatment period was completed by spring 2013, providing baseline data for comparison with intended improvements in habitat conditions and response to varying degrees in human development activity. Winter range habitat improvements resulting in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of2 study areas were completed by April of 2013, and subsequent vegetation responses have met or exceeded expectations through 2016. The post-treatment monitoring period was completed June 2018, with the final year of habitat use and habitat treatment response data collection still pending. Based on final (migration, mule deer behavioral responses, reproductive success and neonate survival) and preliminary data analyses for this 10-year project: (I) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to 103% in North Magnolia, but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge; (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures; (5) mule deer exhibited behavioral plasticity in relation to energy development, where disturbance distance varied relative to diurnal extent and magnitude of development activity, which may provide for several options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months}, but may have reduced neonatal survival (March until birth) when drought conditions persisted during the third trimester of doe parturition. Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning, and final results addressing the 12 V �interaction of mule deer behavioral and demographic factors associated with energy development activity have been submitted for scientific review and publication. Completion of this project, including final data collection, analyses and interpretation ofresults, is anticipated by fall/winter 2020-21. Hay field improvements were completed during 2012 in the North Magnolia study area by WPX Energy to fulfill a Wildlife Management Plan (WMP) agreement with CPW; rapid and continued elk (Cervus e/aphus) use of these areas was evident, but mule deer response has been minor. A similar WMP agreement between ExxonMobil/XTO Energy and CPW allowed completion and continued monitoring of mechanical habitat improvements in the Magnolia study areas. Collaborative research with agency biologists, graduate students, and university professors has produced 19 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011, Peterson et al. 2018b); mule deer migration (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013); mule deer response to helicopter capture and handling (Northrup et al. 2014a); potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014 ); mule deer genetics in relation to body condition and migration (Northrup et al. 2014b); spatial and temporal factors influencing auditory vigilance in mule deer (Lynch et al. 2014); the relationship of plant phenology with mule deer body condition (Seral et al. 2015); approaches to identify cause-specific mortality in mule deer from field necropsies (Stonehouse et al. 2016); the influence of individual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman et al. 2018); and mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 2015, 2016a, 2016b, Peterson et al. 2017, 2018a). These publications are summarized in Appendix A and results describing mule deer concentration areas among study areas are reported in Appendix B. We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado. 13 �LITERATURE CITED Anderson, C.R., Jr., and D. J. Freddy. 2008. Population perfonnance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Final Study Plan, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 201 I. Population perfonnance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Division of Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2012. Population perfonnance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Job Progress Report, Colorado Parks and Wildlife, Ft. Collins, CO, USA. Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns ofadult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79 th North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Bartmann, R. M. 1975. Piceance deer study-population density and structure. Job Progress Report, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. 8., and S. F. Steinert. 1981. Distribution and movements of mule deer in the White River Drainage, Colorado. Special Report No. 51, Colorado Division of Wildlife, Fort Collins, Colorado, USA. Bartmann, R. M., G. C. 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Thesis, Colorado State University, Fort Collins, Colorado, USA. 14 V V �Home, J. S., E. 0. Garton, S. M Krone, and J. S. Lewis. 2007. Analyzing animal movements using Brownian bridges. Ecology 88:2354-2363. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 52:457-481. Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.org/10. Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: 10.1371/joumal.pone.0064548 Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/10.10 I6/i .mambio.2014.05.005 Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: 10.1093/beheco/aru 158. McCiintock, B. T., G. C. White, K. P~ Burnham, and M. A. Pride. 2008. A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement. Pages 271- 289 in D. L. Thompson, E.G. Cooch, and M. J. Conroy, editors, Modeling demographic processes is marked populations. Springer, New York, New York, USA. Northrup, J.M. 2015. Behavioral response of mule deer to natural gas development in the Piceance Basin. Dissertation, Colorado State University, Fort Collins, USA. Northrup, J.M., M. B. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731738; DOI: 10.1002/jwmg.705 Northrup, J.M., A. B. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Northrup, J.M., C. R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037. Northrup, J. M., C. R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:2746-2757 Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: 10.1111/wlb.00341 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82:1135-1148, DOI: 10.1002/jwmg.21476 Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. C. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15. Sawyer, H., M. J. Kauffman, A. D. Middleton, T. A. Morrison, R. M. Nielson, and T. B. Wycoff. 2012. A framework for understanding semi-permeable barrier effects on migratory ungulates. Journal of Applied Ecology 50:68-78. 15 �Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549; DOI IO. 1007/s00442-015-3348-9 Stephens, G. J. 2014. Understory responses to mechanical removal of pinyon-juniper overstory. MS Thesis, Colorado State University, Ft. Collins USA. Stephenson, T. R., V. C. Bleich, 8. M. Pierce, and G. P. Mulcahy. 2002. Validation of mule deer body composition using in vivo and post-mortem indices of nutritional condition. Wildlife Society Bulletin 30:557-564. Stephenson, T. R., K. J. Hundertmark, C. C. Swartz, and V. Van Ballenberghe. 1998. Predicting body fat and mass in moose with untrasonography. Canadian Journal of Zoology 76:717-722. Stonehouse, K. F., C. R. Anderson Jr., M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer ( Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. Unsworth, J. W., D. F. Pack, G. C. White, and R. M. Bartmann. 1999. Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management 63:315-326. Van Reenen, G. 1982. Field experience in the capture of red deer by helicopter in New Zealand with reference to post-capture sequela and management. Pages 408-421 in L. Nielsen, J.C. Haigh, and M. E. Fowler, editors. Chemical immobilization of North American wildlife. Wisconsin Humane Society, Milwaukee, USA. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked individuals. Bird Study 46: 120-139. White, G. C., and B. C. Lubow. 2002. Fitting population models to multiple sources of observed data. Journal of Wildlife Management 66:300-309. Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader V 16 �Table I. Survival rate estimates (S) of fawn (I Dec. 2017-15 June 2018) and adult female (1 July 201730 June 2018) mule deer from 4 winter range study areas of the Piceance Basin in northwest Colorado. Cohort Study area Initial sample size (n) March doe samplea (n) 8(95% CI) Fawns Ryan Gulch 53 0.335 (0.207-0.464) South Magnolia 53 0.479 (0.343-0.616) North Magnolia 57 0.665 (0.543-0.788) North Ridge 58 0. 757 (0.645-0.868) Adult females '..,,,_/ Ryan Gulch 27 52 0.832 (0.722-0.942) South Magnolia 32 56 0.869 (0. 757-0.980) North Magnolia 27 49 0.781 (0.65~.913) North Ridge 24 45 0. 731 (0.583--0.878) aAdult female sample sizes following capture and radio-collaring efforts March, 2018. 17 �Table 2. Mean rump fat (mm) and % ingesta-free body fat8 (% fat) of adult female mule deer from 4 study areas in the Piceance Basin of northwest Colorado, March and December, 2009-2018. Values in parentheses= SD. March 2009 Study Area Rump fat Ryan Gulch 1.73(1.78) 7.08 (1.27) South Magnolia %fat December 2009 Rump fat %fat March 2010 December 20 I 0 Rump fat %fat Rump fat %fat 8.35 (6.36) 10.54 (3.72) 2.31 (1.44) 6.37 (1.41) 7.26 (6.36) 9.69 (3.56) 1.29 (0.47) 6.74 (2.27) 10.05 (6.19) 11.44 (3.50) 3.12 (2.20) 7.11 ( 1.69) 9.85 (6.78) 11.27 (3.75) North Magnolia 1.31 (1.01) 7.15 (1.63) 10.67 (5.76) 11.94 (3.39) 3.15 (2.34) 7.54 (1.53) 9.55 (6.49) 10. 79 (4.26) North Ridge 1.57 (1.22) 6.81 (1.68) 5.25 (5.65) 1.77 (I. I I) 6.39 (1.45) 7.25 (5.41) 9.85 (3.02) 9.37 (3.08) Table 2. Continued. March 2011 Study Area Rump fat Ryan Gulch 1.55 (0.60) 6.72 (1.37) South Magnolia %fat December 2011 Rump fat %fat March 2012 December 2012 Rump fat %fat Rump fat %fat 13.41 (6.39) 13.17 (3.64) 2.15 (1.44) 7.22 (1.16) 6.34 (4.35) 9.34 (2.43) 1.65 (0.75) 6.15 (1.75) 8.18 (5.45) 10.34 (3.28) 1.66 (0.77) 7.03(1.13) 8.30 (5.71) 10.32 (3.23) North Magnolia 1.65 (0.67) 6.79 (1.47) 8.76 (5.76) 10.73 (3.14) 1.90 (0.76) 7.61 (0.96) 9.66 (6.41) I 1.18 (3.64) North Ridge 1.45 (0.76) 6.30 (1.65) 8.86 (5.65) 10.77 (3.33) 2.24 (1.58) 7.26 (1.05) 5.76 (4.10) 9.06 (2.31) 15 C ( ( �( ( Table 2. Continued. March 2013 Study Area Rump fat Ryan Gulch 1.87 (0.90) 7.14 (0.89) South Magnolia %fat December 2013 Rump fat %fat March 2014 December 2014 Rump fat %fat Rump fat % fat 9.27 (6.29) 10.61 (3.76) 1.69 (0.85) 7.03 (0.99) 8.50 (6.76) 10.56 (3.70) 2.06 (0.77) 7.19 (0.66) 11.27 (8.40) 11.40 (4.16) 2.57 (1.61) 7.75 (0.68) 10.96 (6.82) 11.98 (3.81) North Magnolia 1.76 (0.91) 6.87 (1.11) 9.00 (6.15) 10.48 (3.25) 2.33 (2.12) 7.31 (1.43) 9.52 (5.83) 11.18 (3.32) North Ridge 1.87 (0.73) 6.70 (1.12) 11.17 (5.28) 11.66 (2.69) 2.38 (1.52) 7. 16 (1.14) 7.93 (5.50) 10.20 (3.01) Table 2. Continued. March 2015 Study Area Rump fat % fat Ryan Gulch 2.62 (0.95) 7.44 (0.53) South Magnolia December 2015 Rump fat %fat March 2016 December 2016 Rump fat %fat Rump fat %fat 12.80 (6.83) 12.89 (3.72) 2.29 (0.64) 7.29 (0.52) 8.20 (4.90) 10.46 (2.70) 2.66 (1.36) 7.62 (0.74) 6.93 (4.83) 9.83 (2.69) 2.07 (1.39) 7.46 (0.93) 6.27 (4.62) North Magnolia 2.25 (0.97) 7.49 (0.90) 8.79 (6.01) 10.81 (3.54) 2.43 (1.01) 7.17 (0.87) 7.90 (5.52) 10.34 (3.14) North Ridge 2.28 (1.37) 7.43 (1.05) 5.47 (5.49) 1.58 (0.70) 6.73 (1.26) 7.74 (5.48) 10.01 (3.09) 9.35 (2.75) 16 9.37 (2.53) �Table 2. Continued. March 2017 December 2017 March 2018 Study Area Rump fat %fat Rump fat %fat Rump fat %fat Ryan Gulch 2.39 (0.74) 6.78 (0.97) 4.47 (3.57) 8.62 (1.80) 2.13 (0.76) 7.40 (0.50) South Magnolia 2.48 (0.77) 7.09 (0.63) 6.67 (5.23) 9.56 (2.73) 2.19 (1.18) 7.40 (0.72) North Magnolia 1.82 (0.72) 7.05 (0.58) 6.16 (4.32) 9.23 (2.47) 1.87 (0.63) 7.15(1.11) North Ridge 2.30 (1.37) 7.23 (1.21) 6.60 (4.29) 9.38 (2.35) 2.35 (0.80) 7.73 (1.03) 8 lngesta-free body fat calculated following Cook et al. (2009). 17 C ( �Table 3. Mark-resight abundance (N) and density estimates of mule deer from 4 winter range herd segments in the Piceance Basin, northwest Colorado, 26-31 March 2018. Data represent 4 helicopter resight surveys from 3 of 4 study areas, with South Magnolia receiving 5 surveys. Study area Mean No. sighted Mean No. marked N(95% Cl) Density (deer/km 2) Ryan Gulch 418 19 1,397 ( 1, 186-1 ,674) 9.9 South Magnolia 239 26 764 (678-874) 8.1 North Magnolia 319 27 899 (774-1,070) 9.8 North Ridge 305 32 838 (748-954) 15.8 18 �Mule Deer Winter Range Study Areas Mule deer study areas Well Pads & Facilities D Nonn Magnolia Soutn Magnoha ! 1n development ► Pr0<1uc1ng well _ Oevelopm ent lac11,1,es 10 M ilts Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development facilities in the Piceance Basin of northwest Colorado. winter 20 13/14 (Accessed http://cogcc.state.co.us/. Dec.31.201 3). Development activity has subsided with minimal drilling activity since fall 20 12. 19 �Winter fawn survival 2010-11- 2017-18 1.00 ~T , j j,__ Ji j~ j 0.90 0.80 - I- I- I- I- I-- I-- 1-- 1-- 0.70 0.60 0.50 ....... 0.40 0.30 0.20 0.10 0.00 T ' T I- '- I- I- '- I-- 1-- I- I- I- I- I- I-- '- □ South Mag noli3 '- I I- I-- 1-- I-- I- 1-- I- I- '- 1-- ._ I•' I- 1-- I- I- '-- '- I-- I- - I- - I- ,, ~- -~ ' - f.l a Ryan Gulch ■ North Magnolia - □ North Ridge Ii 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Figure 2. Over-winter (Dec- June) mule deer fawn survival (5) from 4 study areas in the Piceance Basin. northwest Colorado. 20 I 0-11 - 2017- 18. ErTor bars = 95% C l. Fawn survival estimates past late March unavai lable for winter 2008-09 and 2009-1 0 due to premature collar drop. but survival estimates were comparable to 20 11-1 2 and 20 12- 13 (excl uding 2012-1 3 North Ridge) during 2008-09 and to the hi gher survival rates from 20 I3- 14 and 20 I 4- 15 during 2009- I 0. 20 �Male fawn weights 42.0 40.0 ~ .JII 38.0 Q0 - ... ='=- 36.0 .t:: ._ ·a; 34.0 3: 32.0 _11 I 30.0 ~ ,. lrll! ~ - - - - -.... I- I- I- '- I- I- I- '- .... .... L- L- ~ ,__ .,_ ,- - ..... 28.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 -- I• Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2016 D Ryan Gulch ... D South Magnolia ■ North M agnolia D North Ridge Dec 2017 Female fawn weights 42.0 40.0 Q0 38.0 ... D Rvan Gulch ='=- 36.0 .t:: D south Magnolia -~ 34.0 3: ■North Magnolia 32.0 D Nonh Ridgo 30.0 28.0 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2016 Dec 2017 Figure 3. Mean male and female fawn weights and 95% CI (error bars) from 4 mule deer study areas in the Piceance Basin. northwest Colorado. December 2008- 20 17. 21 �Early winter rump fat {mm) 16 14 12 10 8 6 4 2 0 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2016 Dec 2017 ■ North Ridge ■ North Magnolia ■ Ryan Gulch ■ South Magnolia Late winter rump fat {mm) 4.0 3.5 3.0 2.5 2.0 II 1.5 1.0 0.5 0.0 Mar 2009 Mar 2010 Mar 2011 Mar 2012 Mar 2013 Mar 2014 Mar 2015 Mar 2016 Mar 2017 Mar 2018 ■ North Ridge ■ North Magnolia ■ Ryan Gulch ■ South Magnolia Figure 4. Mean early (early Dec., Top) and late winter (early Mar .. Bottom) body condition (mm rump fat) of adult female mule deer from 4 winter range study areas in the Piceance Basin of northwest Colorado. March 2009-March 10 18. E1Tor bars = 95% C l. �Piceance Basin late winter mule deer density 35.00 30.00 25.00 } 20.00 ";::- - t 15.00 C - North Ridge • • • • • • Ryan Gulch 10.00 5.00 - • North Magnolia - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 5. Mule deer density estimates and 95% CI (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2018. Estimates for were adjusted upward ( using GPS migration data) to account for early migration from winter range prior to and during surveys during years when early migration biased estimates low (North Ridge 2014-2017, North Magnolia 2015 and 2017, Ryan Gulch 2017). 24 -------------- ------- �North Ridge and North Magnolia Summer Range Figure 6. Mule deer study areas in the Piceance Basin of northwestern Colorado. USA (Top). spring 2009 migration routes of adult female mule deer (11 = 52; Lower left). and active natural-gas well pads (black dots) and roads (state, county. and natural-gas; white lines) from May 2009 (Lower right: from Lendrum et al. 2012). 25 �j O _.,t·- - - - · - - - -+- - - - ... - - - - -.. - - - - - , ,- - - t" ~ 'E .... - I~ 13 --+----+ I ' e 8 7M I I I T I I J Prod 400 Prod 600 Prod 800 Prod 1000 Drill 400 Covariates I Drill 600 OriO 800 Drill 1000 Prod 400 Prod 600 Prod 800 Prod 1000 Drill 600 Drill 800 Drlll 1000 C") I Drill 400 Covariates Figure 7. Posterior distributions of population-level coefficients related to natural gas development for RSF models during the (a) day and (b) night for 53 adult female mule deer in the Piceance Basin, Northwest Colorado. Dashed line indicates 0 selection or avoidance of the habitat features. 'Drill' and 'Prod' refer to well pads where there was active drilling or producing pads, respectively. The numbers following 'Drill' or ·Prod' represent the concentric buffer over which the number of well pads was calculated (e.g., 'Drill 600' is the number of well pads with active drilling between 400-600 m from the deer location; from Northup et al. 2015). 26 �,......,_ 1.00 - '-- 0.80 Q) --~ ·, I, ~ ro 0.60 > -~ :::, (/) cu 0.40 Q) u. 0.20 0.00 2013 2012 2014 Year □ High development □ Low development I Figure 8. Model-averaged estimates of fetal s urvi val (± 95% Cl) of mule deer fetuses from early March until birth (late May- June) in high and low energy development study areas of the Piceance Basin, northwest Colorado, 20 12-20 14 (from Peterson et al. 2017). f;' 0.016 iii ,::: 0 E 0 0.012 ~ :a m .g 0.008 5. ~ 7il Cl 0.004 development Low development development Low development development Low development 2012 2012 2013 2013 2014 2014 High High High Study area □ Predation c Malnutrition ■ Unknown mortality Figure 9. Mean daily probability of death by predation. malnutrition, or unknown mortality(± 95% Cl) of mule deer fawns (0 to 6 months old) in high and low energy development study areas of the Piceance Basin Colorado, 2012-2014 (from Peterson et al. 2018). 27 �Norlh M agnoba tr<alement sates (587 aoes) C Bea,Sel_ I 5_35b_andG Bea,Sel _ I _BandA_E c::__, 8 earSe1_36_54andJ GreasewoodSe1_g I 6_929 GreasewoodSe1_g1 _915 I GreasewooaSe1_g30_!)'l2 LeeOvers,ghts_a_tand 16_ 17 Med'lanrc,1 treatment compansoo 15-1 acres) - - Norlh Hatch Pilot Treatments 111 6 acres ) South Magnolia Figure I 0. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin. northwest Colorado (Top: cyan polygons completed Jan. 201 I using hydro-axe; yellow polygons completed Jan. 20 I 2 using hydro-axe, roller-chop. and chaining: and remaining polygons completed April 2013 using hydro-axe). January 2011 hydro-axe treatment-site photos from North Hatch Gulch during Apri l (Lower left. aerial view) and October. 20 I I (Lower ri ght. ground view). 25 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP', CHARLES R. ANDERSON Jr. 1, DANIEL P. WALSH 1, ERIC J. BERGMAN 1, PETER KUECHLE 2, and JOHN ROTH2 'Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA 2 Advanced Telemetry Systems, Isanti, Minnesota 55040 USA Citation: Bishop, C. J., C. R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8): 1797-1806; DOI: I0.1002~wmg.229 '.._I ABSTRACT Our understanding of factors that limit mule deer (Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expelled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability of a VIT being retained until parturition was 0.766 (SE= 0.0605) and the probability ofa VIT being retained to within 3 days of parturition was 0.894 (SE = 0.0441 ). In a similar study using the original VIT wings (Bishop et al. 2007), the probability of a VIT being retained until parturition was 0.447 (SE= 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE= 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE= 0.0765) and VIT retention to within 3 days of parturition by 0.271 (SE= 0.0634). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least I neonate was 0.952 (SE= 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer.© 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM', CHARLES R. ANDERSON JR. 2, RY AN A. LONG 1, JOHN G. KIE 1, AND R. TERRY BOWYER' 'Department of Biological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2cotorado Parks and Wildlife, Grand Junction, Colorado 81505 USA Citation: Lendrum, P. E., C.R. Anderson Jr., R. A. Long, J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http·//dx.doi.org/10.1890/ES 12-00165.1 Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoileus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 26 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum', Charles R. Anderson Jr.2, Kevin L Monteith 1,J, Jonathan A. Jenks", R. Terry Bowyer' 1 Department ofBiological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA.4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: I0.13711oumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Met/1odology/Prindpal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range offemale mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-20 I0. Conclusions/Signijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP', MEVIN B. HOOTEN 1,2,J, CHARLES R. ANDERSON JR.", AND GEORGE WITTEMYER1 1 Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., M. B. Hooten, C. R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. http:l/dx.doi.org/l0.1890/12-1688. I Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a useravailability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most u~availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using OPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 27 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP 1, CHARLES R. ANDERSON JR2, AND GEORGE WITTEMYER 1 1Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., C. R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71% of deer returning in the first day, and 91% returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 2014 The Wildlife Society. ~ Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns offorage quality Patrick E. Lendrum•, Charles R. Anderson Jr.\ Kevin L Monteithc, Jonathan A. Jenksd, R. Terry Bowyer8 • Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA b Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81505, USA "Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, 3166, 1000 East University Avenue, Laramie 82071, USA d Department of Natural Resource Management, South Dakota State University, Box 2140B, Brookings 57007, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/l0.1016/j.mambio.2014.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns offecal nitrogen and remotely sensed greenness of vegetation (Normalized Difference Vegetation Index; NOVI) in spring 2010 in the Piceance Basin of northwestern Colorado, USA. NDVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values offecal nitrogen corresponded with increasing NOVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NOVI and fecal nitrogen. Moreover, these deer 'jumped,. rather than "surfed" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation and malnutrition during migration. 28 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN', RANDY T. LARSEN', MARKE. PETERSON2, CHARLES R. ANDERSON JR.3, KENT R. HERSEY', AND BROCK R. McMILLAN' 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, lIT 84602, USA 2 Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81 SOS, USA 4 Utah Division of Wildlife Resources, 1594 W North Temple, Salt Lake City, lIT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: IO. I002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to malebiased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/JOO females) and a relatively low (Monroe, UT, USA; 14 males/lO0 females) male/female ratio. We detennined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= 1.258; SE= 1.672; t = 0.752; P = 0.454), or synchrony of parturition (F = 1.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected.© 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Norlhrup, 1 Aaron B. A. Shafer,2 Charles R. Anderson Jr/ David W. Coltman4 and George Wittemyer 1 I Department offish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Department ofBiological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J.M., A. B. Shafer, C.R. Anderson Jr., D. W. Colbnan, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management, even in widely distributed panmictic species. 30 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch,• Joseph M. Northrup,b Megan F. McKenna,c Charles R. Anderson Jr,d Lisa Angeloni,a.e and George Wittemye.,..i, Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA ~partment of Fish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA ~atural Sounds and Night Skies Division, National Parle Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, dMammals Research Section, Colorado Pruks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA ceepartment of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA 1 Citation: Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology~ doi: I0.1093/beheco/aru 158. While visual fonns of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to detennine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a form of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA Citation: Anderson, C. R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 7'11' North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing penneability along migration routes by applying dispersed development plans (>2 well pads/km 2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 31 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle' • Mindy B. Rice2 • Charles R. Anderson 2 • Chad Bishop2 • N. T. Hobbs3 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 OQB. UK 2 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80S26, USA 3 Department of Ecosystem Science and Sustainability. Colorado State University, Fort Collins 80524, CO, USA 1 Citation: Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 201S. Asynchronous vegetation phenology enhances winter body condition ofa large mobile herbivore. Oecologia ISSN 0029-8S49~ 00110.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes fonns a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP 1, CHARLES R. ANDERSON JR.,2 and GEORGE WITTEMYER 1 • 3 1Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 20 IS. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: IO.I I l l/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over 500/4 of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers, mangers, and industry when attempting to mitigate habitat loss due to energy development. 32 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup•, Charles R. Anderson Jr and George Wittemyer•.l 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA 3 Citation: Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, 001: 10.111 J/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8-78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup,• Charles R. Anderson Jr.,2 Mevin B. Hooten,3 and George Wittemye.-4 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA 3 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 Department offish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Citation: Northrup, J.M., C. R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoi/eus hemionus) in the Piceance Basin of Colorado, USA, an area ofongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 33 �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse, 1.J Charles R. Anderson Jr., 1 Mark E. Peterson,•.2 and David R. Collins• 1 Mammals Research Section. Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90S26 USA 2Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80S23 USA Citation: Stonehouse, K. F., C.R. Anderson Jr.• M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations ofcaus~specific mortality in mule deer {Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly, these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The infonnation presented here is based primarily on field experience gained from a long tenn research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/leam/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes ofmortaJity; efforts discerning ultimate cause will require specific tissue sample collections, where possible, submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document, be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy fonn to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson,1 Charles R. Anderson Jr., 2 Joseph M. Northrup•, and Paul F. Doherty Jr. 1 1Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80S23 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90S26 USA Citation: Peterson, M. E., C. R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: IO. I l I l/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat, particularly for ungulate species. Understanding how this development impacts reproductive success metrics that are influential for ungulate population dynamics is important to guide management of ungulates. However, the influences of natural gas development on reproductive success metrics of mule deer Odocoi/eus hemionus have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceance Basin, northwest Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utero fetal rates were high and statistically indistinguishable between study areas. Fetal survival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival, particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning, such as implementing habitat treatments to improve forage availability and quality, minimizing disturbance to hiding and foraging habitat particularly during parturition, and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 34 �Variation in ungulate body fat: individual versus temporal effects Eric J. Bergman,1 Charles R. Anderson Jr., 1 Chad J. Bishop, 1 A. Andrew Holland, 1 and Joseph M. Northrup1 1Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Bergman, E. J., C. R. Anderson Jr., C. J. Bishop, A. A. Holland, and J.M. Northrup. 2018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82: 130-137, 001: I0: I002/jwmg.21334 ABSTRACT The use ofultrasonograhic measurements of muscle and body fat represent a relatively new data stream that can be used to address questions regarding ungulate condition. We have learned that measurements of body fat and presumably overall body condition among individual animals, even those taken from the same herd at that same time, are highly variable. Relatively little consideration has been given to the sources of variation in body fat and other physiological parameters in wildlife populations. We evaluated the components of variation in late-winter mule deer (Odocoileus hemionus) body fat estimates: sampling variation (i.e., variation induced by the particular set of individuals that were sampled) and process variation (i.e., variation stemming from biological processes) with a long-tenn data set (2002-2015) from Colorado, USA. We collected our data from across Colorado as part of historicaJ research, ongoing research, and periodic population monitoring programs. Mean percent ingesta-free body fat (%IFBF) for sampled mule deer was 7.20 :1: 1.20% (SD). Covariates related to individuaJ deer explained approximately 4% of the total variation in %IFBF and annual effects explained an additional 13% of the variation. Substantial residua] variation in %1FBF (83%) remained unexplained. The source of the 83% of unexplained variation is partially linked to fine-scale spatial dynamics but also additional individual metrics we were unable to capture, primarily the presence or absence of dependent young. We speculate that the primary factors influencing late-winter mule deer body fat and overall condition are individual in nature. These results present a cautionary check on herd level inference that can be made from individuaJ late-winter body fat estimates and we postulate that for mule deer, alternative and additional body condition metrics may offer added utility in management scenarios. However, an important next step to better understand wildlife population health is to evaJuate the sources and magnitude of variation within other body condition metrics, with the goal of further refining data that can better allow biologists to incorporate herd health into population management recommendations. Mortality of mule deer fawns in a natural gas development area Mark E. Peterson,' Charles R. Anderson Jr.,1 Joseph M. Northrup 1,and Paul F. Doherty Jr.' 1 Department ofFish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018. Mortality of mule deer fawns in a natural gas development area. Journal ofWildlifeManagement 82:1135-1148, 001: 10.1002/jwmg.2l476 ABSTRACT Recent natural gas development has caused concern among wildlife managers, researchers, and stakeholders over the potential effects on wildlife and their habitats. Specifically, understanding how this development and other factors influence mule deer (Odocoileus hemionus) fawn (i.e., 0--6 months old) mortality rates, recruitment, and subsequently population dynamics have been identified as knowledge gaps. Thus, we tested predictions concerning the relationship between natural gas development, adult female, fawn birth, and temporal (weather) characteristics on fawn mortality in the Piceance Basin of northwestern Colorado, USA, from 2012-2014.We captured and radio-collared 184 fawns and estimated apparent cause-specific mortality in areas with relatively high or low levels of natural gas development using a multi-state model. Mean daily predation probability was similar in the high versus low development areas. Predation was the leading cause of fawn mortality in both areas and decreased from 0-14 days old. Black bear (Ursus americanus; 22% of all mortalities, n = 17) and cougar (Fe/is concolor; 36% of all mortalities, n =6) predation was the leading cause of mortality in the high and low development areas, respectively. Predation of fawns was negatively correlated with the distance from a female's core area to a producing well pad on winter or summer range. Contrary to expectations, predation of fawns was positively correlated with rump fat thickness of adult females. Well pad densities and development activity were relatively low during our study, indicating that the observed intensity of development did not appear to influence daily predation probability. Our results suggest maintaining development activity thresholds at levels we observed to potentially minimize the effects of development on fawn mortality. However, we caution that higher development intensity and drilling activity in flatter, less rugged areas with less concealment cover could influence fawn mortality. Managers should maintain low development densities in areas where topography and vegetation offer less concealment. Overall, region-specific data (e.g., development intensity, topography, predator assemblages, and associated predation risk) are needed to better understand the effects of natural gas development on fawn mortality. 35 �Using maternal mule deer movements to estimate timing of parturition and assist fawn captures Mark E. Peterson, 1 Charles R. Anderson Jr.,2 Mathew W. Alldredge 2,and Paul F. Doherty Jr. 1 1 Depanment offish, Wildlife and Conservation Biology, Colorado State University, Fon Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 90526 USA Citation: Peterson, M. E., C. R. Anderson Jr., M. W. Alldredge, and P. F. Doherty Jr. 2018. Using maternal mule deer movements to estimate timing of parturition and assist fawn captures. Wildlife Society Bulletin; DOI: l0.1002/wsb.935 ABSTRACT Movement patterns of maternal ungulates have been used to determine parturition dates and aid in locating fawns, which may be important for understanding reproductive rates (e.g., pregnancy and fetal), but such methods have not been validated for mule deer (Odocoileus hemionus). We first determined timing of parturition using vaginal implant transmitters (VITs) and then predicted timing of parturition using VITs in conjunction with Global Positioning System collar data in the Piceance Basin of northwestern Colorado, USA, during 2012-2014. We examined daily movement rate to determine differences in movement rate among days (7 days pre- and postpartum) and for movement patterns indicative of parturition. Mean daily movement rate (m/day) of I 02 maternal deer decreased by 46% from I day preparturition (mean = 1,253, SD = 1,091) to parturition date (mean =682, S = 574), and remained at this low rate 1-7 days postpartum. We applied an independent data set to validate predicted parturition dates based on daily movement rate. We estimated day of parturition correctly (i.e., day 0), within 1-3 days postparturition, and_4 days postparturition offield-reported dates for 10 (29%), 21 (60%), and 4 (II%) maternal females, respectively. For novel data sets, we predict that a mule deer female whose daily movement rate decreases by_46% and remains low _3 days postparturition particularly when preceded by a sudden increase in movement-has given birth. However, we caution that disturbance of deer by field crews should be minimized, and if birth sites are not found, neonatal mortality will be underestimated. Our results can help determine timing and general location of parturition as an aid in capturing fawns when the use ofVITs is not feasible, with the ultimate objective of estimating pregnancy, fetal, and fawn survival rates if birth sites are found. 36 V �u Appendix B. Winter utilization distributions of adult female mule deer during a post-drilling, production phase (2012 -2018) in the Piceance Basin, Colorado USA. u 37 �Winter Range Analysis for Piceance Mule Deer Summary Prepared for: Colorado Parks and Wildlife 317 W. Prospect Rd ., Fort Collins, CO 80526 Prepared by: Hall Sawyer and Andrew Telander Western Ecosystems Technology, Inc. 200 South 2nd St. , Laramie, Wyoming November 2018 ~ NATURAL RESOURCES • SCIENTIFIC SOLUTIONS WESli 38 �~ OVERVIEW The purpose of this analysis was to delineate winter distribution patterns within four designated study areas (North Magnolia, South Magnolia, Ryan Gulch, and North Ridge) using GPS data collected from mule deer during six winters (2012-13 through 2017-18). The GPS data were used to estimate overall utilization distributions for each study area, so that managers could map and visualize intensity of use within the four winter ranges. Recognizing which winter range parcels are used more intensively than others can help inform management and land-use decisions associated with this important mule deer herd. APPROACH Data Collection Colorado Parks and Wildlife (CPW) provided us with GPS data collected during the winters of, 2012-13, 201314, 2014-15, 2015-16, 2016-17, and 2017-18. These data were combined into one data frame so that all fields could be organized, cleaned and formatted for analysis. Winter Distribution To ensure data reflected winter use rather than late-autumn or early-spring migration, we restricted our analysis to data collected between December 01 and March 15 each winter. We further restricted analysis to individuals that collected 30 or more days of data. Table 1 shows sample sizes listed by year and study area. Once winter sequences were extracted for each animal, we used the Brownian bridge movement model (BBMM: Horne et al. 2007) and the "BBMM" package in R to estimate a winter utilization distribution (UD) for each animal. The BBMM was preferred over traditional kernel-based estimators because it incorporates movement of the animal by using sequential locations common with GPS studies. Winter distribution patterns were estimated using 444,822 locations collected from 925 GPS-collared deer during the winters 2012-2017 (Fig. 1, Table 1). Within each study area (n = 4), we then averaged the individual animal UDs to create a population-level UD for each winter, including 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18. As a final step, we then averaged all 6 winter UDs together in each study area to produce a final UD intended to represent the overall winter distribution (Fig. 2). As an extra step, we created a shapefile of UD contours ranging from 0.1 to 0.9, to assist with visualizing and differentiating high-use from low-use areas (Figs.3 - 8). Such contours (e.g., 0.50) are often used to help agencies identify or modify existing crucial winter ranges boundaries and target areas for habitat improvements. 39 �WY Clly UT D North Magnolia me * Soltla'<> a co "o.w North Ridge w+ Ryan Gulch " • 0 f 3 °"1a Source H•to-"lof G«,Q,~ m, Cmr~AIW'Sy!-litffl UA0 198]UTM Zonc 13:.1 0 C.. 1118/20 1& Aur:'lor R~MM"I ~ WESli Figure 1. GPS locations (n = 444,822) locations collected from 925 GPS-collared mule deer during the winters of 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18 in the Piceance Basin of northwest Colorado. 40 �Intensity of Use WY • ,. C'O = D High North Ridge Low Ryan Gulch l m, .,,, • Dal.a Sourte Na.tl0n3J Geogar,rvc Coou>rnaie System N;.o 1983 UT M Zent tlN 0.1e i 14'2'018 ,\Utnol R And~uon ~ WESli Figure 2. Winter utilization distributions (UD) of mule deer, averaged from winters 2011-12, 2012-13, 201314, 2014-15, 2015-16, 2016-17, and 2017-18. 41 �North Magnolia North Ridge Ryan Gulch South Magnolia contour contour contour contour 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 Contem may noc rer>e(;1 Natl0a3 Geogtapr11c·s e-urr•.: 1map p(>JIC) Sourcep ~a;.ona:1 ,Geoo1aph1t Es,, OeLo1me 1-fERE .UNEP \\'Cl.IC 'USG1:t rl ASA, ESA ◄'-IR:Afl GEBC'1 1,t)r,.p. mcr~men1PCo<p •.,en. Figure 3. Selected contou rs (0. 1, 0.2., 0.3, 0.4, 0.5) from winter utilization distributions (UD) of mule deer, averaged from winters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18. 42 �contour contour 0.1 0.1 contour 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 . 0.1 Figure 4. Selected contours (0.1, 0.2., 0.3, 0.4, 0.5) from w inter utilization distributions (UD) of mule deer, averaged from winters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18. 43 �Figure 5. Selected contours (0.1, 0.2., 0.3, 0.4, 0.5) from winter utilization distributions (UD) of mule deer in North Ridge study area, averaged from winters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18. 44 �Figure 6. Selected contours (0.1, 0.2., 0.3, 0.4, 0.5) from winter utilization distributions (UD) of mule deer in North Magnolia study area, averaged from winters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017- 18. 45 �Figure 7. Selected contours (0.1, 0.2., 0.3, 0.4, 0.5) from winter utilization distributions (UD} of mule deer in South Magnolia study area, averaged from w inters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 201718. 46 �Figure 8. Selected contours (0.1, 0.2., 0.3, 0.4, 0.5) from winter utilization distributions (UD) of mule deer in Ryan Gulch study area, averaged from winters 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18. Table 1. Sample sizes of GPS-collared mule deer by winter and study area. 47 �Study Area North Magnolia North Magnolia North Magnolia North Magnolia North Magnolia North Magnolia North Magnolia North Ridge North Ridge North Ridge North Ridge North Ridge North Ridge North Ridge Ryan Gulch Ryan Gulch Ryan Gulch Ryan Gulch Ryan Gulch Ryan Gulch Ryan Gulch South Magnolia South Magnolia South Magnolia South Magnolia South Magnolia South Magnolia South Magnolia All Study Areas Winter Period 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Sub-total 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Sub-total 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Sub-total 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 Sub-total Total n 28 43 44 54 52 16 237 20 39 43 43 46 18 209 27 42 42 54 48 24 237 27 41 45 56 46 27 242 925 REFERENCES Horne, J. S., E. 0. Garton, S. M. Krone, and J. S. Lewis. 2007. Analyzing animal movements using Brownian Bridges. Ecology 88:2354-2363. Sawyer, H., M. J. Kauffman, R. M. Nielson, and J. S. Horne. 2009. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecological Applications 19:2016-2025. 48 --•---- ----------- �Colorado Parks and Wildlife July I, 2019- June 30, 2020 WILDLIFE RESEARCH REPORT State of _______C.. ;ao__,;lo:a. ; .ra=-d__oa.,.__ _ _ _ : __P=ar__ksaa.. .,; . ,. a...... n d__W.. . . . .,il___ dl__iTI___ e _ _ _ _ _ _ _ _ _ _ __ Cost Center 3430 : . __M__a=m=m . . . a_____l___s ___R__e__ se__a___rc__h_______________ Work Package 3001 : =D=e=er,.._C=-o=n=s=erv---=a=ti=on=-=-------------Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:_--'-'W---'-2;;:..;4~3-=--R.;::..;4,_____ _ __ Period Covered: July 1, 2019 - June 30, 2020 Author: C. R. Anderson, Jr. Personnel: D. Bilyeu-Johnston, D. Collins, B. deVergie, D. Finley, L. Gepfert, T. Knowles, B. Petch, J. Rivale, Z. Swennes, M. Way, CPW; L. Belmonte, BLM; J. Northrup, B. Gerber, G. Wittemyer, Colorado State University; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, Caerus Oil and Gas LLC, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTMTY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Recover remaining GPS collars to address the final year of adult female mule deer habitat use and behavioral patterns in 4 study areas experiencing varying levels of energy development in the Piceance Basin, northwest Colorado. 2. Finalizing monitoring of vegetation responses to habitat treatments for assessing efficacy of habitat improvement projects to mitigate energy development disturbances to mule deer. 3. Complete evaluation of large herbivore use of habitat treatments during summer/fall using remote camera sampling. PROJECT OVERVIEW AND RESEARCH SUMMARY We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent preliminary and final results of a 10-year research project addressing habitat improvements as mitigation and evaluation of deer responses to energy development activities to inform future development planning options on important seasonal ranges. From2008-2019, we monitored deer on 4 winter range study areas representing relatively high (Ryan Gulch, South Magnolia) and low (North Magnolia, North Ridge) levels of development activity (Fig. 1) to address factors influencing deer behavior and demographics and to evaluate success of habitat treatments as a mitigation option. We recorded adult female habitat use and movement patterns; estimated neonatal, overwinter fawn and annual adult female survival; estimated annual early and late winter body condition, pregnancy and fetal rates of adult females; and estimated annual mule deer abundance among study areas. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of2 treatment areas (Fig. 2) with minor (North Magnolia) and extensive (South Magno1ia) energy development, respectively. During this research segment, we recovered the remaining store-on-board GPS collars from adult female mule deer during spring/summer 2019, completed the final year of measuring vegetation 2 �responses of habitat treatments completed spring 2013 and collected camera grid detections of summer/fall herbivore use of habitat treatment and control sites (preliminary results reported in Appendix B). Based on final (migration, mule deer behavioral responses. reproductive success and neonate survival; see Anderson 2019 for detailed methods and results and Appendix A for publication abstracts) and preliminary data analyses (vegetation and herbivore response to habitat treatments, Appendix B) for this I0-year project: ( 1) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition. annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas. with higher variability among study areas early winter, primarily due to December lactation rates. and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to 103% in North Magnolia. but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge (Fig. 3); (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas. and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures (Fig. 4); (5) mule deer exhibited behavioral plasticity in relation to energy development. without evidence of demographic effects. where disturbance distance varied relative to diurnal extent and magnitude of development activity (Fig 5), which provide for useful mitigation options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) during 2012 when drought conditions persisted during the third trimester of doe parturition (Fig. 6). Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning. Final data collection efforts for this project were completed by spring 2020. Collaborative research with agency biologists. graduate students, and university professors has produced 22 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011. Peterson et al. :!O 18b); development and evaluation of a remote mule deer collaring device (Bishop et al. 2019); mule deer migration relative to energy development (Lendrum et al. 2012, 20 I3. 20 I4; Anderson and Bishop 20 I4). improved approaches to address animal habitat use pattems (Northrup et al. 2013 ); mule deer response to helicopter capture and handling (Northrup et al. 2014a); potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014); mule deer genetics in relation to body condition and migration (Northrup et al. 2014b); acoustic monitoring to investigate spatial and temporal factors influencing mule deer vigilance (Lynch et al. 2014) and foraging behavior (Northrup et al. 2019); the relationship of plant phenology with mule deer body condition (Seral et al. 2015); approaches to identify cause-specific mortality in mule deer from field necropsies (Stonehouse et al. 2016); the influence of individual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman et al. 2018); and mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 20 I5, 2016a. 20 I6b, in press. Peterson et al. 2017. 2018a). These publications are summarized in Appendix A and preliminary results describing vegetation and herbivore responses to habitat treatments are reported in Appendix B. We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado and elsewhere. 3 �~, ' Mule Deer Winter Range Study Areas Mule deer study areas Well Pad• & Facilities Q ! 1nc:~lop:nen1 - De-w"!k)f)mtnt f.JCll'IIIE-5 uew1nMagnooa SOuin "1apn0ha 10 Mdo Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development fac ilities in the Piceance Basin of n011hwest Colorado. winter 2013/ 14 (Accessed http://cogcc.state.co.us/ December 31.20 13: energy development drilling activity has been minor since 2013). 4 �Nollh Magnolia treatement sites (587 acres) Bea,Set_ 15_35b_andG 8e,rSel_ 1_BancA_E I 8e3rSft_36_54andJ GreasewoooSet_g16_g~9 Greasev-.ood5 et_g1 _g 1$ Gre a; awoodSet_ g30_g4 2 l eeOvers,ghts_J_fand 16_ 17 Mechantcal treatment comparison {54 acres) - - No11h Hatch Pilot Treatmen ts! 116 1cres1 Soutn Magnolia J Figure 2. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin. northwest Colorado (Top; cyan polygons completed Jan 20 I I using hydro-axe: yellow pol ygons completed Jan 20 12 using hydro-axe. ro ller-chop. and chaining; and remaining polygons completed Apr 2013 using hydro-axe). January 201 1 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left. aerial view) and October. 20 I I (Lower right. ground view). 5 �Piceance Basin late w inter mule deer density 35.00 30.00 25.00 NE -"" -:::(IJ (IJ / 20.00 15.00 .I. 0 4 .. ....... 10.00 :I: 5.00 - - North Ridge • • • • • • Ryan Gulch - • North Magnolia - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 3. Mule deer density estimates and 95% Cl (enor bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado. late w inter 1009- 10 18. Figure 4. M ule deer study areas in the Piceance Basin of northwestern Colorado. USA (Top). spring 2009 migration routes of adult fema le mule deer (n = 51: Lower left). and active natural-gas well pads (black dots) and roads (state. county. and natural-gas: white lines) from May 1009 (Lower right: from Lendrum et al. 20 12: http://d:-:.doi.om/ I 0. 1890 ES 11-00165. 1). 6 �'i' t _ _ _ _ _ r Ptod 400 Prod 600 P,ud 800 Prc,d 1000 Oriti ,IOt, 01111 600 Oriti 600 Oull 1000 On~ 500 Onll BOC 01111 100~ Covarut1os - r -- 'i'' ·r r Prod 400 Proo 600 P1od 800 Proa '1000 Outt 1\ t)~ n I <t Figure 5. Posterior distributions of populati on-level coeffi cients related to natural gas development for RSF models during the day (top) and ni ght (bottom) for 53 adu lt fema le mul e deer in the Piceance Basi n. northwest Colorado. Dashed line indicates 0 selection or avoidance (below the line) of the habitat features. ' Drill' and 'Prod' represent d rilling and producing well pads, respectively. T he numbers fo llow ing ' Dri ll' or ' Prod' represent the distance from respect ive well pads evaluated (e.g., 'Drill 600' is the number of well pads with active dri lling between 400-600 m from the deer location; from No1thrup et al. 20 15; http://o nli nelibrarv.wilev.com/do i/ I0.1 I 1l /gcb.13037/abstract). Road disturbance was relatively minor (- 60-120 m, not illustrated above). 1.00 (1) 0.80 r-t- - ..._ ~ .._ rt co.... ro> 0 .60 -~ ~ Cl) ro 0.40 Q) LL I I 0 .20 0.00 2012 20 13 2014 Year o High development □ Low development I Figure 6. Model averaged estimates of mu le deer fetal s urvival from early Marc h unti l bi 1th (late May-June) in high and low energy develo pment study areas of the Piceance Basin, northwest Colorado, 2012- 20 14 (from Peterson et a l. 20 17; http://www. bioone.org/doi/ pdf/ I 0.298 1/wlb.00341 ). 7 �LITERATURE CITED Anderson, C.R., Jr. 2019. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Federal Aid in Wildlife Restoration Annual Report W-243-R3, Ft. Collins, CO USA. Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79th North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Bergman, E. J., C.R. Anderson Jr., C. J. Bishop, A. A. Holland, and J.M. Northrup. 2018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82:130-137, DOI: 10:1002/jwmg.21334 Bishop, C. J., C.R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8): 1797-1806; DOI: 10.1002/jwmg.229 Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman, C. R. Anderson Jr., D. Kilpatrick, J. Bakel, and C. Fabvre. 2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725; doi.org/10.1002/wsb.1034 Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson, Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 Lendrum, P. E., C.R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.org/10. Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: 10.1371/journal.pone.0064548 Lendrum, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.org/10.1016/j .mambio.2014.05.005 Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: 10.1093/beheco/aru 158. Northrup, J. M., M. 8. Hooten, C. R. Anderson, Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731738; DOI: 10.1002/jwmg.705 Northrup, J.M., A. B. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12 l 89 Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037. Northrup, J.M., C.R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016a. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:27462757 8 �Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016b. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.11 11 /ddi. I2417 Northrup, J.M., A. Avrin, C.R. Anderson Jr., E. Brown, and G. Wittemyer. 2019. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal ofMammalogy 100:14791489; https://doi.org/10.1093/imammal/gyzl 24 Northrup, J.M., C.R. Anderson Jr., 8. D. Gerber, and G. Wittemyer. In Press. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs. Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: 10.1111/wlb.00341 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018a. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82:1135-1148, DOI: 10.1002/jwmg.21476 Peterson, M. E., C. R. Anderson Jr., M. W. Alldredge, and P. F. Doherty Jr. 2018b. Using maternal mule deer movements to estimate timing of parturition and assist fawn captures. Wildlife Society Bulletin 42:616-621; DOI: 10.1002/wsb.935 Searle, K. R., M. 8. Rice, C. R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549; DOI 10.1007/s00442-015-3348-9 Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883 . ...,_; Prepared by_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ Charles R. Anderson, Jr., Mammals Research Leader 9 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP', CHARLES R ANDERSON Jr. 1, DANIEL P. WALSH', ERIC J. BERGMAN', PETER KUECHLE2, and JOHN ROTH 2 1 Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA Advanced Telemetry Systems, Isanti, Minnesota 55040 USA 2 Citation: Bishop, C. J., C. R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8):1797-1806; DOI: 10.1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoi/eus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expelled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood oflocating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability ofa VIT being retained until parturition was 0.766 (SE= 0.0605) and the probability of a VIT being retained to within 3 days of parturition was 0.894 (SE= 0.0441). In a similar study using the original VIT wings (Bishop et al. 2007), the probability ofa VIT being retained until parturition was 0.447 (SE= 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE= 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE= 0.0765) and VIT retention to within 3 days of parturition by 0.271 (SE= 0.0634). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least 1 neonate was 0.952 (SE= 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately I 2 person-hours per detected neonate. As a guide for researchers planning future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer.© 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM 1, CHARLES R ANDERSON JR 2, RYAN A. LONG 1, JOHN G. KIE', AND R TERRY BOWYER' 1 0epartment of Biological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2colorado Parks and Wildlife. Grand Junction. Colorado 81505 USA Citation: Lendrum, P. E., C. R. Anderson Jr., R. A. Long. J. G. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http://dx.doi.org/ I0.1890/ES 12-00165. I Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoi/eus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence of natural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = I 67) among four study areas that had varying degrees ofnatural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes. where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate to,vards migration routes. If avoidance is feasible, then deer may select areas further from development. whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 10 u �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum', Charles R. Anderson Jr.\ Kevin L. Monteith'·\ Jonathan A. Jenks\ R. Terry Bowyer' 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,~ Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: 10.137l~oumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GPS collars, and observed patterns of spring migration during 2008-20 l 0. Condusions/Signijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shoner migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP 1, MEVIN 8. HOOTEN 1,2,3, CHARLES R. ANDERSON JR.\ AND GEORGE \VITTEMYER 1 1 Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA ~Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA 2 Citation: Northrup, J. M., M. 8. Hooten, C. R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): I 4S6-1463. http://dx.doi.org/10. I 890/12-1688.1 Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a us~availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer OPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics. exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 11 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR2, AND GEORGE WITIEMYER 1 'Department of Fish. Wildlife. and Conservation Biology, Colorado State University. 1474 Campus Delivery. Fort Collins. Colorado 80523 USA Mammals Research Section Colorado Parks and Wildlife. 711 Independent Avenue. Grand Junction, Colorado 81505 USA 2 Citation: Northrup. J. M.. C. R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured {controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71 % of deer returning in the first day, and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 2014 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns offorage quality Patrick E. Lendrum•, Charles R. Anderson Jr.b, Kevin L. Monteith~, Jonathan A. Jenksd, R. Terry Bowyer• a Department ofBiological Sciences, Idaho State University, 921 South 8th Avenue. Stop 8007. Pocatello 83209, USA " Mammals Researth Section Colorado Parks and Wildlife, 71 I Independent Avenue. Grand Junction 81505, USA .: Wyoming Cooperative Fish and Wildlife Research Unit. Department of Zoology and Physiology. University of Wyoming.3166. 1000 East University Avenue, Laramie 82071. USA "Department of Natural Resource Management. South Dakota State University, Box 21408. Brookings 57007. USA Citation: Lendrum. P. E .• C.R. Anderson Jr.• K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 2014. Relating the movement ofa rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http·//dx.doi.org/J0.1016/j mambio.20)4.05 005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn. which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greeMess of vegetation (Nonnalized Difference Vegetation Index; NOVI) in spring 2010 in the Piceance Basin of northwestern Colorado, USA. NOVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NOVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NOVI and fecal nitrogen. Moreover, these deer "jumpect•· rather than ·•surfed.. the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation and malnutrition during migration. 12 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN', RANDY T. LARSEN 1, MARKE. PETERSON2, CHARLES R. ANDERSON JR. 3, KENT R. HERSEY', AND BROCK R. McMILLAN' 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA 2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81505, USA 4 Utah Division of Wildlife Resources, 1594 W North Temple, Salt Lake City, UT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to male• biased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males/100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= 1.258; SE= 1.672; t = 0.152; P = 0.454), or synchrony of parturition (F= 1.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected.© 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup', Aaron B. A. Sharer2, Charles R. Anderson Jr.3, David W. Coltman", and George Wittemyer 1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO. USA 2 Department of Evolutionary Biology. Evolutionary Biology Centre. Uppsala University. Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J. M., A. B. Shafer, C. R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: 10.1111/eva.12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management. even in widely distributed panmictic species. 13 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch•, Joseph M. Northrupti, Megan F. Mc Kenna~. Charles R. Anderson Jr. d, Lisa Angeloniu, and George Wittemye..-.t, Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA "Department offish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 'Natural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, dMammals Research Section. Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA COepanment ofBiology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA 0 Citation: Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: 10.1093/beheco/aru I58. While visual fonns of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways extemaJ stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to detennine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a fonn of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA Citation: Anderson, C. R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-S0 in Transactions of the 7f1h North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners. PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013: Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer ( Odocoi/eus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing penneability along migration routes by applying dispersed development plans (<2 well pads/km2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly. we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 14 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle1 • Mindy 8. Rice2 • Charles R. Anderson 2 • Chad Bishop2 • N. T. Hobbs3 1 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 OQB, UK 2 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80S26, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80524, CO, USA Citation: Searle, K. R., M. B. Rice, C. R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029-8549; DOI I0.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP 1, CHARLES R. ANDERSON JR. 2, and GEORGE WITTEMYER 1 • 3 1Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2Manunals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO. USA Citation: Northrup, J.M., C.R. Anderson, Jr.. and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: 10.1111/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers, mangers, and industry when attempting to mitigate habitat loss due to energy development 15 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup•, Charles R. Anderson Jr, and George Wittemyer 1.J 'Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado. USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individuaJ deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8-78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup•, Charles R. Anderson Jr. 2, Mevin B. Hooten 3, and George Wittemyer' 'Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA 3U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 Department of Fish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Northrup, J.M., C.R. Anderson. Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado, USA, an area ofongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mu le deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development. with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development. but only to a degree. At higher development densities than seen in this area. such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 16 V �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse•.2, Charles R. Anderson Jr. 1, Mark E. Peterson•.2, and Da,·id R. Collins' 1Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 90526 USA 2 Department offish, Wildlife and Conservation Biology, Colorado State University, Fon Collins, Colorado 80523 USA Citation: Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoi/eus hemionus). Colorado Parks and Wildlife Technical Repon No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly, these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The information presented here is based primarily on field experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/leam/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality; efforts discerning ultimate cause will require specific tissue sample collections, where possible, submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document, be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson', Charles R. Anderson Jr. 2,Joseph M. Northrup 1,and Paul F. Doherty Jr. 1 1 Department offish, Wildlife and Conservation Biology, Colorado State University, Fon Collins, Colorado 80523 USA Mammals Researeh Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 90526 USA 2 Citation: Peterson, M. E., C. R. Anderson Jr., J.M. Nonhrup, and P. F. Doheny Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: IO. I I I I/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat, particularly for ungulate species. Understanding how this development impacts reproductive success metrics that are influential for ungulate population dynamics is important to guide management of ungulates. However, the influences of natural gas development on reproductive success metrics of mule deer Odocoi/eus hemionus have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceance Basin, northv,est Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utero fetal rates were high and statistically indistinguishable between study areas. Fetal survival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival, particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning, such as implementing habitat treatments to improve forage availability and quality, minimizing disturbance to hiding and foraging habitat particularly during parturition, and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 17 �Variation in ungulate body fat: individual versus temporal effects EricJ. Bergman•. Charles R. Anderson Jr:, Chad J. Bishop'. A. Andrew Holland 1• and Joseph M. Northrup2 1 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2Oepartment of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Bergman, E. J., C.R. Anderson Jr., C. J. Bishop, A. A. Holland, and J.M. Northrup. 2018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82:130-137, 001: 10:1002/jwmg.21334 ABSTRACT The use of ultrasonograhic measurements of muscle and body fat represent a relatively new data stream that can be used to address questions regarding ungulate condition. We have learned that measurements of body fat and presumably overall body condition among individual animals, even those taken from the same herd at that same time, are highly variable. Relatively little consideration has been given to the sources of variation in body fat and other physiological parameters in wildlife populations. We evaluated the components of variation in late-winter mule deer (Odocoileus hemionus) body fat estimates: sampling variation (i.e., variation induced by the particular set of individuals that were sampled) and process variation (i.e., variation stemming from biological processes) with a long-term data set (2002-2015) from Colorado, USA. We collected our data from across Colorado as part of historical research, ongoing research, and periodic population monitoring programs. Mean percent ingesta-free body fat (%1FBF) for sampled mule deer was 7.20 :I: 1.20% (SD). Covariates related to individual deer explained approximately 4% of the total variation in %1FBF and annual effects explained an additional 13%ofthe variation. Substantial residual variation in %IFBF (83%) remained unexplained. The source of the 83% of unexplained variation is partially linked to fine-scale spatial dynamics but also additional individual metrics we were unable to capture, primarily the presence or absence of dependent young. We speculate that the primary factors influencing late-winter mule deer body fat and overall condition are individual in nature. These results present a cautionary check on herd level inference that can be made from individual late-winter body fat estimates and we postulate that for mule deer, alternative and additional body condition metrics may offer added utility in management scenarios. However, an important next step to better understand wildlife population health is to evaluate the sources and magnitude of variation within other body condition metrics, with the goal of further refining data that can better allow biologists to incorporate herd heaJth into population management recommendations. Mortality of mule deer fawns in a natural gas development area Mark E. Peterson•, Charles R. Anderson Jr. 2• Joseph M. Northrup 1,and Paul F. Doherty Jr. 1 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2 Citation: Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82:1135-1148, DOI: 10.1002/jwmg.21476 ABSTRACT Recent natural gas development has caused concern among wildlife managers, researchers, and stakeholders over the potential effects on wildlife and their habitats. Specifically, understanding how this development and other factors influence mule deer (Odocoileus hemionus) fawn (i.e., 0-6 months old) mortality rates, recruitment, and subsequently population dynamics have been identified as knowledge gaps. Thus, we tested predictions concerning the relationship between natural gas development. adult female, fawn birth, and temporal (weather) characteristics on fawn mortality in the Piceance Basin of northwestern Colorado, USA, from 2012-2014.We captured and radio-collared 184 fawns and estimated apparent cause-specific mortality in areas with relatively high or low levels of natural gas development using a multi-state model. Mean daily predation probability was similar in the high versus low development areas. Predation was the leading cause of fawn mortality in both areas and decreased from 0-14 days old. Black bear ( Ursus americanus; 22% of all mortalities, n = 17) and cougar (Fe/is concolor; 36% of all mortalities, n = 6) predation was the leading cause of mortality in the high and low development areas, respectively. Predation of fawns was negatively correlated with the distance from a female's core area to a producing well pad on winter or summer range. Contrary to expectations, predation of fawns was positively correlated with rump fat thickness of adult females. Well pad densities and development activity were relatively low during our study, indicating that the observed intensity of development did not appear to influence daily predation probability. Our results suggest maintaining development activity thresholds at levels we observed to potentially minimize the effects of development on fawn mortality. However, we caution that higher development intensity and drilling activity in flatter, less rugged areas with less concealment cover could influence fawn mortality. Managers should maintain low development densities in areas where topography and vegetation offer less concealment. Overall, region-specific data (e.g., development intensity, topography, predator assemblages, and associated predation risk) are needed to better understand the effects of natural gas development on fawn mortality. 18 �Using maternal mule deer movements to estimate timing of parturition and assist fawn captures Mark E. Peterson•. Charles R. Anderson Jr. 2, Mathew W. Alldredge2.and Paul F. Doherty Jr.' 'Department of Fish, Wildlife and Conservation Biology, Colorado State University. Fon Collins, Colorado 80523 USA 2Mammals Research Section. Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 90526 USA Citation: Peterson. M. E., C. R. Anderson Jr., M. W. Alldredge, and P. F. Doherty Jr. 2018. Using maternal mule deer movements to estimate timing of parturition and assist fawn captures. Wildlife Society Bulletin 42:616-621; DOI: 10.1002/wsb.935 ABSTRACT Movement patterns of maternal ungulates have been used to detennine parturition dates and aid in locating fawns, which may be important for understanding reproductive rates (e.g., pregnancy and fetal), but such methods have not been validated for mule deer (Odocoileus hemionus). We first detennined timing of parturition using vaginal implant transmitters (VITs) and then predicted timing of parturition using VITs in conjunction with Global Positioning System collar data in the Piceance Basin of northwestern Colorado, USA, during 2012-2014. We examined daily movement rate to detennine differences in movement rate among days (7 days pre- and postpartum) and for movement patterns indicative of parturition. Mean daily movement rate (m/day) of 102 maternal deer decreased by 46% from 1 day preparturition (mean = 1,253, SD= 1,091) to parturition date (mean = 682, S = 574), and remained at this low rate 1-7 days postpartum. We applied an independent data set to validate predicted parturition dates based on daily movement rate. We estimated day of parturition correctly (i.e., day 0), within 1-3 days postparturition, and_4 days postparturition of field-reported dates for 10 (29%), 21 (60%), and 4 ( 11 %) maternal females, respectively. For novel data sets, we predict that a mule deer female whose daily movement rate decreases by _46% and remains low _3 days postparturition particularly when preceded by a sudden increase in movement-has given birth. However, we caution that disturbance of deer by field crews should be minimized, and if birth sites are not found, neonatal monality will be underestimated. Our results can help detennine timing and general location of parturition as an aid in capturing fawns when the use of VITs is not feasible, with the ultimate objective of estimating pregnancy, fetal, and fawn survival rates if birth sites are found. On-animal acoustic monitoring provides insight to ungulate foraging behavior Joseph M. Northrup•, Alexandra Avrin 1• Charles R. Anderson, Jr. 2, Emma Brownl, and George Wittemyer 1 'Department of Fish, Wildlife and Conservation Biology, Colorado State University. Fort Collins, Colorado 80523 USA ?Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 90526 USA 3 National Parle Service Natural Sounds and Night Skies Division, Fon Collins, CO 80525 USA Citation: Nonhrup, J.M., A. Avrin, C.R. Anderson Jr., E. Brown, and G. Wittemyer. 2019. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal of Mammalogy I 00: 1479-1489: https·//doi org/ I0.1093/jmammal/gyz 124 Abstract Foraging behavior underpins many ecological processes; however. robust assessments of this behavior for free-ranging animals are rare due to limitations to direct observations. We leveraged acoustic monitoring and GPS tracking to assess the factors influencing foraging behavior of mule deer (Odocoileus hemionus). We deployed custom-built acoustic collars with GPS radiocollars on mule deer to measure location-specific foraging. We quantified individual bites and steps taken by deer, and quantified two metrics of foraging behavior: the number of bites taken per step and the number of bites taken per unit time. which relate to foraging intensity and efficiency. We fit statistical models to these metrics to examine the individual, environmental. and anthropogenic factors influencing foraging. Deer in poorer body condition took more bites per step and per minute and foraged for longer irrespective of landscape properties. Other patterns varied seasonally with major changes in deer condition. In December, when deer were in better condition, they took fewer bites per step and more bites per minute. Deer also foraged more intensely and efficiently in areas of greater forage availability and greater movement costs. During March, when deer were in poorer condition, foraging was not influenced by landscape features. Anthropogenic factors weakly structured foraging behavior in December with no relationship in March. Most research on animal foraging is interpreted under the framework of optimal foraging theory. Departures from predictions developed under this framework provide insight to unrecognized factors influencing the evolution of foraging. Our results only confonned to our predictions when deer were in better condition and ecological conditions were declining, suggesting foraging strategies were state-dependent. These results advance our understanding of foraging patterns in wild animals and highlight novel observational approaches for studying animal behavior. 19 �A noninvasive automated device for remotely collaring and weighing mule deer Chad J. Bishop', Mathew W. Alldredge 1, Daniel P. Walsh 1, Eric J. Bergman', Charles R. Anderson Jr. 1, Darlene Kilpatrick, Joe Bakel 2,and Christophe Fabvre2 1Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526 USA 20ynamic Group Circuit Design, Inc., 2629 Redwing Road, Fort Collins, CO 80525 USA Citation: Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman, C.R. Anderson Jr., D. Kilpatrick, J. Bakel, and C. Fabvre. 2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725; doi.org/10.1002/wsb.1034 ABSTRACT Wildlife biologists capture deer (Odocoileus spp.) annually to attach transmitters and collect basic information (e.g., animal mass and sex) as part of ongoing research and monitoring activities. Traditional capture techniques induce stress in animals and can be expensive, inefficient, and dangerous. They are also impractical for some urbanized settings. We designed and evaluated a device for mule deer (0. hemionus) that automatically attached an expandable radiocollar to a ~-month-old fawn and recorded the fawn's mass and sex, without physically restraining the animal. The device did not require on-site human presence to operate. Students and faculty in the Mechanical Engineering Department at Colorado State University produced a conceptual model and early prototype. Professional engineers at Dynamic Group Circuit Design, Inc. in Fort Collins, Colorado, USA, produced a fully functional prototype of the device. Using the device. we remotely collared, weighed, and identified sex of8 free-ranging mule deer fawns during winters 2010-2011 and 2011-2012. Collars were modified to shed from deer approximately I month after the collaring event. Two fawns were successfully recollared after they shed the first collars they received. Thus, we observed 10 successful collaring events involving 8 unique fawns. Fawns demonstrated minimal response to collaring events, either remaining in the device or calmly exiting. A fawn typically required :::I weeks of daily exposure before fully entering the device and extending its head through the outstretched collar, which was necessary for a collaring event to occur. This slow acclimation period limited utility of the device when compared with traditional capture techniques. Future work should focus on device modifications and altered baiting strategies that decrease fawn acclimation period, and in turn. increase collaring rates, providing a noninvasive and perhaps cost-effective alternative for monitoring mid- to large-sized mammal species. © 2019 The Wildlife Society. Behavioral and demographic responses of mule deer to energy development on winter range Joseph M. Northrup', J. M., Charles R Anderson Jr. 2, Brian D. Gerber, and George Wittemyer 1 1Department offish, Wildlife and Conservation Biology, Colorado State University. 1474 Campus Deliveiy, Fort Collins, CO 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fon Collins, CO 80S26 USA Department of Natural Resources Science, University of Rhode Island. I Greenhouse Road, Kingston, RI 02881 USA 3 Citation: Northrup, J.M., C.R. Anderson Jr., B. D. Gerber, and G. Wittemyer. /n Press. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs. ABSTRACT Anthropogenic habitat modification is a major driver of global biodiversity loss. In North America, one of the primary sources of habitat modification over the last two decades has been exploration for and production of oil and natural gas (hydrocarbon development), which has led to demographic and behavioral impacts to numerous wildlife species. Developing effective measures to mitigate these impacts has become a critical task for wildlife managers and conservation practitioners. However, this task has been hindered by the difficulties involved in identifying and isolating factors driving population responses. Current research on responses of wildlife to development predominantly quantifies behavior, but it is not always clear how these responses scale to demography and population dynamics. Concomitant assessments of behavior and population-level processes are needed to gain the mechanistic understanding required to develop effective mitigation approaches. We simultaneously assessed the demographic and behavioral responses of a mule deer population to natural gas development on winter range in the Piceance Basin of Colorado, USA from 2008 to 2015. Notably, this was the period when development declined from high levels of active drilling to production phase activity (i.e., no drilling). We focused our data collection on two contiguous mule deer winter range study areas that experienced starkly different levels of hydrocarbon development within the Piceance Basin. We assessed mule deer behavioral responses to a range of development features with varying levels of associated human activity by examining habitat selection patterns of nearly 400 individual adult female mule deer. Concurrently, we assessed the demographic and physiological effects of natural gas development by comparing annual adult female and over-winter fawn (6-monthold animals) survival, December fawn mass, adult female late and early winter body fat, age, pregnancy rates, fetal counts and lactation rates in December across the two study areas. Strong differences in habitat selection between the two study areas were apparent. Deer in the less developed study area avoided development during the day and night, while selecting habitat presumed to be used for foraging. Deer in the heavily developed study area selected habitat presumed to be used for thermal and security cover to a greater degree. Deer faced with higher densities of development avoided areas with more well pads during the day and responded neutrally or selected for these areas at night. Deer in both study areas showed a strong reduction in use of areas around well pads that were being drilled, which is the phase of energy development associated with the greatest amount of human presence, vehicle traffic, noise and artificial light. Despite divergent habitat selection patterns, we found no effects of development on individual condition or reproduction 20 V �and found no differences in any of the physiological or vital rate parameters measured at the population level. However, deer density and annual increases in density were higher in the low development area. Our results indicated that deer in the less developed area avoided development, whereas those in the heavily developed area altered their behavioral patterns to use habitat with more cover and areas near development during times when there was reduced human activity. The recorded behavioral alterations did not appear to be associated with demographic or physiological costs, possibly because populations are below winter range carrying capacity. We discuss potential drivers of the difference in population density between the two areas, suggesting development caused a population decline prior to our study (when development was initiated) or that there were area specific differences in habitat quality, juvenile dispersal, neonatal or juvenile survival; however, we lack the required data to contrast evidence for these mechanisms. Given our results, it appears that deer can adjust to relatively high densities of well pads in the production phase (the period with markedly lower human activity on the landscape), provided there is sufficient vegetative and topographic cover afforded to them and populations are below carrying capacity. The strong reaction to wells in the drilling phase of development suggests mitigation efforts should focus on this activity and stage of development. Many of the wells in this area were directionally drilled from multiple well pads, leading to a reduced footprint of disturbance, but still drove strong behavioral responses. Our results also indicate the likely value of mitigation efforts focusing on reducing human activity (i.e., vehicle traffic, light and noise). In combination, these findings indicate that attention should be paid to the spatial configuration of the final development footprint to ensure adequate cover. In our study system, minimizing the road network through landscape-level development planning would be valuable (i.e., exploring a maximum road density criteria). Lastly, our study highlights the importance of concomitant assessments of behavior and demography to provide a comprehensive understanding of how wildlife respond to habitat modification. 21 �Appendix B. Preliminary results of habitat treatment responses and herbivore use of treated sites. Vegetation and camera data to accompany the study' Population performance ofPiceance Basin mule deer in response to natural gas resource selection and mitigation efforts to address human activity and habuatdegradation' Principal Investigators: Danielle Johnston (Danielle.bilyeu@state.co.us). Chuck Anderson (chuck.anderson@state.co.us) Collaborators: Colorado Parks and Wildlife, BLM-White River Field Office, Idaho State University, Colorado State University, Federal Aid in Wildlife Restoration, EnCana Corp., ExxonMobil Prod. Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, WPX Energy, Colorado Mule Deer Assn., Muley Fanatic Found., Colorado Mule Deer Found., Colorado State Severance Tax Fund, Boone & Crocket Club, and Safari Club Int. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In 2011 and 2013, about 1,200 acres of pinyon and juniper (PJ) mastication treatments were completed in the Magnolia region of the Piceance Basin. Treated parcels averaged 7 acres in size, and were intended to increase winter range quality for deer. The treatments were part of a study to evaluate the effectiveness of PJ removal as mitigation for impacts of natural gas development on deer, with outcomes assessed in terms of deer population and demographic parameters. This summary addresses some side questions relevant to the main study, with outcomes assessed in terms of vegetation response and animal use of vegetation treatments. We were interested in quantifying the understory forage produced by the mastication treatments. We used paired masticated/control point-intercept transects on a subset of parcels (Graham 2013) to quantify cover of plant groups relevant to deer nutrition. We used belt transects and trained ocular estimation, with benchmarks (Johnston 2018), to estimate summer utilization on individual shrubs, then scaled these to the plot level (Bilyeu. Cooper et al. 2007). We used belt transects of shrub canopy measurements, coupled with biomass equations developed for the study area (Johnston 2018) to quantify winter forage production of key browse species. Winter forage production was defined as current-year stems, not including leaves, not including biomass removed by summer browsing, and not including very small stems which would likely be shed prior to winter (Johnston 2018). We were interested in how summer use of treatments, and use of treatments by non-target animals, impacted winter forage availability. Ten cattle exclosures, distributed broadly throughout the study area (Figure 1), were built within mastication treatments in 2011 and 2013. We assessed plant cover and summer shrub utilization within these using techniques described above. On paired masticated/control transects, we deployed Reconyx Hyperfire cameras July-November 2018-2019. These were programmed to facilitate creating an index of use: 5 pictures per motion trigger, 3 second interval between pictures, a 5 minute wait time between triggers, and a sensitivity setting of High (Rhodes, Larsen et al. 2018). An animal observed with their head down or other indication of foraging in one or more of the photos in a 5 photo set was counted as one foraging event, and non-foraging occurrences were counted similarly. Sampling efforts by year are given in Table I. Because the plant cover data contained many zeros, we modeled presence/absence of each plant group separately from its cover where present (Fletcher, Mackenzie et al. 2005), using the lme4 package in R (Bates 2005). For both analyses, treatment, year, and their interaction were considered fixed effects, year was included as a categorical variable, and pair ID and plot ID were included as random effects. We used a similar approach for camera data for cattle and elk, which also contained many zeros. 22 �1 ~ ~ In general, grasses responded positively to treatment (Figure 2a). Wheatgrass presence, wheatgrass cover, and needlegrass presence were higher in treated than untreated plots. Poa grass presence was higher in treated plots by 2018, although poa grass presence and cover initially had a negative response to treatment. Cheatgrass presence also responded positively to treatment (Figure 2a). Wheatgrasses, poas, and cheatgrass all had significant year•treatment interactions for either presence or cover. Interannual variation in cover was greater in masticated plots than in control plots for these species groups (Figure 2a). Forbs responded positively to treatment. Annual forb and perennial forb presence were higher in treated than untreated plots (Figure 2b ). Some shrubs responded positively to treatment, while others did not. Snowberry cover was lower in treated plots in 2013, but in 2016 and 2018, cover was higher in treated plots (Figure 2c). Variation in snowberry cover was greater in masticated than in control plots (Figure 2c). Bitterbrush did not display any significant effects until 2018, when cover was higher in treated plots (Figure 2c). Serviceberry cover was lower in treated plots over all years (Figure 2d). Sagebrush cover was initially lower in treated plots, but by 2018 this difference was no longer significant (Figure 2d). Summer utilization of serviceberry and mountain mahogany in 2018 was significantly higher in masticated than in control plots, but no differences were detected in bitterbrush or sagebrush. Winter forage production, which was summed over serviceberry, mountain mahogany, and bitterbrush, was significantly higher in masticated plots than in unmasticated plots in all years except 2016, when the pattern was reversed (Figure 3). There was no significant effect of exclosures on any plant cover group or on summer utilization in 2018. Deer, horse, elk, and cattle all foraged more often in masticated plots than in controls in 2018 (Figure 4). Cattle were only observed foraging at 6 of20 locations, horse were observed at 9, deer at 19, and elk at 6. Mastication treatments had many positive effects on forage availabilty, including higher cover of desirable grass groups such as poa grasses and wheatgrasses, higher cover of perennial forbs, and usually higher productivity of winter-available shrub forage. There were some negative effects and some differences in effects among years, however. Cheatgrass was higher in masticated plots than in controls, and snowberry cover was higher in masticated plots in 2016 and 2018. 2016 was an unusual year compared to other years of this study, with very high productivity of grasses (including cheatgrass, especially in masticated plots), and unusually high productivity of winter-available forage of desirable shrubs in control but not masticated plots. Summer shrub utilization in 2018 was higher in masticated plots than in controls. We lack any data on utilization from 2016, which might have helped explain if the lower production of winter-available forage in masticated plots was due to higher summer utilization in those plots that year. Another explanation for the 2016 results is that good conditions for grass, cheatgrass, and/or snowberry productivity in masticated plots led to increased competition which lessened productivity of desirable forage shrubs. All four of the large herbivores of interest foraged more frequently in summer and fall in masticated plots than in control plots in 2018. The impact of cattle was concentrated in only a few plots, but they did forage frequently in plots where they occurred. Cattle use ended in September, prior to the period of heavy use by deer in October. The data from the cattle exclosures does not indicate that cattle are having any measurable negative effect on forage resources. In summary the impact of cattle on the forage resources available to deer in mastication treatments seems minimal. However, the effect of the sum of cattle, horse, and elk foraging may have some impact. In 2019, we collected vegetation data and camera data. 2019 is the last year of data collection for this study, and final analyses will be incorporated into publications in 2020-21. LITERATURE CITED Bates, D. (2005). "Fitting linear mixed models in R." R news 5( 1). Bilyeu, D. M., D. J. Cooper and N. T. Hobbs (2007). "Assessing impacts of large herbivores on shrubs: tests of scaling factors for utilization rates from shoot-level measurements." Journal of Applied Ecology 44(1 ): 168-175. 23 �Fletcher, D., D. D. Mackenzie and E. Villouta (2005). "Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression." Environmental and ecological statistics 12: 45-54. Graham, T. (2013). Magnolia habitat manipulation project vegetative monitoring: June 2013 notes on data collection and methods used, Ranch Advisory Partners, LLC: 7. Johnston, D. B. (2018). Wildlife Research Report: Examining the effectiveness of mechanical treatments as a restoration technique for mule deer habitat. Fort Collins, CO, Colorado Parks and Wildlife. Rhodes, A. C., R. T. Larsen and S. B. S. Clair (2018). "Differential effects of cattle, mule deer, and elk herbivory on aspen forest regeneration and recruitment." Forest Ecology and Management 422: 273280. u 24 �Table I. Number of transects sampled for a given data type each year. 2011 2012 2013 2014 2015 2016 2018 Variables quantified 69 90* 90* 159 145 107t Percent cover of plant functional groups 70t 27t 63 Winter-available forage of bitterbrush, serviceberry, mountain mahogany (ShrubMassPerArea) Summer utilization of bitterbrush, serviceberry, mountain mahogany, and sagebrush Index of deer, elk, horse, and cattle use in summer and fall, as determined by trail camera (EventsPerDay) * Pretreatment data collected 2011-2012 will be added to a later report. tlncludes 24-30 locations taken at exclosure sites. 25 ------------------------ -··-·- ··-------- 2019 40 (camera sites) 75t 75t 75t 75t 40 (2 40 (2 cameras each) cameras each) �Figurl.! I. Sampling locations ll'ithin the .\lagnolia region o(the Piceance Basin. 16 �b 10 o- a .... Q) 15 - .... Q) > > 0 0 (.) 'E Q) 7 5- (.) 10 - ....Q) (.) wht;.,Sss + . PC 'E Q) ' a. # # a. 5· 5.0 - ~ Q) o- 2500- I 2013 ' 2016 2014 2018 2013 year C , -.... ,r-' \ (.) C \ 6- 2016 2018 d 0- sno~berr,,+, C ,, > 0 2018 year T 9.... Q) I ' 2016 ' 2014 ' Q) (.) Q) a. 3- '\ .... Q) 6 > s 0 (.) 'E Q) 4 - ....Q) (.) 'T 0. 2- 2013 2014 2016 20 13 2018 ' 2014 year year Figure]. Cover ofsome pla111f11nctional groups and spl!Cies i111porta111.fhr eval11a1i11g hahilal <Jllaliz,•. Dashed lines indicale maslica!ed plots and solid lines are co111m/.1·. .·/ .. _ .. or .. _.. sign indica/es significant posit in: or ncgatin: 111uin effect of 111asticatiu11 C1c:ro.u _1·c w ·.,· (<l 0 . 051. .. l' .. indic:at,•.,· that !he sign[ficant ej /ect was obsen•ed in the prese11ce 1ahse11ce ww~i·s is. and "( ' .. i11dicales a si~11i/icant effect in !he cover-where -prese111 analysis. 27 �- 1....... : i: - . ~ Treatment "'~ "' .D • masbcatec 2 9C - control -t 1 .r:: V, .., C: ::, a., a. <l> b.O r.n - ~ .2 .!!! .D * * ~ "' "'> 30 • "' .:.. 1 1 * T T 1 * + T -'- 2:! C: 3 . o· :"\ •J 20 1 : .' 016 . o,e year Figure 3. Mass <?f'11·i11ter-a1•ailahle.fi>rnge rcurrent-year stem muss measured in Septe111her. 1101 i11cludi11g /eal'es or mass re11101•ed hy s11111111er hrmrsingJ p<!r unit shrub area. Data are .1·11111111ed urer .1·erl'icehen:1·. mountain 111ahoga11y. and hillerhrush. ,\'= 8.fi>r 1013 and 1015 and 15-31.fhr other years. No 1ra11sects i11side.fe11ce.1· ll'ere i11cluded Error har.1· = SE. Stars indicate sign(fica111 d(fferences at alpha = (J.()5 28 �Not foraging, near camera Foraging, near camera b 75 · >. 75 · (l! 0 iii c.. (l! I Treatment 50 · iii 50 · c.. f/) control Q) ■ masticated c > w en .S 25· en 25 · ~ 0 u.. O· O· ' callle ' elk ' horse canle mule deer ' elk horse mule' deer Herbivore Herbivore Fig ure -I. a) Average number a/foraging events per hectare per day between mid-July and midNovember, 2018 in control versus masticated plots. Stars indicate signijlcant differences at a. = 0. 05. t indicates a sign(ficant d(fference in presence offoraging events. b) Average number of non.foraging observations per hectare per day. 29 �"--'. Colorado Parks and Wildlife July I, 2020 - June 30, 2021 WILDLIFE RESEARCH REPORT Colorado : Parks and Wildlife ---------==--------3430 : =M=amm==a=l~s.; ;. ;R;. a:; es=--e--ar__c__h______________ State of Cost Center Work Package Task No. .:..=.=.::.::...=;=-~===------------- 3001 6 : =D-"'"e___ er_C__o__n__s___ erv~at___io__n_______________ : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project: ______ W__-___ 24.....3.....-.aa..;RS _______ Period Covered: July l, 2020 - June 30, 2021 Author: C.R. Anderson, Jr. Personnel: D. Bilyeu-Johnston, K. Aagaard, CPW; J. Northrup, Ontario Ministry ofNatural Resources and Forestry; B. Gerber, University of Rhode Island; G. Wittemyer, Colorado State University. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, Caerus Oil and Gas LLC, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, Williams and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Colo Para Wlklllfa RaNnll'I Ub 11~111mm1mm11~mm~1~1~111111 3 2333 00000 1059 I �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Complete publication of mule deer behavioral and demographic responses to energy development activity. 2. Continue analyses and begin manuscript preparation to address effectiveness of habitat treatments as a mitigation option for mule deer management and energy development planning. PROJECT OVERVIEW AND RESEARCH SUMMARY We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer ( Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent preliminary and final results of a 10-year research project addressing habitat improvements as mitigation and evaluation of deer responses to energy development activities to inform future development planning options on important seasonal ranges. From2008-2019, we monitored deer on 4 winter range study areas representing relatively high (Ryan Gulch, South Magnolia) and low (North Magnolia, North Ridge) levels of development activity (Fig. 1) to address factors influencing deer behavior and demographics and to evaluate success of habitat treatments as a mitigation option. We recorded adult female habitat use and movement patterns; estimated neonatal, overwinter fawn and annual adult female survival; estimated annual early and late winter body condition, pregnancy and fetal rates of adult females; and estimated annual mule deer abundance among study areas. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 treatment areas (Fig. 2) with minor (North Magnolia) and extensive (South Magnolia) energy development, respectively. During this research segment, we finalized publication of mule deer behavioral and demographic responses to energy development activity (Northrup et al. 2021; Appendix A) and continued ~nalyses of vegetation and mule deer responses to habitat treatments intended as a mitigation option to offset energy development disturbance (preliminary results reported in Appendix B). Based on final (migration, mule deer behavioral and demographic responses, reproductive success and neonate survival; see Anderson 2019 for detailed methods and results and Appendix A for publication abstracts) and preliminary data 2 V �analyses (vegetation and herbivore response to habitat treatments, Appendix B) for this 10-year project: (1) annual adult female survival was consistent among areas averaging 79-87% annual1y, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas. with higher variability among study areas early winter, primarily due to December lactation rates. and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to I03% in North Magnolia, but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge (Fig. 3); (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures (Fig. 4); (5) mule deer exhibited behavioral plasticity in relation to energy development. without evidence of demographic effects, where disturbance distance varied relative to diurnal extent and magnitude of development activity (Fig 5), which provide for useful mitigation options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) during 2012 when drought conditions persisted during the third trimester of doe parturition (Fig. 6). Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning. Final data collection efforts for this project were completed by spring 2020 (final GPS collar recovery). Collaborative research with agency biologists, graduate students. and university professors has produced 22 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011, Peterson et al. 2018b); development and evaluation of a remote mule deer collaring device (Bishop et al. 2019); mule deer migration relative to energy development (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013); mule deer response to helicopter capture and handling (Northrup et al. 2014a); potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014 ); mule deer genetics in relation to body condition and migration (Northrup et al. 2014b); acoustic monitoring to investigate spatial and temporal factors influencing mule deer vigilance (Lynch et al. 2014) and foraging behavior (Northrup et al. 2019); the relationship of plant phenology with mule deer body condition (Sera) et al. 2015); approaches to identify cause-specific mortality in mule deer from field necropsies (Stonehouse et al. 2016); the influence of individual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman et al. 2018); and mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 2015, 2016a. 2016b, 2021. Peterson et al. 2017, 2018a). These publications are summarized in Appendix A and preliminary results describing vegetation and herbivore responses to habitat treatments are reported in Appendix B. We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation·s energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado and elsewhere. 3 �Mule Deer Winter Range Study Areas Mule deer 9tucly areas Well Pl dl & FacllltiOI CJ llOM uaono,.a ! 1n .leY~k>pm~ nl soorn Magno.aa 11"'111 ~""1• 10 Figure 1. Mule deer winter range study areas relati ve to active natural gas well pads and energy development fac ilities in the Piceance Basin of northwest Colorado. wi nter 20 13/14 (Accessed http://cogcc.state.co.us/ December 31. 20 13: energy development dri ll ing activity has been minor since 20 13). 4 �North M agnolia trea tement sites (587 acre s) LJ BearSet_ 15_ 35b_andG SearSet_ 1_8anc1A_E c::::J BearSet_36_54 andJ GreasewoodSet_g 16_g29 GreasewoodSet_g1 _g 15 c::::J Greasewood5et_g30_g42 LeeOvers,ghts_a_fandl 6_ 17 Mechanical treatrrent companson {54 acres) - - North Hatch Pilot Treatments ( 116 acres) South Magnol•a Figure 2. Habitat treatment site de! ineations in 2 mule deer study areas (604 acres each) of the Piceance Basin. northwest Colorado (Top; cyan polygons completed Jan 2011 using hydro-axe; yellow polygons completed Jan 20 12 using hydro-axe. roller-chop. and chaining: and remaining polygons completed Apr 2013 using hydro-axe). January 20 11 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left. aerial view) and October. 20 I I (Lower right. ground view). 5 �Piceance Basin late winter mule deer density 35.00 30.00 25.00 e 20.00 ~ - ~ cu 15.00 cu - North Ridge • • • • • • Ryan Gulch 0 10.00 5.00 - • North Magnolia - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 3. Mule deer density estimates and 95% Cl (e1rnr bars) from 4 wi nter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-2018. Figure 4. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult female mule deer (11 = 52: Lower left). and active natural-gas well pads (black dots) and roads (state. county, and natural-gas: white lines) from May 2009 (Lower right: from Lendrum et al. 20 12; http://dx.doi.org/ I 0. 1890/ES 12-00 165. I). 6 �I:~<f •• "' ..... •··r -r ·+ --+ g 7 ... ' -' - - - - - - , - - - , - - - M Prod 400 Prod 600 Prod 600 Prod 1000 Onn i:oo Onll 600 Onll 800 Crill 1000 Onfl 600 Ortn 800 Qt-NI H)OO Ca1anates '- M P,oa 400 Ptod 600 Prod 800 Proa 1000 Ot\11 400 ' I Figure 5. Posterior distributions of population-level coefficients related to natural gas development for RSF models during the day (top) and night (bonom) for 53 adult female mule deer in the Piceance Basin. northwest Colorado. Dashed line indicates 0 selection or avoidance (below the line) of the habitat features. ' Drill' and ·Prod ' represent drilling and producing well pads, respectively. The numbers following 'Drill' or 'Prod· represent the distance from respective well pads evaluated (e.g., 'Drill 600' is the number of well pads with active drilling between 400-600 m from the deer location: from Northrup et al. 20 15; http://onlinelibrary.wilev.com/doi/ I 0. 111I /gcb. 13037/abstract). Road disturbance was relatively minor (- 60-120 m. not illustrated above). 1.00 QJ 0.80 r---a-- - ._ -- + "§ ro> 0.60 ·2: :::; en cii 0.40 Q) u.. 0.20 0.00 2012 2013 Year □ High development 2014 □ Low development I Figure 6. Model averaged estimates of mule deer fetal survival from early March until birth (late May-June) in high and low energy development study areas of the Piceance Basin. no11hwest Colorado. 2012- 2014 (from Peterson et al. 2017: http://www.bioone.org/doi/pdf/l 0.298 I/wlb.00341 ). 7 �LITERATURE CITED Anderson, C. R., Jr.20 19. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation effo11s to address human activity and habitat degradation. Federal Aid in Wildlife Restoration Annual Repo11 W-243-R3. Ft. Collins. CO USA. Anderson. C.R.. Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79•h North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee. eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Bergman, E. J., C.R. Anderson Jr .. C. J. Bishop. A. A. Holland. and J.M. Northrup. 1018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82:130-137. DOI : 10:1002/jwmg.21334 Bishop. C. J.. C. R. Anderson Jr.. D. P. Walsh. E. J. Bergman. P. Kuechle. and J. Roth.20 11. Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8): 1797-1806; DOI : I0.1002/jwmg.229 Bishop. C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman. C.R. Anderson Jr.. D. Kilpatrick. J. Bake!, and C. Fabvre.2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:7 17-725; doi.org/ I0.1002/wsb. l 034 Freeman. E. D., R. T. Larsen, M. E. Peterson. C. R. Anderson. Jr.. K. R. Hersey. and B. R. McMillan. 20 14. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing ofpa11urition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 Lendrum. P. E.. C. R. Anderson, Jr.. R. A. Long, J. K. Kie. and R. T. Bowyer. 20 12. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.org/ I0. Lendrum, P. E.. C.R. Anderson. Jr .. K. L. Monteith. J. A. Jenks. and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: I 0.1371 /joumal.pone.0064548 Lendrum. P. E.. C.R. Anderson. Jr.. K. L. Monteith. J. A. Jenks. and R. T. Bowyer. 20 14. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http:, dx.doi.om ' J0. 10 16 j.mambio.20 1-tOS.005 Lynch. E., J. M. Northrup. M. F. McKenna. C. R. Anderson Jr.. L. Angeloni, and G. Wittemyer. 20 14. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: I 0.1093/beheco/aru 158. Northrup. J. M., M. B. Hooten, C. R. Anderson. Jr.. and G. Wittemyer. 2013 . Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456- 1463. Northrup, J.M .. C.R. Anderson, Jr.. and G. Wittemyer. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):73 1738; DOI: 10.1002/jwmg.705 Northrup, J.M .. A. B. Shafer, C.R. Anderson Jr.. D. W. Coltman. and G. Whittemyer. 20 14b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-457 1; doi: I0.1 I 11 /eva. 11 I89 Northrup, J.M .. C.R. Anderson. Jr.. and G. Wittemyer.2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology. doi: IO.I I I I/gcb. 13037. Northrup. J.M .. C.R. Anderson. Jr .. M. B. Hooten. and G. Wittemyer. 2016a. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:27462757 8 �Northrup. J.M .. C. R. Anderson. Jr.. and G. Winemyer. 2016b. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557. DOI : I0.11 11 /ddi. I24 17 Northrup. J.M., A. Avrin. C. R. Anderson Jr.. E. Brown. and G. Winemyer. 201 9. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal of Mammalogy I00: 14791489: https://doi .ore./ 10. 1093/jmamma]/gyz 12-1 Northrup. J. M.. C. R. Anderson Jr.. B. D. Gerber. and G. Wittemyer. 202 1. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs 208: 1-37; 202 1: DOI : 10.1002/wmon.1060 Peterson. M. E.. C.R. Anderson Jr.. J.M. Northrup. and P. F. Doherty Jr. 2017 . Reproductive success of mule deer in a natural gas development area. Wildli fe Biology doi : 10.111 l/wlb.0034 1 Peterson. M. E.. C. R. Anderson Jr.. J. M. Northrup. and P. F. Doherty Jr. 20 18a. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82: 11 35-11 48. DOI: 10.1002/jwmg.2 1476 Peterson. M. E.. C. R. Anderson Jr.. M. W. Alldredge. and P. F. Doherty Jr. 20 18b. Using maternal mule deer movements to estimate timing of pa1turition and assist fawn captures. Wildlife Society Bulletin 42:6 16-62 1: DOI: I0.1002/wsb.935 Searle. K. R., M. B. Rice. C.R. Anderson. C. Bishop and N. T. Hobbs. 20 15. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549: DOI I0.1007/s00442-0 15-3348-9 Stonehouse. K. F.. C. R. Anderson Jr.. M. E. Peterson. and D. R.Collins. 20 16. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus he111io1111s). Colorado Parks and Wildlife Technical Repo11 No. 48. First Edition. 317 W. Prospect Rd.. Ft. Collins. CO USA. DOW-R-T-48-16. ISSN 0084-8883. Prepared by_ _ _ _ __ __ _ __ _ __ _ _ _ __ Charles R. Anderson. Jr.. Mammals Research Leader 9 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research colla borations. Abstract form at specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHADJ. BISII OP1, CIIARLES R. ANDERSON .Jr. ', DANIEL P. \\'.-\LSll 1, ERIC ,I. II ERGI\IAN 1, PETER h: l iECIII.E', and .JOH N ROTH' 'Colorado Parks and Wildlili:. Fon Collins. Colorado 80526 USA 'Advanced Tdcmwy Systems. Isanti. Minnesota 550~0 US/\ Citation: Bishop. C. J . C R Anderson Jr.. D. P. Walsh. E J. Bergman. P. Kuechle. and J. Roth. 20 1 I. Effecti,·cness ofa redesigned vaginal implant transmincr in mule deer. Journal of Wildlifi: Managenwn 75(8 ): 1797-1806. DOI. IO I00'.:!/j11mg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoilrms he111io1111s) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions. which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocoll arcd adult females is use of vaginal implant transmitters (VITs). To date. VITs have allowed research opponunitics that were not previously possible: however. vrrs arc ollcn expelled from adult lcmalcs prcpanum. which limits their cffcctivc.;ncss. We rcdcsigned an existing VIT manufactured by Advanced Telemetry Systems (ATS: Isanti. MN) by leng1hcning and widening wings used Lo retain the VIT in an adult female. Our objective was to increase VIT retention rates and lhercby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probabi lity of re1ention to parturition and the probability of detecting ncwbom fawns. We also developed an equation for detern1ining VIT sample size necessary to achieve a specified sample size of neonates. The probabil ity ofa VIT being retained until parturition was 0.766 (SE = 0.0605) and the probability ofa VIT being retained to within 3 days of parturition was 0.894 (SE = 0.044 1). In a similar study using the original VIT wings (Bishop ct al. 2007). the probabi lity of a VIT being retained unti l parturition was 0.447 (SE = 0.0468) and the probabil ity of retention to within 3 days or parturition was 0.623 (SI: = 0.0456). Thus. our design modification increased VIT retention to pai1urition by 0.3 19 (SE = 0.0765) and VIT retention to within 3 days of parturition by 0.27 1 (SE = 0.0634). Considering dams that retained VITs to within 3 days of parturition. the probabil ity of detecting at least I neonate was 0.952 (SE= 0.033-l) and the probability of detecting both fawns from twin lillcrs was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future stud ies. we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands oppo1tunities for conducting research that links adult female attributes to producth·ity and olTspring survival in mule deer. t 20 1-t The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICh: E. LENDRlli\1 1, CHA RLES R. ANDERSON .m.'. RYAN A. LONG'. ,JOII N G. h:IE1, .\ND R. TERRY BOWYER' Depanment of Biological Sciences. Idaho State University. l'ocoldlo. Idaho 83209 tJS/\ 'Colorado Parks and Wildlife. Grand Junction. Colorado 81505 USA 1 Citation: Lendrum. I' E.. C R. Anderson Jr.. R. /I Long. J G Kie. and R. T Bo11)cr 2012 1labilal sd cction by mule deer during m1gra11on. ellccts of landscape slnicturc and natural-gas development. Ecosphcrc 3( 9 ):82 hnr 1J, Jn, ore, Ill I N9ll'ES Ic-lllll r,5 I A bstract. The disruption of trad itional migratory routes by anthropogenic disturbances has shifted pallcms of resource selection by many species. and in some instances has caused populalions to decline. Moreover. in recent decades popu lations of mule deer (Odocoileus he111io1111s) have declined throughout much of their historic range in the western United Slates. We used resourceselection functions to detem1ine if the presence of natural-gas development altered pauems of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (11 = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 20 IO in the Piceance Basin of n011hwcs1 Colorado. USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally. deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover. whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to \\•ell pad, and avoided roads in all instances except along the most highly developed migratory routes. where road densities may have been too high for deer to avoid roads without deviating substantial ly from established migration routes. These results indicate that behavioral 1endencies toward avoidance of anthropogenic disturbance can be ovc1,-iddcn during migration by the strong lidelity ungulates demonstrate towards migration routes. If avoidance is feas ible. then d1:cr may select areas further from development. whereas in highly developed areas. deer may simply increase their rate of travel along establ ished migration routes. 10 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum•, Charles R. Anderson Jr. 2, Ke,·in L. Monteith 1.J, Jonathan A. Jenb~, R. Terry Bowyer' 1 Department of Biological Sciences. Idaho State University. Pocatello. Idaho. USA.; Colorado Division of Parks and Wildlife, Grand Junction. Colorado. USA.~ Wyoming Cooperative Fish and Wildlife Research Unit University of Wyoming. Laramie. Wyoming. USA.~ Deparunent of Natural Resource Management. South Dakota State University. Brookings. South Dakota. USA Citation: Lendrum. P. E.. C.R. Anderson Jr.. K. L. Monteith. J. A. Jenks. R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): c64548. DOI: I0.1371/joumal.pone.0<164548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes. many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning. because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns. and whether ungulate migration is sufficiently plastic to compensate for such changes. warrants additional study to better understand this critical conservation issue. Met/1odolog)'/Principal Fi11dings: We studied timing and S)11Chrony of departure from winter range and arrival to summer range offemale mule deer (Odocoileus /remiom,s) in northwestern Colorado. USA. which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather. plant phcnology. and individual life-history characteristics. patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer. equipped them with GPS collars. and observed patterns of spring migration during 2008--20 I0. Condusions/Sig11ijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation. which varied among years. but was highly synchronous across study areas within years. Additionally. timing of migration was influenced by the collective effects of anthropogenic disturbance. rate of travel. distance traveled. and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure. but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas. especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design 1 JOSEPH M. NORTHRllP , ME\'IN 8. HOOTEN'"• CHARI.ES R. ANDERSON JR.~. AND GEORGE WITTEMYER 1 1 Depanment of Fish. Wildlife. and Conservation Biology. Colorado State University. 1474 Campus Delivery. Fort Collins. Colorado 80523 USA :u.s. Geological Survey. Colorado Cooperative Fish and Wildlife Research Unit 1474 Campus Delivery. Fon Collins. Colorado 80523 USA ·'Colorado State University. Department of Statistics. Colorado State University. 1474 Campus Delivery. Fon Collins. Colorado 80523 USA ~Mammals Research Section Colorado Parks and Wildlife. 711 Independent Avenue. Grand Junction. Colorado 81505 USA Citation: Northrup. J.M .. M. B. Hooten. C.R. Anderson Jr.. and G. Wittemyer. :?013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7 ): 1456-1463. hnp://dx.doi.org/10.1890/12-1688.1 Abstract. Habitat selection is a fundamental aspect of animal ecology. the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework. whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in foct spatial point process models. they often are fit using logistic regression. This framework offers numerous methodological challenges. for which the literature provides little guidance. Specifically. the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates. which is common for landscape characteristics. exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data. which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and. where bias is likely. take care with interpretations and use cross validation to assess robustness. 11 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH l\l. NORTHRUP', CHARLES R. A;-.OERSON JR'. .-\~O GEORGE \\'ITTEl\lYER1 'Department of Fish. Wildlife. and Conservation Biology. Colorado State Universir:,. 1-17-l Campus Delivery. Fort Collins. Colorado 80523 USA 'Mammals Research S<'Clion Colorado Parks and Wildlife. 711 Independent Avenue. Grand Junction. Colorado 81505 USA Citation: Northrup. J. M.. C. R. Anderson Jr.. and G. Wittemyer. 201-l Effects ofhdicopter capture and handling on mowment behavior of mule deer. Journal of Wildlife Management 78(4):731 -738: DOI: 10. IOO:!/J\\1ng.705 ABSTRACT Research on wildlife movement. physiology. and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior. which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoile11s he111io1111s). a focal species for research in North America. we investigated pre- and post-recapture movements of collared individuals. and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (1n/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition. we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following. recapture. with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture. we found no differences in displacement. but movement rates demonstrated seasonal effects. with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements. recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks fo llowing recapture. The median time lo return to the pre-recapture home range was 13 hours. with 71 % of deer returning in the first day. and 91 % returning " ~thin 4 days. These results indicate a shon period of elevated movements following recaptures. likely due to the deer returning to their home ranges. followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day o r data post capture from analyses is strongly supponed. and removing additional days until the individual returns to its home range will control for the majority of impacts from capture. © 20 14 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendrum', Charles R. Anderson Jr. 1' . h:cvin L. :\lonteith', Jonallrnn .-\. ,Jenks'', R. Terry Bowyer-" ' Department ofBiolog,cal Sciences. Idaho State University. 9::? I South 8th Avenue. Stop 8007. Pocotdlo 83209. USA 1' Mammals Research Section Colorado Parks and Wildlik 711 Independent Avenue. Grand Junction 81505. USA ' Wyoming Cooperative Fish and Wildlife Research Unit Department of Zoology and Physiology. Universny of Wyoming. 3166. I000 East University Avenue. Laramie 82071. USA J Department of Natural Resource Management. South Dakota State University. Box ::?1-108. Brookings 57007. USA Citation: Lendrum. I'. E.. C. R. Anderson Jr.. KL. Monteith. J. A. Jenks. and R. T. Bo\\yer. 201-1. Relating the movement ofa rapidly migrating ungulate 10 spatiotemporal patterns of forage quality. Mammalian Biology: hnn 1/Jx dn, unµ'I 11 IOI6 11nrnmhsn 101-l 05 OIJ:\ ABSTRACT: Migratory ungulates exhibit recurring movements. o ften along trad itional routes between seasonal ranges each spring and autumn. which allow them lo track resources as they become avaiIable on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus he111io1111s) and forage quality. as indexed by spatiotemporal pauerns of fecal nitrogen and remotely sensed greenness of vegetation (Nom1al izcd Difference Vegetation Index: NOV I) in spring 2010 in the Piceance Basin ofnorthwestem Colorado. USA. NDVI increased throughout spring. and was affected primarily by snow depth when snow was present. and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration. increased rapidly to an asymptote during migration. and remained relatively high when deer reached summer range. Values of fecal nitrogen corresponded with increasing NDV I during migration. Spring migration for mule deer provided a way for these large mammals to increase access lo a high-qual ity diet. which was evident in pauerns of NOVI and fecal nitrogen. Moreover. these deer ·jumped.. rather than ..surfed.. the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for panurition. and to minimize detrimental factors such as predation and malnutrition during migration. 12 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN'. RAND\' T. LARSEN 1, MARKE. PETF.RSON2, CHARLES R. ANDERSON JR.J. KENT R. HERSEY\ AND BROCK R. Mcl\llLLAN 1 1 Depanment of Plant and Wildlife Sciences. Brigham Young University. 275 WIDB. Provo. UT 84602. USA : Depanment of Fish. Wildlife. and Conservation Biology. Colorado State University. 1474 Campus Delivery. Fon Collins. CO 80523, USA ·' Colorado Parks and Wildlife. 711 Independent Avenue. Grand Junction. CO 81 SOS. USA ~ Utah Division of Wildlife Resources. 1594 W Nonh Temple. Salt Lake City. UT 84114. USA Citation: Freeman. E. D.. R. T. Larsen. M. E. Peterson. C.R. Anderson Jr.. K. R. Hersey. and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rat~-s of pregnancy. synchrony. and timing of panurition. Wildlife Society Bulletin: DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example. in mule deer (Odocoileus hemi01ms). changes in male/female ratio due to malebiased harvest may alter rates of pregnancy. timing of parturition. and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios. recruitment may be reduced (e.g .. fewer births. later parturition resulting in lower survival of favms. and a less S)nchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy. synchrony of parturition. and timing of parturition between exploited mule deer populations with a relatively high (Piceance. CO. USA: 26 males/JOO females) and a relatively low (Monroe. UT. USA: 14 males/JOO females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%: z = 0.821: P = 0.794). timing of parturition (estimate= 1.258: SE= 1.672: t = 0. 752: P = 0.454 ). or S)11chrony of parturition (F = 1.073: P = 0.859) between Monroe Mountain and Piceance Basin. respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population d)11amics because recruitment remains unaffected. C 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup', Aaron B. A. Shafer, Charles R. Anderson Jr.J, Da,·id W. Coltman~. and George \\'ittemyer1 I Department offish. Wildlife. and Conservation Biology. Colorado State University. Fon Collins. CO. USA 2 Department of Evolutionary Biology. Evolutionary Biology Centre. Uppsala University. Uppsala. Sweden 3 Mammals Research Section. Colorado Parks and Wildlife. Grand Jun~1ion. CO. USA 4 Depanment of Biological Sciences. University of Alberta. Edmonton. AB. Canada. Citation: Nonhrup. J.M .. A. B. Shafer. C.R. Anderson Jr.. D. W. Coltman. and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a \\ild cervid. Evolutionary Applications ISSN 1752-4571: doi: IO. I I I l/eva.12189 Abstract The relationship between genetic variation and phcnotypic traits is fundamental to the study and management of natural populations. Such relationships often arc investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus). we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing. (ii) screen for mitochondrial haplotypes associated ,,ith migration timing. and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns). one of which is situated near a knO\m fat metabolism gene in mammals. Despite being focused on a widespread panmictic species. these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions. and these correlates highlight the need to consider evolutionary mechanisms in management. even in widely distributed panmictic species. 13 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch•, Joseph M. Northrupb, Megan F. McKennaC. Charles R. Anderson Jr.", Lisa Angeloni~, and George Wittemye~• "Graduate Degree Program in Ecology. Colorado State University. 1474 Campus Delivery. Fon Collins. CO 80513. USA 1lepanment offish, Wildlife and Conservation Biology. Colorado State University. 1474 Campus Delivery. Fon Collins. CO 80523. USA "Natural Sounds and Night Skies Division, National Park Service. 1201 Oakridge Drive. Fon Collins. CO 80525. USA. JMammals Research Section, Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Coll ins. CO 80526, USA cDepartment of Biology, Colorado State University, 1878 Campus Delivery. Fon Collins. CO 80513. USA Citation: Lynch, E., J.M. Nonhrup, M. F. McKenna. C.R. Anderson Jr.. L. Angeloni. and G. Winemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology: doi: I0.1093/beheco/aru 158. While visual fonns of vigilance behavior and their relationship with predation risk have been broadly examined. animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance. auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study. but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoi/eus hemionus) within bouts of rumination-based maslication. To better understand the ecological properties that structure this behavior. we investigate spatial and temporal factors related to these pauses to detennine ifresults are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night where visual vigilance was likely to be less effective. Additionally. deer paused more in areas of moderate background sound levels. though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a fonn of auditory vigilance that is responsive to landscape variables. Further e~-ploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development V Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Collins. CO 80526. USA Citation: Anderson, C.R .. Jr.. and C. J. Bishop. 2014. Migration panems of adult female mule deer in response to energy development. Pages 47.50 in Transactions of the 79lh North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee. eds.). Wildlife Manae.ement Institute. Gardners. PA. USA. ISSN 0078•1355. - Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes. many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns. and whether ungulate migration is sufficiently prepared to compensate for such changes. has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012. 2013: Sawyer et al. 2012). Lendrum et al. (2012. 2013) and Sawyer et al. (2012) address mule deer (Odoc:oi/eus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming. respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012. 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate. migration routes. and stopover use. if present may be altered at high development intensities. In addition. migratory mule deer may benefit by maintaining security cover along migration paths. and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing penneability along migration routes by applying dispersed development plans (<2 well pads/km:?) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible. habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible. directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly. we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow con·idors that arc most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available. we encourage agencies to incorporate such migration corridors into land-use plans (e.g .. resource management plans) and National Environmental Policy Act documents. 14 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle' • Mindy B. Rice 2 • Charles R. Anderson 2 • Chad Rishop 2 • N. T. HobbsJ 1 NERC Centre for Ecology and Hydrology. Bush Estate. Penicuik El 126 0QB. UK ~ Colorado Parks and Wildlife. 317 W. Prospect Road. Fort Collins. CO 80526. USA ~ Department of Ecosystem Science and Sustainability. Colorado State University. Fon Collins 80524. CO. USA Citation: Searle. K. R.. M. B. Rice. C. R. Anderson. C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Occologia ISSN 0029-8549: DOI I 0.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes fonns a central challenge in ecology. Individual responses to heterogeneity shape population dynamics. therefore understanding these responses is central to sustainable population management. Emerging evidence has sho\\11 that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phcnology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns m1d topographical variables on vegetation phenology in the home ranges of mule deer. Crucially. temporal patterns of vegetation phenology were linked with differences in body condition. ,,ith deer tending to show poorer body condition in areas ,-.ith less asynchronous vegetation green-up and later vegetation onset The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH 1\1. NORTHRUP'. CHARLES R. ANDERSON JR. 2• and GEORGE WIITE!\IYER1.J 'Department of Fish. Wildlife and Conservation Biology. Colorado State University. Fon Collins. CO. USA ~Mammals Research Section. Colorado Parks and Wildlife. Fort Collins. CO. USA )Graduate Degree Program in Ecology. Colorado State University. Fon Collins. CO. USA Citation: Nonhrup. J.M .. C. R. Anderson. Jr.. and G. Wittcmyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology. doi: IO. I I I l/gcb.13037 Abstract Extraction ofoil and natural gas (hydrocarbons) from shale is increasing rapidly in North America. with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent this development is set to become a major driver of global land-use change. It is increasingly critical to quantil)' spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat oflscts. Habitat selection is a fundamental ecological process. influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their ,\inter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework. with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns. with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure. avoiding pads with active production and roads to a greater degree during the day than night. In aggregate. these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions. the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study. and the methods we employed. provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers. mangers. and industry when attempting to mitigate habitat loss due to energy development. 15 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup•, Charles R. Anderson Jr. and George Wittemyer 1.., 1Department of Fish. Wildlife and Conservation Biology, Colorado State University. Fon Collins. CO. USA ~Mammals Research Section. Colorado Parks and Wildlife, Fon Collins. CO, USA JGraduate Degree Program in Ecology. Colorado State University. Fon Collins. CO. USA Citation: Nonhrup, J. M.. C. R. Anderson. Jr., and G. Winemycr. .::?0 16. Environmental dynamics and anthropogenic development alter philopatl)' and space-use in a Nonh American cervid. Diversity and Db1ributions .:?.:?: 547-557. DOI: I0. I l I l/ddi. l.::?417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs. reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology. conservation and management. Location The Piceance Basin. Colorado. USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage). individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry. with a median of 50% overlap (range: 8-78%) in year-to-year utilization disuibutions. Environmental conditions were the primary driver of both philopatry and range size. with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and. therefore. factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategics. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup1, Charles R. Anderson Jr.:. Mnin B. Hooten". and George Wittemye~ 1 Depanment of Fish. Wildlife and Conservation Biology, Colorado State University. Fon Collins. Colorado 80523 USA :Mammals Research Section. Colorado Parks and Wildlife. Fon Collins. Colorado 805:!3 USA 3U.S. Geological Survey. Colorado Cooperative Fish and Wildlife Research Unit. Depanment of Fish. Wildlife and Conservation Biology. Colorado State University. Fon Collins. Colorado 80523 USA ~Depanment of Fish. Wildlife and Conservation Biology and Graduate Degree Program in Ecology. Colorado State University. Fon Collins. Colorado 80523 USA Citation: Nonhrup, J.M .. C.R. Anderson. Jr.. M. B. Hooten. and G. Wincmyer. 2016. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes. but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado. USA. an area of ongoing natural gas development We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movementc;). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features. avoiding well pads and the areas closest to roads at all scales. though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features. including land cover type and habitat edges. Selection differed between night and day at the finest scales. but homogenized as scale increased. Deer displayed functional responses to development with deer inhabiting the least developed ranges more strongly avoiding development relative to those \\'1th more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer. sb1lcturing their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development. but only to a degree. At higher development densities than seen in this area. such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities "ill be a critical best management practice as development expands globally. 16 �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse•.2. Charles R. Anderson Jr. 1, Mark E. Peterson•.2, and David R. Collins' 1Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fort Collins. CO 90526 USA :Department offish. Wildlife and Conservation Biology. Colorado Stute University. Fort Collins. Colorado 80523 USA Citation: Stonehouse. K. F.. C.R. Anderson Jr.. M. E. Peterson. and D.R. Collins . .2016. Approaches to field investigations of cause-specific monality in mule deer (Odocoi/eus hemionus). Colorado Parks and Wildlife Technical Report No. 48. First Edition. 317 W. Prospect Rd .. Ft. Collins. CO USA. DOW-R-T-48-16. ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly. these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The infonnation presented here is based primarily on lield experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/leam/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality: efforts discerning ultimate cause will require specific tissue sample collections. where possible. submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this documenL be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson 1, Charles R. Anderson Jr. 2, Joseph M. Northrup 1,and Paul F. Doherty Jr. 1 'Department of Fish. Wildlife and Conservation Biology. Colorado State University. Fort Collins. Colorado 80523 USA :Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fort Collins. CO 90526 USA Citation: Peterson. M. E.. C. R. Anderson Jr.. J.M. Northrup. and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: IO. I I I I/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat. particularly for ungulate species. Understanding how this development impacts reproductive success metrics that are influential for ungulate population dynamics is important to guide management of ungulates. However. the influences of natural gas development on reproductive success metrics of mule deer Odocoi/eus hemio11us have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceance Basin. northwest Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utero fetal rates were high and statistically indistinguishable between study areas. Fetal sun•ival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival. particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning. such as implementing habitat treatments to improve forage availability and quality. minimizing disturbance to hiding and foraging habitat particularly during parturition. and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 17 �Variation in ungulate body fat: individual versus temporal effects Eric J. Bergman•, Charles R. Anderson Jr. 1• Chad J. Bishop•, A, Andrew Holland 1, and Joseph 1\1, Northrup? 'Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Collins. CO 90526 USA :Depanment of Fish. Wildlife and Conservation Biology. Colorado State University. Fon Collins. Colorado 805::?3 USA Citation: Bergman, E. J.. C. R. Anderson Jr.. C. J. Bishop. A. A. Holland. and J. M. Nonhrup. .'.!0 18. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 8.'.!: I30-137. DOI: IO: I00.'.!/j\\mg.::? 133.J ABSTRACT The use ofultrasonograhic measurements of muscle and body fat represent a relatively new data stream that can be used to address questions regarding ungulate condition. We have learned that measurements of body fat and presumably overall body condition among individual animals. even those taken from the same herd at that same time. are highly variable. Relatively little consideration has been given to the sources of variation in body fat and other physiological parameters in "•;Jdlife populations. We evaluated the components of variation in late-winter mule deer (Odocoileus hemiomts) body fat estimates: sampling variation (i.e .. variation induced by the particular set of individuals that were sampled) and process variation (i.e .. variation stemming from biological processes) with a long-tenn data set (2002-2015) from Colorado. USA. We collected our data from across Colorado as part of historical research. ongoing research. and periodic population monitoring programs. Mean percent ingesta-frce body fat (%1FBF) for sampled mule deer was 7.20 ± 1.20% (SD). Covariates related to individual deer explained approximately-'% of the total variation in %1FBF and annual effects explained an additional 13% of the variation. Substantial residual variation in %1FBF (83%) remained unex'J)lained. The source of the 83% of unexplained variation is partially linked to line-scale spatial dynamics but also additional individual metrics we were unable to capture, primarily the presence or absence of dependent young. We speculate that the primary factors influencing late-winter mule deer body fat and overall condition are individual in nature. These results present a cautionary check on herd level inference that can be made from individual late-winter body fat estimates and we postulate that for mule deer, alternative and additional body condition metrics may offer added utility in management scenarios. 1lowever. an important next step to better understand wildlife population health is to evaluate the sources and magnitude of variation within other body condition metrics. with the goal of further refining data that can better allow biologists to incorporate herd health into population management recommendations. Mortality of mule deer fawns in a natural gas development area Mark E. Peterson•, Charles R. Anderson Jr. 2, Joseph l\l. Northrup•, and Paul F. Doherty Jr. 1 'Department offish. Wildlife and Conservation Biology. Colorado State Uniwrsity. Fon Collins, Colorado 80523 USA ?Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Collins. CO 905.'.!6 USA Citation: Peterson. M. E.. C.R. Anderson Jr .. J.M. Nonhrup. and P. F. Doheny Jr. ::?018. Monality of mule deer fa\\TIS in a natural gas development area. Journal of Wildlife Management 82:1135-1148. DOI: 10. I00::?/j,,mg.21476 ABSTRACT Recent natural gas development has caused concern among wildlife managers. researchers. and stakeholders over the potential effects on wildlife and their habitats. Specifically. understanding how this development and other factors influence mule deer (Odocoileus hemi01ms) fawn (i.e .. 0-6 months old) mortality rates. recruitment and subsequently population dynamics have been identified as knowledge gaps. Thus. we tested predictions concerning the relationship between natural gas development adult female. fa,..11 birth. and temporal (weather) characteristics on fa, ..n mortality in the Piccance Basin of northwestern Colorado. USA. from 2012-2014.We captured and radio-collared 184 fawns and estimated apparent cause-specific mortality in areas with relatively high or low levels of natural gas development using a multi-state model. Mean daily predation probability was similar in the high versus low development areas. Predation was the leading cause offawn mortality in both areas and decreased from 0-14 days old. Black bear ( Ursus americam1s: 22% of all mortalities. 11 = 17) and cougar (Fe/is co11color: 36% of all mortalities. 11 = 6) predation was the leading cause of mortality in the high and low development areas. respectively. Predation of fawns was negatively correlated with the distance from a female"s core area to a producing well pad on winter or summer range. Contrary to expectations. predation of fawns was positively correlated with rump fat thickness of adult females. Well pad densities and development activity were relatively low during our study. indicating that the observed intensity of development did not appear to influence daily predation probability. Our results suggest maintaining development activity thresholds at levels we observed to potentially minimize the effects of development on fawn mortality. However. we caution that higher development intensity and drilling activity in flatter. less rugged areas with less concealment cover could influence fawn mortality. Managers should maintain low development densities in areas where topography and vegetation offer less concealment. Overall. region-specific data (e.g .. development intensity. topography. predator assemblages. and associated predation risk) arc needed to better understand the effects of natural gas development on fawn mortality. 18 �Using maternal mule deer movements to estimate timing of parturition and assist fawn captures Mark E. Peterson', Charles R. Anderson ,Jr.', Mathew W. Alldredge', and Paul F. Doherty ,I r.' 1Depamnent of Fish. Wildlife and Conservation Biology. Colorado State Uni\'crsily. Fon Collins. Colorado 80523 USA 'Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Coll ms. CO 90526 USA Citation: Peterson. M. E.. C R. Anderson Jr.. M. W. Alldredge. and P. F Doheny Jr 2018. Using maternal mule deer movements lo estimate liming of panurilion and assist fa\\T1 captures Wildlife Society Bullelin ➔2 : 616-6~1 : DOI: 10 1002/wsb,935 ABSTRACT Movement patterns of maternal ungulates have been used Lo determine parturition dates and aid in locating fawns. which may be important for understanding reproductive rates (e.g .. pregnancy and fetal). but such methods have not been validated for mule deer ( Odocoi/eus he111ia1111s). We first detem1ined timing of parturition using vaginal implant transmitters (VITs) and then predicted timing of parturition using VlTs in conjunction with Global Positioning System collar data in the Piceance Basin of northwestern Colorado. USA. during 20 12-20 1--1. We examined daily movement rate to determine differences in movement rate among days (7 days pre• and postpartum) and for movement patterns indicative of parturition, Mean daily movement rate (m/day) of I 02 maternal deer decreased by --16% from I day preparturition (mean = l.253. SD = 1.091) to parturition date (mean = 682. S = 574). and remained at this low rate 1-7 days postpartum. We applied an independent data set to validate predicted parturition dates based on daily movement rate. We estimated day of parturition cotTcctly (i.e .. day 0). with in 1-3 days postparturition. and _ 4 days postparrurition of field-n:ported dates for IO (29%). 2 1 (60%). and 4 ( 11%) maternal lcmalcs. respecti vely. For novel data s.:ts. w.: predict that a mule deer female whose dai ly movement rate decreases by _ 46% and remains low _3 days postparturition particularly when preceded by a sudden increase in movement-has given birth. However. we caution that disturbance of deer by field crews should be minimized. and if birth sites are not found. neonatal mortal ity will be underestimated. Our results can help determine timing and general location of parturition as an aid in capturing fawns when the use of VITs is not feasible. with the ultimate objective of estimating pregnancy. fetal. and fawn survival rates ifbirth sites arc found. On-animal acoustic monitoring provides insight to ungulate foraging behavior Joseph M. Northrup'. Alexandra Anin 1, Charles R. Anderson. Jr.'. Emma Browns, and Gcori:c Wittcmycr 1 1Depanmenl of Fish. Wildlife and Conservation Biolog,. Colorado Stale Unhersit~. Fon Collins. Colorado 80523 USA 1Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Collins. CO 905'.!6 USA ' National Park Service Natural Sounds and Night Skies Division. Fon Collins. CO 805~5 USA Citalion: Northrup. J. M.. A i\vnn. C. R. Anderson Jr.. E. Bro\\n. and G. Wiuem~er. '.!019. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal of Mammalogy I00: I-l 79-1 ➔89: hnps //do, nrt!/IIJ.10'/3h111urnm:1II;;\/ 11-l Abstract Foraging behavior underpins many ecological processes: ho"·cvcr. rohust assessments of this behavior for free-ranging animals arc rare due to limitations to direct obse1vations. We leveraged acoustic mon itoring and GPS tracking to assess the factors influencing foragi ng behavior of mule deer (Odocoile us he111io1111s). We deployed custom-built acoustic collars with GPS radiocollars on mule deer to measure location-specific foraging. We quantified individual bites and steps taken by deer. and quantified two metrics of forag ing behavior: the number of bites taken p~r step and the number of bites taken per unit time. which relate to foragi ng intensity and efficiency. We fi t statistical models to these metrics to examine the individual. environmental. and anthropogenic factors influencing foraging. Deer in poorer body condition took more bites per step and per minute and foraged for longer irrespective of landscape properties. Other patterns varied seasonally with major changes in deer condition. In December. when deer were in better condition. they took fewer bites per step and more bites per minute. Deer also fo raged more intensely and efliciently in areas of greater forage availability and greater movement costs. During March. when deer were in poorer condition. foraging was not influenced by landscape features. Anthropogenic factors weakly su·uctured foraging behavior in December "ith no relationship in March. Most research on animal foraging is interpreled under the framework of optimal foraging theory. Departures from predictions developed under this framework provide insight to unrecogn ized factors influenc.ing the evolution of foraging. Our results on ly conformed to our pred ictions when deer were in better condition and ecological conditions were declini ng. suggesting foraging strategies were state-dependent. These results advance our understanding of foragi ng patterns in wild animals and highlight novel observational approaches for studying animal behavior. 19 �A noninvasive automated device for remotely collaring and weighing mule deer Chad J. Bishop•, Mathew W, Alldredge•, Daniel P. Walsh 1. Eric: J, Bergman 1. Charles R. Anderson Jr.'. Darlene Kilpatrick. Joe Bakel 1, and Christophe Fabvre1 1 Colorado Parks and Wildlife. 317 W. Prospect Road. Fon Collins. CO 80526 USA ~Dynamic Group Circuit Design. Inc .. 2629 Redwing Road, Fort Collins. CO 80525 USA Citation: Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman, C.R. Anderson Jr .. D. Kilpatrick. J. Bakel. and C. Fabvrc. 2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725: doi.org/10.1002/wsb. I034 ABSTRACT Wildlife biologists capture deer (Odocoi/eus spp.) annually to attach transmitters and collect basic information (e.g .. animal mass and sex) as part of ongoing research and monitoring activities. Traditional capture techniques induce stress in animals and can be expensive. inefficient and dangerous. They are also impra"-tical for some urbanized settings. We designed and evaluated a device for mule deer (0. hemionus) that automatically attached an expandable radiocollar to a 2::6-month-old fawn and recorded the fawn's mass and sex. without physically restraining the animal. The device did not require on-site human presence to operate. Students and faculty in the Mechanical Engineering Department at Colorado State University produced a conceptual model and early prototype. Professional engineers at D)11amic Group Circuit Design. Inc. in Fort Collins. Colorado. USA. produced a fully functional prototype of the device. Using the device. we remotely collared, weighed. and identified sex of 8 free-ranging mule deer fa\\ns during winters 2010-2011 and 2011-2012. Collars were modified to shed from deer approximately I month after the collaring event. Two fawns were successfully recollared after they shed the first collars they received. Thus. we observed IO successful collaring events involving 8 unique fawns. Fawns demonstrated minimal response to collaring events. either remaining in the de,,ice or calmly exiting. A fawn typically required 2::1 weeks of daily e~-posure before fully entering the device and extending its head through the outstretched collar. which was necessary for a collaring event to occur. This slow acclimation period limited utility of the device when compared with traditional capture techniques. Future work should focus on device modifications and altered baiting strategies that decrease fawn acclimation period, and in tum, increase collaring rates, providing a noninvasive and perhaps cost-effective alternative for monitoring mid- to large-sized mammal species. © 2019 The Wild Ii fe Society. Behavioral and demographic responses of mule deer to energy development on winter range Joseph M. Northrup', J.M., Charles R. Anderson Jr. 1, Brian D. Gerber. and George Wittemycr 1 1Department of Fish, Wildlife and Conservation Biology, Colorado State University. 14 74 Campus Delivery, Fort Collins. CO 80523 USA 2Mammals Research Section. Colorado Parks and Wildlife. 317 W. Prospect Road. Fort Collins. CO 80526 USA 'Department of Natural Resources Science, University of Rhode Island. I Gr~nhousc Road. Kingston. RI 02881 USA Citation: Nonhrup, J. M.. C. R. Anderson Jr.. B. D. Gerber. and G. Winemyer. 2021. Bcha\'ioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs 208: 1-37: 2021: DOI: 10.1002/\\mon.1060. ABSTRACT Anthropogenic habitat modification is a major driver of global biodiversity loss. In North America. one of the primary sources of habitat modification over the last 2 decades has been exploration for and production of oil and natural gas (hydrocarbon development). which has led to demographic and behavioral impacts to numerous wildlife species. Developing effective measures to mitigate these impacts has become a critical task for wildlife managers and conservation practitioners. However. this task has been hindered by the difficulties involved in identifying and isolating factors driving population responses. Current research on responses of v.ildlife to development predominantly quantifies behavior. but it is not always clear how these responses scale to demography and population dynamics. Concomitant assessments of behavior and population-level processes are needed to gain the mechanistic understanding required to develop effective mitigation approaches. We simultaneously assessed the demographic and behavioral responses of a mule deer population to natural gas development on winter range in the Piceance Basin of Colorado. USA. from 2008 to 2015. Notably. this was the period when development declined from high levels of active drilling to only production phase activity (i.e .. no drilling). We focused our data collection on 2 contiguous mule deer winter range study areas that experienced starkly different levels of hydrocarbon development within the Piceance Basin. We assessed mule deer behavioral responses to a range of development features with varying levels of associated human activity by examining habitat selection patterns of nearly 400 individual adult female mule deer. Concurrently. we assessed the demographic and physiological effects of natural gas development by comparing annual adult female and overwinter fawn (6-monthold animals) survival. December fa,m mass. adult female late and early winter body fat. age. pregnancy rates. fetal counts. and lactation rates in December between the 2 study areas. Strong differences in habitat selection between the 2 study areas were apparent. Deer in the less-developed study area avoided development during the day and night and selected habitat presumed to be used for foraging. Deer in the heavily developed study area selected habitat presumed to be used for thermal and security cover to a greater degree. Deer faced with higher densities of development avoided areas with more well pads during the day and responded neutrally or 20 u �\__,I selected for these areas at night. Deer in both study areas showed a strong reduction in use of areas around well pads that were being drilled. which is the phase of energy development associated with the greatest amount of human presence. vehicle traffic. noise. and artificial light. Despite divergent habitat selection patterns. we found no effects of development on individual condition or reproduction and found no differences in any of the physiological or vital rate parameters measured at the population level. However. deer density and annual increases in density were higher in the low-development area. Thus. the recorded behavioral alterations did not appear to be associated with demographic or physiological costs measured at the individual level. possibly because populations are below winter range carrying capacity. Differences in population density between the 2 areas may be a result of a population decline prior to our study (when development was initiated) or area-specific differences in habitat quality, juvenile dispersal. or neonatal or juvenile survival: however. we lack the required data to contrast evidence for these mechanisms. Given our results. it appears that deer can adjust to relatively high densities of well pads in the production phase (the period with markedly lower human activity on the landscape). provided there is sufficient vegetative and topographic cover afforded to them and populations are below carrying capacity. The strong reaction to wells in the drilling phase of development suggests mitigation efforts should focus on this activity and stage of development Many of the wells in this area were directionally drilled from multiplewell pads. leading to a reduced footprint of disturbance. but were still related to strong behavioral responses. Our results also indicate the likely value of mitigation efforts focusing on reducing human activity (i.e .. vehicle tratlic. lighL and noise). In combination. these findings indicate that attention should be paid to the spatial configuration of the final development footprint to ensure adequate cover. In our study system. minimizing the road network through landscape-level development planning would be valuable (i.e.~ exploring a maximum road density criteria). Lastly. our study highlights the imponancc of concomitant assessments of behavior and demography to provide a comprehensive understanding of how wildlife respond to habitat modification. 0 2021 The Wildlife Society. 21 �Appendix B. Preliminary results of habitat treatment responses and herbivore use of treated sites. Vegetation and camera data to accompany the study ' Population performance of Piceance Basin mule deer in response to natural gas resource selection and mitigation efforts to address human activi~1• and habitat degradation ' Principal Investigators: Danielle Johnston (Danielle.bil\'eu ll state.co.us). Chuck Anderson (chuck.anderson@state.co. us) Collaborators: Colorado Parks and Wildlife. BLM-White River Field Office. Idaho State University, Colorado State University, Federal Aid in Wildlife Restoration, EnCana Corp.. ExxonMobil Prod. Co./XTO Energy, Marathon Oil Corp., Shell Petroleum WPX Energy. Colorado Mule Deer Assn .. Muley Fanatic Found. , Colorado Mule Deer Found .. Colorado State Severance Tax Fund. Boone & Crocket Club, and Safari Club Int. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In 2011 and 2013, about I ,200 acres of pinyon and juniper (PJ) mastication treatments were completed in the Magnolia region of the Piceance Basin. Treated parcels averaged 7 acres in size. and were intended to increase winter range quality for deer. The treatments were pa11 of a study to evaluate the effectiveness of PJ removal as mitigation for impacts of natural gas development on deer. with outcomes assessed in terms of deer population and demographic parameters. This summary addresses some side questions relevant to the main study, with outcomes assessed in terms of vegetation response and animal use of vegetation treatments. We were interested in quantifying the understory forage produced by the mastication treatments. We used paired masticated/control point-intercept transects on a subset of parcels (Graham 20 I 3) to quantify cover of plant groups relevant to deer nutrition. We used belt transects and trained ocular estimation, with benchmarks (Johnston 2018), to estimate summer utilization on individual shrubs. then scaled these to the plot level (Bilyeu. Cooper et al. 2007). We used belt transects of shrub canopy measurements. coupled with biomass equations developed for the study area (Johnston 2018) to quantify winter forage production of key browse species. Winter forage production was defined as current-year stems. not including leaves. not including biomass removed by summer browsing. and not including very small stems which wou ld likely be shed prior to winter (Johnston 20 18). We were interested in how summer use of treatments. and use of treatments by non-target animals. impacted winter forage availability. Ten cattle exclosures. distributed broadly throughout the study area (Figure 1), were built within mastication treatments in 20 11 and 20 13. We assessed plant cover and summer shrub uti lization within these using techniques described above. On paired masticated/control transects, we deployed Reconyx Hypertire cameras July-November 20I8-2019. These were programmed to facilitate creating an index of use: 5 pictures per motion trigger. 3 second interval between pictures. a 5 minute wait time between triggers, and a sensitivity sening of High (Rhodes. Larsen et al. 20 18). An animal observed with their head down or other indication of foraging in one or more of the photos in a 5 photo set was counted as one foraging event, and non-foraging occurrences were counted similarl y. Sampl ing efforts by year are given in Table I. Because the plant cover data contained many zeros. we modeled presence/absence of each plant group separately from its cover where present (Fletcher. Mackenzie et al. 2005). using the lme4 package in R (Bates 2005). For both analyses, treatment. year. and their interaction were considered ft:xed effects. year was included as a categorical variable. and pair ID and plot ID were included as random effects. We used a similar approach for camera data for cattle and elk. which also contained many zeros. 22 �--- ---- ----- In general, grasses responded positively to treatment (Figure 2a). Wheatgrass presence, wheatgrass cover, and needlegrass presence were higher in treated than untreated plots. Poa grass presence was higher in treated plots by 2018, although poa grass presence and cover initially had a negative response to treatment. Cheatgrass presence also responded positively to treatment (Figure 2a). Wheatgrasses, poas, and cheatgrass all had significant year*treatment interactions for either presence or cover. Interannual variation in cover was greater in masticated plots than in control plots for these species groups (Figure 2a). Forbs responded positively to treatment. Annual forb and perennial forb presence were higher in treated than untreated plots (Figure 2b). Some shrubs responded positively to treatment, while others did not. Snowberry cover was lower in treated plots in 2013, but in 2016 and 2018, cover was higher in treated plots (Figure 2c). Variation in snowberry cover was greater in masticated than in control plots (Figure 2c). Bitterbrush did not display any significant effects until 2018, when cover was higher in treated plots (Figure 2c). Serviceberry cover was lower in treated plots over all years (Figure 2d). Sagebrush cover was initially lower in treated plots, but by 2018 this difference was no longer significant (Figure 2d). Summer utilization of serviceberry and mountain mahogany in 2018 was significantly higher in masticated than in control plots, but no differences were detected in bitterbrush or sagebrush. Winter forage production, which was summed over serviceberry, mountain mahogany, and bitterbrush, was significantly higher in masticated plots than in unmasticated plots in all years except 2016, when the pattern was reversed (Figure 3). There was no significant effect of exclosures on any plant cover group or on summer utilization in 2018. Deer, horse, elk, and cattle all foraged more often in masticated plots than in controls in 2018 (Figure 4). Cattle were only observed foraging at 6 of 20 locations, horse were observed at 9, deer at 19, and elk at 6. Mastication treatments had many positive effects on forage availabilty, including higher cover of desirable grass groups such as poa grasses and wheatgrasses. higher cover of perennial forbs, and usually higher productivity of winter-available shrub forage. There were some negative effects and some differences in effects among years, however. Cheatgrass was higher in masticated plots than in controls, and snowberry cover was higher in masticated plots in 2016 and 2018. 2016 was an unusual year compared to other years of this study, with very high productivity of grasses (including cheatgrass, especially in masticated plots), and unusua11y high productivity of winter-available forage of desirable shrubs in control but not masticated plots. Summer shrub utilization in 2018 was higher in masticated plots than in controls. We lack any data on utilization from 2016, which might have helped explain if the lower production of winter-available forage in masticated plots was due to higher summer utilization in those plots that year. Another explanation for the 2016 results is that good conditions for grass, cheatgrass, and/or snowberry productivity in masticated plots led to increased competition which lessened productivity of desirable forage shrubs. All four of the large herbivores of interest foraged more frequently in summer and fa11 in masticated plots than in control plots in 2018. The impact of cattle was concentrated in only a few plots, but they did forage frequently in plots where they occurred. Cattle use ended in September, prior to the period of heavy use by deer in October. The data from the cattle exclosures does not indicate that cattle are having any measurable negative effect on forage resources. In summary the impact of cattle on the forage resources available to deer in mastication treatments seems minimal. However, the effect of the sum of cattle, horse, and elk foraging may have some impact. In 2019, we collected vegetation data and camera data. 2019 is the last year of data collection for this study, and final analyses will be incorporated into publications in 2020-21. LITERATURE CITED Bates, D. (2005). "Fitting linear mixed models in R." R news 5(1). Bilyeu, D. M., D. J. Cooper and N. T. Hobbs (2007). "Assessing impacts of large herbivores on shrubs: tests of scaling factors for utilization rates from shoot-level measurements." Journal of Applied Ecology 44( I): I68-175. 23 �Fletcher, D., D. D. Mackenzie and E. Villouta (2005). "Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression." Environmental and ecological statistics 12: 45-54. Graham, T. (2013). Magnolia habitat manipulation project vegetative monitoring: June 2013 notes on data collection and methods used, Ranch Advisory Partners, LLC: 7. Johnston, D. 8. (2018). Wildlife Research Report: Examining the effectiveness of mechanical treatments as a restoration technique for mule deer habitat. Fort Collins, CO. Colorado Parks and Wildlife. Rhodes, A. C., R. T. Larsen and S. B. S. Clair (2018). "Differential effects of cattle, mule deer, and elk herbivory on aspen forest regeneration and recruitment." Forest Ecology and Management 422: 273280. 24 �Table 1. Number of transects sampled for a given data t:ype each year. Variables quantified 2011 2012 2013 2014 2015 2016 2018 90* 90* 145 69 Percent cover of plant l07t 159 functional groups Winter-available forage of bitterbrush, serviceberry, mountain mahogany (ShrubMassPerArea) Summer utilization of bitterbrush, service berry, mountain mahogany, and sagebrush Index of deer, elk, horse, and 70t 27t 63 2019 75t 40 (camera sites) 75t 75t 75t 40 40 cattle use in summer and fall, (2 (2 as determined by trail camera (EventsPerDay) * Pretreatment data collected 20 I 1-20 I2 will be added to a later report. tlncludes 24-30 locations taken at exclosure sites. cameras each) cameras each) 25 �Figure 1. Sampling locations within the Magnolia region r~f"the Pic:eance Basin. 26 �b 10.0 - a .... Q) e 15 · ra s I > ._ a, > 7.5 - 0 0 _!: 10 · ca, 5.o - (.) C: Q) ~ ~ Q) Q) a. a. 5 · o- 2.5 · 0.0 • ' 2013 ' 2014 ' 2018 ' 2016 ' 2013 .... Q) snowb~ +, C '' ,, 8 c 6· , ' Q) ~ 2016 2018 d 8- I 9- ' 2018 year year C ' 2016 ' 2014 ' ai 6 • > 8 ~ 4 _ sa ....Q) (.) Q) a. a. 3· 2· 20'13 ' 2016 2014 2013 2018 ' 2014 year year Figure 2. Cover ofsome p!antfimcriona! groups and species imporranr.for evaluaring habitar quality. Dashed lines indicare masricared plots and solid lines are confrols. A "+ " or .. _.. sign indicates significant posifive or negative main effect of masticafion across years (a = 0. 05). "P .. indicates that the sign[ficant ejject was observed in the prese11celabse11ce ana(rsis. and "C •• indicates a significant effect in the cover-where-present ana(vsis. 27 �_ 20 · NE --- T ~ Treatment "'~ __.. control ..0 "' 2 90 - • masbcated .c l "' * ..1.. ...1.. I I 2013 20 14 2015 2016 2018 year Figure 3. Mass of winter-available forage (curre111-year stem mass measured in September, not including leaves or mass removed by summer browsing) per unit shrub area. Data are summed over serviceberry, mountain mahogany. and bitterbrush. N=8.fhr 2013 and 2015 and ]5-31.for other years. No transects inside fences were included. Error bars = SE. Stars indicate siKn(ficant differences at alpha = 0. 05 28 �Not foraging, near camera Foraging, near camera a 75 · .,,>- 75 • 0 ai Cl. .,, :I: Treatment 50 · ai 50 • Cl. control 1/l ■ masticated C: Ql > w Cl .!: 25 • 25 • * Cl !!! 0 u.. O· O· cattle elk horse callle mule deer elk horse mule deer Herbivore Herbivore Figure -I. a) Average number offoraging eve111s per hectare per day between mid-July and midNovember. 2018 in control versus masticated plots. Stars indicate sign!ficant differences at a = 0.05. t indicates a sig11(ficcmt d(ffere11ce in presence <~fjoraging events. h) Average number of non.foraging observations per hectare per day. 29 �> Colorado Parks and Wildlife July 1, 2021 - June 30, 2022 WILDLIFE RESEARCH REPORT State of _ _ _ _ _____.:::C=o=lo=r=ad=o=--_ _ _ _ _ : ~P=ar:..!.:ks=an:.:.:d::....:..W:..:i=ld:::.:li=fe=--------------Cost Center 3430 : .:.:.M=a=mm=a=l=s=R=e=se=a=rc=h=-------------Work Package 3001 : .::::D~e.:.:er~C::::.o=n=s=erv:...:..::a=ti=on~-----------Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:. ______W__-"""""24__3a.. .-""""'R'"""'-6a.. .__ _ __ Period Covered: July 1, 2021 - June 30, 2022 Author: C. R. Anderson, Jr. Personnel: D. Bilyeu-Johnston, CPW; J. Northrup, Ontario Ministry ofNatural Resources and Forestry; R. Marrotte and Helena Rheault, Environmental and Life Sciences Graduate Program, Trent University; B. Gerber, University of Rhode Island. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, Caerus Oil and Gas LLC, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, Williams and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. 1 �WILDLIFE RESEARCH REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES l. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES I. Finalize publication of results addressing the role of memory in seasonal habitat selection patterns of migratory mule deer during different time scales. 2. Continue analyses of vegetation and mule deer responses to habitat treatments intended as a mitigation option to offset energy development disturbance. ' 3. Submit final results addressing vegetation and mule deer responses to 3 mechanical treatment methods on pinyon-juniper winter range for publication. 4. Construct a web-based energy development planning tool to guide future energy development to minimize and/or mitigate mule deer disturbance on winter range. PROJECT OVERVIEW AND RESEARCH SUMMARY We propose to experimentally evaluate winter range habitat treatments and human-activity management alternatives intended to enhance mule deer (Odocoileus hemionus) populations exposed to energy-development activities. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent preliminary and final results of a 10-year research project addressing habitat improvements as mitigation and evaluation of deer responses to energy development activities to inform future development planning options on important seasonal ranges. From2008-2019, we monitored deer on 4 winter range study areas representing relatively high (Ryan Gulch, South Magnolia) and low (North Magnolia, North Ridge) levels of development activity (Fig. 1) to address factors influencing deer behavior and demographics and to evaluate success of habitat treatments as a mitigation option. We recorded adult female habitat use and movement patterns; estimated neonatal, overwinter fawn and annual adult female survival; estimated annual early and late winter body condition, pregnancy and fetal rates of adult females; and estimated annual mule deer abundance among study areas. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinion-juniper/mountain shrub habitats in each of 2 treatment areas (Fig. 2) with minor (North Magnolia) and extensive (South Magnolia) energy development, respectively. 2 �During this research segment, we finalized publication of results investigating the role of memory in seasonal habitat selection of migratory mule deer over different time scales (Rheault et al. 2021; Appendix A), continued analyses of vegetation and mule deer responses to habitat treatments intended as a mitigation option to offset energy development disturbance (preliminary results reported in Appendix B), submitted final results addressing vegetation and mule deer responses to 3 mechanical treatment methods on pinyon-juniper winter range for publication (Johnston and Anderson /11 review), and constructed a web-based energy development planning tool to guide future energy development to minimize and/or mitigate mule deer disturbance on winter range (Marrotte et al. In prep). Based on final (migration, mule deer behavioral and demographic responses, reproductive success and neonate survival; see Anderson 2019 for detailed methods and results and Appendix A for publication abstracts) and preliminary data analyses (vegetation and herbivore response to habitat treatments, Appendix B) for this 10-year project: (I) annual adult female survival was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31 % to 95% within study areas, with annual and study area differences primarily due to early winter fawn condition, annual weather conditions, and factors associated with predation on winter range; (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from 50% in North Ridge to I03% in North Magnolia, but have stabilized recently in 3 of the 4 study areas with recent decline evident in North Ridge (Fig. 3); (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures (Fig. 4); (5) mule deer exhibited behavioral plasticity in relation to energy development, without evidence of demographic effects, where disturbance distance varied relative to diurnal extent and magnitude of development activity (Fig 5), which provide for useful mitigation options in future development planning; and (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) during 2012 when drought conditions persisted during the third trimester of doe parturition ( Fig. 6). Final results are pending to address vegetation and mule deer responses to assess habitat treatment mitigation options for energy development planning and spatial planning tool development is in progress. Final data collection efforts for this project was completed by spring 2020 (final GPS collar recovery). Collaborative research with agency biologists. graduate students. and university professors has produced 23 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011, Peterson et al. 2018b); development and evaluation of a remote mule deer collaring device (Bishop et al. 2019); mule deer migration relative to energy development (Lendrum et al. 2012. 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013; Rheault et al. 2021 ); mule deer response to helicopter capture and handling (Northrup et al. 2014a ); potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014); mule deer genetics in relation to body condition and migration (Northrup et al. 2014b); acoustic monitoring to investigate spatial and temporal factors influencing mule deer vigilance (Lynch et al. 2014) and foraging behavior (Northrup et al. 2019); the relationship of plant phenology with mule deer body condition (Searle et al. 2015); approaches to identify cause-specific mortality in mule deer from field necropsies (Stonehouse et al. 2016); the influence of individual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman ct al. 2018 ); and mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 2015, 2016a, 2016b, 2021, Peterson ct al. 2017, 2018a). These publications arc summarized in Appendix A and preliminary results describing vegetation and herbivore responses to habitat treatments are reported in Appendix B. Wc anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado and elsewhere. 3 �Wl!II Pads & Faclll~es South MagnOf•a ,i:,rtn R idge .C In oevelopme nt l Producing 'n'CII _ Development tac1:1t1 es 10 f,t.le-s Figure I. Mule deer winter range study areas relative to active natural gas well pads and energy development facil ities in the Piceance Basin of northwest Colorado, winter 2013/14 (Accessed http://cogcc.state.co. us/ December 3 1, 2013; energy development drilling activity has been minor since 20 12). 4 �North Magnoha treatement sites (587 acres) D Be;; rSet_15_35b_andC, r--• BearSet_ 1_8andA_E D BearSet_36_54andJ GreasewoodSet_g I 6_g29 GreasewoodSet_g I _g 15 D GreasewoodSet_g30_g-l 2 LeeOversrghts_a_fand 16_ 17 t,lechanrcar treatment companson (54 acres) - - North Hatch Prlo t Trea tments ( 116 acres) le Deer Study Areas North Magnolra South Magnolia 4 Figure 2. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan 2011 using hydro-axe; yellow polygons completed Jan 201 2 using hydro-axe, roller-chop, and chaining; and remaining polygons completed Apr 201 3 using hydro-axe). January 20 I I hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 20 11 (Lower right, ground view). 5 �Piceance Basin late winter mule deer density 35.00 30.00 25.00 1-:::- 20.00 - t 15.00 0 - North Ridge • • • • • • Ryan Gulch 10.00 - • North Magnolia 5.00 - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 3. Mule deer density estimates and 95% C I (error bars) from 4 winter range herd segments in the Piceance Basin. northwest Colorado, late winter 2009-2018. Figure 4. Mule deer study areas in the Piceancc Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult female mule deer (11 = 52: Lower left), and acti ve natural-gas well pads (black dots) and roads (state, county, and natural-gas: white lines) from May 2009 (Lower right; from Lendrum et al. 20 12: http://dx.doi.org/l 0. I 890/ES I "'-00 l 65 . 1). 6 �M I Prod 400 Prod 600 Prod 800 Prod 1000 Drill 400 Dnll 6(10 Drill 800 Dnll 1000 Dr<ll 600 Drill SQQ Drill 1000 Covanaces M I Prod 400 Prod 600 Prod 800 Prod 1000 Dr<II 400 Figure 5. Posterior distributions of population-level coefficients related to natural gas development for RSF models during the day (top) and night (bottom) for 53 adult female mule deer in the Piceance Bas in, northwest Colorado. Dashed line indicates 0 selection or avoidance (below the line) of the habitat features. 'Drill ' and ' Prod ' represent drilling and producing well pads, respectively. The numbers fo llowing ' Drill ' or ' Prod' represent the distance from respective well pads evaluated (e.g.. ' Drill 600' is the number of well pads with active drilling between 400- 600 m from the deer location; from Northrup et al. 2015; http://onlinelibrary.wiley.com/doi/ I 0. 1111 /gcb.13037/abstract). Road disturbance was relatively minor (~60- 120 m, not illustrated above). 1.00 0.80 Q) ro I... ro> 0.60 -~ ::, Cf) ro 0.40 a> lL 0.20 0.00 2013 2012 2014 Year □ High development □ Low development I Figure 6. Model averaged estimates of mule deer fetal survival from early March until bi11h (late MayJune) in high and low energy development study areas of the Piccancc Basin, northwest Colorado, 20 122014 (from Peterson et al. 2017; http://www.bioone.org/doi/ pdf/l 0.298 I/w lb.0034 1). 7 �LITERATURE CITED Anderson, C. R., Jr. 2019. Population performance of Piceance Basin mule deer 111 response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. Federal Aid in Wildlife Restoration Annual Report W-243-R3, Ft. Collins. CO USA. Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79th North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners. PA, USA. ISSN 0078- I 355. Bergman. E. J.. C. R. Anderson Jr., C. J. Bishop. A. A. Holland, and J. M. Northrup. 20 18. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82: 130-137, DOI: 10: 1002/jwmg.21334 Bishop, C. J .. C. R. Anderson Jr.. D. P. Walsh. E. J. Bergman. P. Kuechle. and J. Roth. 20 11 . Effectiveness of a redesigned vaginal implant transmitter in mule deer. Journal of Wildlife Management 75(8): 1797-1806; DOI : 10.1002/j wmg.229 Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman. C.R. Anderson Jr., D. Kilpatrick, J. Bake!, and C. Fabvre. 2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725; doi.org/10.1002/wsb. l 034 Freeman, E. D., R. T . Larsen, M. E. Peterson, C. R. Anderson, Jr., K. R. Hersey, and B. R. Mc Millan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: I 0.1002/wsb.450 Johnston, D. B., and C. R. Anderson Jr. /11 Re view. Plant and mule deer responses to pinyon-juniper removal by three mechanical methods. Wildlife Society Bulletin. Lendrum, P. E. , C. R. Anderson, Jr., R. A. Long, J. K. Kie, and R. T. Bowyer. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http ://dx.doi.org/10. Lendrum, P. E. , C.R. Anderson, Jr.. K. L. Monteith. J. A. Jenks, and R. T. Bowyer. 2013. Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: l 0.13 7 l/joumal.pone.0064548 Lendmm, P. E., C.R. Anderson, Jr., K. L. Monteith, J. A. Jenks, and R. T. Bowyer. 20 14. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.orn/ l 0. 10 16/ j.mambio.20 14.05.005 Lynch, E., J . M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic featu res influence th e u se of auditory vig ilance by mule deer . Behavioral Ecology; doi: 10.1093/beheco/aru 158. Marrotte, R. R., C. R. Anderson Jr., and J. M. Northrup. /11 Prep . Developing a spatial planning tool for natural gas development on mule deer winter range. Northrup, J. M., M. B. Hooten, C.R. Anderson, Jr., and G. Wittemyer. 2013 . Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463 . Northrnp, J. M., C.R. Anderson, Jr., and G . Wittemycr. 2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4 ):73 1738; DOI: 10.1002/jwmg.705 Northrup, J. M., A. B. Shafer, C. R. Anderson Jr., D. W. Coltman, and G. Whittcmyer. 2014b. Finescale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571 ; doi : 1O. l l l l/eva. 121 89 Northrup, J. M., C.R. Anderson, Jr., and G. Wittcmyer.201 5. Quantifying spatial habitat loss from h ydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi : I 0.1111/gcb. 13037. 8 �Northrup, J.M., C.R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016a. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications 26:27462757 Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016b. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 Northrup, J.M., A. Avrin, C.R. Anderson Jr., E. Brown, and G. Wittemyer. 2019. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal of Mammalogy 100: 14791489; https://doi.org/lO. I093/jmammal/gyz124 Northrup, J.M., C. R. Anderson Jr., B. D. Gerber, and G. Wittemyer. 2021. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs 208: 1-37; 2021; DOI: 10.1002/wmon.1060 Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: IO. l l l l/wlb.00341 Peterson, M. E., C. R. Anderson Jr., J. M. Northrup, and P. F. Doherty Jr. 2018a. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82:1135-1148, DOI: l0.1002/jwmg.21476 Peterson, M. E., C. R. Anderson Jr., M. W. Alldredge, and P. F. Doherty Jr. 2018b. Using maternal mule deer movements to estimate timing of parturition and assist fawn captures. Wildlife Society Bulletin 42:616-621; DOI: 10.l002/wsb.935 Rheault, H., C. R. Anderson Jr., M. Bonar, R. R. Marrotte, T. R. Ross, G. Wittemyer, and J. M. Northrup. 2021. Some memories never fade: inferring multi-scale memory effects on habitat selection of a migratory ungulate using step-selection functions. Frontiers in Ecology and Evolution 9:702818; doi: I 0.3389/fevo.2021. 702810 Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia ISSN 0029- 8549; DOI 10.1007/s00442-015-3348-9 Stonehouse, K. F., C. R. Anderson Jr., M. E. Peterson, and D. R. Collins.2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. Prepared by Ch UC k And erson Digitally signed by Chuck Anderson Date:2022.10,0605:49:08-06'00' Charles R. Anderson, Jr., Mammals Research Leader 9 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CHAD J. BISHOP 1, CHARLES R. ANDERSON Jr. 1, DANIEL P. WALSH 1, ERIC J. BERGMAN', PETER KUECHLE 2, and JOHN ROTH2 1 Colorado Parks and Wildlife, Fort Collins, Colorado 80526 USA 2 Advanccd Telemetry Systems, Isanti, Minnesota 55040 USA Citation: Bishop, C. J., C.R. Anderson Jr., D. P. Walsh, E. J. Bergman, P. Kuechle, and J. Roth. 2011. Effectiveness ofa redesigned vaginal implant transmitter in mule deer. Journal ofWildlife Management 75(8):1797-1806; DOI: 10.1002/jwmg.229 ABSTRACT Our understanding of factors that limit mule deer (Odocoileus hemionus) populations may be improved by evaluating neonatal survival as a function of dam characteristics under free-ranging conditions, which generally requires that both neonates and dams are radiocollared. The most viable technique facilitating capture of neonates from radiocollared adult females is use of vaginal implant transmitters (VITs). To date, VITs have allowed research opportunities that were not previously possible; however, VITs are often expelled from adult females prepartum, which limits their effectiveness. We redesigned an existing VIT manufactured by Advanced Telemetry Systems (ATS; Isanti, MN) by lengthening and widening wings used to retain the VIT in an adult female. Our objective was to increase VIT retention rates and thereby increase the likelihood of locating birth sites and newborn fawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of retention to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT sample size necessary to achieve a specified sample size of neonates. The probability of a VIT being retained until parturition was 0.766 (SE= 0.0605) and the probability of a VIT being retained to within 3 days of parturition was 0.894 (SE= 0.0441 ). In a similar study using the original VIT wings (Bishop et al. 2007), the probability of a VIT being retained until parturition was 0.447 (SE= 0.0468) and the probability of retention to within 3 days of parturition was 0.623 (SE= 0.0456). Thus, our design modification increased VIT retention to parturition by 0.319 (SE= 0.0765) and VIT retention to within 3 days of parturition by 0.271 (SE = 0.0634 ). Considering dams that retained VITs to within 3 days of parturition, the probability of detecting at least I neonate was 0.952 (SE= 0.0334) and the probability of detecting both fawns from twin litters was 0.588 (SE = 0.0827). We expended approximately 12 person-hours per detected neonate. As a guide for researchers planning future studies, we found that VIT sample size should approximately equal the targeted neonate sample size. Our study expands opportunities for conducting research that links adult female attributes to productivity and offspring survival in mule deer.© 2014 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development PATRICK E. LENDRUM 1, CHARLES R. ANDERSON JR.2, RYAN A. LONG 1, JOHN G. KIE 1, AND R. TERRY BOWYER1 1Department ofBiological Sciences, Idaho State University, Pocatello, Idaho 83209 USA 2 Colorado Parks and Wildlife, Grand Junction, Colorado 81 SOS USA Citation: Lendrum, P. E., C.R. Anderson Jr., R. A Long, J. G. Kie, and R. T. Bowyer. 2012. Habit:it selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3(9):82 http://dx.doi.org/lO. l 890/ES 12-00165. l Abstract. The disruption of traditional migratory routes by anthropogenic disturbances has shifted patterns of resource selection by many species, and in some instances has caused populations to decline. Moreover, in recent decades populations of mule deer (Odocoileus hemionus) have declined throughout much of their historic range in the western United States. We used resourceselection functions to determine if the presence ofnatural-gas development altered patterns of resource selection by migrating mule deer. We compared spring migration routes of adult female mule deer fitted with GPS collars (n = 167) among four study areas that had varying degrees of natural-gas development from 2008 to 2010 in the Piceance Basin of northwest Colorado, USA. Mule deer migrating through the most developed area had longer step lengths (straight-line distance between successive GPS locations) compared with deer in less developed areas. Additionally, deer migrating through the most developed study areas tended to select for habitat types that provided greater amounts of concealment cover, whereas deer from the least developed areas tended to select habitats that increased access to forage and cover. Deer selected habitats closer to well pads and avoided roads in all instances except along the most highly developed migratory routes, where road densities may have been too high for deer to avoid roads without deviating substantially from established migration routes. These results indicate that behavioral tendencies toward avoidance of anthropogenic disturbance can be overridden during migration by the strong fidelity ungulates demonstrate towards migration routes. If avoidance is feasible, then deer may select areas further from development, whereas in highly developed areas, deer may simply increase their rate of travel along established migration routes. 10 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum•, Charles R. Anderson Jr.2, Kevin L. Monteith 1.3, Jonathan A. Jenks", R. Terry Bowyer' 1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, 3 Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogcnically Altered Landscapes. PloS ONE 8(5): e64548. DOI: I0.1371/journal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently plastic to compensate for such changes, warrants additional study to better understand this critical conservation issue. Methodology/Principal Findings: We studied timing and synchrony of departure from winter range and arrival to summer range offemale mule deer (Odocoileus hemionus) in northwestern Colorado, USA, which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather, plant phenology, and individual life-history characteristics, patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer, equipped them with GP$ collars, and observed patterns of spring migration during 2008-20 I0. Concl11sions/Signijicance: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation, which varied among years, but was highly synchronous across study areas within years. Additionally, timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled, and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure, but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas, especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP 1, MEVIN B. HOOTEN 1,2,3, CHARLES R. ANDERSON JR.4, AND GEORGE WITTEMYER1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 3 Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J.M., M. B. Hooten, C.R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology 94(7): 1456-1463. http://dx.doi.org/ 10.1890/12-1688.l Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. 11 �Effects of Helicopter Capture and Handling on Movement Behavior of Mule Deer JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JRZ, AND GEORGE WITTEMYER 1 'Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J.M., C. R. Anderson Jr., and G. Wittemyer. 2014. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731-738; DOI: 10.1002/jwmg.705 ABSTRACT Research on wildlife movement, physiology, and reproductive biology often requires capture and handling of animals. Such invasive treatment can alter behavior, which may bias results or invalidate assumptions regarding representative behaviors. To assess the impacts of handling on mule deer (Odocoileus hemionus), a focal species for research in North America, we investigated pre- and post-recapture movements of collared individuals, and compared them to deer that were not recaptured (controls). We compared pre- and post-recapture movement rates (m/hr) and 24-hour straight-line displacement among recaptured and control deer. In addition, we examined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate were marginally elevated relative to monthly averages for 24 hours following recapture, with non-significant elevation continuing for up to 7 days. Comparing movements averaged over 30 days before and after recapture, we found no differences in displacement, but movement rates demonstrated seasonal effects, with faster movements post- relative to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relative to control deer movements, recaptured deer movement rates in March were higher immediately after recapture and lower in the second and third weeks following recapture. The median time to return to the pre-recapture home range was 13 hours, with 71 % of deer returning in the first day, and 91 % returning within 4 days. These results indicate a short period of elevated movements following recaptures, likely due to the deer returning to their home ranges, followed by weaker but non-significant depression of movements for up to 3 weeks. Censoring of the first day of data post capture from analyses is strongly supported, and removing additional days until the individual returns to its home range will control for the majority ofimpacts from capture. © 2014 The Wildlife Society. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendrum■, Charles R. Anderson Jr. b' Kevin L. MontelthC, Jonathan A. Jenksd, R. Terry Bowyer• Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello 83209, USA b Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction 81505, USA c Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, 3166, 1000 East University Avenue, Laramie 82071, USA .s Department of Natural Resource Management, South Dakota State University, Box 2140B, Brookings 57007, USA u Citation: Lendrum, P. E., C.R. Anderson Jr., K. L Monteith, J. A Jenks, and R. T. Bowyer. 2014. Relating the movement of a rapidly migrating ungulate to spatiotemporal patterns of forage quality. Mammalian Biology: http://dx.doi.ony'l0.1016/j.mambio.2014.05.005 ABSTRACT: Migratory ungulates exhibit recurring movements, often along traditional routes between seasonal ranges each spring and autumn, which allow them to track resources as they become available on the landscape. We examined the relationship between spring migration of mule deer (Odocoileus hemionus) and forage quality, as indexed by spatiotemporal patterns of fecal nitrogen and remotely sensed greenness of vegetation (Normalized Difference Vegetation Index; NOVI) in spring 20 l Oin the Piceance Basin of northwestern Colorado, USA. NOVI increased throughout spring, and was affected primarily by snow depth when snow was present, and temperature when snow was absent. Fecal nitrogen was lowest when deer were on winter range before migration, increased rapidly to an asymptote during migration, and remained relatively high when deer reached summer range. Values offecal nitrogen corresponded with increasing NOVI during migration. Spring migration for mule deer provided a way for these large mammals to increase access to a high-quality diet, which was evident in patterns of NOVI and fecal nitrogen. Moreover, these deer 'jumped" rather than "surfed" the green wave by arriving on summer range well before peak productivity of forage occurred. This rapid migration may aid in securing resources and seclusion from others on summer range in preparation for parturition, and to minimize detrimental factors such as predation and malnutrition during migration. 12 �\..,_I Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN 1, RANDY T. LARSEN 1, MARKE. PETERSON2, CHARLES R. ANDERSON JR.3, KENT R. HERSEY\ AND BROCK R. McMILLAN 1 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA 2 Department offish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, CO 81505, USA 4 Utah Division of Wildlife Resources, 1594 WNorth Temple, Salt Lake City, UT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlifo Society Bulletin; DOI: I0.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example, in mule deer (Odocoileus hemionus), changes in male/female ratio due to male- biased harvest may alter rates of pregnancy, timing of parturition, and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g., fewer births, later parturition resulting in lower survival of fawns, and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy, synchrony of parturition, and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO, USA; 26 males/100 females) and a relatively low (Monroe, UT, USA; 14 males/100 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%; z = 0.821; P = 0.794), timing of parturition (estimate= 1.258; SE= 1.672; I = 0. 752; P = 0.454), or synchrony of parturition (F = 1.073; P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected.© 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup•, Aaron B. A. Shafe.-%, Charles R. Anderson Jr.3, David W. Coltman', and George Wlttemyer 1 I Department offish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Palks and Wildlife, Grand Junction, CO, USA 4 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J.M., AB. Shafer, C.R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-457 l; doi: I0.1111/eva 12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoileus hemionus), we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing, (ii) screen for mitochondrial haplotypes associated with migration timing, and (iii) test whether nuclear beterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial baplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns), one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species, these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management, even in widely distributed panmictic species. 13 �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynch•, Joseph M. Northrupb, Megan F. MeKennaC, Charles R. Anderson Jr.d, Lisa Angeloni¥, and George Wlttemyer-,b •Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA bDepartmcnt offish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA •Natural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, dMammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA 9)cpartmcnt ofBiology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA Citation: Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittcmycr. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: I0.1093/beheco/aru I58. While visual forms of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeoffs associated with visual vigilance, auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field affect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior, we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night, where visual vigilance was likely to be less effective. Additionally, deer paused more in areas of moderate background sound levels, though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a fonn of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA Citation: Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79rJt North American Wildlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns, and whether ungulate migration is sufficiently prepared to compensate for such changes, has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming, respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identified by Lendrum et al. (2012, 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate, migration routes, and stopover use, if present, may be altered at high development intensities. In addition, migratory mule deer may benefit by maintaining security cover along migration paths, and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing permeability along migration routes by applying dispersed development plans (<2 well pads/krn2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible, habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible, directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly, we emphasize that OPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available, we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 14 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle1 • Mindy B. Rlce2 • Charles R. Anderson2 • Chad Bishop2 • N. T. Hobbs3 1 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 OQB, UK 2 Colorado Parlcs and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA 3 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80524, CO, USA Citation: Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phcnology enhances winter body condition of a large mobile herbivore. Occologia ISSN 0029-8549; DOI I0.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoi/eus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Cmcially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer JOSEPH M. NORTHRUP 1, CHARLES R. ANDERSON JR. 2, and GEORGE WITTEMYER 1• 3 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA 2 Citation: Northrup, J. M., C. R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: IO.I I I l/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America, with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework, with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns, with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate, these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions, the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study, and the methods we employed, provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers, mangers, and industry when attempting to mitigate habitat loss due to energy development. 15 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup', Charles R. Anderson Jr, and George Wittemyer•.J 1Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA 3Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Diversity and Distributions 22: 547-557, DOI: 10.1111/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8-78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup 1, Charles R. Anderson Jr. 2, Mevin 8. Hooten 3, and George Wittemyer4 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA 3 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 Department offish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Citation: Northrup, J.M., C.R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoileus hemionus) in the Piceance Basin of Colorado, USA, an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 16 �~ Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) Kourtney F. Stonehouse•.i, Charles R. Anderson Jr. 1, Mark E. Peterson•.i, and David R. Collins• 1Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2 Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations ofcausc-spccifie mortality in mule deer (Odocoi/eus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices are also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly. these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The information presented here is based primarily on field experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.state.co.us/learn/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality; efforts discerning ultimate cause will require specific tissue sample collections, where possible, submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document, be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson•, Charles R. Anderson Jr.2, Joseph M. Northrup 1,and Paul F. Doherty Jr.' 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospi:ct Road, Fort Collins, CO 90526 USA \..-I Citation: Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology doi: I0.1 I I l/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat, particularly for ungulate species. Understanding how this development impacts reproductive success metrics that are influential for ungulate population dynamics is important to guide management of ungulates. However, the influences of natural gas development on reproductive success metrics of mule deer Odocoileus hemionus have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceance Basin, northwest Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utero fetal rates were high and statistically indistinguishable between study areas. Fetal survival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival, particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning, such as implementing habitat treatments to improve forage availability and quality, minimizing disturbance to hiding and foraging habitat particularly during parturition, and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 17 �Variation in ungulate body fat: individual versus temporal effects Eric J. Bergman 1, Charles R. Anderson Jr. 1, Chad J. Bishop', A. Andrew Holland', and Joseph M. Northrup 2 'Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Bergman, E. J., C.R. Anderson Jr., C. J. Bishop, A. A. Holland, and J.M. Northrup. 2018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82:130-137, DOI: 10:1002/jwmg.21334 ABSTRACT The use of ultrasonograhic measurements of muscle and body fat represent a relatively new data stream that can be used to address questions regarding tmgulate condition. We have learned that measurements of body fat and presumably overall body condition among individual animals, even those taken from the same herd at that same time, are highly variable. Relatively little consideration has been given to the sources of variation in body fat and other physiological parameters in wildlife populations. We evaluated the components of variation in late-winter mule deer (Odocoileus hemionus) body fat estimates: sampling variation (i.e., variation induced by the particular set of individuals that were sampled) and process variation (i.e., variation stemming from biological processes) with a long-term data set (2002-20 IS) from Colorado, USA. We collected our data from across Colorado as part of historical research, ongoing research, and periodic population monitoring programs. Mean percent ingesta-free body fat (%IFBF) for sampled mule deer was 7.20 ± 1.20% (SO). Covariates related to individual deer explained approximately 4% of the total variation in %IFBF and annual effects explained an additional 13% of the variation. Substantial residual variation in %IFBF (83%) remained unexplained. The source of the 83% of unexplained variation is partially linked to fine-scale spatial dynamics but also additional individual metrics we were unable to capture, primarily the presence or absence of dependent young. We speculate that the primary factors influencing late-winter mule deer body fat and overall condition are individual in nature. These results present a cautionary check on herd level inference that can be made from individual late-winter body fat estimates and we postulate that for mule deer, alternative and additional body condition metrics may offer added utility in management scenarios. However, an important next step to better understand wildlife population health is to evaluate the sources and magnitude of variation within other body condition metrics, with the goal of further refining data that can better allow biologists to incorporate herd health into population management recommendations. Mortality of mule deer fawns in a natural gas development area Mark E. Peterson•, Charles R. Anderson Jr.2,Joseph M. Northrup 1,and Paul F. Doherty Jr.' 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C. R. Anderson Jr., J. M. Northrup, and P. F. Doherty Jr. 20 I8. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82:1135-1148, DOI: 10.1002/jwmg.21476 ABSTRACT Recent natural gas development has caused concern among wildlife managers, researchers, and stakeholders over the potential effects on wildlife and their habitats. Specifically, understanding how this development and other factors influence mule deer (Odocoileus hemionus) fawn (i.e., 0-6 months old) mortality rates, recruitment, and subsequently population dynamics have been identified as knowledge gaps. Thus, we tested predictions concerning the relationship between natural gas development, adult female, fawn birth, and temporal (weather) characteristics on fawn mortality in the Piceance Basin of northwestern Colorado, USA, from 2012-2014.We captured and radio-collared 184 fawns and estimated apparent cause-specific mortality in areas with relatively high or low levels of natural gas development using a multi-state model. Mean daily predation probability was similar in the high versus low development areas. Predation was the leading cause of fawn mortality in both areas and decreased from 0-14 days old. Black bear (Ursus americanus; 22% of all mortalities, n = 17) and cougar (Felis conco/or; 36% of all mortalities, n =6) predation was the leading cause of mortality in the high and low development areas, respectively. Predation of fawns was negatively correlated with the distance from a female's core area to a producing well pad on winter or summer range. Contrary to expectations, predation of fawns was positively correlated with rump fat thickness of adult females. Well pad densities and development activity were relatively low during our study, indicating that the observed intensity of development did not appear to influence daily predation probability. Our results suggest maintaining development activity thresholds at levels we observed to potentially minimize the effects of development on fawn mortality. However, we caution that higher development intensity and drilling activity in flatter, less rugged areas with less concealment cover could influence fawn mortality. Managers should maintain low development densities in areas where topography and vegetation offer less concealment. Overall, region-specific data (e.g., development intensity, topography, predator assemblages, and associated predation risk) are needed to better understand the effects of natural gas development on fawn mortality. 18 �,__., Using maternal mule deer movements to estimate timing of parturition and assist fawn captures Mark E. Peterson•, Charles R. Anderson Jr.2, Mathew W. Alldredge2,and Paul F. Doherty Jr.' 1Departmcnt of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C.R. Anderson Jr., M. W. Alldredge, and P. F. Doherty Jr. 2018. Using maternal mule deer movements to estimate timing of parturition and assist fawn captures. Wildlife Society Bulletin 42:616-621; DOI: I0.1002/wsb.935 ABSTRACT Movement patterns of maternal ungulates have been used to determine parturition dates and aid in locating fawns, which may be important for understanding reproductive rates (e.g., pregnancy and fetal), but such methods have not been validated for mule deer (Odocoileus hemionus). We first determined timing of parturition using vaginal implant transmitters (VITs) and then predicted timing of parturition using VITs in conjunction with Global Positioning System collar data in the Piceance Basin of northwestern Colorado, USA, during 2012-2014. We examined daily movement rate to determine differences in movement rate among days (7 days pre- and postpartum) and for movement patterns indicative of parturition. Mean daily movement rate (m/day) of 102 maternal deer decreased by 46% from 1 day preparturition (mean = 1,253, SD= 1,091) to parturition date (mean = 682, S = 574), and remained at this low rate 1-7 days postpartum. We applied an independent data set to validate predicted parturition dates based on daily movement rate. We estimated day of parturition correctly (i.e., day 0), within 1-3 days postparturition, and_4 days postparturition offield-reported dates for IO (29%), 21 (60%), and 4 (11%) maternal females, respectively. For novel data sets, we predict that a mule deer female whose daily movement rate decreases by_46% and remains low _3 days postparturition particularly when preceded by a sudden increase in movement-has given birth. However, we caution that disturbance of deer by field crews should be minimized, and if birth sites are not found, neonatal mortality will be underestimated. Our results can help determine timing and general location of parturition as an aid in capturing fawns when the use of VITs is not feasible, with the ultimate objective of estimating pregnancy, fetal, and fawn survival rates if birth sites are found. On-animal acoustic monitoring provides insight to ungulate foraging behavior Joseph M. Northrup•, Alexandra Avrin 1, Charles R. Anderson, Jr. 2, Emma Brown3, and George Wittemyer 1 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 3National Parle Service Natural Sounds and Night Skies Division, Fort Collins, CO 80525 USA Citation: Northrup, J.M., A. Avrin, C.R. Anderson Jr., E. Brown, and G. Wittemyer. 2019. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal ofMammalogy 100:1479-1489; https://doi.org/10.1093/jmammaVgyzl24 Abstract Foraging behavior underpins many ecological processes; however, robust assessments of this behavior for free-ranging animals are rare due to limitations to direct observations. We leveraged acoustic monitoring and GPS tracking to assess the factors influencing foraging behavior of mule deer (Odocoileus hemionus). We deployed custom-built acoustic collars with GPS radiocollars on mule deer to measure location-specific foraging. We quantified individual bites and steps taken by deer, and quantified two metrics of foraging behavior: the number of bites taken per step and the number of bites taken per unit time, which relate to foraging intensity and efficiency. We fit statistical models to these metrics to examine the individual, environmental, and anthropogenic factors influencing foraging. Deer in poorer body condition took more bites per step and per minute and foraged for longer irrespective of landscape properties. Other patterns varied seasonally with major changes in deer condition. In December, when deer were in better condition, they took fewer bites per step and more bites per minute. Deer also foraged more intensely and efficiently in areas of greater forage availability and greater movement costs. During March, when deer were in poorer condition, foraging was not influenced by landscape features. Anthropogenic factors weakly structured foraging behavior in December with no relationship in March. Most research on animal foraging is interpreted under the framework of optimal foraging theory. Departures from predictions developed under this framework provide insight to unrecognized factors influencing the evolution of foraging. Our results only conformed to our predictions when deer were in better condition and ecological conditions were declining, suggesting foraging strategies were state-dependent. These results advance our understanding of foraging patterns in wild animals and highlight novel observational approaches for studying animal behavior. 19 �A noninvasive automated device for remotely collaring and weighing mule deer Chad J. Bishop 1, Mathew W. Alldredge 1, Daniel P. Walsh 1, Eric J. Bergman 1, Charles R. Anderson Jr. 1, Darlene Kilpatrick, Joe Bakel2, and Christophe Fabvre1 1 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526 USA 2 Dynamic Group Circuit Design, Inc., 2629 Redwing Road, Fort Collins, CO 80525 USA Citation: Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman, C.R. Anderson Jr., D. Kilpatrick, J. Bake!, and C. Fabvre. 2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725; doi.org/ 10.1002/wsb. I034 ABSTRACT Wildlife biologists capture deer (Odocoileus spp.) annually to attach transmitters and collect basic information (e.g., animal mass and sex) as part of ongoing research and monitoring activities. Traditional capture techniques induce stress in animals and can be expensive, inefficient, and dangerous. They are also impractical for some urbanized settings. We designed and evaluated a device for mule deer (0. hemionus) that automatically attached an expandable radiocollar to a ~6-month-old fawn and recorded the fawn's mass and sex, without physically restraining the animal. The device did not require on-site human presence to operate. Students and faculty in the Mechanical Engineering Department at Colorado State University produced a conceptual model and early prototype. Professional engineers at Dynamic Group Circuit Design, Inc. in Fort Collins, Colorado, USA, produced a fully functional prototype of the device. Using the device, we remotely collared, weighed, and identified sex of 8 free-ranging mule deer fawns during winters 2010-2011 and 2011-2012. Collars were modified to shed from deer approximately I month after the collaring event. Two fawns were successfully recollared after they shed the first collars they received. Thus, we observed IO successful collaring events involving 8 unique fawns. Fawns demonstrated minimal response to collaring events, either remaining in the device or calmly exiting. A fawn typically required ~l weeks of daily exposure before fully entering the device and extending its head through the outstretched collar, which was necessary for a collaring event to occur. This slow acclimation period limited utility of the device when compared with traditional capture techniques. Future work should focus on device modifications and altered baiting strategies that decrease fawn acclimation period, and in tum, increase collaring rates, providing a noninvasive and perhaps cost-effective alternative for monitoring mid- to large-sized mammal species. © 2019 The Wildlife Society. Behavioral and demographic responses of mule deer to energy development on winter range Joseph M. Northrup 1,J. M., Charles R. Anderson Jr. 2, Brian D. Gerber, and George Wittemyer 1 1Department of Fish, Wildlife and Conservation Biology, Colorado State University. 1474 Campus Delivery, Fort Collins, CO 80523 USA 2Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526 USA 3Departrnent of Natural Resources Science, University of Rhode Island. 1 Greenhouse Road, Kingston, RI 02881 USA Citation: Northrup, J.M., C.R. Anderson Jr., B. D. Gerber, and G. Wittemyer. 2021. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs 208:1-37; 2021; DOI: 10.1002/wmon.1060. ABSTRACT Anthropogenic habitat modification is a major driver of global biodiversity loss. In North America, one of the primary sources of habitat modification over the last 2 decades has been exploration for and production of oil and natural gas (hydrocaroon development), which has led to demographic and behavioral impacts to numerous wildlife species. Developing effective measures to mitigate these impacts has become a critical task for wildlife managers and conservation practitioners. However, this task has been hindered by the difficulties involved in identifying and isolating factors driving population responses. Current research on responses of wildlife to development predominantly quantifies behavior, but it is not always clear how these responses scale to demography and population dynamics. Concomitant assessments of behavior and population-level processes are needed to gain the mechanistic understanding required to develop effective mitigation approaches. We simultaneously assessed the demographic and behavioral responses of a mule deer population to natural gas development on winter range in the Piceance Basin of Colorado, USA, from 2008 to 2015. Notably, this was the period when development declined from high levels of active drilling to only production phase activity (i.e., no drilling). We focused our data collection on 2 contiguous mule deer winter range study areas that experienced starkly different levels of hydrocarbon development within the Piceance Basin. We assessed mule deer behavioral responses to a range of development features with varying levels of associated human activity by examining habitat selection patterns of nearly 400 individual adult female mule deer. Concurrently, we assessed the demographic and physiological effects of natural gas development by comparing annual adult female and overwinter fawn (6-monthold animals) survival, December fawn mass. adult female late and early winter body fat, age. pregnancy rates. fetal counts, and lactation rates in December between the 2 study areas. Strong differences in habitat selection between the 2 study areas were apparent. Deer in the less-developed study area avoided development during the day and night, and selected habitat presumed to be used for foraging. Deer in the heavily developed study area selected habitat presumed to be used for thermal and security cover to a greater degree. Deer faced with higher densities of development avoided areas with more well pads during the day and responded neutrally or 20 V �~ selected for these areas at night. Deer in both study areas showed a strong reduction in use of areas around well pads that were being drilled, which is the phase of energy development associated with the greatest amount of human presence, vehicle traffic, noise, and artificial light. Despite divergent habitat selection patterns, we found no effects of development on individual condition or reproduction and found no differences in any of the physiological or vital rate parameters measured at the population level. However, deer density and annual increases in density were higher in the low-development area. Thus, the recorded behavioral alterations did not appear to be associated with demographic or physiological costs measured at the individual level, possibly because populations are below winter range carrying capacity. Differences in population density between the 2 areas may be a result of a population decline prior to our study (when development was initiated) or area-specific differences in habitat quality, juvenile dispersal, or neonatal or juvenile survival; however, we lack the required data to contrast evidence for these mechanisms. Given our results, it appears that deer can adjust to relatively high densities of well pads in the production phase (the period with markedly lower human activity on the landscape), provided there is sufficient vegetative and topographic cover afforded to them and populations are below carrying capacity. The strong reaction to wells in the drilling phase of development suggests mitigation efforts should focus on this activity and stage of development. Many of the wells in this area were directionally drilled from multiplewell pads, leading to a reduced footprint of disturbance, but were still related to strong behavioral responses. Our results also indicate the likely value of mitigation efforts focusing on reducing human activity (i.e., vehicle traffic, light, and noise). In combination, these findings indicate that attention should be paid to the spatial configuration of the final development footprint to ensure adequate cover. In our study system, minimizing the road network through landscape-level development planning would be valuable (i.e., exploring a maximum road density criteria). Lastly, our study highlights the importance of concomitant assessments of behavior and demography to provide a comprehensive understanding of how wildlife respond to habitat modification. © 2021 The Wildlife Society. Some memories never fade: inferring multi-scale memory effects on habitat selection of a migratory ungulate using step-selection functions Helena Rheault 1, Charles R. Anderson Jr.2, Meagwin Bonar', Robby R. Marrotte•, Tyler R. Rossl, Geroge Wittemyer4, and Joseph M. Northrup •.s 'Environmental and Life Sciences Graduate Program, Trent University, Peterborough, ON, Canada 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, United States 3 Department of Biology, York University, Toronto, ON, Canada 4 Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, United States, 5 Ontario Ministry of Natural Resources and Forestry, Peterborough, ON, Canada Citation: Rheault, H., C.R. Anderson Jr., M. Bonar, R.R. Marrotte, T. R. Ross, G. Wittemyer, and J.M. Northrup. 2021. Some memories never fade: inferring multi-scale memory effects on habitat selection of a migratory wigulate using step-selection functions. Frontiers in Ecology and Evolution 9:702818; doi: I0.3389/fevo.202 l.702810 ABSTRACT: Understanding how animals use infonnation about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store infonnation about experienced landscapes, however, remains an understudied topic in movement ecology. By studying how species select for familiar locations, visited recently and in the past, we can gain insight to how they store and use local infonnation in multiple memory types. In this study, we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado, United States to investigate the influence of spatial experience over different time scales on seasonal range habitat selection. We inferred the influence of short and long-tenn memory from the contribution to habitat selection of previous space use within the same season and during the prior year, respectively. We fit step-selection functions to GPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection, inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals, models incorporating covariates representing both recent and past experience and environmental covariates perfonned best. In the top model, locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-tenn memory in driving seasonal range habitat selection. Further, the influence of previous space uses was stronger in the summer relative to winter, which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-tenn infonnation about seasonal ranges, which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly, these findings provide novel insight into how species store and use infonnation over different time scales. 21 �Appendix B. Preliminary results of habitat treatment responses and herbivore use of treated sites. Vegetation and camera data to accompany the study 'Population performance of Piceance Basin mule deer in response to natural gas resource selection and mitigation efforts to address human activity and habuatdegradation' Principal Investigators: Danielle Johnston (Danielle.bilyeu@state.co.us), Chuck Anderson (chuck.anderson@state.co.us) Collaborators: Colorado Parks and Wildlife, BLM-White River Field Office, Idaho State University, Colorado State University, Federal Aid in Wildlife Restoration, EnCana Corp., ExxonMobil Prod. Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, WPX Energy, Colorado Mule Deer Assn., Muley Fanatic Found., Colorado Mule Deer Found., Colorado State Severance Tax Fund, Boone & Crocket Club, and Safari Club Int. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In 2011 and 2013, about 1,200 acres of pinyon and juniper (PJ) mastication treatments were completed in the Magnolia region of the Piceance Basin. Treated parcels averaged 7 acres in size, and were intended to increase winter range quality for deer. The treatments were part of a study to evaluate the effectiveness of PJ removal as mitigation for impacts of natural gas development on deer, with outcomes assessed in terms of deer population and demographic parameters. This summary addresses some side questions relevant to the main study, with outcomes assessed in terms of vegetation response and animal use ofvegetation treatments. We were interested in quantifying the understory forage produced by the mastication treatments. We used paired masticated/control point-intercept transects on a subset of parcels (Graham 2013) to quantify cover of plant groups relevant to deer nutrition. We used belt transects and trained ocular estimation, with benchmarks (Johnston 2018), to estimate summer utilization on individual shrubs, then scaled these to the plot level (Bilyeu, Cooper et al. 2007). We used belt transects of shrub canopy measurements, coupled with biomass equations developed for the study area (Johnston 2018) to quantify winter forage production of key browse species. Winter forage production was defined as current-year stems, not including leaves, not including biomass removed by summer browsing, and not including very small stems which would likely be shed prior to winter (Johnston 2018). We were interested in how summer use of treatments, and use of treatments by non-target animals, impacted winter forage availability. Ten cattle exclosures, distributed broadly throughout the study area (Figure 1), were built within mastication treatments in 2011 and 2013. We assessed plant cover and summer shrub utilization within these using techniques described above. On paired masticated/control transects, we deployed Reconyx Hyperfire cameras July-November 2018-2019. These were programmed to facilitate creating an index of use: 5 pictures per motion trigger, 3 second interval between pictures, a 5 minute wait time between triggers, and a sensitivity setting of High (Rhodes, Larsen et al. 2018). An animal observed with their head down or other indication of foraging in one or more of the photos in a 5 photo set was counted as one foraging event, and non-foraging occurrences were counted similarly. Sampling efforts by year are given in Table I. Because the plant cover data contained many zeros, we modeled presence/absence of each plant group separately from its cover where present (Fletcher, Mackenzie et al. 2005), using the lme4 package in R (Bates 2005). For both analyses, treatment, year, and their interaction were considered fixed effects, year was included as a categorical variable, and pair ID and plot ID were included as random effects. We used a similar approach for camera data for cattle and elk, which also contained many zeros. 22 �In general, grasses responded positively to treatment (Figure 2a). Wheatgrass presence, wheatgrass cover, and needlegrass presence were higher in treated than untreated plots. Poa grass presence was higher in treated plots by 2018, although poa grass presence and cover initially had a negative response to treatment. Cheatgrass presence also responded positively to treatment (Figure 2a). Wheatgrasses, poas, and cheatgrass all had significant year*treatment interactions for either presence or cover. Interannual variation in cover was greater in masticated plots than in control plots for these species groups (Figure 2a). Forbs responded positively to treatment. Annual forb and perennial forb presence were higher in treated than untreated plots (Figure 2b). Some shrubs responded positively to treatment, while others did not. Snowberry cover was lower in treated plots in 2013, but in 2016 and 2018, cover was higher in treated plots (Figure 2c). Variation in snowberry cover was greater in masticated than in control plots (Figure 2c). Bitterbrush did not display any significant effects until 2018, when cover was higher in treated plots (Figure 2c). Serviceberry cover was lower in treated plots over all years (Figure 2d). Sagebrush cover was initially lower in treated plots, but by 2018 this difference was no longer significant (Figure 2d). Summer utilization of serviceberry and mountain mahogany in 2018 was significantly higher in masticated than in control plots, but no differences were detected in bitterbrush or sagebrush. Winter forage production, which was summed over serviceberry, mountain mahogany, and bitterbrush, was significantly higher in masticated plots than in unmasticated plots in all years except 2016, when the pattern was reversed (Figure 3). There was no significant effect of exclosures on any plant cover group or on summer utilization in 2018. Deer, horse, elk, and cattle all foraged more often in masticated plots than in controls in 2018 (Figure 4). Cattle were only observed foraging at 6 of20 locations, horse were observed at 9, deer at 19, and elk at 6. Mastication treatments had many positive effects on forage availabilty, including higher cover of desirable grass groups such as poa grasses and wheatgrasses, higher cover of perennial forbs, and usually higher productivity of winter-available shrub forage. There were some negative effects and some differences in effects among years, however. Cheatgrass was higher in masticated plots than in controls, and snowberry cover was higher in masticated plots in 2016 and 2018. 2016 was an unusual year compared to other years of this study, with very high productivity of grasses (including cheatgrass, especially in masticated plots), and unusually high productivity of winter-available forage of desirable shrubs in control but not masticated plots. Summer shrub utilization in 2018 was higher in masticated plots than in controls. We lack any data on utilization from 2016, which might have helped explain if the lower production of winter-available forage in masticated plots was due to higher summer utilization in those plots that year. Another explanation for the 2016 results is that good conditions for grass, cheatgrass, and/or snowberry productivity in masticated plots led to increased competition which lessened productivity of desirable forage shrubs. All four of the large herbivores of interest foraged more frequently in summer and fall in masticated plots than in control plots in 2018. The impact of cattle was concentrated in only a few plots, but they did forage frequently in plots where they occurred. Cattle use ended in September, prior to the period of heavy use by deer in October. The data from the cattle exclosures does not indicate that cattle are having any measurable negative effect on forage resources. In swnmary the impact of cattle on the forage resources available to deer in mastication treatments seems minimal. However, the effect of the sum of cattle, horse, and elk foraging may have some impact. In 2019, we collected vegetation data and camera data. 2019 is the last year of data collection for this study, and final analyses will be incorporated into publications in 2020-21. LITERATURE CITED Bates, D. (2005). "Fitting linear mixed models in R." R news 5(1). Bilyeu, D. M., D. J. Cooper and N. T. Hobbs (2007). "Assessing impacts oflarge herbivores on shrubs: tests of scaling factors for utilization rates from shoot-level measurements." Journal of Applied Ecology 44(1): 168-175. 23 �Fletcher, D., D. D. Mackenzie and E. Villouta (2005). "Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression." Environmental and ecological statistics 12: 45-54. Graham, T. (2013). Magnolia habitat manipulation project vegetative monitoring: June 2013 notes on data collection and methods used, Ranch Advisory Partners, LLC: 7. Johnston, D. B. (2018). Wildlife Research Report: Examining the effectiveness of mechanical treatments as a restoration technique for mule deer habitat. Fort Collins, CO, Colorado Parks and Wildlife. Rhodes, A. C., R. T. Larsen and S. B. S. Clair (2018). "Differential effects of cattle, mule deer, and elk herbivory on aspen forest regeneration and recruitment." Forest Ecology and Management 422: 273280. \..,! V 24 �Table 1. Number of transects sampled for a given data type each year. 2011 2012 2013 2014 2015 2016 2018 Variables quantified 145 90* 90* 159 69 Percent cover of plant 107t functional groups Winter-available forage of bitterbrush, serviceberry, mountain mahogany 70t 27t 63 75t 2019 40 (camera sites) 75t 75t 75t 40 (2 cameras each) 40 (2 cameras each) (ShrubMassPerArea) Summer utilization of bitterbrush, serviceberry, mountain mahogany, and sagebrush Index of deer, elk, horse, and cattle use in summer and fall, as determined by trail camera (EventsPerDay) * Pretreatment data collected 2011-2012 will be added to a later report. tincludes 24-30 locations taken at exclosure sites. 25 �Figure 1. Sampling locatio11s within the Magnolia region o/'the Piceance Basin. 26 �b 10.0 - a 9 ras 15 '- ._ Q) > Q) > 7.5 - 0 0 (.) ~ 10 - c<ll s.o- c: Q) e (.) ,_ Q) Q) a. a. 5 - 0- 2.5 - 0.0 I I I I I I I I 2013 2014 2016 2018 2013 201 4 2016 201 8 year I d 8- snowb~ +, C , \ , ai 5 - C 9'Q) > 0 (.) year \ , 'E 6 Q) ~ Q) a. 3- ' '' > S 0 (.) ~ 4 _ sa '1 ~ Q) a. 2- I I I I I I I I 2013 2014 2016 2018 2013 2014 2016 2018 year year Figure 2. Cover ofsome p/antfunctional groups and species important.for evaluating habitat quality. Dashed lines indicate masticated plots and solid lines are controls. A " +" or "-" sign indicates sign~ficant positive or negative main effect of mastication across years (o. = 0. 05). "P" indicates that the signjficant effect was obsen 1ed in the presence/absence a11a~vsis, and ··c" indicates a sig11!fica11t effect in the cover-where-present a11azvsis. 27 �_ 20 · NE ....... .:!'! re ~ re Treatment ..... control .D .... . masticated .c T.. 2 90 · ...., "' /1 / C :, .... QJ I * * a. I ~ 60 · "'.... .E * ~ .D re re .f \ l\ T- > * ..L.. '1' 30 • ~ ~C 3 T 2013 20,~ 2015 ,016 2016 year Figure 3. Mass o_f-winrer-availableforage (curre11t-.1·ear stem mass measured in September. not including leaves or mass removed by s11111111er browsing) per unit shrub area. Data are summed over serviceberry. mountain mahogany. and bitterbrush. N=lifor 2013 and 2015 and 25-3/for other years. No transects inside.fences " ·ere included. Error bars= SE. Srars indicate sign(ficant d(fferences at alpha= 0.05 28 �Foraging, near camera Not foraging, near camera b a 75 · >. 75· n, Cl ai Cl. n, ::c Treatment 50 • ai 50 • Cl. control rJ> i: Q) ■ masticated > w Cl 25 · .!: 25 · Cl * ~ i 0 u. O· cattle ' elk O· ' horse mule' deer Ill cattle Herbivore I elk horse mule deer Herbivore Figure 4. a) Average number o.fforag ing events per hectare per day between 111id-J11(1' and midNovember, 2018 in control versus masticated plots. Stars indicate sig11[!ica11t d(lfere11ces vt a. = 0.05. indicates a sign[ficant d[fference in presC:'11ce o.fforaging e1·ents. b) ,-l1•erage 1111mher <?f"nonforaging observations per hectare per day. t 29 �Colorado Parks and Wildlife July 2022 -June 2023 WILDLIFE RESEARCH FINAL REPORT State of __________C.. ;;;o__Io__r__ad=o~_ _ _ _ _: __P=ar=k__s __a__ nd___W ___i__ld__I___iti__ e _ _ _ _ _ _ _ _ _ _ _ __ Cost Center 3430 : .;;..;.M=a=m.....m .......... al__s__R.....e__se.....a__r..... ch_______________ Work Package 3001 : .....D.....e__ er__C_o_n.....s.....e_rv__a___ti_o_n_ _ _ _ _ _ _ _ _ _ _ __ Task No. 6 : Population Performance of Piceance Basin Mule Deer in Response to Natural Gas Resource Extraction and Mitigation Efforts to Address Human Activity and Habitat Degradation Federal Aid Project:_ ___,;,W.....-,_2__ 43__-__R__-7_______ Period Covered: July 1, 2022 - June 30, 2023 Author: C. R. Anderson, Jr. Personnel: D. Bilyeu-Johnston, CPW; J. Northrup, Ontario Ministry of Natural Resources and Forestry; R. Marrotte and M. Bonar, Environmental and Life Sciences Graduate Program, Trent University; B. Gerber, University of Rhode Island. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, Caerus Oil and Gas LLC, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, Williams and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW's right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. �WILDLIFE RESEARCH FINAL REPORT POPULATION PERFORMANCE OF PICEANCE BASIN MULE DEER IN RESPONSE TO NATURAL GAS RESOURCE EXTRACTION AND MITIGATION EFFORTS TO ADDRESS HUMAN ACTIVITY AND HABITAT DEGRADATION CHARLES R. ANDERSON, JR PROJECT NARRITIVE OBJECTIVES 1. To determine experimentally whether enhancing mule deer habitat conditions on winter range elicits behavioral responses, improves body condition, increases fawn survival, and ultimately, population density on mule deer winter ranges exposed to extensive energy development. 2. To determine experimentally to what extent modification of energy development practices enhance habitat selection, body condition, fawn survival, and winter range mule deer densities. SEGMENT OBJECTIVES 1. Finalize publication of results addressing vegetation and mule deer responses to 3 mechanical treatment methods on pinyon-juniper winter range. 2. Finalize development of a web-based energy development planning tool to guide future energy development to minimize and/or mitigate mule deer disturbance on winter range. 3. Submit final Federal Aid in Wildlife Restoration report to complete this research project. PROJECT OVERVIEW AND RESEARCH SUMMARY We experimentally evaluated mule deer (Odocoileus hemionus) response to energy-development activities and habitat treatments to address energy development planning and mitigation options. The Piceance Basin of northwestern Colorado was selected as the project area due to ongoing natural gas development in one of the most extensive and important mule deer winter and transition range areas in Colorado. The data presented here represent final results of an I I-year research project addressing habitat improvements as mitigation and evaluation of deer responses to energy development activities to inform future development planning options on important seasonal ranges. From 2008-2019, we monitored deer on 4 winter range study areas representing relatively high (Ryan Gulch, South Magnolia) and low (North Magnolia, North Ridge) levels of development activity (Fig. 1) to address factors influencing deer behavior and demographics and to evaluate success of habitat treatments as a mitigation option. We recorded adult female habitat use and movement patterns; estimated neonatal, overwinter fawn and annual adult female survival; estimated annual early and late winter body condition, pregnancy and fetal rates of adult females; and estimated annual mule deer abundance among study areas. Winter range habitat improvements completed spring 2013 resulted in 604 acres of mechanically treated pinyon-juniper/mountain shrub habitats in each of 2 treatment areas (Fig. 2) with minor (North Magnolia) and extensive (South Magnolia) energy development, respectively. During this final research segment, we finalized publication of results addressing vegetation and mule deer responses to 3 mechanical treatment methods on pinyon-juniper winter range (Johnston and Anderson 2023), and finalized development of a web-based energy development planning tool to guide future energy development to minimize and/or mitigate mule deer disturbance on winter range (Marrotte et al. 2022; Appendix B). Based on final data analyses (see Anderson 2019 for detailed methods and results and Appendix A for publication abstracts) for this I I-year project: (1) annual adult female survival 2 V �was consistent among areas averaging 79-87% annually, but overwinter fawn survival was variable, ranging from 31% to 95% within study areas, with annual and study area differences primarily due to early winter fawn weights, annual weather conditions, and factors associated with predation on winter range (e.g., crusted snow); (2) mule deer body condition early and late winter was generally consistent within areas, with higher variability among study areas early winter, primarily due to December lactation rates, and late winter condition related to seasonal moisture and winter severity; (3) late winter mule deer densities increased through 2016 in all study areas, ranging from a 50% increase in North Ridge to a 103% increase in North Magnolia, but stabilized through 2018 in 3 of the 4 study areas with a recent decline evident in North Ridge (Fig. 3); (4) migratory mule deer selected for areas with increased cover and increased their rate of travel through developed areas, and avoided negative influences through behavioral shifts in timing and rate of migration, but did not avoid development structures (Fig. 4); (5) mule deer exhibited behavioral plasticity in relation to energy development, without evidence of demographic effects, where disturbance distance varied relative to diurnal extent and magnitude of development activity (Fig 5), which provide for useful mitigation options in future development planning; (6) energy development activity under existing conditions did not influence pregnancy rates, fetal rates or early fawn survival (0-6 months), but may have reduced neonatal survival (March until birth) during 2012 when drought conditions persisted during the third trimester of doe parturition (Fig. 6); (7) rollerchopped plots provided the best combination of hiding cover and winter forage relative to mule deer use on winter range (Fig. 7), but mastication or chaining, applied leaving dispersed security cover, may be better options at large scales or when invasive species concerns exist; and (8) these results informed development of a spatial planning tool to guide future energy development on mule deer winter range (Appendix B). Final data collection efforts for this project were completed by spring 2020 (final GPS collar recovery). Collaborative research with agency biologists, graduate students, and university professors produced 26 scientific publications addressing improved monitoring techniques for neonate mule deer captures (Bishop et al. 2011, Peterson et al. 2018b); development and evaluation of a remote deer collaring device (Bishop et al. 20 I 9); mule deer migration relative to energy development (Lendrum et al. 2012, 2013, 2014; Anderson and Bishop 2014), improved approaches to address animal habitat use patterns (Northrup et al. 2013; Rheault et al. 2021 ); mule deer response to helicopter capture and handling (Northrup et al. 20 I4a); potential effects of male-biased harvest on mule deer productivity (Freeman et al. 2014); mule deer genetics in relation to body condition and migration (Northrup et al. 2014b, Bonar et al. 2022); acoustic monitoring to investigate spatial and temporal factors influencing mule deer vigilance (Lynch et al. 2014) and foraging behavior (Northrup et al. 2019); the relationship of plant phenology with mule deer body condition (Searle et al. 20 I 5); approaches to identify cause-specific mortality in mule deer from field necropsies (Stonehouse et al. 2016); the influence of individual and temporal factors affecting late winter body condition estimates of adult female mule deer (Bergman et al. 20 I 8); mule deer behavioral and demographic responses to energy development activities to inform future development planning (Northrup et al. 2015, 2016a, 2016b, 2021, Peterson et al. 2017, 2018a); plant and mule deer responses to 3 mechanical treatment methods on winter range (Johnston and Anderson 2023), and application of web-based planning tool to guide future energy development (Marrotte et al. 2022; Appendix B). These publications with management implications where appropriate are summarized in Appendix A and Appendix B. We anticipate the opportunity to work cooperatively toward developing solutions for allowing the nation's energy reserves to be developed in a manner that benefits wildlife and the people who value both the wildlife and energy resources of Colorado and elsewhere. 3 �Well Pads & Facilities SOutn MagnOka l In deve!Opment ! Proaucing w~I _ OevelOpment tacllmes 10 Milts Figure I. Mule deer winter range study areas relati ve to active natural gas well pads and energy development fac ilities in the Piceance Basin o f northwest Colorado. winter 20 I 3/ 14 (A ccessed http://cogcc.state.co. us/ December 31.2013: energy development drilling acti vi ty has been minor since 2012). 4 �North Magnolia treatemem sites (587 acres) LJ BearSet_l5_35b_andG BearSet_ l _BandA._E LJ BearSet_36_54anclJ GreasewoodSet_gl 6_g29 GreasewoodSet_gl _g 15 LJ GreasewoodSet_g30_g42 LeeOvers,ghts_a_fandl 6_ 17 Mechanical treatment companson (54 acres) - - NOr1h Hatch Pilot Treatments ( 116 acres) South Magnolia 2 a Figure 2. Habitat treatment site delineations in 2 mule deer study areas (604 acres each) of the Piceance Basin, northwest Colorado (Top; cyan polygons completed Jan 20 I I using hydro-axe; yell ow polygons completed Jan 20 12 using hydro-axe, roll er-chop, and chaining; and remaining polygons completed Apr 20 13 using hydro-axe). January 20 11 hydro-axe treatment-site photos from North Hatch Gulch during April (Lower left, aerial view) and October, 20 11 (Lower ri ght, ground vi ew). 5 �Piceance Basin late winter mule deer density 35.00 30.00 25.00 1 20.00 - ";::- g: 15.00 C 10.00 5.00 - North Ridge - - - Ryan Gulch - • North Magnolia - South Magnolia 0.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 3. Mule deer density estimates and 95% Cl (error bars) from 4 winter range herd segments in the Piceance Basin, northwest Colorado, late winter 2009-20 18. Figure 4. Mule deer study areas in the Piceance Basin of northwestern Colorado, USA (Top), spring 2009 migration routes of adult fe male mule deer (n = 52; Lower left), and active natural-gas well pads (black dots) and roads (state, county, and natu ral-gas; white lines) from May 2009 (Lower right; from Lendrum et al. 20 12; http://dx.do i.org/ 10.1890/ES 12-00 165. 1). 6 �i~1 f - ,;, - - - + - - - - .... - - ~- - - - - - 7 - - - -t- - - - + J J7 ,., I Prod 400 Proo 600 PrOd 800 PrOd 1000 Or1U 400 Onll 600 o ,m soo Orlll 1000 Covariates - - - 4----4-- ~- --- ?--- A · · l · · f - - + j o ~ ~ I ~ J c;- ,., I Prod • OO PrOd 600 Prod 800 PrOd 1000 Drill 400 ~----·-·-· Orill 600 Onll 800 o nu 1000 Figu re 5. Posterior distri butions of populat ion-level coefficients related to natural gas development for RSF models d uring the day (top) and night (bottom) for 53 adult female mule deer in the Piceance Basin, northwest Colorado. Das hed li ne indicates 0 selection or avo idance (below the li ne) of the habitat features. ' Drill' and ' Prod' represent drilling and produc ing well pads, respectively. The numbers following ' Drill' or ' Prod' represent the distance from respective well pads evaluated (e.g., ' Drill 600' is the number of well pads with active drilling between 400-600 m from the deer location; from Northrup et al. 20 15; http://onli nelibrary.wiley.com/doi/10. 1 I 11/gcb. 13037/abstract). Road disturbance was re lat ively minor (- 60-1 20 m, not illustrated above). 1 .00 a, 0 .80 r---9- - - - - rl- I§ cii 0 .60 > -~ ::, 1/l ro 0 .40 w LL 0.20 0.00 2012 2013 2014 Year □ High development □ Low development I Figure 6. Model averaged esti mates of mu le deer fetal survival from early Ma rch until bi11h (late MayJune) in high and low energy deve lopment study areas of the Piceance Basin, northwest Colorad o. 20 1220 14 ( from Peterson et a l. 20 17; http://www.bioone.org/do i/pdf/ l 0.298 1/wlb.00341 ). 7 �C C 4 co ..c: .._ be ~ 3 ro ab -0 Cl) (/) ::, "Cl) 2 Q) I -0 !,,... (1) ......, C s1 0 CON MAST CHAIN ROLLER Figure 7. Winter mule deer use days from GPS locations (days/ha) over a 5-year period in control plots and plots treated to remove pi nyon and juniper trees by 3 different methods: CON (control). MAST (masticated). CHAIN (chained), and ROLLER (roller-chopped). Bars not sharing letters are significantly different at a = 0.05. Error bars = SE (from Johnston and Anderson 2023; https://doi.org/ I0. 1002/wsb. I42 I). 8 �LITERATURE CITED Anderson. C. R .. Jr. 20 19. Population performance of Piceance Basin mule deer in response to natural gas resource extraction and mitigation efforts to address human acti vity and habitat degradati on. Federal Aid in Wi ldlife Restorati on A nnual Report W-243-R3. Ft. Coll ins. CO USA. A nderson, C. R.. Jr.. and C. J. Bishop.2014. M igrati on patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 79'" North Ameri can Wi ldlife & Natural Resources Conference (R. A. Coon & M. C. Dunfee, eds.). Wildlife M anagement Institute. Gardners, PA, USA. ISSN 0078-1355. Bergman. E. .I.. C. R. Anderson Jr.. C. J. Bishop. A. A. Holl and. and J.M. Northrup. 20 18. Variation in ungulate body fat: individual versus temporal effects. Journal of Wi ldlife Management 82:130- 137. DO I: 10: 1002/j w mg.2 l 334 Bishop, C. J., C. R. Anderson .Ir.. D. P. Walsh. E. .I. Bergman, P. Kuechle. and J. Roth. 20 I I . Effecti veness ora redesigned vaginal implant transmitter in mule deer. Journal or Wild life Management 75(8): 1797-1806. DOI: I 0.1002/jwmg.229 Bishop, C. J., M . W. A lldredge, D. P. Walsh, E. J. Bergman, C. R. Anderson Jr.. D. K ilpatri ck, .I. Bake I, and C. Fabvre. 20 19. A noninvasive automated device for remotely collaring and weighing mule deer. Wildli fe Society Bulletin 43:717-725. doi.org/ 10. 1002/wsb. I 034 Bonar. M. S. J. Anderson, C. R Anderson Jr. G. Wittemyer, J. M. Northrup. and A. B. A. Shafer. 2022 Genomic correlates ror migratory direction in a free-ranging cervid. Proceedings of the Royal Society B 289: 2022 1969. hllps://doi.org/ l O.l098/rspb.2022.1969 Freeman. E. D .. R. T. Larsen. M. E. Peterson. C. R. Anderson. Jr.. K. R. Hersey. and B. R. McM illan. 20 14. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin 38(4):806-8 11. DOI: I 0. I 002/wsb.450 Johnston. 0 . B .. and C.R. A nderson Jr. 2023. Plant and mule deer responses to pinyon-juniper removal by three mechanical methods. Wild Ii fe Society Bulletin 47:e 142 1. https://doi.org/10. 1002/wsb. I 42 I Lendrum. P. E., C.R. An derson, .Ir., R. A. Long, J. K. Kie, and R. T. Bowyer. 20 12. Habitat selection by mule deer during migrati on: effects of landscape structure and natural gas development. Ecosphere 3(9):82. http://dx.doi.om/ I 0. Lendrum. P. E.. C. R. Anderson, .Ir .. K. L. Monteith, J. A . Jenks. and R. T. Bowyer. 20 13. Migrating Mule Deer: Effects o f A nthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. doi: 10. I 371 /journal.pone.0064548 Lendrum, P. E., C. R. A nderson, .Ir., K . L. Monteith, .I. A. Jenks. and R. T. Bowyer.20 14. Relating the movement of a rapid ly migrating ungulate to spatiotemporal patterns o f forage quality. Mammal ian Biology 79(6):369-3 75. http://dx.doi.om/ I 0. 10 16/j .mambio.20 14.05.005 Lynch, E.. J.M. Northrup, M . F. McKenna, C. R. A nderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology 26( I ) :75-82. doi: I 0.1 093/beheco/aru 158. Marrolle, R. R., C. R. A nderson Jr., and .I. M. Northrup. 2022. Developing a spatial planning tool for natural gas development on mule deer winter range. Final Report to Bureau o f' Land Management : Grant Agreement LI 8AC00068. I 4pp. Northrup. J.M .. M. B. Hooten. C.R. Anderson. Jr.. and G. Wittemyer. 20I3. Practical guidance on characteri zing availability in resource selection functi ons under a use-avai lability design. Ecology 94(7): 1456- 1463 . Northrup, J. M .. C. R. Anderson, Jr., and G. Winemy er.2014a. Effects of helicopter capture and handling on movement behavior of mule deer. Journal of Wildlife Management 78(4):731738. DOI: 10. 1002/j wmg.705 9 �Northrup. J.M .. A. B. A. Shafer. C.R. A nderson Jr. . D. W. Coltman. and G. Whi ttemyer. 20 I 4b. Finescale geneti c correlates to condition and migration in a wild cervid. Evolutionary Applications 7(8):937-948: doi: IO. I I l I /eva.12 I89 No11hrup. J. M .. C. R. A nderson. Jr.. and G. Willemyer.20 I 5. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selecti on patterns of mule deer. Global Change Biology 2 1( 11 ):386 1-3970. doi: I 0. I I I l /gcb. I 3037. Northrup. J.M .. C.R. Anderson, Jr.. M. B. Hooten. and G. W itternyer. 2016a. Movement reveals scale dependence in habitat selection or a large ungulate. Ecological Applications 26:27462757. North rup, J.M .. C. R. Anderson. .Ir.. and G. Wirtemyer. 2016b. Environmental dynam ics and anthropogenic development alter philopatry and space-use in a North American cervid . Diversity and Di stributions 22 :547-557 . DOI: I 0. 1I I I /ddi. I24I7 Northrup, J.M. , A . Avrin . C.R. Anderson Jr., E. Brown, and G. Wittemyer. 20 19. On-animal acoustic monitoring provides insight to ungulate foraging behavior. Journal or Marnmalogy I 00: I 4791489. https://doi.org/ I 0. 1093/j mam mal/gyzl 24 Northrup, J.M .. C.R. A nderson .Ir.. B. D. Gerber, and G. Wittemyer. 202 I . Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs208: l -37;202 I : DOI : I 0. I002/wmon.I060 Peterson, M . E.. C. R. A nderson .Ir., .l. M. Northrup, and P. F. Doherty Jr. 20 17. Reproductive success or mule deer in a natural gas development area. Wildlife Bio logy. doi: I 0. I1II / w lb.0034I Peterson. M. E.. C.R. A nderson .Jr.. J.M. Northrup. and P. F. Dohe11y Jr. 20 18a. Mortality of mule deer fawns in a natural gas development area. Journal of Wildli fe Management 82: I I 35-11 48. DOI: 10.1002/j w mg.2 1476 Peterson. M. E.. C. R. A nderson .Ir.. M. W. A lldredge. and P. F. Doherty Jr. 20 18b. Using maternal mule deer movements lo estimate timing of parturition and assist faw n captures. Wildlife Society Bulletin 42:6 16-62 1. DOI: I 0. 1002/wsb.935 Rheault. H .. C. R. A nderson Jr.. M. Bonar. R. R. Marrotte. T. R. Ross. G. W ittemyer, and J. M. Northrup. 202 1. Some memories never fade: inferring multi-scale memory effects on habitat selection of' a migratory ungulate using step-selection functi ons. Frontiers in Ecology and Evolution 9:7028 18. doi : I 0.3389/fevo.202 I. 7028 10 Searl e. K. R., M. B. Rice. C. R. A nderson. C. Bishop and N . T. Hobbs. 20 15. Asynchronous vegetati on phenology enhances w inter body condition of a large mobile herbivore. Oecologia 179:377-39 1. DOI I 0.1 007/s00442-015-3348-9 Stonehouse. K. F.. C. R. Anderson .Ir.. M. E. Peterson. and D. R. Collins.20 16. Approaches to fi eld investi gations or cause-speci fie mortality in mule deer ( Odocoi/eus hemionus). Colorado Parks and Wi ldlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins. CO USA. DOW-R-T-48-I 6. ISSN 0084-8883. Prepared by Chuck Anderson °"Jllally dgl\4'd DYC"hutl. , N... w• °'~ '°"''" ,,,...,.,... Charles R. A nderson. .Jr.. Mammals Research Leader 10 �Appendix A. Abstracts of published manuscripts resulting from Piceance Basin mule deer/energy development interaction research collaborations. Abstract format specific to the respective journal requirements. Effectiveness of a redesigned vaginal implant transmitter in mule deer CIIAD J. BISIIOI'', CIIARLES R i\N DERSON .Jr.', D,\:\IEJ. P. \\'ALSII ', ERIC .I. BERG~IA~ 1• l'ETEn h'. lltCIII.E'. :i nti ,IOII N ROTIJ' 1Colorado Parks and Wildlilc. Fort Collins. Colorado 80526 lJSA 'Advanced Telemetry Systems, lsn1111, Minnesota 55040 USA Citation ' Bishop, C J.. C R. Anderson Jr.. D P. Walsh. I: J. Bergman. r Kuechle, and J l~oth. 2011 Effecu vcn~ss ot'a redesign~d vaginal implant transmillcr 111 mule deer Joumal or Wildlilc Management 75(8 ): I797-1806. DOI I() I002/jwmg 229 ABSTRACT Our understanding of factors that limit mule deer (Odocoileus '1e111io1111s) populations may be improved by evaluating neonata l survival as a funct ion of dam chuructcristics under free-ranging conditions. which generally requires that both neonates and dams are radiocollarcd. l'hc most viable technique lacil i1a1ing capture of neonates lrom radiocollarcd adu lt females is use of vaginal implant transmillcrs (VITs). To date. VITs have allowed research opportunities that were not previously possible: however. VITs arc oflen expelled from adult females prepartum. which limits their effectiveness. We redesigned an existing VIT manufactured by /\dvam:cd Telemetry Systems (t\TS: Isanti. MN) by lengthening and widen ing wings used 10 retain the VIT in an adult lemale. Our objct:tive was to increase VIT retentio n rates und thereby increase 1hc likelihood of locating birth sites and ncwbom lawns. We placed the newly designed VITs in 59 adult female mule deer and evaluated the probability of rc1e111ion to parturition and the probability of detecting newborn fawns. We also developed an equation for determining VIT samplc size necessary 10 ach ieve a specified sample size of neonates. The probability of a VIT being n:tnincd until parturition was 0.766 (SE = 0.0605) and the probability ofa VIT being retained lo within 3 days of parturit ion was 0.894 (SE = 0.044 1). In a similar study using the original VIT wings (Bishop ct al. 2007). the probability ofa VIT being retained unti l parturition was 0.-147 (SE= 0.0468) and the probability of retention 10 within 3 days of parturition was 0.623 (SI~= (J.0456). Thus. our design modification increased VIT retention 10 pal'lurition by 0.3 19 (SE = 0.0765) and VIT retention lo with in 3 day of parturition by 0.271 (SE = 0.063-1 ). Considering dams that retained V l'l s to within 3 days of parturition. the probability of detecting at least I neonate was 0.952 (SE = 0.0334) and the probability ofdc1cc1ing both fa\1ns from twin li11ers was 0.588 ( E = 0.0827). We expended appro.xima1cly 12 person-hours per detected neonate. /\s a guide for researchers planning future studies. we fou nd that VIT sample size: should approximately equal the targeted neonate sample size. Our study expands oppor1uni1ics fo r conducting research that links adult female att ributes 10 prod uctivity and offspring survival in mule deer. © 20 14 The Wildlife Society. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development l'ATRICI< E. LtNDRUM', CIIARLES It A:\'DERSOI'\ .m . 1• n YAN ,,. LONG', ,IOIIN G. h:IE 1, AN D R. TERRY BOWYER' 'Department of Uiological Sciences. Idaho State University, l'ocatello, Idaho 83209 USA ~Colorndo Parks and Wildlilc. Grund Junction. Colorado 81505 lJSA Ci1a11on. Lendrum. P. E., C. R Anderson Jr., R. A Long, J G Kie. and R. r. 1301\ycr 2012 llabitat sclccuon hy mule deer during rnigrnuM c1kc1s of landscape structure and natural-gas development l:cosphcrc 3(9):82 hnp /Id~ do1 ore/ I() 1890/1:S12-001(,5 I Abstract. The disruption of tradit ional migratory routes by anthropogenic disturbances has shil'led pallerns of resource se lection by many species. and in some instances has caused populatio ns 10 decline. Moreover. in recent decades populations of'111ulc deer (Odocoileus he111ion11s) have clcclincd throughout much of their historic range in the western Uni 11.:d States. We ust.:d rcsoure1.:sclec1ion functions 10 determine ii'thc presence ofnaturnl-gas dcvelopmenl altered pallerns of resource selection by migrating mule deer. \Ve compared spring migration routes of adult fema le mule deer lilled with GPS collars (11 = 167) among four study areas 1ha1 had varying degrees of natural-gas development from 2008 to 20 IO in the Piccance Basin of nonlmcst Colorado. US/\. Mule deer migrating through the most developed area had longer step lengths (straight-line distuncc between sueccssivl'. GPS lorntions) compared with deer in less developed areas. Add itionally. deer migrat ing through the most developed study ar1.:as tended 10 select for habitnt types that provided grcntcr amounts or concealment cover. whereas deer li·om the least clcvclopcd areas tended 10 select habitats that increased access 10 forage and cover. Deer sclc1.:1ed habitats closer 10 we ll pads and avoided roads in all instances except along the most highly dl'.vcloped migratory routes. where road densities may have been 100 high for deer 10 avoid roads without deviati ng substantially li-0111 cslllblishcd migration routes. These results indicate that behavioral tendencies toward avoidance o r anthropogenic disturbance can be overridden during migration b) the strong fidelit) ungulates demonstrate towards migration routes. If avoidance is leasiblc. then deer may select areas further from development. whereas in highly dcvelopcd areas. deer may simply increase their rate ol'travcl along eswblishcd migration routes. 10 �Migrating Mule Deer: Effects of Anthropogenically Altered Landscapes Patrick E. Lendrum', Charles R. Anderson Jr.2, Kevin L. Monteith 1.J, Jonathan A. Jenks4, R. Terry Bowyer' 1 Department ofBiological Sciences, Idaho State University, Pocatello, Idaho, USA, 2 Colorado Division of Parks and Wildlife, Grand Junction, Colorado, USA, J Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA,4 Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA Citation: Lendrum, P. E., C.R. Anderson Jr., K. L. Monteith, J. A. Jenks, R. T. Bowyer. 2013. Migrating Mule Deer: Effects of anthropogenically Altered Landscapes. PLoS ONE 8(5): e64548. DOI: I0.1371/joumal.pone.0064548 Abstract Background: Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes, many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning, because it is closely coupled with timing of parturition. The degree to which oil and gas development affects migratory patterns. and whether ungulate migration is sufficiently plastic to compensate for such changes. warrants additional study to better understand this critical conservation issue. Met/,odo/ogy/Principa/ Fim/i11gs: We studied timing and synchrony of departure from winter range and arrival to summer range of female mule deer (Odocoileus hemionus) in northwestern Colorado. USA. which has one of the largest natural-gas reserves currently under development in North America. We hypothesized that in addition to local weather. plant phenology. and individual life-history characteristics. patterns of spring migration would be modified by disturbances associated with natural-gas extraction. We captured 205 adult female mule deer. equipped them with GPS collars. and observed patterns of spring migration during 2008-20 I 0. Conclusi011s/Signijica11ce: Timing of spring migration was related to winter weather (particularly snow depth) and access to emerging vegetation. which varied among years. but was highly synchronous across study areas within years. Additionally. timing of migration was influenced by the collective effects of anthropogenic disturbance, rate of travel, distance traveled. and body condition of adult females. Rates of travel were more rapid over shorter migration distances in areas of high natural-gas development resulting in the delayed departure. but early arrival for females migrating in areas with high development compared with less-developed areas. Such shifts in behavior could have consequences for timing of arrival on birthing areas. especially where mule deer migrate over longer distances or for greater durations. Practical guidance on characterizing availability in resource selection functions under a use-availability design JOSEPH M. NORTHRUP', ME\'IN B. HOOTEN 1·u, CHARLES R. ANDERSON JR. 4, AND GEORGE WITTEMYER1 1 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 2 U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA ~Colorado State University, Department of Statistics, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA 4 Mammals Research Section Colorado Parks and Wildlife, 711 Independent Avenue, Grand Junction, Colorado 81505 USA Citation: Northrup, J. M., M. B. Hooten, C. R. Anderson Jr., and G. Wittemyer. 2013. Practical guidance on characterizing availability in resource selection functions under a USt.'--availability design. Ecology 94(7): 1456-1463. http://dx.doi.org/10.1890/12-1688. I Abstract. Habitat selection is a fundamental aspect of animal ecology. the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically arc analyzed in a use-availability framework. whereby animal locations arc contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models. they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically. the size and spatial extent of the availability sample influences coefficient estimates potentially causing interprctational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates. which is common for landscape characteristics. exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data. which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and. where bias is likely, take care with interpretations and use cross validation to assess robustness. u 1I �Effects of Helicopter Capture and Handling on Movement Behav ior of Mule Deer ,JOSEPII ~I. NORTIIRUP', Clli\Rl.ES H. ,\ 'DERSO .m'. AN D GEORG E W ITTLJ\IYEH' I Dcpanmcnt oi' Fish, Wildl1lc. and Conscrva1ion 13iology, Colorado S1;11c U111vcrsi1y, 1474 Campus Dd 1vcry, Fon Collins. Colomclo 80523 USA cMa111muls Research Sccl ion Colorado Parks and Wildlilc. 7 11 lndcpcndcm /\venue, Grand Junc11on. Colorado 81505 US/\ Ci1a11on : Northrup. J. M .. C R. i\nclcrson Jr .. and G. Wiucmycr 20 14 Effcc1s ol'hclicoplcr caplurc uml handling on movcmcnl behavior oi'mulc deer Journal ofWildlilc Managcmml 78(4).731-738. DOI : 10 1002/jwmg 705 .\BSTRA CT Research on wildl ilc movement physiology. and reproducl ive biology oncn requires caplurc and handling oi' ani mals. Such in vasive lreauncnl cun alter behavior. whid1 may hias results or invalidaie assumptions regarding representat ive behaviors. T o assess the impacts of handling on mule deer (Odocoileus he111io1111s). a focal species for research in North America. we investigated pre- and post-recapture movements of co ll ared indiv iduals. and compared them to deer t hat wen: not rccnpturcd (control s). We compared pre- and post-recapture movement rutcs (111/hr) and 24-hour straight-line displacement among recaptured and contro l deer. In addition. we e:--amined the time it took recaptured deer to return to their pre-recapture home range. Both daily straight-line displacement and movement rate wen: ,narginally elevated relative to monthly averages for 24 hours lo Ilowing recapture. with non- signi Ii cant elevation continuing for up to 7 days. Comparing movements avcrag1:d over 30 da) s before and afler recapture. we found no d i fferences in displac1:111ent. but movement rates demonstrated seasonal effects. with fos ter movements post- relat ive to pre-recapture in March and slower movements post- relative to pre-recapture in December. Relati e to control deer movements. recaptured deer mo, cment rates in March were higher im mediately a lier recapture and lower i n the second and third weeks followi ng recapture. The median time to return to the pre-recapture home range was 13 hours. with 71 % of deer returning in the first day. and 9 1% returning withi n ~ days. r hese results ind icate a short period 0 1· elcvuled movements fo llowing recaptures. likely due to the deer returning to their home ranges. followed by weaker but non-signili cant depression of movements for up to 3 weeks. Censoring ol' thc lirst day of data post capture from analyses is strongly supported. and remov ing additional days unt il the individual returns to ils home range will control for the 111:i_jority o r impacts from capture. V 20 1-l 'Ille W ildl i fe Society. Relating the movement of a rapid ly migrating ungulate to spatiotemporal patterns of forage quality Patrick E. Lendrum•, Charles R. Anderson Jr.", Kevin I.. Monteith' , .lonntlrnn A. ,Jcnki , R. Terry IIOll')'Cr• ·' Department of Biological Sciences. Idaho State University, 921 South 8th /\venue, Stop 8007. l'o<:atcllo 83209. US/\ '' Mammals Research Section Col()rauo Parks and Wildlifo. 711 Independent /\venue. Grand Junction 81505. US/\ ' Wyoming Cooperative Fish and Wilclhlc Research Unit. Dcpartmclll of Zoology and Physiology. University of Wyoming, 3 166, 1000 East Universlly /\venue. Laramie 82071. US/\ " Dcpanmcm of Natural Resource Management. South Dakolu Swtc Univcr.;1ty. Box 2140l3. l3rookmg, 57007, USA C1tut1011 I.end rum. P E., C R. Anderson Jr. K I.. Monteith. J i\ Jenks. and R. T Bm,~•cr. 20I4 , Rclaung the movcmclll or a rnp1clly migrating ungulate 10 spmiotcmporal pancms or forage quality. M amnmhan Bmlogy· http./tdx do1 org/10 10 16/J mamb10 2014 05 005 ABSTRACT: Migratory ungu lates exhibil recurring movements. 011cn along tradilional routes between seasonal ranges each spring and autumn. which allow them 10 track resources as they become available on the landscape. We examined the relat ionship between spri ng migration o r mule deer (Odornileus he111ion11s) and forage qual ity. as indexed by spatiotcmporal putlerns ol'fi::cal nitrogen and remotely sensed greenness of'vegetation (Normalized Diilcrence Vegetat ion Index: NDVI) in spring 20 10 in the Piceancc Basin of'nortbwcstem Colorado. US/\. N D V I increased throughout spring. and was artcctcd primar ily by snow depth when snow was present. and tempcruture when snow was absent. Fecal nitrogen \\US IO\\CSt when deer were on w inter range before m igration. increased rapidly to an asymptote during migration. and remained relatively high when deer reached summer range. Values of foca l nitrogen corresponded with increasing NDVI duri ng migration. Spring migration for mule deer provided a way for these large mam mals to increase access to a high-qua lity diet. which was evident in patterns of N D V I and fecal nitrogen. Moreover. these deer "jumped" rather than "surfed" the green wave by ar riving on summer range well before peak producti vity or forage occurred. This rapid migration may aid in securing resources and seclusion fro m others on summer range in preparation for parturition. and to min imize detrimental factors such ns predat ion and malnutrit ion during m igrat ion. 12 �Effects of Male-Biased Harvest on Mule Deer: Implications for Rates of Pregnancy, Synchrony, and Timing of Parturition ERIC D. FREEMAN', RANDY T. l.ARSEN 1, MARKE. PETERSON 2, CHARLES R. ANDERSON JR.3, KENT R. HERSEY\ AND BROCK R. McMILLAN' 1 Department of Plant and Wildlife Sciences, Brigham Young University, 275 WIDB, Provo, UT 84602, USA 2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA 3 Colorado Parks and Wildlite, 711 Independent Avenue, Grand Junction, CO 81505, USA 1 Utah Division of Wildlife Resources. I594 W North Temple, Salt Lake City, UT 84114, USA Citation: Freeman, E. D., R. T. Larsen, M. E. Peterson, C.R. Anderson Jr., K. R. Hersey, and B. R. McMillan. 2014. Effects of male-biased harvest on mule deer: implications for rates of pregnancy, synchrony, and timing of parturition. Wildlife Society Bulletin; DOI: 10.1002/wsb.450 ABSTRACT Evaluating how management practices influence the population dynamics of ungulates may enhance future management of these species. For example. in mule deer (Odocoileus hemionus), changes in male/female ratio due to male- biased harvest may alter rates of pregnancy. timing of parturition. and synchrony of parturition if inadequate numbers of males are present to fertilize females during their first estrous cycle. If rates of pregnancy or parturition are influenced by decreased male/female ratios, recruitment may be reduced (e.g .. fewer births. later parturition resulting in lower survival of fawns. and a less synchronous parturition that potentially increases susceptibility of neonates to predation). Our objectives were to compare rates of pregnancy. synchrony of parturition. and timing of parturition between exploited mule deer populations with a relatively high (Piceance, CO. USA: 26 males/I 00 females) and a relatively low (Monroe. UT. USA: 14 males/I 00 females) male/female ratio. We determined rates of pregnancy via ultrasonography and timing of parturition via vaginal implant transmitters. We found no differences in rates of pregnancy (98.6% and 96.6%: z = 0.821: P = 0.794). timing of parturition (estimate= 1.258: SE= 1.672: I= 0.752: P = 0.454), or synchrony of parturition (F = 1.073: P = 0.859) between Monroe Mountain and Piceance Basin, respectively. The relatively low male/female ratio on Monroe Mountain was not associated with a protracted period of parturition. This finding suggests that relatively low male/female ratios typical of heavily harvested populations do not influence population dynamics because recruitment remains unaffected.© 2014 The Wildlife Society. Fine-scale genetic correlates to condition and migration in a wild cervid Joseph M. Northrup', Aaron B. A. Shafer2, Charles R. Anderson Jr.J, David W. Coltman~, and George Wittemyer 1 I Department offish, Wildlite, and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Department of Evolutionary Biology, Evolutionary Biology Centre, lJppsala University, Uppsala, Sweden 3 Mammals Research Section, Colorado Parks and Wildlife, Grand Junction, CO, USA 4 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. Citation: Northrup, J. M., A. B. Shafer, C. R. Anderson Jr., D. W. Coltman, and G. Whittemyer. 2014. Fine-scale genetic correlates to condition and migration in a wild cervid. Evolutionary Applications ISSN 1752-4571; doi: IO. l I I l/eva.12189 Abstract The relationship between genetic variation and phenotypic traits is fundamental to the study and management of natural populations. Such relationships often are investigated by assessing correlations between phenotypic traits and heterozygosity or genetic differentiation. Using an extensive data set compiled from free ranging mule deer (Odocoi/eus hemionus). we combined genetic and ecological data to (i) examine correlations between genetic differentiation and migration timing. (ii) screen for mitochondrial haplotypes associated with migration timing. and (iii) test whether nuclear heterozygosity was associated with condition. Migration was related to genetic differentiation (more closely related individuals migrated closer in time) and mitochondrial haplogroup. Body fat was related to heterozygosity at two nuclear loci (with antagonistic patterns). one of which is situated near a known fat metabolism gene in mammals. Despite being focused on a widespread panmictic species. these findings revealed a link between genetic variation and important phenotypes at a fine scale. We hypothesize that these correlations are either the result of mixing refugial lineages or differential mitochondrial haplotypes influencing energetics. The maintenance of phenotypic diversity will be critical to enable the potential tracking of changing climatic conditions, and these correlates highlight the need to consider evolutionary mechanisms in management. even in widely distributed panmictic species. 13 u �Landscape and anthropogenic features influence the use of auditory vigilance by mule deer Emma Lynth•, Joseph M. Northrupi., Megan F. MtKenna", Charles R. Anderson Jr.d, Lisa Angeloniu, and George Wittemyer.,i. •Graduate Degree Program in Ecology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523, USA "Department offish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523. USA "Natural Sounds and Night Skies Division, National Park Service, 1201 Oakridge Drive, Fort Collins, CO 80525, USA, JMammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA "Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA Citation: Lynch, E., J.M. Northrup, M. F. McKenna, C.R. Anderson Jr., L. Angeloni, and G. Wittemyer. 2014. Landscape and anthropogenic features influence the use of auditory vigilance by mule deer. Behavioral Ecology; doi: I0.1093/beheco/aru 158. While visual fonns of vigilance behavior and their relationship with predation risk have been broadly examined, animals also employ other vigilance modalities such as auditory vigilance by listening for the acoustic cues of predators. Similar to the tradeofTs associated with visual vigilance. auditory behavior potentially structures the energy budgets and behavior of animals. The cryptic nature of auditory vigilance makes it difficult to study, but on-animal acoustical monitoring has rapidly advanced our ability to investigate behaviors and conditions related to sound. We utilized this technique to investigate the ways external stimuli in an active natural gas development field aITect periodic pausing by mule deer (Odocoileus hemionus) within bouts of rumination-based mastication. To better understand the ecological properties that structure this behavior. we investigate spatial and temporal factors related to these pauses to determine if results are consistent with our hypothesis that pausing is used for auditory vigilance. We found that deer paused more when in forested cover and at night. where visual vigilance was likely to be less effective. Additionally. deer paused more in areas of moderate background sound levels. though responses to anthropogenic features were less clear. Our results suggest that pauses during rumination represent a fonn of auditory vigilance that is responsive to landscape variables. Further exploration of this behavior can facilitate a more holistic understanding of risk perception and the costs associated with vigilance behavior. Migration Patterns of Adult Female Mule Deer in Response to Energy Development Charles R. Anderson Jr. and Chad J. Bishop Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, fort Collins, CO 80526, USA Citation: Anderson, C.R., Jr., and C. J. Bishop. 2014. Migration patterns of adult female mule deer in response to energy development. Pages 47-50 in Transactions of the 791h North American Wildlife & Natural Resources Conference (R. A Coon & M. C. Dunfee, eds.). Wildlifo Management Institute, Gardners, PA, USA. ISSN 0078-1355. Migration is an adaptive strategy that enables animals to enhance resource availability and reduce risk of predation at a broad geographic scale. Ungulate migrations generally occur along traditional routes. many of which have been disrupted by anthropogenic disturbances. Spring migration in ungulates is of particular importance for conservation planning because it is closely coupled with timing of parturition. The degree to which oil and gas development aITects migratory patterns. and whether ungulate migration is sufficiently prepared to compensate for such changes. has recently been investigated in Colorado and Wyoming (Lendrum et al. 2012, 2013; Sawyer et al. 2012). Lendrum et al. (2012, 2013) and Sawyer et al. (2012) address mule deer (Odocoileus hemionus) migration patterns in relation to energy development from northwest Colorado and south-central Wyoming. respectively. We address results from the Colorado and Wyoming studies and then compare similarities and differences. The interactions between migratory mule deer and energy development identi fled by Lendrum et al. (2012. 2013) and Sawyer et al. (2012) suggest mule deer may benefit from energy development planning by considering thresholds of development that may alter migratory behavior. It appears that migration rate. migration routes. and stopover use, if present. may be altered at high development intensities. In addition. migratory mule deer may benefit by maintaining security cover along migration paths. and improved habitat conditions may facilitate more direct and rapid migration requiring less energy to complete migration. Enhancing penneability along migration routes by applying dispersed development plans (<2 well pads/km2) and minimizing disturbance to vegetation types by maintaining security cover should reduce impacts to migratory mule deer as well as other migratory ungulates. Where feasible. habitat improvement projects on winter range and possibly stopover sites would also enhance migratory mule deer populations by enhancing energy reserves for long-distance movements and parturition shortly after summer range arrival. Where possible. directional drilling could be used to extract energy resources from underneath migration routes while maintaining no surface occupancy. Lastly. we emphasize that GPS studies now allow managers to accurately map migration routes for entire populations and identify relatively narrow corridors that are most heavily used thus allowing for the identification of the most important corridors for migrating ungulates. Where available. we encourage agencies to incorporate such migration corridors into land-use plans (e.g., resource management plans) and National Environmental Policy Act documents. 14 �Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore Kate R. Searle 1 • Mindy B. Rice2 • Charles R. Anderson 2 • Chad Bishop2 • N. T. HobbsJ 1 NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik EI-126 0QB, UK i Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526, USA ) Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80524, CO, USA Citation: Searle, K. R., M. B. Rice, C.R. Anderson, C. Bishop and N. T. Hobbs. 2015. Asynchronous vegetation phenology enhances winter body condition ofa large mobile herbivore. Oecologia ISSN 0029-8549: DOI I0.1007/s00442-015-3348-9 Abstract Understanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics. therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over 3 years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially. temporal patterns of vegetation phenology were linked with differences in body condition. with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer ,JOSEPH M. NORTHRUP', CHARLES R. ANDERSON JR. 2, and GEORGE WITTEMYER 1·J 'Department offish, Wildlilc and Conservation Biology, Colorado State University, Fort Collins, CO, USA 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA ~Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA Citation: Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Global Change Biology, doi: IO. I I I l/gcb.13037 Abstract Extraction of oil and natural gas (hydrocarbons) from shale is increasing rapidly in North America. with documented impacts to native species and ecosystems. With shale oil and gas resources on nearly every continent, this development is set to become a major driver of global land-use change. It is increasingly critical to quantify spatial habitat loss driven by this development to implement effective mitigation strategies and develop habitat offsets. Habitat selection is a fundamental ecological process, influencing both individual fitness and population-level distribution on the landscape. Examinations of habitat selection provide a natural means for understanding spatial impacts. We examined the impact of natural gas development on habitat selection patterns of mule deer on their winter range in Colorado. We fit resource selection functions in a Bayesian hierarchical framework. with habitat availability defined using a movement-based modeling approach. Energy development drove considerable alterations to deer habitat selection patterns. with the most substantial impacts manifested as avoidance of well pads with active drilling to a distance of at least 800 m. Deer displayed more nuanced responses to other infrastructure, avoiding pads with active production and roads to a greater degree during the day than night. In aggregate. these responses equate to alteration of behavior by human development in over 50% of the critical winter range in our study area during the day and over 25% at night. Compared to other regions. the topographic and vegetative diversity in the study area appear to provide refugia that allow deer to behaviorally mediate some of the impacts of development. This study. and the methods we employed. provides a template for quantifying spatial take by industrial activities in natural areas and the results offer guidance for policy makers. mangers. and industry when attempting to mitigate habitat loss due to energy development. 15 �Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup', Charles R. Anderson Jr 2, and George WiUemyer 1·J Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA JGraduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA 1 2 Citation: Northrup, J.M., C.R. Anderson, Jr., and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions 22:547-557, DOI: I0.111 l/ddi.12417 ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs. reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology. conservation and management. Location The Piceancc Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages secondorder properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on intcrannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry. with a median of50% overlap (range: 8-78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size. with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement arc of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Movement reveals scale dependence in habitat selection of a large ungulate Joseph M. Northrup•, Charles R. Anderson Jr. 2, Mevin B. Hootenl, and George WiUemyer-4 Department offish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, Colorado 80523 USA JU.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 4 Department offish, Wildlife and Conservation Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523 USA 1 2 Citation: Northrup, J.M., C.R. Anderson, Jr., M. B. Hooten, and G. Wittemyer. 2016. Movement reveals scale dependence in habitat selection ofa large ungulate. Ecological Applications 26:2746-2757 Abstract. Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes. but examinations of scale dependence in impacts arc infrequent. Such examinations can provide important insight to wildlife-human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer (Odocoi/eus hemionus) in the Piceance Basin of Colorado, USA. an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features. including land cover type and habitat edges. Selection differed between night and day at the finest scales. but homogenized as scale increased. Deer displayed functional responses to development. with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer. structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development. but only to a degree. At higher development densities than seen in this area. such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally. 16 �Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus) u Kourtney F. Stonehouse 1•2, Charles R. Anderson Jr. 1, Mark E. Peterson•.z, and David R. Collins' 'Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA ~Department ofFish, Wildlife and Conservation Biology, Colorado State University. Fort Collins, Colorado 80523 USA Citation: Stonehouse, K. F., C.R. Anderson Jr., M. E. Peterson, and D.R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odoco;/eus hemionus). Colorado Parks and Wildlife Technical Report No. 48, First Edition, 317 W. Prospect Rd., Ft. Collins, CO USA. DOW-R-T-48-16, ISSN 0084-8883. This technical report provides general guidelines for conducting mortality site investigations to help investigators distinguish predation from scavenging and other causes of death. General health indices arc also provided to assess whether or not deer may have died from malnutrition or disease or if these factors may have predisposed deer to predation. Lastly. these guidelines will assist investigators in identifying predatory species or scavengers involved through the examination of physical evidence at deer mortality sites. The information presented here is based primarily on field experience gained from a long term research effort in northwest Colorado investigating mule deer mortality sites over several years (http://cpw.statc.co.us/learn/Pages/ResearchMammalsRP-04.aspx) and literature review where referenced. We acknowledge that proximate and ultimate cause of death can be difficult or impossible to detect from field necropsy alone and examples presented here largely represent proximate causes of mortality; efforts discerning ultimate cause will require specific tissue sample collections. where possible. submitted to a veterinary diagnostic laboratory. Within this technical report are numerous photographs documenting characteristics of predator attacks on mule deer and signs left by predatory and scavenging species. Additional pictures illustrate differences between healthy and unhealthy tissues and organs. While reading this document. be aware that each mortality investigation is unique and observations in the field may differ from illustrations provided here. Appendix I provides a sample necropsy form to assist in conducting mortality investigations. Reproductive success of mule deer in a natural gas development area Mark E. Peterson', Charles R. Anderson Jr.2, Joseph M. Northrup1,and Paul F. Doherty Jr. 1 'Department ofFish. Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA ~Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C. R. Anderson Jr., J. M. Northrup, and P. F. Doherty Jr. 2017. Reproductive success of mule deer in a natural gas development area. Wildlife Biology, doi: 10.1111/wlb.00341 Abstract: Natural gas development is increasing across North America and causing concern over the potential impacts on wildlife populations and their habitat. particularly for ungulate species. Understanding how this development impacts reproductive success metrics that arc influential for ungulate population dynamics is important to guide management of ungulates. However. the influences of natural gas development on reproductive success metrics of mule deer Odocoileus hemionus have not been studied. We used statistical models to examine the influence of natural gas development and temporal factors on reproductive success metrics of mule deer in the Piceancc Basin, northwest Colorado during 2012-2014. We focused on study areas with relatively high or low levels of natural gas development. Pregnancy and in utcro fetal rates were high and statistically indistinguishable between study areas. Fetal survival rates increased over time and survival was lower in the high versus low development study areas in 2012 possibly influenced by drought coupled with habitat loss and fragmentation associated with development. Our novel results suggest managers should be concerned with the influences of development on fetal survival. particularly during extreme environmental conditions (e.g. drought) and our results can be used to guide development planning and/or mitigation. Developers and wildlife managers should continue to collaborate on development planning. such as implementing habitat treatments to improve forage availability and quality. minimizing disturbance to hiding and foraging habitat particularly during parturition. and implementing directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. 17 u �V ariati on in ungulate body fat: individual versus temporal effects Eric J. Bergman•, Charles R. Anderson Jr. 1, Chad J. Bishop1, A. Andrew Holland', and Joseph M. Northrup 2 'Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA 2Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA Citation: Bergman, E. J., C. R. Anderson Jr., C. J. Bishop, A. A. Holland, and J. M. Northrup.2018. Variation in ungulate body fat: individual versus temporal effects. Journal of Wildlife Management 82: 130-137, DOI: 10: 1002/jwmg.21334 ABSTRACT The use ofultrasonograhic measurements of muscle and body fat represent a relatively new data stream that can be used to address questions regarding ungulate condition. We have learned that measurements of body fat and presumably overall body condition among individual animals, even those taken from the same herd at that same time, are highly variable. Relatively little consideration has been given to the sources of variation in body fat and other physiological parameters in wildlife populations. We evaluated the components of variation in late-winter mule deer (Odocoileus hemionus) body fat estimates: sampling variation (i.e .. variation induced by the particular set of individuals that were sampled) and process variation (i.e .. variation stemming from biological processes) with a long-term data set (2002-2015) from Colorado. USA. We collected our data from across Colorado as part of historical research, ongoing research, and periodic population monitoring programs. Mean percent ingesta-free body fat (%1FBF) for sampled mule deer was 7.20 ± 1.20% (SD). Covariates related to individual deer explained approximately 4% of the total variation in %IFBF and annual effects explained an additional 13% of the variation. Substantial residual variation in %IFBF (83%) remained unexplained. The source of the 83% of unexplained variation is partially linked to line-scale spatial dynamics but also additional individual metrics we were unable to capture, primarily the presence or absence of dependent young. We speculate that the primary factors influencing late-winter mule deer body fat and overall condition are individual in nature. These results present a cautionary check on herd level inference that can be made from individual late-winter body fat estimates and we postulate that for mule deer, alternative and additional body condition metrics may offer added utility in management scenarios. However. an important next step to better understand wildlife population health is to evaluate the sources and magnitude of variation within other body condition metrics, with the goal of further refining data that can better allow biologists to incorporate herd health into population management recommendations. Mortality of mule deer fawns in a natural gas development area Mark E. Peterson•, Charles R. Anderson Jr. 2, Joseph M. Northrup', and Paul F. Doherty Jr. 1 'Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 90526 USA Citation: Peterson, M. E., C.R. Anderson Jr., J.M. Northrup, and P. F. Doherty Jr. 2018. Mortality of mule deer fawns in a natural gas development area. Journal of Wildlife Management 82: 1135-1148, DOI: 10.1002/jwmg.21476 ABSTRACT Recent natural gas development has caused concern among wildlife managers, researchers, and stakeholders over the potential effects on wildlife and their habitats. Specifically, understanding how this development and other factors influence mule deer (Odocoileus hemionus) fawn (i.e., 0-6 months old) mortality rates, recruitment, and subsequently population dynamics have been identified as knowledge gaps. Thus, we tested predictions concerning the relationship between natural gas development, adult female, fawn birth, and temporal (weather) characteristics on fawn mortality in the Piceance Basin of northwestern Colorado, USA, from 2012-2014. We captured and radio-collared 184 fawns and estimated apparent cause-specific mortality in areas with relatively high or low levels of natural gas development using a multi-state model. Mean daily predation probability was similar in the high versus low development areas. Predation was the leading cause of fawn mortality in both areas and decreased from 0-14 days old. Black bear ( Ursus americanus; 22% of all mortalities, 11 = 17) and cougar (Fe/is concolor: 36% of all mortalities. 11 = 6) predation was the leading cause of mortality in the high and low development areas. respectively. Predation of fawns was negatively correlated with the distance from a female's core area to a producing well pad on winter or summer range. Contrary to expectations, predation of fawns was positively correlated with rump fat thickness of adult females. Well pad densities and development activity were relatively low during our study, indicating that the observed intensity of development did not appear to influence daily predation probability. Our results suggest maintaining development activity thresholds at levels we observed to potentially minimize the effects of development on fawn mortality. However, we caution that higher development intensity and drilling activity in flatter, less rugged areas with less concealment cover could influence fawn mortality. Managers should maintain low development densities in areas where topography and vegetation offer less concealment. Overall, region-specific data (e.g., development intensity, topography, predator assemblages, and associated predation risk) are needed to better understand the effects of natural gas development on fawn mortality. 18 �Using matern al mule deer movements to estimate timing of parturition and assist fawn captures .\ lark E. l'rterson' , Charil's R. Ande rson .fr.'. 1\ lathrw \\'. i\lldredgr'.and Paul F. Ooht·rt)' .Ir.' 'Dcpanmcm of Fish. Wild hie and Conscrvatmn Biology. Colorndo State U111vcrs1ty. Fort Collins. Colorado 80523 US!\ :Mammals Research Sct·t1on. Colorndo Parks and Wlldl1li:. 317 W Prospect Road. Fort Coll ms. CO 90526 US!\ Citation: Peterson, M E. C R. Anderson Jr.. M W. Alldredge. and I'. F. Doheny Jr 2018. Using maternal mule deer movements tu cstnnatc Liming of parturition uml assist fown enplurcs. Wild lilc Society Bulletin 42:616-621: DOI· IO 1002/wsb.935 ABSTRACT Movement pallerns of maternal ungulates have been used 10 determine parturition daies and aid in locating fawns. which may be im portant for unders1and ing reproduc1 ive rates (e.g.. pregnancy and fe tal). but such methods have not been validated for mule deer (Odocoileus hemionus). We first de1ennined timing of parturition using vaginal implant 1rnnsmi11crs (VfTs) and then predicted timing of parturition using VITs in conjunction \\ith Global Positioning System co llar data in the Piccancc Basin of north\\'estern Colorado. USA. during 20 12-201-1. We examined daily movcmcn1 rate to determine diflcrcnccs in movement rate among days (7 days pre- and postpartum) and li>r movement patlerns indicative or parturition. Mean daily movement rate (m/day) o r I02 maternal deer decreased by 46% from I day prcparlurition (mean = 1.253. SD = 1.09 1) 10 panurition date (mean = 682. SD = 574). and remained at this low rate 1-7 days postpanum. We applied an independent data set 10 va lidate predicted parturition dates based on daily movement rate. We estimated day of' parturition correctly (i.e .. day 0). within 1-3 days pos1parluri1 ion. and 2:4 days postparturition of lie Id-reported dates for IO (29%). 2 1 (60%). and 4 ( 11 %) materna l females. respectively. For novel data sets. we predict thm a mule deer female whose daily movcmcn l rate decreases by 2:46<Vo and remains low 2:3 days postparturition particularly when preceded by a sudden increase in 111ove111c111-has given birth. However. we caut ion that disturbance or deer hy field crews should be minimized. and il' bi11h sites arc 1101 found. neonatal mortality wil l be unclcrcstimaled. Our results can help determine timing and ~cncrnl location of parturition as an aid in capturing fawns when the use of'VITs is not fcasihlc. with the ultimate objective of estimating pregnancy. fetal. and fa" n survival rates if birth sites arc found. £, 20 18 The Wildlife Society. On-animal aco ustic monitoring provides insig ht to ungulate forag ing behavior .Joseph M. North r up', ;\lcxn ndrn i\vrin ', C hurlcs n. A nd erson, Jr.', Emma Brown', :111d George Willcmycr' 'Dcpanmcnt olTish. Wildlil'e and Conservation Biology. Colorado State University, Fon Collins, Colorado 80523 US/\ :Mammals Research Secuon. Colorado Parks and Wildlilc. 317 W Prospect Roud , Fon Collms, CO 90526 US/\ ' National Park Scrv1tc Natural Sounds and Night Skies Division. Fort Collins. CO 80525 USA Citation Nonhni[J. J M . i\ i\vrm. C.R. Anderson Jr . 1,. Bro\\n. and G Wittcmy~r 201 9 On-an11nal acoustic mo111tonng provides msight to ungulate roraging bchavwr Journal or Mammalogy I00: I479-1489. https·//doi orn/10 I093/unammalleH 12~ Abstract foraging behavior underpins many ecological processes: however. robust assessments or1h is behavior tor free-ranging animals are rare due 10 limitations 10 direct observat ions. We leveraged acoustic mon itoring and GPS tracking 10 assess the factors in0ucncing foraging behavior of' mulc deer (Odocoileus he111io1111s). We deployed custom-built acoustic collars with GPS radiornllars on mu le deer to measure lotalion-sp~cific foraging. We quantified individual bites and steps taken by deer. and quantified two metrics o r foraging behavior: lhe number of'bitcs taken per step and the number o r bites taken per uni t time. which re late to foraging intensity and ellicicncy. We fit statistical models 10 these metrics to examine the individual. environmental. and anthropogenic factors influencing foraging. Deer in poorer body condition took more bites per step and per minute and foraged for longer irrespecti ve or landscape properties. Other pallcrns varied sca,onally with major changes in deer condition. In December. when deer were in belier condition. they took fc w..:r bites per step and more bites per minute. Deer also foraged more intensely and efficiently in areas orgreater forage availabil ity and greater movement costs. During March. when deer were in poorer condition. foraging was not influenced by landscape features. Anthropogenic lilctors weakly sLructurcd fora ging behavior in December with no relationship in Murch. Most research on animal foraging is i111Crprc1cd under the framework of'optimal foraging theory. Depa11urcs from predictions deve loped under this framework providc insight 10 unrecognized factors influencing the evolut ion o r forug ing. Our results on ly con lim11ed 10 our predictions when deer were in lx:llcr condition and ecological conditions were declining. suggesting forag ing strategics were stale-dependent. These results advm1cc o ur understanding of forag ing pallcrns in wild an imals and highlight novel observational approaches for studying animal behavior. 19 �A noninvasive automated device for remotely collaring and weighing mule deer Chad J. Bishop1, Mathew W. Alldredge•, Daniel P. Walsh', Eric J. Bergman•, Charles R. Anderson Jr. 1, Darlene Kilpatrick1, Joe Bakel 2, and Christophe Fabvre 2 1 Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526 USA 2 Dynamic Group Circuit Design, Inc., 2629 Redwing Road, Fort Collins, CO 80525 USA Citation: Bishop, C. J., M. W. Alldredge, D. P. Walsh, E. J. Bergman, C. R. Anderson Jr., D. Kilpatrick, J. Bakel, and C. Fabvre.2019. A noninvasive automated device for remotely collaring and weighing mule deer. Wildlife Society Bulletin 43:717-725; doi.org/10.1002/wsb.1034 ABSTRACT Wildlife biologists capture deer (Odocoileus spp.) annually to attach transmitters and collect basic information (e.g .. animal mass and sex) as part of ongoing research and monitoring activities. Traditional capture techniques induce stress in animals and can be expensive, inefficient, and dangerous. They arc also impractical for some urbanized settings. We designed and evaluated a device for mule deer (0. hemionus) that automatically attached an expandable radiocollar to a ~-month-old fawn and recorded the fawn's mass and sex, without physically restraining the animal. The device did not require on-site human presence to operate. Students and faculty in the Mechanical Engineering Department at Colorado State University produced a conceptual model and early prototype. Professional engineers at Dynamic Group Circuit Design. Inc. in Fort Collins, Colorado, USA, produced a fully functional prototype of the device. Using the device, we remotely collared, weighed, and identified sex of 8 free-ranging mule deer fawns during winters 20I0-2011 and 2011-2012. Collars were modified to shed from deer approximately I month after the collaring event. Two fawns were successfully recollared after they shed the first collars they received. Thus. we observed IO successful collaring events involving 8 unique fawns. Fawns demonstrated minimal response to collaring events. either remaining in the device or calmly exiting. A fawn typically required :::1 weeks of daily exposure before fully entering the device and extending its head through the outstretched collar, which was necessary for a collaring event to occur. This slow acclimation period limited utility of the device when compared with traditional capture techniques. Future work should focus on device modifications and altered baiting strategies that decrease fawn acclimation period. and in tum. increase collaring rates, providing a noninvasive and perhaps cost-effective alternative for monitoring mid-to large-sized mammal species.© 2019 The Wildlife Society. Behavioral and demographic responses of mule deer to energy development on winter range Joseph M. Northrup', J.M., Charles R. Anderson Jr. 2, Brian D. Gerber\ and George Wittemyer1 1 Department offish, Wildlife and Conservation Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, CO 80523 USA 2 Mammals Research Section, Colorado Parks and Wildlife, 317 W. Prospect Road, Fort Collins, CO 80526 USA 3 Department of Natural Resources Science, University of Rhode Island, I Greenhouse Road, Kingston, RI 02881 USA Citation: Northrup, J.M., C. R. Anderson Jr., B. D. Gerber, and G. Wittemyer. 2021. Behavioral and demographic responses of mule deer to energy development on winter range. Wildlife Monographs 208: 1-37; 2021; DOI: 10.1002/wmon.1060. ABSTRACT Anthropogenic habitat modification is a major driver of global biodiversity loss. In North America. one of the primary sources of habitat modification over the last 2 decades has been exploration for and production of oil and natural gas (hydrocarbon development), which has led to demographic and behavioral impacts to numerous wildlife species. Developing effective measures to mitigate these impacts has become a critical task for wildlife managers and conservation practitioners. However. this task has been hindered by the difficulties involved in identifying and isolating factors driving population responses. Current research on responses of wildlife to development predominantly quantifies behavior, but it is not always clear how these responses scale to demography and population dynamics. Concomitant assessments of behavior and population-level processes are needed to gain the mechanistic understanding required to develop effective mitigation approaches. We simultaneously assessed the demographic and behavioral responses of a mule deer population to natural gas development on winter range in the Piceance Basin of Colorado. USA. from 2008 to 2015. Notably. this was the period when development declined from high levels of active drilling to only production phase activity (i.e., no drilling). We focused our data collection on 2 contiguous mule deer winter range study areas that experienced starkly different levels of hydrocarbon development within the Piceance Basin. We assessed mule deer behavioral responses to a range of development features with varying levels of associated human activity by examining habitat selection patterns of nearly 400 individual adult female mule deer. Concurrently. we assessed the demographic and physiological effects of natural gas development by comparing annual adult female and overwinter fawn (6-monthold animals) survival. December fawn mass. adult female late and early winter body fat. age. pregnancy rates. fetal counts. and lactation rates in December between the 2 study areas. Strong differences in habitat selection between the 2 study areas were apparent. Deer in the less-developed study area avoided development during the day and night. and selected habitat presumed to be used for foraging. Deer in the heavily developed study area selected habitat presumed to be used for thermal and security cover to a greater degree. Deer faced with higher densities of development avoided areas with more well pads during the day and responded neutrally or 20 �selected for these areas at night. Deer in both study areas showed a strong reduction in use of areas around well pads that were being drilled. which is the phase of energy development associated with the greatest amount of human presence. vehicle traffic, noise. and artificial light. Despite divergent habitat selection patterns. we found no effects of development on individual condition or reproduction and found no differences in any of the physiological or vital rate parameters measured at the population level. However, deer density and annual increases in density were higher in the low-development area. Thus, the recorded behavioral alterations did not appear to be associated with demographic or physiological costs measured at the individual level, possibly because populations are below winter range carrying capacity. Differences in population density between the 2 areas may be a result of a population decline prior to our study (when development was initiated) or area-specific differences in habitat quality,juvenile dispersal, or neonatal or juvenile survival; however, we lack the required data to contrast evidence lor these mechanisms. Given our results. it appears that deer can adjust to relatively high densities of well pads in the production phase (the period with markedly lower human activity on the landscape). provided there is sufficient vegetative and topographic cover afforded to them and populations are below carrying capacity. The strong reaction to wells in the drilling phase of development suggests mitigation efforts should focus on this activity and stage of development. Many of the wells in this area were directionally drilled from multiplewell pads. leading to a reduced footprint of disturbance. but were still related to strong behavioral responses. Our results also indicate the likely value of mitigation efforts focusing on reducing human activity (i.e .. vehicle traffic. light. and noise). In combination. these findings indicate that attention should be paid to the spatial configuration of the final development footprint to ensure adequate cover. In our study system. minimizing the road network through landscape-level development planning would be valuable (i.e .. exploring a maximum road density criteria). Lastly. our study highlights the importance of concomitant assessments of behavior and demography to provide a comprehensive understanding of how wildlife respond to habitat modification.© 2021 The Wildlife Society. Some memories never fade: inferring multi-scale memory effects on habitat selection of a migratory ungulate using step-selection functions Helena Rheault', Charles R. Anderson Jr. 2, Meagwin Bonar•, Robby R. Marrotte•, Tyler R. RossJ, Geroge Wittemyer\ and Joseph M. Northrup•.s 'Environmental and Life Sciences Graduate Program, Trent University, Peterborough, ON, Canada ~Mammals Research Section, Colorado Parks and Wildlife, Fon Collins, CO, United States )Depanment of Biology, York University, Toronto, ON, Canada ~Depanment of Fish, Wildlife and Conservation Biology, Colorado State University, Fon Collins, CO, United States, 5Ontario Ministry of Natural Resources and Forestry, Peterborough, ON, Canada u Citation: Rheault, II., C. R. Anderson Jr .. M. Bonar, R. R. Marrotte, T. R. Ross, G. Wittemyer, and J. M. Nonhrup. 2021. Some memories never fade: inferring multi-scale memory effects on habitat selection of a migratory ungulate using step-selection functions. Frontiers in Ecology and Evolution 9:702818; doi: I0.3389/fevo.2021.702810 ABSTRACT: Understanding how animals use information about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store information about experienced landscapes. however. remains an understudied topic in movement ecology. By studying how species select for familiar locations. visited recently and in the past. we can gain insight to how they store and use local information in multiple memory types. In this study. we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado. United States to investigate the innucncc of spatial experience over different time scales on seasonal range habitat selection. We inferred the innuence of short and long-term memory from the contribution to habitat selection of previous space use within the same season and during the prior year. respectively. We fit step-selection functions to OPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection. inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals. models incorporating covariates representing both recent and past experience and environmental covariates performed best. In the top model. locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-term memory in driving seasonal range habitat selection. Further. the influence of previous space uses was stronger in the summer relative to winter. which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-term information about seasonal ranges. which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly. these findings provide novel insight into how species store and use information over different time scales. 21 u �Genomic correlates for migratory direction in a free-ranging cervid iVtaci:win Uonar1, Spcntcr,J. ;\1ukrson', C harles R. Anderson .Jr', George Witlcmycr 3, Joseph ~I. Northrup,. ·' and Aaron B. A. Shafer' 11':nv,ronmcntal & I.ire Sc iences Graduate Program. Trent University. Peterborough. Ontario. Canada K9L OG2 ' Mammals Research Sccuon, Colorado Parks and Wildlife, Fort Coll111s. CO 8()523. US/\ ' Department or Fish. Wildl1f'c and Conservation !1,ology. Colorado State lJ111vers1ty. Fon Collin,. CO 80513. USA 'W,ldhlc Research and Monitoring Section. Ontario Ministry or Natural Resources & Forestry. Peterborough. Ontario, Canada K9.I 3C7 Citation Bonar, M, S. J Anderson, C. R Anderson Jr. G. Wiltemyer. J. M. Northrup. and i\ H A. Shafer. 2022 Genomic correlates for migrutory direction in a frcc-rangmg ccrv,d Proceed ings 01'1hc Royal Society 13 289· 20221969 hltps//do, or0 l11 1098/rsph 2022 1969 ABSTRACT: Animal migrations arc some of the most ubiquitous and one of the most threatened ecological processes globally./\ wide range of migratory behu viours occur in nature. and this behaviour is not uniform among and within spl:cics. where even individuals in the same population can exhibit differences. While the environment largely drives migratory behaviour. it is necessary to understand the genetic mechanisms influcndng migration to elucidate the potential of migratory species to cope with novel conditions aml adapt to environmental change. In this study. we identified genes associated with a migratory trait by undertaking pooled genome-wide scuns on a natural population of migrating mule deer. We identified genomic regions associated with variation in migratory direction. including flTM I. a gene linked to the fonnation of lipids. and DPPA3. a gl:ne linked to epigenetic modifications of the maternal line. Such a genetic basis for a migratory trait co111 ribu1cs to the adaptive pmcntial of the species and might afTcl:I the flexibility of individuals to change their behaviour in the lace of changes in their environment. Plant and mule deer responses to pinyon-juniper removal by three mechanical methods Dnniclle Bilyeu J ohnston' and C harles R. Anderson .Jr.' 'Colorado Parks and Wildlife. 711 lndcpcndenl Avenue. Grand Junction. CO 81505, USA 1Colorndo Parks and Wildlitc. 317 Prospect Avenue. Fort Collins. CO 80526. lJSA C1ta\1on Johnston. D. 13, and C. R_Anderson Jr 2023 Plam and mule deer responses to Jllll} on-Juniper removal b) 1hrcl' mrcha111cal rncthOtb_ W1ldhfc Soc1c1y Oullct,n ~7:cl412 h11ps//c.lo1 org/10.1002/wsb 1~21 A bstn1ct Land managers in western North America often reverse succession by removing pin yon (/'inus spp.) and juniper (J1111ipern.1· spp.) trees to reduce fi re risk and increase forage Jor wildlife und livestock. Because prescribed fire carries inherent risks. mechanical methods such as chaining, roller-chopping, and mastication arc olicn used. Ml:chanical methods differ in cost and the size or wood) debris produced. and may diflc rcntially impact plum and animal rl:sponses. We i111plcmc111cd a rando111 i1.ccl. complete block. split-plot experiment in December 20 11 in the l'iceancc Basin. northwcslcrn Colorado. USA. to compare mcchnnirnl methods and to explore seeding (subplot) interactions. We assessed vegetation 1-, 2-, 5-, and 6-years post-treatment, and mule deer (Odocoileus he111io11us) response via GPS locations 3-8 years post-treatment. By 2016, treated plots had 3- 5 times higher perennial grass cover and - 10 times higher cheatgrass (Bro11111s recrorum) cover than untreated control plots. Rollcrchopped plots had both the highest non-native annual forb cover. and when seeded. the highest density ofbitterbrush (l'11rshia 1ride111ara). a nutritious shrub used by mule deer. Masticated plots had higher binerbrush use during summer and fa ll. leaving less lorngc avai lable for winter. Days of winter mule deer use from GPS po int locations in chained and rollerchopped plots was - 70% highl:r than in control plots. while wimcr use in masticated plots was similar to control plots. Mule deer use appears related to a combination of hiding cover. resulting from residual woody debris. and winter forage availability. Roller-chopped plots provide the best combination of hiding cover and winter forage. but mastication or chaining. applied leaving dispersed security cover. may be better options at large scah.:s or when invasive species concerns ex ist. 22 �Appendix B. Final Report to U. S. Department of Interior Bureau of Land Management: Developing a spatial planning tool for natural gas development on mule deer winter range. Developing a spatial planning tool for natural gas development on mule deer winter range Robby M. Marrotte, Department of Biological Sciences, Trent University Charles R. Anderson Jr., Mammals Research Section, Colorado Parks and Wildlife Joseph M. Northrup, Environmental and Life Sciences Graduate Program, Trent University Purpose Developing a spatial planning tool for natural gas development on mule deer winter range. Objectives Using existing data collected on mule deer in the Piceance Basin, Colorado, we developed a tool that allows land managers to assess the potential impacts of future hydrocarbon development on mule deer behaviour and populations. This project had two phases: I. Statistical modelling of movement data to optimize predictions of deer habitat selection. 2. Development of a user-friendly, web-based platform to assist in the development planning process by optimizing placement of infrastructure that minimizes disturbance to mule deer utilizing winter range. Yearly Summaries • 2021 o Data and covariate gathering and development ■ We cleaned mule deer location data and filtered transition and summer range data (Table I). ■ We defined the area of interest as the 4 study areas used by the mule deer in the Piceance Basin winter range addressed by Northrup et al. (2021 ): North Ridge, North Magnolia, South Magnolia, and Ryan Gulch (Figure 1). ■ We retrieved all necessary spatial and spatiotemporal data. Locations of roads, pipelines and facilities were digitized from National Agricultural Imagery Program (NAIP) imagery and ground truthed between 2010 and 2015. • Mule deer winter range study areas (Retrieved from Northrup et al. 2021 ). • Digital Elevation Model (DEM; Retrieved from https://earthexplorer.usgs.gov/). • Daily snow depth between 2009-2015 (Retrieved from Northrup et al. 2021). • Road network (Retrieved from Northrup et al. 2021 ). • Facility locations (Retrieved from Northrup et al. 2021 ). 23 �• • o Pipeline network (Retrieved from Northrup et al. 2021 ). Landsat 8 (LS8) imagery between 2012-2019 (Retrieved from Google Earth Engine). • Modis imagery between 2009-2019 (Retrieved from Google Earth Engine). • Location and daily status of wells between 2009-2019 (Retrieved from cogcc.state.co.us). Wells were grouped onto pads and pad boundaries were digitized using NAIP imagery. Status of the pad was assigned as the status of the well with the most active developmente.g., if there were two wells on a pad and one was producing gas and the other was being drilled, the status of the pad was set as "drilling." ■ We built static and spatiotemporal layers (Table 2). • Static layers o Digital Elevation Model (DEM) derived: Elevation, Terrain Ruggedness Index (TRI), Slope, Solar-radiation Aspect Index. o Climate: Long-term average and standard deviation of snow depth. o Roads: Density of roads within I 00-meter distance bands between 0.1-1 km. o Facilities: Density of facilities within I 00-meter distance bands between 0.1-1 km. o Pipelines: Density of pipelines within I 00-meter distance bands between 0.1-1 km. o LS8 derived: summer bands 1-7, summer NOVI, summer NOVI slope, winter bands 1-7, winter NOVI, winter NOVI slope. • Spatiotemporal layers o Modis derived: Biweekly NOVI, red, near-infrared, blue and middle-infrared. o Producing well pads derived: Daily density of producing well pads within I 00-meter distance bands between 0.1-1 km. o Drilling well pads derived: Daily density of producing well pad within I 00-meter distance bands between 0.1-1 km. Model development ■ We created the background (available) and habitat use (i.e., deer GPS collar) locations. We set a ratio of 30 background locations for each habitat use location within each range. We based the probability of each background location on the frequency of use locations during each day between 20092019. Consequently, if 5% of use locations were on January 2nd, 2008, the same proportion of background locations were assigned to this day. • We trained machine learning resource selection functions using Extreme Gradient Boosting (XGBoost; Chen et al. 2015) using 80% of the entire dataset. We used 20% of the training data (i.e., 16% of the entire dataset) for 24 �model validation to help guide and tune the hyperparameters. We then used the remaining 20% of the data for testing the accuracy out-of-sample. o Dashboard • We developed a dashboard using the R Shiny package and Leaflet interactive maps that can be used to predict the impact of the placement of well pads (Figure 2). • 2022 o o o o o o o We deployed a prototype of the application on a University of Rhode Island server (shiny.celsrs.uri.edu/bgerber/). We fixed bugs relating to version differences between the server and the previous infrastructure on which we built the application. We added the ability to predict the impact of roads in addition to well pads (Figure 3). We fine-tuned the artificial intelligence model to increase the model's predictive accuracy on validation data. We used the remaining data (testing data) to determine the weakness of the models across ranges and drilling periods (Table 3). We found that the model was generally capable of accurately predicting the drilling (2008-2012) and producing (2012-2019) periods. Comparatively, the model accuracy was lowest for North Magnolia during the producing period. We invited resource managers to test-run the application and used their feedback to make it more user-friendly. We added the ability to visualize the percent change of habitat use during the dri Hing and producing period (Figure 4 ). We finalized the model and application and uploaded a stable release of the application on the University of Rhode Island server. We wrote the first draft of a manuscript detailing the steps to create the application. We plan on submitting this manuscript to the Journal of Wildlife Management in December 2023. Background Oil and natural gas development has seen significant increases across North America since the turn of the century (USEIA 2015), bringing substantial environmental impacts to developed areas. In western North America, much of this development has overlapped with the ranges of wildlife species (Northrup & Wittemyer 2013). One species for which this development has generated significant concern is the mule deer. Mule deer are an important recreational and economic resource across the intermountain west but have seen large-scale population fluctuations over the last several decades (Unsworth et al. 1999), along with recent declines (Bergman et al. 2015). Hydrocarbon development in mule deer winter range elicits behavioural responses from deer, including relative reductions in the use of large areas in their winter range (Sawyer et al. 2006, 2009; Northrup et al. 2015, 2016a, 2016b ). During winter, deer have a negative energy balance, leading to declining conditions (Monteith et al. 2013) and occasional large-scale mortality events (White & Bartmann 1998). Thus, displacement from preferred areas or increased movements due to human activity could exacerbate these issues. Hydrocarbon extraction is projected to continue to increase for the next two decades (USEIA 2022), modifying substantial areas of new land, much of which will be in the 25 U �intermountain West (McDona ld et al. 2009). Considering this ongoi ng and impend ing development in the mule deer winter range. managers need a more in-depth understandi ng of the impacts of hydrocarbon development. A major need for land and wildli fe managers is spatial decision support too ls that incorporate currentl y ex isting knowledge on how hydrocarbon development impacts mule deer to allow for science-based development and mitigation planni ng. Specifical ly. managers need tools that can be used to determine how much development to allow in an area, where and when to allow development to proceed (e.g .. how to spatia lly configure development infrastructu re on the landscape to red uce impacts to critica l habitat), and the types of mitigation measures to implement to reduce the impacts o f deve lopment on mule deer. We leveraged ex isting large tempora l and spatial scale datasets on mu le deer habitat selection and demography fro m the Piceance Basin of Colorado to develop a spatial planning tool that can be used by managers in an adaptive management framework to plan development infrastructure and guide mitigation planning. We applied IO years or combined movement, survival. and populat ion abundance in fo rmation. Much of these data have been previously analyzed and been used to quantify behav ioral and demographic responses to energy development. We used this ex isti ng in fo rmation in conjunction with new analyses that focused on opti mizing our ability to predict the spatial responses of deer to energy development to produce a plan ning too l. The spatial planning tool that we developed wi ll allow land managers lo assess how the spatial pattern of proposed development would impact mule deer behavior on pinyon juniper winter range. Further, it prov ides estimates of the uncertainty in expected impacts to deer and the opportunity to expl ore impacts under varying winter and moisture conditions. We envision a user- friendly platform that would ultimately allow managers and developers the ability to optimize the development footprint such that impacts to deer populations and habitat can be mini mized. Applicability of Planning Tool and Next Steps The model underlying the shi nyapp developed for this proj ect was tit and tested using winter range data fro m the Piceance Basin. As such, the app is most valid for applicati on to the winter ranges in the Piceance Bas in from which the data originated. However, the mode l has potential utility outside of the Piceance Basin provided sufficient caution is taken in interpretation of the outputs. Several factors will influence how accurate the model is outside of the Piceance Bas in: I) the similarity of the habitat. including both vegetation and topography (e.g., topographica lly diverse dominated by pinion-juni per overstory), 2) the sim ilari ty of the development infrastructu re, and 3) deer density, which is directly linked to their use of habitat. In the coming months. we will directl y test the applicabil ity of the developed models to mule deer habitat use outside of the specific winter ranges within the Picea nce Basi n and using data completely outside of the Piceance Basin. Th is wi ll provide some guidance on the utility of the model for development planning elsewhere. Further. we plan to deve lop a companion tool that wiII allow users to apply the model elsewhere in Colorado. This too l will require user inputs for existing in frastructure of roads. well pads, pipelines. and fac ilities (e.g., compressor stations, gas plants). Prior to development of this com pan ion too l. resource ma nagers can contact Dr. Joseph Northru p at joe.northrup@gmaiI.com to discuss use outside of the Piceance Basin and coordinate application. Such application will again requi re user inputs fo r we ll pads, roads, pi peli nes and fac ilities. Further. caution in interpretation wi ll be needed. 26 �Tables Table 1. The number of individuals and GPS fixes for adult female mule deer monitored on winter range in the Piceance Basin, Colorado USA between December 2008 to March 2019. Number of Does Number of Fixes Winter North Magnolia North Ridge Ryan Gulch South Magnolia 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 2013-2014 2014-2015 2015-2016 2016-2017 2017-2018 2018-2019 Total 4 15 34 52 10 11 27 31 23 36 56 55 48 44 67 56 56 47 43 48 22 43 39 43 27 26 36 19 39 35 43 49 44 48 14 5 12 34 51 60 71 51 49 50 59 67 29 499 387 442 538 38 Total North Magnolia North Ridge Ryan Gulch South Magnolia Total 30 77 142 190 207 220 181 191 173 172 199 84 1,866 1,116 2,515 7,296 18,486 19,337 25,091 19,617 21,782 20,016 17,814 15,597 4,802 173,469 3,338 4,573 8,371 7,091 12,752 15,287 13,998 17,825 11,504 10,791 12,126 6,179 123,835 2,967 3,484 8,232 18,129 2,061 15,681 13,269 19,245 24,145 19,195 20,451 6,549 153,408 1,801 2,081 4,985 17,697 22,162 25,382 19,894 15,372 17,955 19,906 29,424 10,100 186,759 9,222 12,653 28,884 61,403 56,312 81,441 66,778 74,224 73,620 67,706 77,598 27,630 637,471 27 C C C �( ( ( Table 2. Habitat use prediction categories for mule deer does on their winter range in the Piceance Basin, Colorado, USA. Derived predictors Category Variation Elevation (m) Terrain ruggedness index (TRI) Cover Static Forage Static Description Sources Topographic based predictors Obtained from the United States Geological Survey https://earthexplorer .usgs.gov/ Daily modelled snow depth from 2008-2015 Obtained and derived by Liston and Elder (2006), Northrup et al. (2016b), Northrup et al. (2021) For each, median value for December-March 2013-2019 and June-September 2013-2019 USGS Landsat 8 Level 2, Collection 2, Tier 1 https://developers.google.com/earthengine/datasets/catalog/LAN DSAT_LC08_CO2_Tl_L2 Nearest value in time between 2009 and 2019 Obtained from the Google Earth Engine MOD13Ql.006 Terra Vegetation Indices 16-Day Global 250m https://developers.google.com/earthengine/datasets/catalog/MODIS_006_MOD13Q1 The density of roads for several distance bands Obtained from the United States Geological Survey Digitized from aerial imagery obtained from the National Agricultural Imagery Program https://earthexplorer.usgs.gov/ The density of pipelines for several distance bands Obtained from the White River Bureau of Land Management office and supplemented from aerial imagery obtained from the National Agricultural Imagery Program https://earthexplorer.usgs.gov/ Radiation Slope Mean Snow Depth Sd Snow Depth LB Bl Ultra Blue (0.435-0.451 µm) LB B2 Blue (0.452-0.512 µm) LB B3 Green (0.533-0.590 µm) LB B4 Red (0.636-0.673 µm) LB BS NIR (0.851-0.879 µm) Coverand forage Static LS 86 SIR 1 (1.566-1.651 µm) LS B7 SIR 2 (2.107-2.294 µm) NOVI NOVI Slope Modis Red (645nm) Modis NIR (858nm) Modis Blue (469nm) Modis MIR (2130nm/2105 2155nm) Coverand forage Spatiotemporal Modis NOVI Road density within 0-200, 200400, 400-600, 600-800, and 8001000 meters Pipeline density within 0-200, 200400, 400-600, 600-800, and 8001000 meters Anthropogenic Anthropogenic Static Static Static 28 �Facility density within 0-200, 200400, 400-600, 600-800, and 8001000 meters Pad density within 0-200, 200-400, 400-600, 600-800, and 800-1000 meters The density of natural gas facilities for several distance bands Anthropogenic Nearest value in time between 2009 and 2019 for density of drilling and producing well pads for several distance bands Anthropogenic Digitized from aerial imagery obtained from the National Agricultural Imagery Program https://earthexplorer.usgs.gov/ and validated on the ground Obtained from the Colorado Oil & Gas Conservation Commission cogcc.state.co.us Spatiotemporal Pad density within 0-200, 200-400, 400-600, 600-800, and 800-1000 meters 29 ( C ( �Table 3. Model accuracy(%) for adult female mule deer habitat use in 4 winter range study areas in the Piceance Basin, Colorado between 2008-2019. Sensitivity is the accuracy of the locations where mule deer were located from their GPS collars and specificity was the accuracy of the background availability data. Range Development All Ranges North Magnolia North Ridge Ryan Gulch South Magnolia All Ramies North Magnolia North Ridge Ryan Gulch South Magnolia All Ranges North Ma2nolia North Ridge Ryan Gulch South Magnolia Low/High Low None High High Period Trainine: 94.44 93.04 Sensitivity Validation 74.78 73.91 Testin~ 74.76 73.96 95.24 94.44 95.21 73.82 73.94 76.90 74.46 74.13 76.22 75.43 74.00 71.25 93.35 91.00 73.89 72.11 73.62 71.13 72.11 69.40 94.96 94.40 93.75 73.59 73.33 76.29 74.91 73.89 74.96 74.74 73.86 71.63 94.83 93.84 75.10 74.61 75.17 75.06 72.30 69.56 95.36 94.45 95.73 73.91 74.12 77.12 74.27 74.20 76.65 75.72 74.03 71.11 Drilling (20082012) Low/High Low None Hi,i;h High Producing (20122019) Low/High Low None High High Both (20082019) 30 Specificity 72.25 69.52 �Figures J O 10'11 J Q 05'1l - - - " '' ~ JO 00'11 -' / / ~ 39 95'tl 2 :, "' ...J 39 90'tl 39 85'11 I ~ I > I ' ' l \\ Nonh Magnolia I ~ I I I I l \ I I •,, Ryan Gulch South Magnolia ,--.. \; \ '' 39 80'11 108 JO'I\ I ' '- I ' .....I 108 35'\V 108 30'\V 108 25'\V 108 2o•w 108 15'\'V 108 ,o,w Longnude Figure I. Mu le deer winter range study areas in the Piceance Basin, Colorado, USA. The study areas are described in Northrup et al. (2021 ). Study areas contoured in red represent high development areas with numerous active natural gas wells and study areas contoured in black represent low development areas with few (North Magnolia) or no active natural gas wells (North Ridge). 31 �0 D-J Mule Ow / scnpW app • Shiny D X r-:tp:. '127.0.0.1-6291 Hydrocarbon Impact on Mule Deer PrtdlCI Impact R15fl Page (002) (0 2 0 ~, (OJ 06) (06 08) (0 81) Topography SUHI Mop ., Winier Ranges ~ WellPodS "' R~ds Figure 2. Mule deer hydrocarbon impact dashboard for predicting the impact of plac ing natural gas wells and roads within the Magnol ia, Ryan Gu lch, and North Ridge winter range study areas. The model was developed from mule deer GPS co llar data acquired between 2009- 2019. 32 �u Hydrocarbon Impact on Mule Deer • 0 - ~ · -- - Hydrocarbon lmpaC1 on Mule Deer Figu re 3. Example of placing natural gas well pads within the South Magnol ia winter range study area. A) Area of interest for well pad development. B) Placement of new well pads and service roads. 33 �.----Hydrocarbon Impact on Mule Deer ...... _ ..._ ..... .-----.. Hydrocarbon Impact on Mule Deer .----Hydrocarbon Impact on Mule Deer • ..__ ,._,,.. '"""""._.,.....,,,_""""' a ·--·· 0 • -;_.- • - Hydrocarbon Impact on Mule Deer Figure 4. Predicted habitat use by mule deer during the winter months during the drilling phase (A) and during the producing phase (B). Percent change in habitat use during the dri ll ing phase relative to pred icted map with no well pads; green shading indicates an increase in pred icted probabi lity of use, while purple shad ing indicates a decrease in predicted probability of use and (C) producing phase relative to predicted map with no well pads; green shading indicates an increase in predicted probability of use, while purple shading ind icates a decrease in predicted probability of use (D). Pred icted habitat use and change in habitat use are re lative to available habitat and scaled by category. Complete avoidance on ly occurred directly on well pads. The large apparent avoidance and change in habitat use apparent around well pads in the fi gures represent change in habitat use re lat ive to baseline, but are not complete avoidance of the areas. Note that because the percent change is relative, apparentl y large percent changes can occur with sma ll absolute change; e.g., a change from 0.0 I to 0.02 is small in abso lute terms but would be a I00% increase in probabi Iity of use. 34 �References Bergman. E.J ., Doherty. P.F., White, G.C. and Holland, A.A., 20 15. Density dependence in mule deer: a rev iew of evidence. Wild Ii fe Biology, 21 ( I), pp. 18-29. Chen, T., He, T .. Benesiy. M., Khotil ov ich. V.. Tang, Y.. Cho. H. and Chen, K. , 20 15. Xgboost: extreme gradient boosting. R package version 0.4-2. I(4), pp.1 -4. Northrup, J.M. and Wittemyer. G .. 20 13. Characterising the impacts of emerging energy development on wild Ii fe. with an eye towards mi tigation. Ecology letters, 16( I), pp. I I2125. Northrup, J.M .. Anderson Jr, C. R. and Wittemyer, G., 20 15. Quantifying spatial habitat loss from hydrocarbon development through assessi ng habitat selection patterns of mu le deer. Global change biology, 2 1( 11 ), pp.3961 -3970. Northrup. J.M.. Anderson Jr, C. R.. Hooten, M.B. and Wittemyer, G., 20 16a. Movement reveals scale dependence in habitat selection of a large ungulate. Ecological Applications, 26(8), pp.2746-2757. Northrup. J.M., Anderson Jr, C. R. and Wittemyer, G., 20 16b. Environmenta l dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity and Distributions. 22(5). pp.547-557. Northrup. J.M., Anderson Jr. C. R. , Gerber, B.D. and Wiltemyer. G., 202 1. Behavioral and demograph ic responses of mule deer to energy development on wi nter range. Wild life Monographs. 208( I), pp. 1-3 7. McDonald. R.I., Fargione, J., Kiesecker, J., Miller. W.M. and Powell , .I ., 2009. Energy sprawl or energy effi ciency: climate policy impacts on natural habitat for the United States of America. PloS one. 4(8). p.e6802. Monteith. K.L.. Stephenson. T.R.. Bleich. V.C.. Conner, M.M., Pierce, B.M. and Bowyer, R.T.. 2013. Risk-sensitive allocation in seasonal dynamics of fat and protein reserves in a longli ved mammal. Journal of Animal Ecology. 82(2). pp.377-388. Sawyer. H., Kauffman. M..J . and Nielson. R.M .. 2009. Influence of well pad activity on winter habitat se lection patterns of mule deer. The Journal of Wildlife Management, 73(7), pp. I052- 106 1. Sawyer. H.. Nielson. R.M .. Lindzey. F. and McDonald. L.L.. 2006. Winter habitat se lection of mule deer before and during development of a natural gas field. The Journal of Wildlife Management. 70(2). pp.396-403. United States Energy In formation Administration (USE IA).20 15. Crude Oil and Natural Gas Exploratory and Deve lopment Wells. http://www.eia.gov/dnav/ng/NG ENR WELLEND SI A.htm United States Energy In formation Adm inistration (USE IA). 2022. Annual Energy Outlook 2022. https ://www .eia.gov/outlooks/arch ive/aeo2 I/pd f/ A EO Narrative 202 1.pd f Unsworth. .I . W., Pac. D.F .. White. G.C. and Bartmann , R.M .. 1999. Mule deer surv ival in Colorado, Idaho. and Montana. The Journal of Wi ldli fe Management, pp.3 15-326. White. G.C. and Bartmann, R.M .. 1998. Effect of density reduction on overwinter survival of freeranging mule deer fa wns. The Journal of wild life management, pp.21 4-225. 35 � https://cpw.cvlcollections.org/files/original/eb70939c35cfe6de79639420bdf5e1e7.pdf cafa5849028cd3d2bec66ccdf165940c PDF Text Text Colorado Parks and Wildlife July 2018 – June 2019 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Colorado 3430 3002 Task No. Federal Aid Project No. 1 W-242-R-3 : Parks and Wildlife : Mammals Research : Evaluating factors influencing elk recruitment in Colorado : : Period Covered: July 1, 2018 – June 30, 2019 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Personnel: V. Ashby, B. Banulis, N. Bealer, T. Bonacquista, K. Bond, M. Caddy, K. Crane, D. Collins, R. Delpiccolo, J. Dewhirst, K. Duckett, R. de Vergie, W. de Vergie, R. Ebel-Childs, J. Ennis, D. Finley, P. Firmin, M. Fisher, A. Fowler, K. Fox, W. Hiler, B. Holder, E. Jones, A. Kirby, J. Lambert, S. Lambert, A. Larson, D. Lewis, K. Logan, K. Mahaffie, K. Middledorf, M. Miller, C. Murray, B. Nance, W. O’Malley, A. Orlando, M. Ortega, J. Pollock, N. Renneker, M. Richman, M. Shaw, I. Smith, J. Stanton, M. Swaro, L. Sweanor, J. Taylor, L. Temple, M. Trujillo, G. Tuck, A. Vitt, S. Wagner, C. Wallace, N. Waring, K. Yeager, J. Yost, E. Sawa, and L. Wolfe, CPW; J. Clark, J. Kelley, R. Swisher, S. Swisher, A. Orlando, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, CPW Big Game Auction and Raffle, and Bar NI/Cabot Foundation. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. In 2016, Colorado Parks and Wildlife (CPW) began a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. In FY2018-19, CPW expanded this pilot study work into a 3rd study area and for 6 additional years to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. This progress report covers the 1st half of the 1st field season of this new project (March 2019-June 2019). During this past fiscal year, we focused on working with stakeholders and collaborators on research logistics, and capturing and collaring elk. Field efforts were 1 �centered on 2 objectives: 1) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn elk to collect data on calf survival and cause-specific mortality. We captured 71 adult female elk and radio-collared 62 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 86-100% across herds, and estimates of mean ingesta-free body fat ranged from 5.8-7.1% across herds. During the 2019 calving season, we captured and collared 146 elk calves, including 87% (54 of 62 calves) of the calves born to collared females. Averaged across herds, the average date of calving was June 2nd. 2 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic (age, body condition, reproductive status, habitat conditions and selection) and abiotic factors (previous and current weather conditions) on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar newborn elk to collect data on calf survival and cause-specific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with approximately 282,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas 3 �(Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, DeCesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unharvested elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality for an unharvested elk population in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although non-predation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate 4 �predation represents an additive or compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect intra-uterine survival, yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, be born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 5 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. In 2017, the post-hunt population of the Uncompahgre Plateau herd was estimated to be ~8,500 elk. From 2013-2017 juvenile:adult female ratios in E20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. In 2017, the post-hunt population of the Trinchera herd was estimated to be ~16,600 elk. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyonjuniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. In 2017, the post-hunt population of the Bear’s Ears herd was estimated to be 20,000-24,000 elk. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or 6 �VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). We attempted to handle calves for <5 minutes to minimize stress. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk calf carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). We will use the late-winter body condition of prime-aged adult female elk that successfully raised a calf the previous year to assess whether or not our study herds may be experiencing nutritional limitations. RESULTS AND DISCUSSION During March 2019, we captured a total of 71 adult female elk by helicopter net-gunning, 3 from the Bear’s Ears herd, 37 from the Trinchera herd, and 31 from the Uncompahgre Plateau herd. We radio-collared 62 pregnant elk and outfitted them with VITs, 30 each from the Trinchera and Uncompahgre Plateau herds, and 2 from the Bear’s Ears herd. We exceeded the mortality threshold established by our ACUC protocol due to acute mortalities that occurred during the capture process (4 from the Trinchera herd, 1 from the Bear’s Ears herd). Therefore, we ceased capture operations prior to reaching our target sample of 30 collared pregnant female elk in the Bear’s Ears herd. In 2019, we estimated that pregnancy rates of adult female elk were 100% in the Bear’s Ears herd (95% CI = 44-100%; n = 3), 91% in the Trinchera herd (95% CI = 76-97%; n = 33), and 97% in the Uncompahgre Plateau herd (95% CI = 84-100%; n = 31; Fig. 2). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 5.8% from the Bear’s Ears herd, 6.4% from the Trinchera herd, and 7.1% from the Uncompahgre Plateau herd (Fig. 3). When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous 7 �growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). During May and June 2019, we captured and collared 146 elk calves, 51 from the Bear’s Ears herd, 46 from the Trinchera herd, and 49 from the Uncompahgre Plateau herd. From the Bear’s Ears herd, we successfully captured and collared 100% (2/2) of the calves of collared adult female elk outfitted with VITs. From the Trinchera herd, we successfully captured and collared 90% (27/30) of the calves of collared adult female elk outfitted with VITs. From the Uncompahgre Plateau herd, we successfully captured and collared 83% (25/30) of the calves of collared adult female elk outfitted with VITs. The estimated mean date of calving was June 11 in the Bear’s Ears herd, June 1 in the Trinchera herd, and June 3 in the Uncompahgre Plateau herd (Fig. 4). SUMMARY During FY18-19 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the first year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 62 pregnant females with GPS collars and VITs. We did not reach our target sample size of 30 collared pregnant females from the Bear’s Ears herd because we halted capture operations due to acute mortalities that occurred during helicopter net-gunning. As a result, we had to adjust our sampling strategy for elk calves in this area to capture a greater number of opportunistically encountered calves due to the low number of calves available to capture from collared adult female elk. We successfully captured and collared >45 newborn elk from each study area, meeting our sample size objective, and allowing us to collect data on calf survival and causespecific sources of mortality. We will continue to collect data on elk survival and cause-specific sources of mortality throughout the year. LITERATURE CITED Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. 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Elasticity: the relative contribution of demographic parameters to population growth rate. Ecology 67:1427–1431. Linnell, J. D. C., R. Aanes, and R. Andersen. 1995. Who killed Bambi? The role of predation in the neonatal mortality of temperate ungulates. Wildlife Biology 1:209–223. Lukacs, P. M., M. S. Mitchell, M. Hebblewhite, B. K. Johnson, H. Johnson, M. Kauffman, K. M. Proffitt, P. Zager, J. Brodie, K. Hersey, A. A. Holland, M. Hurley, S. McCorquodale, A. Middleton, M. Nordhagen, J. J. Nowak, D. P. Walsh, and P. J. White. 2018. Factors influencing elk recruitment across ecotypes in the western United States. Journal of Wildlife Management 82:698–710. Middleton, A. D., M. J. Kauffman, D. E. Mcwhirter, J. G. Cook, R. C. Cook, A. A. Nelson, M. D. Jimenez, and R. W. Klaver. 2013. Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology 94:1245–1256. Monteith, K. L., V. C. Bleich, T. R. Stephenson, B. M. Pierce, M. M. Conner, J. G. Kie, and R. T. Bowyer. 2014. Life-history characteristics of mule deer: effects of nutrition in a variable environment. Wildlife Monographs 186:1–62. Parker, K. L., P. S. Barboza, and M. P. Gillingham. 2009. Nutrition integrates environmental responses of ungulates. Functional Ecology 23:57–69. Proffitt, K. M., J. A. Cunningham, K. L. Hamlin, and R. A. Garrott. 2014. Bottom-up and topdown influences on pregnancy rates and recruitment of northern Yellowstone elk. Journal of Wildlife Management 78:1383–1393. Proffitt, K. M., M. Hebblewhite, W. Peters, N. Hupp, and J. Shamhart. 2016. Linking landscapescale differences in forage to ungulate nutritional ecology. Ecological Applications 26:2156– 2174. Raithel, J. D., M. J. Kauffman, and D. H. Pletscher. 2007. Impact of spatial and temporal variation in calf survival on the growth of elk population. Journal of Wildlife Management 71:795– 803. Singer, F. J., A. Harting, K. K. Symonds, and M. B. Coughenour. 1997. Density dependence, compensation, and environmental effects on elk calf mortality in Yellowstone National Park. Journal of Wildlife Management 61:12–25. Smith, B. L., and S. H. Anderson. 1996. Patterns of neonatal mortality of elk in northwest Wyoming. Canadian Journal of Zoology 74:1229–1237. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114– 119. Tatman, N. M., S. G. Liley, J. W. Cain, and J. W. Pitman. 2018. Effects of calf predation and nutrition on elk vital rates. Journal of Wildlife Management 82:1417–1428. Vavra, M., C. G. Parks, and M. J. Wisdom. 2007. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. 10 �Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. Prepared by Nathaniel D. Rayl, Wildlife Researcher 11 �Calves:100 adult females (2013-2017) Insufficient data 0 25-300 30-350 35-400 40-450 45-500 50-55 E-4 55-60 E-51 E-99 j E-24 E-33 0 30 60 Miles Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 12 �1.0 3 33 31 0.9 0.8 +-' C cu 0.7 C 0) ~ 0.6 a. C o 0.5 t: 0 g-0.4 ~ o.. 0.3 0.2 0.1 0.0 Bear's Ears Trinchera Uncompahgre Plateau Herd Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2019 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 13 �12.5- ~ 10.0- cf:. --- +-' ~ >, "'O 0 .o 7.5a, Q) I... I ro +-' CJ) Q) C) C 5.Q - 2.5Bear's Ears Trinchera Uncompahgre Plateau Herd Figure 3. The estimated ingesta-free body fat (%) of adult female elk from the Bear’s Ears (n = 3), Trinchera (n = 33), and Uncompahgre Plateau (n = 31) herds during late-winter 2019 in Colorado, USA. 14 �Jun 17 - Jun 10 - .....roQ) "'O cn Jun 03 c "> ro () May 27 - May 20 Bear's Ears Trinchera Herd Uncompahgre Plateau Figure 4. The distribution of calving dates of adult female elk estimated from vaginal implant transmitters (VITs) from the Bear’s Ears (n = 2), Trinchera (n = 30), and Uncompahgre Plateau (n = 30) herds during 2019 in Colorado, USA. 15 �Colorado Parks and Wildlife July 2019 – June 2020 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 1 : : : : Federal Aid Project No. W-242-R4 : Parks and Wildlife Mammals Research Elk Conservation Evaluating factors influencing elk recruitment in Colorado Period Covered: July 1, 2019 – June 30, 2020 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Jr. Personnel: J. Anderson, J. Ashe, B. Banulis, E. Babbitt, T. Bonacquista, K. Bond, G. Bullington, M. Caddy, K. Crane, D. Collins, R. Delpiccolo, K. Deschenes, J. Dewhirst, K. Duckett, W. de Vergie, R. Ebel-Childs, J. Ennis, D. Finley, P. Firmin, M. Fisher, K. Fox, L. Gephert, K. Hayes, W. Hiler, B. Holder, E. Jones, T. Kishimoto, D. Lewis, K. Logan, K. Mahaffie, M. Melton, K. Middledorf, M. Miller, C. Murray, A. Orlando, M. Ortega, J. Pollock, B. Reimann, N. Renneker, M. Richman, T. Robinson, K. Russo, E. Sawa, I. Smith, T. Smith, J. Stanton, M. Swaro, L. Sweanor, J. Taylor, L. Temple, M. Trujillo, G. Tuck, A. Vitt, N. Waring, J. Yost, S. Waters, and L. Wolfe, CPW; J. Clark, H. Cushman, J. Larrivee, A. Orlando, S. Strike, R. Swisher, S. Swisher, T. Triple, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, CPW Big Game Auction and Raffle, and Virginia Wellington Cabot Foundation. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. In 2016, Colorado Parks and Wildlife (CPW) began a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. In FY2018-19, CPW expanded this pilot study work into a 3rd study area and for 6 additional years to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. During the past fiscal year we focused on working with stakeholders and collaborators 1 �on research logistics, and capturing and collaring elk. Field efforts were centered on 2 objectives: 1) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn and 6-month old elk to collect data on calf survival and cause-specific mortality. We radio-collared 98 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 78-93% across herds, and estimates of mean ingesta-free body fat ranged from 6.5-7.5% across herds. We radio-collared 50 6-month old elk calves in December 2019. During the 2020 calving season, we radio-collared127 elk calves, including 91% of the calves born to collared females (85 of 93 calves). Mean calving date across herds was 31 May. 2 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic (age, body condition, reproductive status, habitat conditions and selection) and abiotic factors (previous and current weather conditions) on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with approximately 282,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas 3 �(Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, Decesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unharvested elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although non-predation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate predation represents 4 �an additive or compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect intra-uterine survival, yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, be born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 5 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. In 2017, the post-hunt population of the Uncompahgre Plateau herd was estimated to be ~8,500 elk. From 2013-2017 juvenile:adult female ratios in E20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. In 2017, the post-hunt population of the Trinchera herd was estimated to be ~16,600 elk. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyonjuniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. In 2017, the post-hunt population of the Bear’s Ears herd was estimated to be 20,000-24,000 elk. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or 6 �VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). In December, we captured 6-month old elk calves from each study herd by helicopter netgunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk calf carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). We will use the late-winter body condition of prime-aged adult female elk that successfully raised a calf the previous year to assess whether or not our study herds may be experiencing nutritional limitations. RESULTS AND DISCUSSION During March 2020, we captured 113 adult female elk by helicopter net-gunning, 43 from the Bear’s Ears herd, 27 from the Trinchera herd, and 43 from the Uncompahgre Plateau herd. We radio-collared 98 pregnant elk and outfitted them with VITs, 40 each from the Bear’s Ears and Uncompahgre Plateau herds, and 18 from the Trinchera herd. Additionally, we collared 1 non-pregnant elk from the Trinchera herd. In 2020, we estimated that pregnancy rates of adult female elk were 93% in the Bear’s Ears and Uncompahgre Plateau herds (both 95% CI = 81-98%; n = 43), and 78% in the Trinchera herd (95% CI = 59-89%; n = 27; Fig. 2). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 6.51% from the Bear’s Ears herd, 7.51% from the Trinchera herd, and 7.03% from the Uncompahgre Plateau herd (Fig. 3). When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify 7 �whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). In December 2019, we collared 50 6-month old elk calves, 25 each from the Bear’s Ears and Uncompahgre Plateau elk herds. The mean weight of calves from the Bear’s Ears herd was 101.8 kg (95% CI = 96.5-107.2 kg) from the Bear’s Ears herd and 113.9 kg (95% CI = 108.4119.4 kg) from the Uncompahgre Plateau elk herd. During May-July 2020, we captured and collared 127 elk calves, 54 from the Bear’s Ears herd, 21 from the Trinchera herd, and 52 from the Uncompahgre Plateau herd. From the Bear’s Ears and Uncompahgre Plateau herds, we successfully captured and collared 90% (35/39) of the calves of adult female elk outfitted with VITs. From the Trinchera herd, we successfully captured and collared 100% (15/15) of the calves of adult female elk outfitted with VITs. The estimated mean date of calving was 31 May in the Bear’s Ears and Uncompahgre Plateau herds, and 3 June in the Trinchera herd (Fig. 4). SUMMARY During FY2019-20 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the second year of this study. 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Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114– 119. Tatman, N. M., S. G. Liley, J. W. Cain, and J. W. Pitman. 2018. Effects of calf predation and nutrition on elk vital rates. Journal of Wildlife Management 82:1417–1428. Vavra, M., C. G. Parks, and M. J. Wisdom. 2007. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. 10 �Prepared by Nathaniel D. Rayl, Wildlife Researcher 11 �Calves:100 adult females (2013-2017) Insufficient data 0 25-300 30-350 35-400 40-450 45-500 50-55 E-4 55-60 E-51 E-99 j E-24 E-33 0 30 60 Miles Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 12 �■ Bears Ear's ■ Trinchera 1.0 0.9 31 27 II Uncompahgre Plateau 43 31 43 31 0.8 ..... C ~ 0.7 O> c.. 0.6 (]) C o 0.5 t:'. 8_0.4 0 l- o... 0.3 0.2 0.1 o.o~- 2017 2018 2019 2020 Year Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2017-2020 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 13 �11 - • 1o- •• --- 9- ~ 0 ._.... ..... ~ >, ""O 8- 0 .0 (I) (I) 7- L.. '+I .....roen 6(I) 0) C 5- 4- • 3' Bear's Ears Trinchera Herd ' Uncompahgre Plateau Figure 3. The estimated ingesta-free body fat (%) of adult female elk from the Bear’s Ears (n = 43), Trinchera (n = 25), and Uncompahgre Plateau (n = 42) herds during late-winter 2020 in Colorado, USA. 14 �Jul 01 - • •• Jun 15(]) ...... ro ""O 0) C > ro Jun 01 U I May 15- • ' Bear's Ears ' Trinchera Uncompahgre Plateau Herd Figure 4. The distribution of calving dates of adult female elk estimated from vaginal implant transmitters (VITs) from the Bear’s Ears (n = 39), Trinchera (n = 15), and Uncompahgre Plateau (n = 39) herds during 2020 in Colorado, USA. 15 �Colorado Parks and Wildlife July 2020 – June 2021 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 1 : : : : Federal Aid Project No. W-242-R5 : Parks and Wildlife Mammals Research Elk Conservation Evaluating factors influencing elk recruitment in Colorado Period Covered: July 1, 2020 – June 30, 2021 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Jr. Personnel: J. Anderson, B. Banulis, T. Bonacquista, K. Bond, M. Caddy, A. Cole, K. Crane, S. Crews, R. Delpiccolo, K. Deschenes, J. Dewhirst, K. Duckett, W. de Vergie, D. Finley, P. Firmin, M. Fisher, K. Fox, L. Gephert, K. Hayes, C. Hernandez, W. Hiler, B. Holder, J. Irvin, E. Jones, A. Kircher, T. Kishimoto, D. Lewis, K. Logan, K. Mahaffie, K. Middledorf, M. Miller, H. Mondin, E. Monfort, C. Murray, R. Nielson, P. Nol, A. Orlando, M. Ortega, J. Pollock, N. Renneker, J. Richards, M. Richman, T. Robinson, K. Russo, E. Sawa, I. Smith, T. Smith, M. Swaro, L. Sweanor, J. Taylor, L. Temple, M. Trujillo, G. Tuck, E. VanNatta, A. Vitt, N. Waring, S. Waters, and M. Wood, CPW; J. Clark, H. Cushman, B. Dooling, T. Herby, A. Orlando, R. Swisher, S. Swisher, and T. Triple, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, and CPW Big Game Auction and Raffle. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. Colorado Parks and Wildlife (CPW) is conducting research in 3 study areas to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. From July 1, 2020 – June 30, 2021, we focused on working with stakeholders and collaborators on research logistics, and capturing and collaring elk. Field efforts were centered on 2 objectives: 1) capturing adult female elk, and collaring and 1 �outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn and 6-month old elk to collect data on calf survival and cause-specific mortality. We radio-collared 50 6-month old elk calves in December 2020. In March 2021, we radio-collared 100 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 91-95% across herds, and estimates of mean ingesta-free body fat ranged from 7.01-7.78 across herds. During the 2021 calving season, we radio-collared 126 elk calves. Mean calving date across herds was 2 June. 2 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic (age, body condition, reproductive status, habitat conditions and selection) and abiotic factors (previous and current weather conditions) on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar neonate and 6-month old elk to collect data on calf survival and causespecific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with over 292,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas 3 �(Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, Decesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unharvested elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although non-predation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate predation represents 4 �an additive or compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect intra-uterine survival, yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, be born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 5 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. In 2017, the post-hunt population of the Uncompahgre Plateau herd was estimated to be ~8,500 elk. From 2013-2017 juvenile:adult female ratios in E20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. In 2017, the post-hunt population of the Trinchera herd was estimated to be ~16,600 elk. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyonjuniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. In 2017, the post-hunt population of the Bear’s Ears herd was estimated to be 20,000-24,000 elk. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or 6 �VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). In December, we captured 6-month old elk calves from each study herd by helicopter netgunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2020, we collared 50 6-month old elk calves, 25 each from the Bear’s Ears and Uncompahgre Plateau elk herds. The mean weight of calves from the Bear’s Ears herd was 97.9 kg (95% CI = 90.9-104.9 kg) and 102.8 kg (95% CI = 96.6-108.9 kg) from the Uncompahgre Plateau elk herd. During March 2021, we radio-collared 100 pregnant elk and outfitted them with VITs, 40 each from the Bear’s Ears and Uncompahgre Plateau herds, and 20 from the Trinchera herd. We estimated that pregnancy rates of adult female elk were 93% (95% CI = 82-98%) in the Bear’s Ears herd, 95% (95% CI = 78-100%) in the Trinchera herd, and 91% (95% CI = 80-97%) in the Uncompahgre Plateau herd (Fig. 2). Elk populations experiencing good to excellent summerautumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 7.01% from the Bear’s Ears herd, 7.78% from the Trinchera herd, and 7.22% from the Uncompahgre Plateau herd (Fig. 3). When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). 7 �During May-July 2021, we captured and collared 126 elk calves, 53 from the Bear’s Ears herd, 21 from the Trinchera herd, and 52 from the Uncompahgre Plateau herd. The estimated mean date of calving was 1 June in the Bear’s Ears herd, 3 June in the Trinchera herd, and 4 June in the Uncompahgre Plateau herd. SUMMARY From July 1, 2020 – June 30, 2021 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the third year of this study. During FY2019-20 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the second year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 100 pregnant females with GPS collars and VITs. We successfully captured and collared 126 newborn elk and 50 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. We will continue to collect data on elk survival and cause-specific sources of mortality throughout the year. LITERATURE CITED Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. 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Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. Prepared by Nathaniel D. Rayl, Wildlife Researcher 10 �Calves:100 adult females (2013-2017) Insufficient data 0 25-300 30-350 35-400 40-450 45-500 50-55 E-4 55-60 E-51 E-99 j E-24 E-33 0 30 60 Miles Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 11 �■ Bears Ear's ■ Trinchera 1.0 27 31 2017 2018 II Uncompahgre Plateau 31 0.9 0.8 ..... C ~ 0.7 O> a. 0.6 Q) C o 0.5 t 8_0.4 e CL 0.3 0.2 0.1 0.0 2019 2020 2021 Year Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2017-2021 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 12 �13- • 12• .?f:. 11 - ---.....cu .__ 10~ "'O 0 ..c Q) Q) 9- '.__ .....roen aQ) g> 7 6- 5' Bear's Ears ' Trinchera Herd ' Uncompahgre Plateau Figure 3. The estimated ingesta-free body fat (%) of adult female elk from the Bear’s Ears (n = 44), Trinchera (n = 22), and Uncompahgre Plateau (n = 47) herds during late-winter 2021 in Colorado, USA. 13 �Colorado Parks and Wildlife July 2021 – June 2022 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 1 : : : : Federal Aid Project No. W-242-R6 : Parks and Wildlife Mammals Research Elk Conservation Evaluating factors influencing elk recruitment in Colorado Period Covered: July 1, 2021 – June 30, 2022 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Jr. Personnel: R. Aberle, J. Alley, T. Bonacquista, K. Bond, M. Brown, M. Caddy, M. Calahan, Z. Chrisman, A. Cole, D. Corcoran, K. Crane, S. Crew, A. Davis, B. de Vergie, K. Duckett, L. Emerick, D. Finley, P. Firmin, K. Fischer, M. Fisher, K. Fox, A. Friedel, M. Gallagher, M. George, L. Gephert, J. Goncalves, K. Hayes, A. Kircher, J. Lambert, D. Leer, E. Los, K. Middledorf, L. Miller, M. Miller, S. Mollett, H. Mondin, E. Monfort, E. Newkirk, P. Nol, K. Oldham, S. Olson, M. Ortega, J. Ortiz Calo, J. Pollock, J. Potter, N. Renneker, B. Rubalcaba, E. Sawa, S. Sinclair, G. Smith, B. Smith, R. Sralla, M. Trujillo, E. VanNatta, A. Vitt, S. Waters, H. Westacott, M. Wood, CPW; J. Clark, H. Cushman, B. Dooling, A. Orlando, R. Swisher, S. Swisher, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, and CPW Big Game Auction and Raffle. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. Colorado Parks and Wildlife (CPW) is conducting research in 3 study areas to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. From July 1, 2021 – June 30, 2022, we focused on working with stakeholders and collaborators on research logistics, and capturing and collaring elk. Field efforts were centered on 2 objectives: 1) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk 1 �demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn and 6-month old elk to collect data on calf survival and cause-specific mortality. We radio-collared 50 6-month old elk calves in December 2021. In March 2022, we radio-collared 80 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 87-91% across herds, and estimates of mean ingesta-free body fat ranged from 7.22-8.02 across herds. During the 2022 calving season, we radio-collared 107 elk calves. Mean calving date across herds was 2 June. 2 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic (age, body condition, reproductive status, habitat conditions and selection) and abiotic factors (previous and current weather conditions) on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar neonate and 6-month old elk to collect data on calf survival and causespecific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with over 292,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas 3 �(Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, Decesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unharvested elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although non-predation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate predation represents 4 �an additive or compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect intra-uterine survival, yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, be born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 5 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. In 2017, the post-hunt population of the Uncompahgre Plateau herd was estimated to be ~8,500 elk. From 2013-2017 juvenile:adult female ratios in E20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. In 2017, the post-hunt population of the Trinchera herd was estimated to be ~16,600 elk. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyonjuniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. In 2017, the post-hunt population of the Bear’s Ears herd was estimated to be 20,000-24,000 elk. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or 6 �VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). In December, we captured 6-month old elk calves from each study herd by helicopter netgunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2021, we collared 50 6-month old elk calves, 25 each from the Bear’s Ears and Uncompahgre Plateau elk herds. The mean weight of calves from the Bear’s Ears herd was 100.2 kg (95% CI = 94.7-105.7 kg) and 103.7 kg (95% CI = 99-108.3 kg) from the Uncompahgre Plateau elk herd. During March 2022, we radio-collared 100 pregnant elk and outfitted them with VITs, 40 each from the Bear’s Ears and Uncompahgre Plateau herds. We estimated that pregnancy rates of adult female elk were 91% (95% CI = 79-96%) in the Bear’s Ears herd, and 87% (95% CI = 7494%) in the Uncompahgre Plateau herd (Fig. 2). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 7.22% from the Bear’s Ears herd and 8.02% from the Uncompahgre Plateau herd (Fig. 3). When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). 7 �During May-July 2021, we captured and collared 107 elk calves, 54 from the Bear’s Ears herd, and 53 from the Uncompahgre Plateau herd. The estimated mean date of calving was 1 June in the Bear’s Ears herd, and 2 June in the Uncompahgre Plateau herd. SUMMARY From July 1, 2021 – June 30, 2022 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the fourth year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 80 pregnant females with GPS collars and VITs. We successfully captured and collared 107 newborn elk and 50 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. LITERATURE CITED Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Bastille-Rousseau, G., J. A. Schaefer, K. P. Lewis, M. A. Mumma, E. H. Ellington, N. D. Rayl, S. P. Mahoney, D. Pouliot, and D. L. Murray. 2016. Phase-dependent climate-predator interactions explain three decades of variation in neonatal caribou survival. Journal of Animal Ecology 85:445–456. Bowyer, T., D. K. Person, and M. P. Becky. 2005. 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Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114– 119. Tatman, N. M., S. G. Liley, J. W. Cain, and J. W. Pitman. 2018. Effects of calf predation and nutrition on elk vital rates. Journal of Wildlife Management 82:1417–1428. Vavra, M., C. G. Parks, and M. J. Wisdom. 2007. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. Prepared by Nathaniel D. Rayl, Wildlife Researcher 10 �Calves:100 adult females (2013-2017) Insufficient data 0 25-300 30-350 35-400 40-450 45-500 50-55 E-4 55-60 E-51 E-99 j E-24 E-33 0 30 60 Miles Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 11 �■ E-2 Bear's Ears 1.0 31 E-20 Uncompat1gre Plateau ■ E-33 Trinchera 31 32 43 42 4 5 46 30 0.9 0.8 _. C cu 0.7 C O> ~ 0.6 a. C .Q 0.5 t 0 g-0.4 ,_ 0... 0.3 0.2 0.1 0.0 J....__ 2_0,,_1-7..___ _ 20~1-8...____ _ 2_0...., 19 - -- 2020 2021 2022 Year Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2017-2022 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 12 �13• • •• 12.?f2. 11 - ---.....cu .._ 10>"'O 0 ..c Q) Q) 9- • • L.. .._ .....coen 8Q) g> 76- 5- I • E-2 Bear's Ears E-20 Uncompahgre Plateau Herd Figure 3. The estimated ingesta-free body fat (%) of adult female elk from the Bear’s Ears (n = 46) and Uncompahgre Plateau (n = 46) herds during late-winter 2022 in Colorado, USA. 13 �Colorado Parks and Wildlife July 2022 – June 2023 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 1 : : : : Federal Aid Project No. W-242-R-7 : Parks and Wildlife Mammals Research Elk Conservation Evaluating factors influencing elk recruitment in Colorado Period Covered: July 1, 2022 – June 30, 2023 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Jr. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. Colorado Parks and Wildlife (CPW) is conducting research in 3 study areas to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. From July 1, 2022 – June 30, 2023, we focused on working with stakeholders and collaborators on research logistics, and capturing and collaring elk. Field efforts were centered on 2 objectives: 1) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn and 6-month old elk to collect data on calf survival and cause-specific mortality. We radio-collared 46 6-month old elk calves in December 2022. In March 2023, we radio-collared 80 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 95-98% across herds, and estimates of mean ingesta-free body fat ranged from 6.19-6.87% across herds. During the 2023 calving season, we radio-collared 97 elk calves. 1 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic (age, body condition, reproductive status, habitat conditions and selection) and abiotic factors (previous and current weather conditions) on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar neonate and 6-month old elk to collect data on calf survival and causespecific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with over 292,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas 2 �(Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, Decesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unhunted elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although nonpredation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate predation represents an additive or 3 �compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect reproductive failures (i.e., abortions or still births), yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat reserves to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 4 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyon-juniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns (Keiss 1969). We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified nonpregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor 5 �and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). In December, we captured 6-month old elk calves from each study herd by helicopter netgunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2022, we collared 46 6-month old elk calves, 21 from the Bear’s Ears and 25 from the Uncompahgre Plateau elk herd. The mean weight of calves from the Bear’s Ears herd was 105.1 kg (95% CI = 99.1-111.0 kg) and 111.3 kg (95% CI = 104.5-118.1 kg) from the Uncompahgre Plateau elk herd. During March 2023, we radio-collared 80 pregnant elk and outfitted them with VITs, 40 each from the Bear’s Ears and Uncompahgre Plateau herds. We estimated that pregnancy rates of adult female elk were 98% (95% CI = 87-100%) in the Bear’s Ears herd, and 95% (95% CI = 8599%) in the Uncompahgre Plateau herd (Fig. 2). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 6.19% from the Bear’s Ears herd and 6.87% from the Uncompahgre Plateau herd. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). During May-July 2023, we captured and collared 97 elk calves, 43 from the Bear’s Ears herd, and 54 from the Uncompahgre Plateau herd. 6 �SUMMARY From July 1, 2022 – June 30, 2023 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the fifth year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 80 pregnant females with GPS collars and VITs. We successfully captured and collared 97 newborn elk and 46 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. 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Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology 94:1245–1256. 8 �Monteith, K. L., V. C. Bleich, T. R. Stephenson, B. M. Pierce, M. M. Conner, J. G. Kie, and R. T. Bowyer. 2014. Life-history characteristics of mule deer: effects of nutrition in a variable environment. Wildlife Monographs 186:1–62. Parker, K. L., P. S. Barboza, and M. P. Gillingham. 2009. Nutrition integrates environmental responses of ungulates. Functional Ecology 23:57–69. Proffitt, K. M., J. A. Cunningham, K. L. Hamlin, and R. A. Garrott. 2014. Bottom-up and topdown influences on pregnancy rates and recruitment of northern Yellowstone elk. Journal of Wildlife Management 78:1383–1393. Proffitt, K. M., M. Hebblewhite, W. Peters, N. Hupp, and J. Shamhart. 2016. Linking landscapescale differences in forage to ungulate nutritional ecology. Ecological Applications 26:2156– 2174. Raithel, J. D., M. J. Kauffman, and D. H. Pletscher. 2007. Impact of spatial and temporal variation in calf survival on the growth of elk population. Journal of Wildlife Management 71:795– 803. Singer, F. J., A. Harting, K. K. Symonds, and M. B. Coughenour. 1997. Density dependence, compensation, and environmental effects on elk calf mortality in Yellowstone National Park. Journal of Wildlife Management 61:12–25. Smith, B. L., and S. H. Anderson. 1996. Patterns of neonatal mortality of elk in northwest Wyoming. Canadian Journal of Zoology 74:1229–1237. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114– 119. Tatman, N. M., S. G. Liley, J. W. Cain, and J. W. Pitman. 2018. Effects of calf predation and nutrition on elk vital rates. Journal of Wildlife Management 82:1417–1428. Vavra, M., C. G. Parks, and M. J. Wisdom. 2007. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. Prepared by Nathaniel D. Rayl, Wildlife Researcher 9 �Calves:100 adult females (2013-2017) Insufficient data 0 25-300 30-350 35-400 40-450 45-500 50-55 E-4 55-60 E-51 E-99 j E-24 E-33 0 30 60 Miles Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 10 �■ E-2 Bear's Ears 27 1.0 0.9 31 31 32 30 31 E-20 Uncompat1gre Plateau ■ E-33 Trinchera 43 42 j 0.8 _. C cu 0.7 C 0) ~ 0.6 a. C .Q 0.5 t 0 g-0.4 ,_ 0.. 0.3 0.2 0.1 0.0.,____2_0.,,._1..... 7'----- 2--'0,18 - --2~0-19..._____ 2020 2021 2022 2023 Year Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2017-2023 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 11 �Colorado Parks and Wildlife July 2023 – June 2024 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 1 : : : : Federal Aid Project No. W-242-R-8 : Parks and Wildlife Mammals Research Elk Conservation Evaluating factors influencing elk recruitment in Colorado Period Covered: July 1, 2023 – June 30, 2024 Authors: N.D. Rayl, M.W. Alldredge, and C.R. Anderson Jr. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Over the last two decades, wildlife managers in Colorado have become increasingly concerned about declining winter elk calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival, they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival. Thus, there is a need for elk research in Colorado based upon monitoring of marked individuals to evaluate factors affecting each stage of production and survival. Colorado Parks and Wildlife (CPW) is conducting research in 3 study areas to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado. From July 1, 2023 – June 30, 2024, we focused on working with stakeholders and collaborators on research logistics, and capturing and collaring elk. Field efforts were centered on 2 objectives: 1) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 2) capturing and collaring newborn and 6-month old elk to collect data on calf survival and cause-specific mortality. We radio-collared 50 6-month old elk calves in December 2023. In March 2024, we radio-collared 80 pregnant elk and outfitted them with VITs. Estimates of average pregnancy rates ranged from 87-98% across herds, and estimates of mean ingesta-free body fat ranged from 7.32-8.03% across herds. During the 2024 calving season, we radio-collared 102 elk calves. 1 �WILDLIFE RESEARCH REPORT EVALUATING FACTORS INFLUENCING ELK RECRUITMENT IN COLORADO NATHANIEL D. RAYL, MAT W. ALLDREDGE, AND CHUCK R. ANDERSON JR. PROJECT NARRATIVE OBJECTIVES The objectives of this project are to 1) estimate elk calf survival and cause-specific mortality rates from birth to age 1 to evaluate the importance of mortality sources for elk calf survival, 2) evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions) and abiotic factors (previous and current weather conditions) on seasonal mortality risk of elk calves from birth to age 1, 3) assess the health of elk herds by quantifying pregnancy rates and percent ingesta-free body fat (IFBF) of adult female elk, and 4) evaluate the influence of biotic and abiotic factors on pregnancy rates and IFBF. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 6, 10, 11, and 18, and private landowners on field research logistics. 2. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 3. Capture and collar neonate and 6-month old elk to collect data on calf survival and causespecific sources of mortality. INTRODUCTION In Colorado, elk (Cervus canadensis) are an important natural resource that are valued for ecological, consumptive, aesthetic, and economic reasons. In 1910, fewer than 1,000 elk remained in Colorado (Swift 1945), but today the state population is estimated to be the largest in the country, with over 292,000 elk. Over the last two decades, however, there has been increasing concern among wildlife managers in Colorado about declining winter calf recruitment (estimated using juvenile:adult female ratios) in the southern portion of the state (Fig. 1; Colorado Parks and Wildlife, unpublished data). In many elk populations, similar declining trends in juvenile recruitment have been observed. A recent synthesis examining elk recruitment in the western United States from 19892010 found evidence of a long-term reduction of 0.48 juveniles/100 adult females/year (Lukacs et al. 2018). Lukacs et al. (2018) discovered associations between recruitment and forage productivity that suggested nutritional conditions on either summer or winter ranges had the most influence on elk recruitment. These associations varied by geographic region: winter range conditions appeared to be more influential in southern areas (Colorado, Utah, and parts of Wyoming), whereas summer range conditions appeared to be more influential in northern areas (Idaho, Montana, Oregon, Washington, parts of Wyoming). Lukacs et al. (2018) also found that 2 �lower total winter precipitation in the previous winter was associated with lower recruitment the next year. The presence of wolves (Canis lupus) and grizzly bears (Ursus arctos) were also associated with lower recruitment in northern areas. Although juvenile:adult female ratios are often highly correlated with juvenile elk survival (Raithel et al. 2007, Harris et al. 2008), they are an imperfect estimate of recruitment because they are affected by harvest, pregnancy rates, juvenile survival, and adult female survival (Caughley 1974, Gaillard et al. 2000, Harris et al. 2008, Decesare et al. 2012, Lukacs et al. 2018). This makes it difficult to identify ultimate factors influencing population dynamics using age ratio data alone, as multiple scenarios can produce equivalent ratios (Caughley 1974). Thus, long-term demographic studies based on monitoring of marked individuals are necessary to reliably test biological hypotheses and evaluate factors affecting each stage of production and survival (Gaillard et al. 2000, Clutton-Brock and Sheldon 2010, Proffitt et al. 2014). In the absence of harvest, the population dynamics of ungulates are normally characterized by high and stable adult female survival and variable juvenile survival (Gaillard et al. 1998, 2000). Among vital rates, changes in adult female survival have the potential to exert the most influence on population growth rates, but usually do not because of low year-to-year variability (Gaillard et al. 1998, 2000). Instead, adult female survival is typically buffered against moderate environmental variation, and thus not strongly influenced by climatic or densitydependent factors (Gaillard et al. 2000). Indeed, in a large-scale meta-analysis, Brodie et al. (2013) demonstrated that adult female elk survival was principally related to human harvest, and found that this harvest was an additive source of mortality. In contrast to adult female survival, and despite its lower elasticity (de Kroon et al. 1986), juvenile survival frequently has a greater effect on population growth rates of ungulates because of its high variability (Gaillard et al. 1998, 2000, Raithel et al. 2007). Juvenile survival of ungulates may be influenced by abiotic or biotic factors such as environmental conditions, forage quality or quantity, population density, maternal body condition, and predation (Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014, Bastille-Rousseau et al. 2016). Complex interactions among these factors frequently make it difficult to identify the relative role of top-down and bottom-up factors affecting calf survival (Linnell et al. 1995). Further complexity may be introduced if top-down and bottom-up forces are simultaneously and variably influencing survival (Bowyer et al. 2005, Monteith et al. 2014). Juvenile survival has been found to vary substantially among and within elk populations on an annual basis (Garrott et al. 2003, Raithel et al. 2007, Griffin et al. 2011). Griffin et al. (2011) synthesized elk neonatal survival across 12 populations in the northwestern United States, and found that survival declined after warmer previous summers and with more predator species, and increased with higher May precipitation. In resource-limited populations, weather conditions may heavily influence juvenile survival. For example, in an unhunted elk population, prior to the establishment of wolves, Garrott et al. (2003) found that the dominant source of calf mortality in Yellowstone National Park was starvation, with more severe winters leading to lower calf survival. Elsewhere, mortality due to predation has been identified as the most significant cause of death for elk calves (e.g., Barber-Meyer et al. 2008). In some systems black bears (Ursus americanus) are the dominant predator (White et al. 2010, Tatman et al. 2018), whereas in others mountain lions (Puma concolor) kill the most calves (Eacker et al. 2016). Although nonpredation deaths (i.e., disease, starvation) may be low where high levels of juvenile predation occur, it remains difficult to determine whether neonate predation represents an additive or 3 �compensatory source of mortality (Linnell et al. 1995, Barber-Meyer et al. 2008, Griffin et al. 2011, Monteith et al. 2014). Individual calf characteristics may also influence risk of mortality. To date, evidence for sex-biased mortality in elk calves is equivocal. Some studies have documented increased vulnerability of male calves to predation (Smith and Anderson 1996, Eacker et al. 2016), whereas others have demonstrated increased vulnerability of female calves (White et al. 2010). It has been suggested that differences may be due to the hunting behavior of the dominant predator, with stalking predators, such as mountain lions, disproportionately killing male calves, which may engage in riskier exploratory behavior (Eacker et al. 2016). Conclusions about the effect of birth mass on elk calf survival are similarly ambiguous. Some studies have reported that birth mass influenced survival probability (Singer et al. 1997, White et al. 2010), but others have not detected an influence of birth mass on neonate mortality (Smith and Anderson 1996, Eacker et al. 2016). The ability for nutritional resources on the landscape to support ungulate populations is reflected in the nutritional condition of individuals within those populations (Parker et al. 2009, Cook et al. 2013, Monteith et al. 2014). As nutritional resources decline there is typically a predictable sequence of changes in the vital rates of large herbivore populations: first, juvenile survival decreases, then age of first reproduction increases, followed by decreased fertility of adult females, and finally increased mortality rates of adults (Eberhardt 1977a, 1977b, 2002, Gaillard et al. 1998, 2000). This general sequence has been confirmed for elk, with forage quality and associated nutritional body condition demonstrated to affect intra-uterine survival, yearling and adult pregnancy rates, birth dates, birth weight, calf growth, and winter survival of juveniles and adults (Cook et al. 2004a, 2004b, 2013, Proffitt et al. 2016). When nutritional resources are limited, elk calves may be lighter at birth, born later, and have slower growth rates during summer, which may predispose them to predation mortality. Cook et al. (2004a, 2013) demonstrated that summer-autumn nutrition can play a central role in determining elk productivity, as, during this time, animals must meet the demands of lactation and accrue sufficient fat reserves to get pregnant and survive the winter. For elk, nutritional resources on the landscape may be affected by wild and domestic herbivory (Vavra and Sheehy 1996, Vavra et al. 2007), timber management (Visscher and Merrill 2009), climatic conditions (Middleton et al. 2013), and fire history (Proffitt et al. 2016). To properly determine factors affecting juvenile ungulate survival bottom-up nutritional effects and top-down predation effects should be evaluated together (Monteith et al. 2014). Understanding how these effects are influencing elk population dynamics in Colorado is critical for guiding management actions. In 2016, Colorado Parks and Wildlife (CPW) initiated a 2-year pilot study to investigate factors influencing elk recruitment in 2 study areas in the state. During the pilot study, researchers collected data on annual elk calf survival, pregnancy rates, and latewinter body condition of adult female elk. In July 2018 – July 2019, we expanded this research into a 3rd study area to better determine how predators, habitat, and weather conditions are impacting elk recruitment in Colorado, and to provide management recommendations for increasing juvenile recruitment. STUDY AREAS For management purposes, the elk population in Colorado is divided into 43 Data Analysis Units (DAUs), each of which encompass the year-round range of an elk herd. This 4 �project is being conducted in 3 DAUs, 2 with low juvenile:adult female ratios (E-20, E-33), and 1 with high juvenile:adult female ratios (E-2), which will serve as a reference area (Fig. 1). The Uncompahgre Plateau elk herd (DAU E-20; 5,858 km2) is on the Uncompahgre Plateau in southwest Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-20 averaged 30 calves per 100 adult females. Landownership is a mixture of BLM (38%), USFS (37%), private (24%), and state (1%) lands. Elevations range from 1,390 to 3,150 m. The plateau is characterized by a mixture of pinyon-juniper (Pinus edulis, Juniperus osteosperma) woodlands and sage-grassland communities (Artemisia spp., Cercocarpus montanus, Achnatherum hymenoides) at lower elevations. At mid elevations, ponderosa pine (Pinus ponderosa) and mountain shrub communities (Amelanchier alnifolia, Arctostaphylos, Artemisia spp., Quercus gambelii, Symphoricarpos spp.) predominate. Spruce-fir (Picea engelmannii, Abies lasiocarpa, Pseudotsuga menziesii) and aspen (Populus tremuloides) forests dominate at higher elevations. The Trinchera elk herd (DAU E-33; 8,601 km2) is in southeast Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-33 averaged 26 calves per 100 adult females. Landownership is a mixture of private (89%), USFS (3%), state (3%), BLM (2%), U.S. Fish and Wildlife Service (USFWS; 1%), and other (2%) lands. Elevations range from 1,640 to 4,370 m. Lower elevations are characterized by agriculture and sage-grassland communities. At mid elevations, pinyon-juniper woodlands, ponderosa pine and mountain shrub forests, and spruce-fir and aspen forests predominate. Alpine tundra communities dominate at higher elevations. The Bear’s Ears elk herd (DAU E-2; 7,293 km2) is in northwest Colorado, USA. From 2013-2017 juvenile:adult female ratios in E-2 averaged 56 calves per 100 adult females. Landownership is a mixture of private (50%), USFS (25%), BLM (19%), state (5%) and other (1%) lands. Elevations range from 1,730 to 3,710 m. The DAU is characterized by sagegrassland communities at lower elevations. At mid elevations, mountain shrub communities predominate. Spruce-fir and aspen forests dominate at higher elevations. METHODS Capture and handling — We captured adult female elk ≥2 years of age from each study herd by helicopter net-gunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. From each study herd, we outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor 5 �and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). In December, we captured 6-month old elk calves from each study herd by helicopter netgunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2023, we collared 50 6-month old elk calves, 25 each from the Bear’s Ears and Uncompahgre Plateau elk herds. The mean weight of calves from the Bear’s Ears herd was 100.4 kg (95% CI = 94.3-106.5 kg) and 103.2 kg (95% CI = 96.8-109.5 kg) from the Uncompahgre Plateau elk herd. During March 2023, we radio-collared 80 pregnant elk and outfitted them with VITs, 40 each from the Bear’s Ears and Uncompahgre Plateau herds. We estimated that pregnancy rates of adult female elk were 87% (95% CI = 74-94%) in the Bear’s Ears herd, and 98% (95% CI = 88100%) in the Uncompahgre Plateau herd (Fig. 2). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 7.32% from the Bear’s Ears herd and 8.03% from the Uncompahgre Plateau herd. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). During May-July 2024, we captured and collared 102 elk calves, 50 from the Bear’s Ears herd, and 52 from the Uncompahgre Plateau herd. 6 �SUMMARY From July 1, 2023 – June 30, 2024 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the sixth year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 80 pregnant females with GPS collars and VITs. We successfully captured and collared 102 newborn elk and 50 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. 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Caswell. 1986. Elasticity: the relative contribution of demographic parameters to population growth rate. Ecology 67:1427–1431. Linnell, J. D. C., R. Aanes, and R. Andersen. 1995. Who killed Bambi? The role of predation in the neonatal mortality of temperate ungulates. Wildlife Biology 1:209–223. Lukacs, P. M., M. S. Mitchell, M. Hebblewhite, B. K. Johnson, H. Johnson, M. Kauffman, K. M. Proffitt, P. Zager, J. Brodie, K. Hersey, A. A. Holland, M. Hurley, S. McCorquodale, A. Middleton, M. Nordhagen, J. J. Nowak, D. P. Walsh, and P. J. White. 2018. Factors influencing elk recruitment across ecotypes in the western United States. Journal of Wildlife Management 82:698–710. Middleton, A. D., M. J. Kauffman, D. E. Mcwhirter, J. G. Cook, R. C. Cook, A. A. Nelson, M. D. Jimenez, and R. W. Klaver. 2013. Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology 94:1245–1256. 8 �Monteith, K. L., V. C. Bleich, T. R. Stephenson, B. M. Pierce, M. M. Conner, J. G. Kie, and R. T. Bowyer. 2014. Life-history characteristics of mule deer: effects of nutrition in a variable environment. Wildlife Monographs 186:1–62. Parker, K. L., P. S. Barboza, and M. P. Gillingham. 2009. Nutrition integrates environmental responses of ungulates. Functional Ecology 23:57–69. Proffitt, K. M., J. A. Cunningham, K. L. Hamlin, and R. A. Garrott. 2014. Bottom-up and topdown influences on pregnancy rates and recruitment of northern Yellowstone elk. Journal of Wildlife Management 78:1383–1393. Proffitt, K. M., M. Hebblewhite, W. Peters, N. Hupp, and J. Shamhart. 2016. Linking landscapescale differences in forage to ungulate nutritional ecology. Ecological Applications 26:2156– 2174. Raithel, J. D., M. J. Kauffman, and D. H. Pletscher. 2007. Impact of spatial and temporal variation in calf survival on the growth of elk population. Journal of Wildlife Management 71:795– 803. Singer, F. J., A. Harting, K. K. Symonds, and M. B. Coughenour. 1997. Density dependence, compensation, and environmental effects on elk calf mortality in Yellowstone National Park. Journal of Wildlife Management 61:12–25. Smith, B. L., and S. H. Anderson. 1996. Patterns of neonatal mortality of elk in northwest Wyoming. Canadian Journal of Zoology 74:1229–1237. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114– 119. Tatman, N. M., S. G. Liley, J. W. Cain, and J. W. Pitman. 2018. Effects of calf predation and nutrition on elk vital rates. Journal of Wildlife Management 82:1417–1428. Vavra, M., C. G. Parks, and M. J. Wisdom. 2007. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Forest Ecology and Management 246:66–72. Vavra, M., and D. P. Sheehy. 1996. Improving elk habitat characteristics with livestock grazing. Rangelands 18:182–185. Visscher, D. R., and E. H. Merrill. 2009. Temporal dynamics of forage succession for elk at two scales: implications of forest management. Forest Ecology and Management 257:96–106. White, C. G., P. Zager, and M. W. Gratson. 2010. Influence of predator harvest, biological factors, and landscape on elk calf survival in Idaho. Journal of Wildlife Management 74:355–369. Prepared by Nathaniel D. Rayl, Wildlife Researcher 9 �Figure 1. Number of elk calves per 100 adult females observed during December-February aerial surveys (5-year average from 2013-2017) within elk Data Analysis Units (DAUs; labeled with black text) in Colorado, USA. 10 �Figure 2. Estimated average pregnancy rates of adult female elk from the Bear’s Ears, Trinchera, and Uncompahgre Plateau herds sampled during late winter 2017-2024 in Colorado, USA. The sample size is given at the top of the 95% binomial confidence intervals (black lines). 11 � https://cpw.cvlcollections.org/files/original/99b97322203e8f53f30b744bcbef4e39.pdf 0366640cc05defa506b6f588d22e209e PDF Text Text Colorado Parks and Wildlife July 2019 – June 2020 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 2 : : : : Federal Aid Project No. W-242-R-4 : Parks and Wildlife Mammals Research Elk Conservation Response of Elk to Human Recreation at Multiple Scales: demographic shifts and behaviorally mediated fluctuations in local abundance Period Covered: July 1, 2019 - June 30, 2020 Authors: E.J. Bergman and N.D. Rayl Personnel: R. Baker, T. Brtis, M. Fisher, L. Gepfert, J. Groves, A. Hart, K. Hayes, W. Hiler, T. Kishimoto, D. Lewis, J. Mao, A. McLaine, K. Middledorf, A. Orlando, K. Russo, K. Tesch, L. Wolfe, and M. Yamashita, CPW; J. Clark, H. Cushman, J. Larrivee, A. Orlando, S. Strike, R. Swisher, S. Swisher, and T. Triple, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Great Outdoors Colorado, Pitkin County Open Space and Trails, and Rocky Mountain Elk Foundation. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT During the reporting period we focused on working with stakeholders and collaborators on research logistics, deploying field cameras, and capturing and collaring elk. Field efforts were centered on 3 objectives: 1) deploying/retrieving field cameras (i.e., cameras capable of timelapse and motion activation to estimate elk abundance), 2) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 3) capturing and collaring newborn and 6-month old elk calves to collect data on calf survival and cause-specific mortality. We retrieved 119 cameras (deployed during fall 2019) and we redeployed 238 cameras (spring 2020) across 8 study units, and radio-collared 40 pregnant elk and outfitted them with VITs. Estimated the pregnancy rate was 95%, and the mean ingesta-free body fat of adult females was 8.1%. We radio-collared 25 6-month old elk calves in December 2019. During the 2020 calving 1 �season, we radio-collared 54 elk calves, including 93% of the calves born to collared females. We estimated that the average date of calving was 3 June. Cameras deployed during spring 2020 will be retrieved from the field during the fall of 2020 and photo processing will occur during the winter of 2020–2021. 2 �WILDLIFE RESEARCH REPORT RESPONSE OF ELK TO HUMAN RECREATION AT MULTIPOLE SCALES: DEMOGRAPHIC SHIFTS AND BEHAVIORALLY MEDIATED FLUCTUATIONS IN LOCAL ABUNDANCE ERIC J. BERGMAN AND NATHANIEL D. RAYL PROJECT NARRATIVE OBJECTIVES This project has objectives on 2 scales. At the broad, elk herd-level scale, we will estimate pregnancy rates, calf survival rates, and cause-specific mortality rates to evaluate the importance of mortality sources for elk calf survival. More specifically, we will evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions), abiotic (previous and current weather conditions), and human-induced factors (i.e., relative exposure to recreational activities) on seasonal mortality risk of elk calves from birth to age 1 and on pregnancy rates of mature female elk. At the narrower geographic and temporal scale, we will use short-term (~3-4 weeks) changes in elk abundance within small study units (<65 km2) as a tool to evaluate the influence of human recreation on elk distribution. At this narrower scale, the primary objective is to evaluate the role that human recreation (e.g., hiking, mountain biking, horseback riding, trail running, hunting, etc.) has on the behavioral distribution of elk on spring calving, summer, and fall transition ranges. Coupled to the objective of detecting behaviorally influenced changes in abundance and density, we will evaluate the effectiveness of current recreational closures maintained by ski areas, counties, and federal land management agencies. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 8 and 10, and private landowners on field research logistics. 2. Deploy field cameras (i.e., cameras capable of time-lapse) to estimate elk abundance. 3. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 4. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. INTRODUCTION The role of outdoor recreation within the state of Colorado is difficult to overstate. According to Colorado's Statewide Comprehensive Outdoor Recreation Plan (SCORP), outdoor recreation contributes 511,000 jobs, $62.5 billion in economic output, and $9.4 billion in local, state, and federal tax revenue (State of Colorado 2019). Outdoor recreation includes multiple activities such as biking, camping, climbing, fishing, hiking, horseback riding, hunting, shooting, skiing and wildlife watching. The Outdoor Foundation estimates that in the United States, there 3 �are nearly 30 million hikers, just under 7 million mountain bikers, over 14 million hunters, and nearly 23 million wildlife watchers (Outdoor Foundation 2018). While difficult to quantify, it is a reasonable assumption that many individual outdoor enthusiasts actively participate in more than one of these activities. Thus, the economies of Colorado, its counties, and its communities, rely on managing the landscape for a multitude of outdoor recreational opportunities. However, there is also evidence that human activities have an impact on wildlife. While trail-based recreation has the potential to impact many species, recent concerns in Colorado have focused on elk (Cervus canadensis; Durango Herald 2018, Steamboat Pilot and Today 2018, Vail Daily 2018). The sensitivity of elk to human presence and human activity has been a topic of interest for many decades. Preliminary research, focused on the effects of logging and vehicle use along road networks, provided consistent and clear evidence that elk-use declined in areas with high road densities, and as road use increased (Lyon and Christensen 2002). Similarly, research in Colorado evaluating the repeated displacement and disturbance of elk by people on foot provided evidence of suppressed recruitment rates following human disturbance (Phillips and Alldredge 2000, Shively et al. 2005). Experimental evaluation of the impact of hunter presence on elk movements and elk distribution has also occurred in Colorado (Conner et al. 2001, Vieira et al. 2003). This research demonstrated that the presence of hunters shifted elk off public lands and onto neighboring private lands. More recently, recreational trail use (all-terrain vehicle (ATV) riding, hiking, biking, and horseback riding) impacts on elk use of areas with trails was experimentally evaluated in Oregon. Wisdom et al. (2018) found that elk avoided areas with trails when recreationists of any type were present. Thus, regardless of human activity, behavioral displacement of elk by humans is well documented. In Colorado, increasing public concerns over human recreational use have coincided with declines in elk productivity, but a direct relationship to this activity in Colorado remains unaddressed. During FY 2016–2017, Colorado Parks and Wildlife (CPW) initiated a large-scale pilot study designed to evaluate pregnancy rates, elk calf survival, and causes of elk calf mortality (Alldredge 2016). At the onset, it was recognized that many factors contribute to suppression of pregnancy rates and calf survival. In addition to hunting, deteriorating habitat quality, habitat loss, and predation are key factors that may influence Colorado’s ungulate herds. Likewise, factors such as disease and competition may also play a role. Less clear, however, are the effects that human recreation may exert on the population dynamics of elk and other large ungulates. Past research has also reported individual behavioral responses of elk exposed to recreational stimuli. However, an alternate approach to studying behavioral displacement would shift the focus away from individual animals and link elk distribution to specific geographic areas. One limitation to studying individual animals is that the presence or absence of unmarked animals within the study area is largely ignored. However, access management and land management planning decisions are intrinsically tied to geographic areas. Thus, knowledge about the presence, absence, and abundance of a species of interest is of great value to managers. 4 �STUDY AREAS This study is occurring in two study areas. The northern study area focuses on the Bear’s Ears elk herd between Craig and Steamboat Springs. Within the Bear’s Ears herd, the fine scale camera-based behavioral portion of this study is centered on the Routt County segment of the herd that uses the Elk River drainage near the community of Steamboat Springs. The Bear’s Ears study area will be sampled using 4 study units: Mad Creek, Buffalo Pass, Walton Rim, and Hwy 40/Ferndale. The northernmost Mad Creek study unit has few existing trails but has been identified as a potential site for future trail development. Immediately south of the Mad Creek study unit is the Buffalo Pass study unit. Extensive trail development in this study area occurred during the past 5-10 years and it is currently an important and key area for many trail-based recreational activities. Further south is the Walton Rim study unit. Bounded to the north by the Steamboat Springs ski area, Walton Rim currently has little or no recreational trail use and plans for future trail development in this unit currently do not exist. Finally, immediately south of Walton Rim falls the Hwy 40/Ferndale study unit. Currently the Hwy 40/Ferndale study unit has nominal trail development and use, but plans for future trail construction in this unit are being considered. With the exception of Walton Rim, all of the camera-based study units in the Bear’s Ears study area have the potential to experience extensive trail development and use. The southern study area is focused on the Avalanche Creek elk herd along the Roaring Fork River between Glenwood Springs and Aspen. Four camera-based study units in the Avalanche Creek study area have also been identified. The southernmost of these units is the Snowmass unit. This unit, managed by Pitkin County and White River National Forest, has existing trails but is managed with seasonal closures (low elevation trail closures in place until May 16th, and high elevation trail closures in place until June 21st) to protect elk wintering and calving areas. Near the Snowmass study unit (and also in the southern portion of this study area) is the Wildcat study unit. The Wildcat study unit is centered on private property and has nominal recreational trail use, allowing it to serve as a reference area. Further north and nearer the community of Carbondale are 2 additional study units. The eastern most of these additional 2 units is The Crown, which is managed by the Bureau of Land Management and has extensive recreational use, but also has winter closures for mechanized and motorized recreation. Immediately to the south and west of The Crown is the fourth study unit. This unit, Two Shoes Ranch, is privately managed and has little recreational use and minimal trail development. METHODS Camera Sampling — Recent development of non-invasive abundance estimation techniques provide opportunities to quantify species in finite areas over relatively short periods. Camera based Space-To-Event (STE) and Instantaneous Sampling (IS) methods provide tools to estimate abundance without expensive flight time (Moeller et al. 2018). An inherent property of these new techniques is that the scope of inference applies to geographic areas and not individual animals. We deployed remote field cameras (HP2X, Reconyx, Holmen, Wisconsin, USA) to estimate elk abundance and density within small geographic areas (<65 km2) and during short time frames (~3–4 weeks). We deployed cameras across 8 study units (4 within the Bear’s Ears study area and 4 within the Avalanche Creek study area). We designed grids composed of 1.6 km cells to overlay each study unit. Within each cell, we used generalized random-tessellation stratification (GRTS) sampling (Stevens and Olsen 2004, Kincaid and Olsen 2017) to select 2 5 �coarse camera locations. We selected final camera site locations (<250 m of the randomly selected coarse locations) in the field with the specific objective of maximizing detection probability of elk. We deployed cameras during the spring and early summer seasons. We programmed the cameras to take pictures at 10-minute intervals throughout the day. Elk capture and handling — We captured adult female elk ≥2 years of age by helicopter netgunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. We outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. After receiving a birth notification from a VIT, we went to the birth site to capture and collar the newborn elk calf. We blindfolded calves, and wore latex gloves to minimize the transfer of human scent. We measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted elk neonates with expandable GPS radio-collars or VHF proximity collars that communicated with the collar on the adult female, and are designed to drop off after 12 months. We also opportunistically located, captured, and collared additional neonates to increase sample sizes. We collected additional measurements (hair moisture, incisor and upper canine eruption, hoof, dew claw, and navel condition) from opportunistically captured calves to estimate age at capture following Johnson (1951) and Eacker (2015). We attempted to handle calves for <5 minutes to minimize stress. In December, we captured 6-month old elk calves by helicopter net-gunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk calf carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We also collected calf carcasses when they were available to verify field assessments with laboratory necropsies performed by a CPW veterinarian. Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not 6 �they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004, 2013). We will use the late-winter body condition of prime-aged adult female elk that successfully raised a calf the previous year to assess whether or not our study herds may be experiencing nutritional limitations. RESULTS AND DISCUSSION During March 2020, we radio-collared 40 pregnant elk from the Avalanche Creek elk herd and outfitted them with VITs. Estimated the pregnancy rate was 95% (95% CI = 85-99%; n = 43). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 8.1%. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). In December 2019, we collared 25 6-month old elk calves from the Avalanche Creek elk herd. The mean weight of 6-month old calves was 115.8 kg (95% CI = 110.8-120.8 kg). During May-July 2020, we captured and collared 54 elk calves from the Avalanche Creek herd. We successfully captured and collared 93% (37/40) of the calves of adult female elk outfitted with VITs. The estimated mean date of calving was 3 June for the Avalanche Creek herd. During the summer of 2019, a total of 384,455 photos were taken by the 118 cameras deployed across 8 study units. Automated photo recognition software is being developed and applied to these photos to expedite future analyses. SUMMARY During FY2019-20 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the second year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 40 pregnant females with GPS collars and VITs. We successfully captured and collared 54 newborn elk and 25 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. We will continue to collect data on elk survival and cause-specific sources of mortality throughout the year. Field cameras were also successfully deployed. LITERATURE CITED Alldredge, M. 2016. Pilot study – elk recruitment and habitat use in Colorado. Program Narrative Study Plan, Colorado Parks and Wildlife, Fort Collins, CO, USA. Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Conner, M.M., G.C. White, and D.J. Freddy. 2001. Elk movement in response to early-season hunting in northwest Colorado. Journal of Wildlife Management 65:926–940. Cook, J. G., B. K. Johnson, R. C. Cook, R. A. Riggs, T. Delcurto, L. D. Bryant, and L. L. Irwin. 2004. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildlife Monographs 155:1–61. 7 �Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Myers, S. M. Mccorquodale, D. J. Vales, L. L. Irwin, P. B. Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, and P. J. Miller. 2010. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880–896. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. McCorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, B. L. Murphie, K. A. Schoenecker, F. Geyer, P. B. Hall, R. D. Spencer, D. A. Immell, D. H. Jackson, B. L. Tiller, P. J. Miller, and L. Schmitz. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1–44. Durango Herald. 2018. Where have all the elk gone? Published 15 November 2018, accessed 16 November 2018 (https://durangoherald.com/articles/250613-where-have-all-the-elkgone). Eacker, D. R. 2015. Linking the effects of risk factors on annual calf survival to elk population dynamics in the Bitterroot Valley, Montana. M.S. thesis, University of Montana, Missoula, MT, USA. Eacker, D. R., M. Hebblewhite, K. M. Proffitt, B. S. Jimenez, M. S. Mitchell, and H. S. Robinson. 2016. Annual elk calf survival in a multiple carnivore system. Journal of Wildlife Management 80:1345–1359. Johnson, D. E. 1951. Biology of the elk calf, Cervus canadensis nelsoni. Journal of Wildlife Management 15:396–410. Kincaid, T.M., and A.R. Olsen. 2017. Spsurvey: spatial survey design and analysis. R package version 3.4. https://CRAN.R-roject.org/package=spsurvey. Lyon, L.J., and A.G. Christensen. 2002. Elk and land management in D.E. Toweill and J.W. Thomas, eds., North American Elk: ecology and management. Smithsonian Institute Press, Washington D.C., USA. Moeller, A.K., P.M. Lukacs, and J.S. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9:e02331. Phillips, G.E. and A.W. Alldredge. 2000. Reproductive success of elk following disturbance by humans during calving season. Journal of Wildlife Management 64:521–530. Shively, K.J., A.W. Alldredge, and G.E. Phillips. 2005. Elk reproductive response to removal of calving season disturbance by humans. Journal of Wildlife Management 69:1073–1080. State of Colorado, Colorado Statewide Comprehensive Outdoor Recreation Plan. 2019. https://cpw.state.co.us/Documents/Trails/SCORP/Final-Plan/2019-SCORP-Report.pdf Accessed 23 January 2019. Steamboat Pilot and Today. 2018. Newly formed group advocates to slow trail building in Routt National Forest to protect wildlife. Published 21 October 2018, accessed 16 November 2018 (https://www.steamboatpilot.com/news/newly-formed-group-advocates-toslow-trail-building-in-routt-national-forest-to-protect-wildlife/). Stevens, D.L., and A.R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262–278. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Outdoor Foundation. 2018. Outdoor Participation Report. https://outdoorindustry.org/resource/2018-outdoor-participation-report/. Accessed 26 September 2018. 8 �Vail Daily. 2018. Eagle County officials concerned by wildlife population declines. Published 14 September 2018, accessed 16 November 2018 (https://www.vaildaily.com/news/eaglecounty-officials-concerned-by-wildlife-population-declines/). Vieira, M.E.P., M.M. Conner, G.C. White, and D.J. Freddy. 2003. Effects of archery hunter numbers and opening dates on elk movement. Journal of Wildlife Management 67:717– 728. Wisdom, M.J., H.K. Preisler, L.M. Naylor, R.G. Anthony, B.K. Johnson, and M.M. Rowland, 2018. Elk responses to trail-based recreation on public forests. Forest Ecology and Management 411:223–233. Prepared by Eric J. Bergman, Wildlife Researcher 9 �Colorado Parks and Wildlife July 2020 – June 2021 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 2 : : : : Federal Aid Project No. W-242-R-5 : Parks and Wildlife Mammals Research Elk Conservation Response of Elk to Human Recreation at Multiple Scales: demographic shifts and behaviorally mediated fluctuations in local abundance Period Covered: July 1, 2020 – June 30, 2021 Authors: E.J. Bergman and N.D. Rayl Personnel: R. Baker, Z. Durbin, R. Ebel-Childs, M. Fisher, L. Gepfert, J. Groves, W. Hiler, D. Lewis, J. Mao, K. Middledorf, S. Mollett, E. Monfort, P. Nol, A. Orlando, S. Sandefur, E. Sawa, N. Starling, K. Tesch, and M. Yamashita, CPW; J. Clark, H. Cushman, B. Dooling, T. Herby, A. Orlando, R. Swisher, S. Swisher, and T. Triple, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, and CPW Big Game Auction and Raffle. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT During the reporting period we focused on working with stakeholders and collaborators on research logistics, deploying field cameras, and capturing and collaring elk. Field efforts were centered on 3 objectives: 1) deploying/retrieving field cameras (i.e., cameras capable of timelapse and motion activation to estimate elk abundance), 2) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 3) capturing and collaring newborn and 6-month old elk calves to collect data on calf survival and cause-specific mortality. During spring (FY 19-20) and early summer (FY 20-21) of 2020 we deployed 238 cameras across 8 study units. During fall, 154 of these cameras were retrieved. Retrieval of 84 cameras was prevented by closures due to the Middle Fork Fire near Steamboat Springs, and the 1 �subsequent onset of winter. The remaining cameras were retrieved during spring of 2021. Downloading and cataloging of photos collected during the summer of 2020 occurred during summer 2021, with approximately 4.6 million photos collected. We radio-collared 25 6-month old elk calves in December 2020. In March 2021, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate was 85% and the mean ingesta-free body fat of adult females was 8.2%. During the 2021 calving season, we radio-collared 51 elk calves. We estimated that the average date of calving was 2 June. WILDLIFE RESEARCH REPORT RESPONSE OF ELK TO HUMAN RECREATION AT MULTIPOLE SCALES: DEMOGRAPHIC SHIFTS AND BEHAVIORALLY MEDIATED FLUCTUATIONS IN LOCAL ABUNDANCE ERIC J. BERGMAN AND NATHANIEL D. RAYL PROJECT NARRATIVE OBJECTIVES This project has objectives on 2 scales. At the broad, elk herd-level scale, we will estimate pregnancy rates, calf survival rates, and cause-specific mortality rates to evaluate the importance of mortality sources for elk calf survival. More specifically, we will evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions), abiotic (previous and current weather conditions), and human-induced factors (i.e., relative exposure to recreational activities) on seasonal mortality risk of elk calves from birth to age 1 and on pregnancy rates of mature female elk. At the narrower geographic and temporal scale, we will use short-term (~3-4 weeks) changes in elk abundance within small study units (<65 km2) as a tool to evaluate the influence of human recreation on elk distribution. At this narrower scale, the primary objective is to evaluate the role that human recreation (e.g., hiking, mountain biking, horseback riding, trail running, hunting, etc.) has on the behavioral distribution of elk on spring calving, summer, and fall transition ranges. Coupled to the objective of detecting behaviorally influenced changes in abundance and density, we will evaluate the effectiveness of current recreational closures maintained by ski areas, counties, and federal land management agencies. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 8 and 10, and private landowners on field research logistics. 2. Deploy field cameras (i.e., cameras capable of time-lapse) to estimate elk abundance. 3. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 4. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. 2 �INTRODUCTION The role of outdoor recreation within the state of Colorado is difficult to overstate. According to Colorado's Statewide Comprehensive Outdoor Recreation Plan (SCORP), outdoor recreation contributes 511,000 jobs, $62.5 billion in economic output, and $9.4 billion in local, state, and federal tax revenue (State of Colorado 2019). Outdoor recreation includes multiple activities such as biking, camping, climbing, fishing, hiking, horseback riding, hunting, shooting, skiing and wildlife watching. The Outdoor Foundation estimates that in the United States, there are nearly 30 million hikers, just under 7 million mountain bikers, over 14 million hunters, and nearly 23 million wildlife watchers (Outdoor Foundation 2018). While difficult to quantify, it is a reasonable assumption that many individual outdoor enthusiasts actively participate in more than one of these activities. Thus, the economies of Colorado, its counties, and its communities, rely on managing the landscape for a multitude of outdoor recreational opportunities. However, there is also evidence that human activities have an impact on wildlife. While trail-based recreation has the potential to impact many species, recent concerns in Colorado have focused on elk (Cervus canadensis; Durango Herald 2018, Steamboat Pilot and Today 2018, Vail Daily 2018). The sensitivity of elk to human presence and human activity has been a topic of interest for many decades. Preliminary research, focused on the effects of logging and vehicle use along road networks, provided consistent and clear evidence that elk-use declined in areas with high road densities, and as road use increased (Lyon and Christensen 2002). Similarly, research in Colorado evaluating the repeated displacement and disturbance of elk by people on foot provided evidence of suppressed recruitment rates following human disturbance (Phillips and Alldredge 2000, Shively et al. 2005). Experimental evaluation of the impact of hunter presence on elk movements and elk distribution has also occurred in Colorado (Conner et al. 2001, Vieira et al. 2003). This research demonstrated that the presence of hunters shifted elk off public lands and onto neighboring private lands. More recently, recreational trail use (all-terrain vehicle (ATV) riding, hiking, biking, and horseback riding) impacts on elk use of areas with trails was experimentally evaluated in Oregon. Wisdom et al. (2018) found that elk avoided areas with trails when recreationists of any type were present. Thus, regardless of human activity, behavioral displacement of elk by humans is well documented. In Colorado, increasing public concerns over human recreational use have coincided with declines in elk productivity, but a direct relationship to this activity in Colorado remains unaddressed. During FY 2016–2017, Colorado Parks and Wildlife (CPW) initiated a large-scale pilot study designed to evaluate pregnancy rates, elk calf survival, and causes of elk calf mortality (Alldredge 2016). At the onset, it was recognized that many factors contribute to suppression of pregnancy rates and calf survival. In addition to hunting, deteriorating habitat quality, habitat loss, and predation are key factors that may influence Colorado’s ungulate herds. Likewise, factors such as disease and competition may also play a role. Less clear, however, are the effects that human recreation may exert on the population dynamics of elk and other large ungulates. Past research has also reported individual behavioral responses of elk exposed to recreational stimuli. However, an alternate approach to studying behavioral displacement would shift the focus away from individual animals and link elk distribution to specific geographic areas. One limitation to studying individual animals is that the presence or absence of unmarked animals within the study area is largely ignored. However, access management and land 3 �management planning decisions are intrinsically tied to geographic areas. Thus, knowledge about the presence, absence, and abundance of a species of interest is of great value to managers. STUDY AREAS This study is occurring in two study areas. The northern study area focuses on the Bear’s Ears elk herd between Craig and Steamboat Springs. Within the Bear’s Ears herd, the fine scale camera-based behavioral portion of this study is centered on the Routt County segment of the herd that uses the Elk River drainage near the community of Steamboat Springs. The Bear’s Ears study area will be sampled using 4 study units: Mad Creek, Buffalo Pass, Walton Rim, and Hwy 40/Ferndale. The northernmost Mad Creek study unit has few existing trails but has been identified as a potential site for future trail development. Immediately south of the Mad Creek study unit is the Buffalo Pass study unit. Extensive trail development in this study area occurred during the past 5-10 years and it is currently an important and key area for many trail-based recreational activities. Further south is the Walton Rim study unit. Bounded to the north by the Steamboat Springs ski area, Walton Rim currently has little or no recreational trail use and plans for future trail development in this unit currently do not exist. Finally, immediately south of Walton Rim falls the Hwy 40/Ferndale study unit. Currently the Hwy 40/Ferndale study unit has nominal trail development and use, but plans for future trail construction in this unit are being considered. With the exception of Walton Rim, all of the camera-based study units in the Bear’s Ears study area have the potential to experience extensive trail development and use. The southern study area is focused on the Avalanche Creek elk herd along the Roaring Fork River between Glenwood Springs and Aspen. Four camera-based study units in the Avalanche Creek study area have also been identified. The southernmost of these units is the Snowmass unit. This unit, managed by Pitkin County and White River National Forest, has existing trails but is managed with seasonal closures (low elevation trail closures in place until May 16th, and high elevation trail closures in place until June 21st) to protect elk wintering and calving areas. Near the Snowmass study unit (and also in the southern portion of this study area) is the Wildcat study unit. The Wildcat study unit is centered on private property and has nominal recreational trail use, allowing it to serve as a reference area. Further north and nearer the community of Carbondale are 2 additional study units. The eastern most of these additional 2 units is The Crown, which is managed by the Bureau of Land Management and has extensive recreational use, but also has winter closures for mechanized and motorized recreation. Immediately to the south and west of The Crown is the fourth study unit. This unit, Two Shoes Ranch, is privately managed and has little recreational use and minimal trail development. METHODS Camera Sampling — Recent development of non-invasive abundance estimation techniques provide opportunities to quantify species in finite areas over relatively short periods. Camera based Space-To-Event (STE) and Instantaneous Sampling (IS) methods provide tools to estimate abundance without expensive flight time (Moeller et al. 2018). An inherent property of these new techniques is that the scope of inference applies to geographic areas and not individual animals. We deployed remote field cameras (HP2X, Reconyx, Holmen, Wisconsin, USA) to estimate elk abundance and density within small geographic areas (<65 km2) and during short 4 �time frames (~3–4 weeks). We deployed cameras across 8 study units (4 within the Bear’s Ears study area and 4 within the Avalanche Creek study area). We designed grids composed of 1.6 km cells to overlay each study unit. Within each cell, we used generalized random-tessellation stratification (GRTS) sampling (Stevens and Olsen 2004, Kincaid and Olsen 2017) to select 2 coarse camera locations. We selected final camera site locations (<250 m of the randomly selected coarse locations) in the field with the specific objective of maximizing detection probability of elk. We deployed cameras during the spring and early summer seasons. We programmed the cameras to take pictures at 10-minute intervals throughout the day. Elk capture and handling — We captured adult female elk ≥2 years of age by helicopter netgunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. We outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. In December, we captured 6-month old elk calves by helicopter net-gunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2020, we collared 25 6-month old elk calves from the Avalanche Creek herd. The mean weight of calves was 109.0 kg (95% CI: 103.3-114.8 kg). During March 2021, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy 5 �rate of adult female elk was 85% (72-93%). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 8.2%. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summerautumn or winter nutrition (R. Cook, personal communication). During the summer of 2020, approximately 4.6 million photos were taken by the 238 cameras deployed across 8 study units. These photos are actively being archived. Automated photo recognition software continues to be developed and will be applied to these photos to expedite future analyses. However, subsampling of photos (and longer time intervals) will occur prior to analysis. SUMMARY During July 2020 – June 2021 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the third year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 40 pregnant females with GPS collars and VITs. We successfully captured and collared 51 newborn elk and 25 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. We will continue to collect data on elk survival and cause-specific sources of mortality throughout the year. Field cameras were also successfully deployed. LITERATURE CITED Alldredge, M. 2016. Pilot study – elk recruitment and habitat use in Colorado. Program Narrative Study Plan, Colorado Parks and Wildlife, Fort Collins, CO, USA. Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Conner, M.M., G.C. White, and D.J. Freddy. 2001. Elk movement in response to early-season hunting in northwest Colorado. Journal of Wildlife Management 65:926–940. Cook, J. G., B. K. Johnson, R. C. Cook, R. A. Riggs, T. Delcurto, L. D. Bryant, and L. L. Irwin. 2004. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildlife Monographs 155:1–61. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Myers, S. M. Mccorquodale, D. J. Vales, L. L. Irwin, P. B. Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, and P. J. Miller. 2010. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880–896. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. McCorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, B. L. Murphie, K. A. Schoenecker, F. Geyer, P. B. Hall, R. D. Spencer, D. A. Immell, D. H. Jackson, B. L. Tiller, P. J. Miller, and L. Schmitz. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1–44. Durango Herald. 2018. Where have all the elk gone? Published 15 November 2018, accessed 16 November 2018 (https://durangoherald.com/articles/250613-where-have-all-the-elkgone). 6 �Eacker, D. R. 2015. Linking the effects of risk factors on annual calf survival to elk population dynamics in the Bitterroot Valley, Montana. M.S. thesis, University of Montana, Missoula, MT, USA. Eacker, D. R., M. Hebblewhite, K. M. Proffitt, B. S. Jimenez, M. S. Mitchell, and H. S. Robinson. 2016. Annual elk calf survival in a multiple carnivore system. Journal of Wildlife Management 80:1345–1359. Johnson, D. E. 1951. Biology of the elk calf, Cervus canadensis nelsoni. Journal of Wildlife Management 15:396–410. Kincaid, T.M., and A.R. Olsen. 2017. Spsurvey: spatial survey design and analysis. R package version 3.4. https://CRAN.R-roject.org/package=spsurvey. Lyon, L.J., and A.G. Christensen. 2002. Elk and land management in D.E. Toweill and J.W. Thomas, eds., North American Elk: ecology and management. Smithsonian Institute Press, Washington D.C., USA. Moeller, A.K., P.M. Lukacs, and J.S. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9:e02331. Phillips, G.E. and A.W. Alldredge. 2000. Reproductive success of elk following disturbance by humans during calving season. Journal of Wildlife Management 64:521–530. Shively, K.J., A.W. Alldredge, and G.E. Phillips. 2005. Elk reproductive response to removal of calving season disturbance by humans. Journal of Wildlife Management 69:1073–1080. State of Colorado, Colorado Statewide Comprehensive Outdoor Recreation Plan. 2019. https://cpw.state.co.us/Documents/Trails/SCORP/Final-Plan/2019-SCORP-Report.pdf Accessed 23 January 2019. Steamboat Pilot and Today. 2018. Newly formed group advocates to slow trail building in Routt National Forest to protect wildlife. Published 21 October 2018, accessed 16 November 2018 (https://www.steamboatpilot.com/news/newly-formed-group-advocates-toslow-trail-building-in-routt-national-forest-to-protect-wildlife/). Stevens, D.L., and A.R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262–278. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Outdoor Foundation. 2018. Outdoor Participation Report. https://outdoorindustry.org/resource/2018-outdoor-participation-report/. Accessed 26 September 2018. Vail Daily. 2018. Eagle County officials concerned by wildlife population declines. Published 14 September 2018, accessed 16 November 2018 (https://www.vaildaily.com/news/eaglecounty-officials-concerned-by-wildlife-population-declines/). Vieira, M.E.P., M.M. Conner, G.C. White, and D.J. Freddy. 2003. Effects of archery hunter numbers and opening dates on elk movement. Journal of Wildlife Management 67:717– 728. Wisdom, M.J., H.K. Preisler, L.M. Naylor, R.G. Anthony, B.K. Johnson, and M.M. Rowland, 2018. Elk responses to trail-based recreation on public forests. Forest Ecology and Management 411:223–233. Prepared by 7 �Eric J. Bergman, Wildlife Researcher 8 �Colorado Parks and Wildlife July 2021 – June 2022 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 2 : : : : Federal Aid Project No. W-242-R-6 : Parks and Wildlife Mammals Research Elk Conservation Response of Elk to Human Recreation at Multiple Scales: demographic shifts and behaviorally mediated fluctuations in local abundance Period Covered: July 1, 2021 – June 30, 2022 Authors: E.J. Bergman and N.D. Rayl Personnel: R. Aberle, J. Arntson, R. Baker, R. Black, K. Bond, D. Corcoran, S. Crews, Z. Durbin, P. Firmin, K. Fischer, M. Fisher, K. Fox, M. Gallagher, L. Gephert, J. Groves, K. Hatch, W. Hiler, Julie Mao, E. Los, M. McDaniel, K. Middledorf, L. Miller, E. Monfort, E. Newkirk, P. Nol, A. Orlando, J. Pollock, J. Potter, E. Sawa, A. Schneller, B. Smith, K. Tesch, E. VanNatta, S. Waters, M. Wood, M. Yamashita, CPW; J. Clark, H. Cushman, B. Dooling, A. Orlando, R. Swisher, S. Swisher, Quicksilver Air, Inc.. Project support received from Federal Aid in Wildlife Restoration, Rocky Mountain Elk Foundation, and CPW Big Game Auction and Raffle. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT During the reporting period we focused on working with stakeholders and collaborators on research logistics, deploying field cameras, and capturing and collaring elk. Field efforts were centered on 3 objectives: 1) deploying/retrieving field cameras (i.e., cameras capable of timelapse and motion activation to estimate elk abundance), 2) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 3) capturing and collaring newborn and 6-month old elk calves to collect data on calf survival and cause-specific mortality. During spring (FY 20-21) and early summer (FY 21-22) of 2021 we deployed 238 cameras across 8 study units. During spring (FY21-22) and summer (FY2022-23) of 2022 data cards were 1 �retrieved. Downloading and cataloging of photos collected during the summer of 2021 occurred during summer 2022, with approximately 4.1 million photos collected. We radio-collared 25 6month old elk calves in December 2021. In March 2022, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate was 95% and the mean ingesta-free body fat of adult females was 7.9%. During the 2022 calving season, we radio-collared 53 elk calves. We estimated that the average date of calving was 3 June. WILDLIFE RESEARCH REPORT RESPONSE OF ELK TO HUMAN RECREATION AT MULTIPOLE SCALES: DEMOGRAPHIC SHIFTS AND BEHAVIORALLY MEDIATED FLUCTUATIONS IN LOCAL ABUNDANCE ERIC J. BERGMAN AND NATHANIEL D. RAYL PROJECT NARRATIVE OBJECTIVES This project has objectives on 2 scales. At the broad, elk herd-level scale, we will estimate pregnancy rates, calf survival rates, and cause-specific mortality rates to evaluate the importance of mortality sources for elk calf survival. More specifically, we will evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions), abiotic (previous and current weather conditions), and human-induced factors (i.e., relative exposure to recreational activities) on seasonal mortality risk of elk calves from birth to age 1 and on pregnancy rates of mature female elk. At the narrower geographic and temporal scale, we will use short-term (~3-4 weeks) changes in elk abundance within small study units (<65 km2) as a tool to evaluate the influence of human recreation on elk distribution. At this narrower scale, the primary objective is to evaluate the role that human recreation (e.g., hiking, mountain biking, horseback riding, trail running, hunting, etc.) has on the behavioral distribution of elk on spring calving, summer, and fall transition ranges. Coupled to the objective of detecting behaviorally influenced changes in abundance and density, we will evaluate the effectiveness of current recreational closures maintained by ski areas, counties, and federal land management agencies. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 8 and 10, and private landowners on field research logistics. 2. Deploy field cameras (i.e., cameras capable of time-lapse) to estimate elk abundance. 3. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 4. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. 2 �INTRODUCTION The role of outdoor recreation within the state of Colorado is difficult to overstate. According to Colorado's Statewide Comprehensive Outdoor Recreation Plan (SCORP), outdoor recreation contributes 511,000 jobs, $62.5 billion in economic output, and $9.4 billion in local, state, and federal tax revenue (State of Colorado 2019). Outdoor recreation includes multiple activities such as biking, camping, climbing, fishing, hiking, horseback riding, hunting, shooting, skiing and wildlife watching. The Outdoor Foundation estimates that in the United States, there are nearly 30 million hikers, just under 7 million mountain bikers, over 14 million hunters, and nearly 23 million wildlife watchers (Outdoor Foundation 2018). While difficult to quantify, it is a reasonable assumption that many individual outdoor enthusiasts actively participate in more than one of these activities. Thus, the economies of Colorado, its counties, and its communities, rely on managing the landscape for a multitude of outdoor recreational opportunities. However, there is also evidence that human activities have an impact on wildlife. While trail-based recreation has the potential to impact many species, recent concerns in Colorado have focused on elk (Cervus canadensis; Durango Herald 2018, Steamboat Pilot and Today 2018, Vail Daily 2018). The sensitivity of elk to human presence and human activity has been a topic of interest for many decades. Preliminary research, focused on the effects of logging and vehicle use along road networks, provided consistent and clear evidence that elk-use declined in areas with high road densities, and as road use increased (Lyon and Christensen 2002). Similarly, research in Colorado evaluating the repeated displacement and disturbance of elk by people on foot provided evidence of suppressed recruitment rates following human disturbance (Phillips and Alldredge 2000, Shively et al. 2005). Experimental evaluation of the impact of hunter presence on elk movements and elk distribution has also occurred in Colorado (Conner et al. 2001, Vieira et al. 2003). This research demonstrated that the presence of hunters shifted elk off public lands and onto neighboring private lands. More recently, recreational trail use (all-terrain vehicle (ATV) riding, hiking, biking, and horseback riding) impacts on elk use of areas with trails was experimentally evaluated in Oregon. Wisdom et al. (2018) found that elk avoided areas with trails when recreationists of any type were present. Thus, regardless of human activity, behavioral displacement of elk by humans is well documented. In Colorado, increasing public concerns over human recreational use have coincided with declines in elk productivity, but a direct relationship to this activity in Colorado remains unaddressed. During FY 2016–2017, Colorado Parks and Wildlife (CPW) initiated a large-scale pilot study designed to evaluate pregnancy rates, elk calf survival, and causes of elk calf mortality (Alldredge 2016). At the onset, it was recognized that many factors contribute to suppression of pregnancy rates and calf survival. In addition to hunting, deteriorating habitat quality, habitat loss, and predation are key factors that may influence Colorado’s ungulate herds. Likewise, factors such as disease and competition may also play a role. Less clear, however, are the effects that human recreation may exert on the population dynamics of elk and other large ungulates. Past research has also reported individual behavioral responses of elk exposed to recreational stimuli. However, an alternate approach to studying behavioral displacement would shift the focus away from individual animals and link elk distribution to specific geographic areas. One limitation to studying individual animals is that the presence or absence of unmarked animals within the study area is largely ignored. However, access management and land 3 �management planning decisions are intrinsically tied to geographic areas. Thus, knowledge about the presence, absence, and abundance of a species of interest is of great value to managers. STUDY AREAS This study is occurring in two study areas. The northern study area focuses on the Bear’s Ears elk herd between Craig and Steamboat Springs. Within the Bear’s Ears herd, the fine scale camera-based behavioral portion of this study is centered on the Routt County segment of the herd that uses the Elk River drainage near the community of Steamboat Springs. The Bear’s Ears study area will be sampled using 4 study units: Mad Creek, Buffalo Pass, Walton Rim, and Hwy 40/Ferndale. The northernmost Mad Creek study unit has few existing trails but has been identified as a potential site for future trail development. Immediately south of the Mad Creek study unit is the Buffalo Pass study unit. Extensive trail development in this study area occurred during the past 5-10 years and it is currently an important and key area for many trail-based recreational activities. Further south is the Walton Rim study unit. Bounded to the north by the Steamboat Springs ski area, Walton Rim currently has little or no recreational trail use and plans for future trail development in this unit currently do not exist. Finally, immediately south of Walton Rim falls the Hwy 40/Ferndale study unit. Currently the Hwy 40/Ferndale study unit has nominal trail development and use, but plans for future trail construction in this unit are being considered. With the exception of Walton Rim, all of the camera-based study units in the Bear’s Ears study area have the potential to experience extensive trail development and use. The southern study area is focused on the Avalanche Creek elk herd along the Roaring Fork River between Glenwood Springs and Aspen. Four camera-based study units in the Avalanche Creek study area have also been identified. The southernmost of these units is the Snowmass unit. This unit, managed by Pitkin County and White River National Forest, has existing trails but is managed with seasonal closures (low elevation trail closures in place until May 16th, and high elevation trail closures in place until June 21st) to protect elk wintering and calving areas. Near the Snowmass study unit (and also in the southern portion of this study area) is the Wildcat study unit. The Wildcat study unit is centered on private property and has nominal recreational trail use, allowing it to serve as a reference area. Further north and nearer the community of Carbondale are 2 additional study units. The eastern most of these additional 2 units is The Crown, which is managed by the Bureau of Land Management and has extensive recreational use, but also has winter closures for mechanized and motorized recreation. Immediately to the south and west of The Crown is the fourth study unit. This unit, Two Shoes Ranch, is privately managed and has little recreational use and minimal trail development. METHODS Camera Sampling — Recent development of non-invasive abundance estimation techniques provide opportunities to quantify species in finite areas over relatively short periods. Camera based Space-To-Event (STE) and Instantaneous Sampling (IS) methods provide tools to estimate abundance without expensive flight time (Moeller et al. 2018). An inherent property of these new techniques is that the scope of inference applies to geographic areas and not individual animals. We deployed remote field cameras (HP2X, Reconyx, Holmen, Wisconsin, USA) to estimate elk abundance and density within small geographic areas (<65 km2) and during short time frames (~3–4 weeks). We deployed cameras across 8 study units (4 within the Bear’s Ears 4 �study area and 4 within the Avalanche Creek study area). We designed grids composed of 1.6 km cells to overlay each study unit. Within each cell, we used generalized random-tessellation stratification (GRTS) sampling (Stevens and Olsen 2004, Kincaid and Olsen 2017) to select 2 coarse camera locations. We selected final camera site locations (<250 m of the randomly selected coarse locations) in the field with the specific objective of maximizing detection probability of elk. We deployed cameras during the spring and early summer seasons. We programmed the cameras to take pictures at 10-minute intervals throughout the day. Elk capture and handling — We captured adult female elk ≥2 years of age by helicopter netgunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. We outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 h. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. In December, we captured 6-month old elk calves by helicopter net-gunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2021, we collared 25 6-month old elk calves from the Avalanche Creek herd. The mean weight of calves was 116.5 kg (95% CI: 111.3-121.7 kg). During March 2022, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate of adult female elk was 95% (85-99%). Elk populations experiencing good to excellent 5 �summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 7.9%. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summerautumn or winter nutrition (R. Cook, personal communication). During the summer of 2021, approximately 4.1 million photos were taken by the 238 cameras deployed across 8 study units. These photos are actively being archived. Automated photo recognition software continues to be developed and will be applied to these photos to expedite future analyses. However, subsampling of photos (and longer time intervals) will occur prior to analysis. SUMMARY During July 2021 – June 2022 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the third year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 40 pregnant females with GPS collars and VITs. We successfully captured and collared 53 newborn elk and 25 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. We will continue to collect data on elk survival and cause-specific sources of mortality throughout the year. Field cameras were also successfully deployed. LITERATURE CITED Alldredge, M. 2016. Pilot study – elk recruitment and habitat use in Colorado. Program Narrative Study Plan, Colorado Parks and Wildlife, Fort Collins, CO, USA. Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Conner, M.M., G.C. White, and D.J. Freddy. 2001. Elk movement in response to early-season hunting in northwest Colorado. Journal of Wildlife Management 65:926–940. Cook, J. G., B. K. Johnson, R. C. Cook, R. A. Riggs, T. Delcurto, L. D. Bryant, and L. L. Irwin. 2004. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildlife Monographs 155:1–61. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Myers, S. M. Mccorquodale, D. J. Vales, L. L. Irwin, P. B. Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, and P. J. Miller. 2010. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880–896. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. McCorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, B. L. Murphie, K. A. Schoenecker, F. Geyer, P. B. Hall, R. D. Spencer, D. A. Immell, D. H. Jackson, B. L. Tiller, P. J. Miller, and L. Schmitz. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1–44. Durango Herald. 2018. Where have all the elk gone? Published 15 November 2018, accessed 16 November 2018 (https://durangoherald.com/articles/250613-where-have-all-the-elkgone). Eacker, D. R. 2015. Linking the effects of risk factors on annual calf survival to elk population 6 �dynamics in the Bitterroot Valley, Montana. M.S. thesis, University of Montana, Missoula, MT, USA. Eacker, D. R., M. Hebblewhite, K. M. Proffitt, B. S. Jimenez, M. S. Mitchell, and H. S. Robinson. 2016. Annual elk calf survival in a multiple carnivore system. Journal of Wildlife Management 80:1345–1359. Johnson, D. E. 1951. Biology of the elk calf, Cervus canadensis nelsoni. Journal of Wildlife Management 15:396–410. Kincaid, T.M., and A.R. Olsen. 2017. Spsurvey: spatial survey design and analysis. R package version 3.4. https://CRAN.R-roject.org/package=spsurvey. Lyon, L.J., and A.G. Christensen. 2002. Elk and land management in D.E. Toweill and J.W. Thomas, eds., North American Elk: ecology and management. Smithsonian Institute Press, Washington D.C., USA. Moeller, A.K., P.M. Lukacs, and J.S. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9:e02331. Phillips, G.E. and A.W. Alldredge. 2000. Reproductive success of elk following disturbance by humans during calving season. Journal of Wildlife Management 64:521–530. Shively, K.J., A.W. Alldredge, and G.E. Phillips. 2005. Elk reproductive response to removal of calving season disturbance by humans. Journal of Wildlife Management 69:1073–1080. State of Colorado, Colorado Statewide Comprehensive Outdoor Recreation Plan. 2019. https://cpw.state.co.us/Documents/Trails/SCORP/Final-Plan/2019-SCORP-Report.pdf Accessed 23 January 2019. Steamboat Pilot and Today. 2018. Newly formed group advocates to slow trail building in Routt National Forest to protect wildlife. Published 21 October 2018, accessed 16 November 2018 (https://www.steamboatpilot.com/news/newly-formed-group-advocates-toslow-trail-building-in-routt-national-forest-to-protect-wildlife/). Stevens, D.L., and A.R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262–278. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Outdoor Foundation. 2018. Outdoor Participation Report. https://outdoorindustry.org/resource/2018-outdoor-participation-report/. Accessed 26 September 2018. Vail Daily. 2018. Eagle County officials concerned by wildlife population declines. Published 14 September 2018, accessed 16 November 2018 (https://www.vaildaily.com/news/eaglecounty-officials-concerned-by-wildlife-population-declines/). Vieira, M.E.P., M.M. Conner, G.C. White, and D.J. Freddy. 2003. Effects of archery hunter numbers and opening dates on elk movement. Journal of Wildlife Management 67:717– 728. Wisdom, M.J., H.K. Preisler, L.M. Naylor, R.G. Anthony, B.K. Johnson, and M.M. Rowland, 2018. Elk responses to trail-based recreation on public forests. Forest Ecology and Management 411:223–233. Prepared by Eric J. Bergman, Wildlife Researcher 7 �8 �Colorado Parks and Wildlife July 2022 – June 2023 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 2 : : : : Federal Aid Project No. W-242-R-7 : Parks and Wildlife Mammals Research Elk Conservation Response of Elk to Human Recreation at Multiple Scales: demographic shifts and behaviorally mediated fluctuations in local abundance Period Covered: July 1, 2022 – June 30, 2023 Authors: E.J. Bergman and N.D. Rayl All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT During the reporting period we focused on working with stakeholders and collaborators on research logistics, deploying field cameras, and capturing and collaring elk. Field efforts were centered on 3 objectives: 1) deploying/retrieving field cameras (i.e., cameras capable of timelapse and motion activation to estimate elk abundance), 2) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 3) capturing and collaring newborn and 6-month old elk calves to collect data on calf survival and cause-specific mortality. During spring (FY 21-22) and early summer (FY 22-23) of 2021 we deployed 238 cameras across 8 study units. Concurrently, data from the summer of 2022 data cards were retrieved. Downloading and cataloging of photos collected during the summer of 2022 is ongoing, with an expected total of 3.5–4.0 million photos collected. During FY21-22, a research contract with Colorado State University was developed, with the intent of having a postdoctoral researcher develop an artificial intelligence processing and work flow system for analyzing all unclassified photos. Similarly, a collaborative relationship with the USFS Rocky Mountain Research Station was developed, for the purpose of using cell phone data to estimate human activity in study areas associated with this project. We radio-collared 25 6-month-old elk calves in December 2022. In March 2023, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the 1 �pregnancy rate was 93% and the mean ingesta-free body fat of adult females was 8.1%. During the 2023 calving season, we radio-collared 60 elk calves. 2 �WILDLIFE RESEARCH REPORT RESPONSE OF ELK TO HUMAN RECREATION AT MULTIPOLE SCALES: DEMOGRAPHIC SHIFTS AND BEHAVIORALLY MEDIATED FLUCTUATIONS IN LOCAL ABUNDANCE ERIC J. BERGMAN AND NATHANIEL D. RAYL PROJECT NARRATIVE OBJECTIVES This project has objectives on 2 scales. At the broad, elk herd-level scale, we will estimate pregnancy rates, calf survival rates, and cause-specific mortality rates to evaluate the importance of mortality sources for elk calf survival. More specifically, we will evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions), abiotic (previous and current weather conditions), and human-induced factors (i.e., relative exposure to recreational activities) on seasonal mortality risk of elk calves from birth to age 1 and on pregnancy rates of mature female elk. At the narrower geographic and temporal scale, we will use short-term (~3-4 weeks) changes in elk abundance within small study units (<65 km2) as a tool to evaluate the influence of human recreation on elk distribution. At this narrower scale, the primary objective is to evaluate the role that human recreation (e.g., hiking, mountain biking, horseback riding, trail running, hunting, etc.) has on the behavioral distribution of elk on spring calving, summer, and fall transition ranges. Coupled to the objective of detecting behaviorally influenced changes in abundance and density, we will evaluate the effectiveness of current recreational closures maintained by ski areas, counties, and federal land management agencies. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 8 and 10, and private landowners on field research logistics. 2. Deploy field cameras (i.e., cameras capable of time-lapse) to estimate elk abundance. 3. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 4. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. INTRODUCTION The role of outdoor recreation within the state of Colorado is difficult to overstate. According to Colorado's Statewide Comprehensive Outdoor Recreation Plan (SCORP), outdoor recreation contributes 511,000 jobs, $62.5 billion in economic output, and $9.4 billion in local, state, and federal tax revenue (State of Colorado 2019). Outdoor recreation includes multiple activities such as biking, camping, climbing, fishing, hiking, horseback riding, hunting, shooting, 3 �skiing and wildlife watching. The Outdoor Foundation (2018) estimates that in the United States, there are nearly 30 million hikers, just under 7 million mountain bikers, over 14 million hunters, and nearly 23 million wildlife watchers. While difficult to quantify, it is a reasonable assumption that many individual outdoor enthusiasts actively participate in more than one of these activities. Thus, the economies of Colorado, its counties, and its communities, rely on managing the landscape for a multitude of outdoor recreational opportunities. However, there is also evidence that human activities have an impact on wildlife. While trail-based recreation has the potential to impact many species, recent concerns in Colorado have focused on elk (Cervus canadensis; Durango Herald 2018, Steamboat Pilot and Today 2018, Vail Daily 2018). The sensitivity of elk to human presence and human activity has been a topic of interest for many decades. Preliminary research, focused on the effects of logging and vehicle use along road networks, provided consistent and clear evidence that elk-use declined in areas with high road densities, and as road use increased (Lyon and Christensen 2002). Similarly, research in Colorado evaluating the repeated displacement and disturbance of elk by people on foot provided evidence of suppressed recruitment rates following human disturbance (Phillips and Alldredge 2000, Shively et al. 2005). Experimental evaluation of the impact of hunter presence on elk movements and elk distribution has also occurred in Colorado (Conner et al. 2001, Vieira et al. 2003). This research demonstrated that the presence of hunters shifted elk off public lands and onto neighboring private lands. More recently, recreational trail use (all-terrain vehicle (ATV) riding, hiking, biking, and horseback riding) impacts on elk use of areas with trails was experimentally evaluated in Oregon. Wisdom et al. (2018) found that elk avoided areas with trails when recreationists of any type were present. Thus, regardless of human activity, behavioral displacement of elk by humans is well documented. In Colorado, increasing public concerns over human recreational use have coincided with declines in elk productivity, but a direct relationship to this activity in Colorado remains unaddressed. During FY 2016–2017, Colorado Parks and Wildlife (CPW) initiated a large-scale pilot study designed to evaluate pregnancy rates, elk calf survival, and causes of elk calf mortality (Alldredge 2016). At the onset, it was recognized that many factors contribute to suppression of pregnancy rates and calf survival. In addition to hunting, deteriorating habitat quality, habitat loss, and predation are key factors that may influence Colorado’s ungulate herds. Likewise, factors such as disease and competition may also play a role. Less clear, however, are the effects that human recreation may exert on the population dynamics of elk and other large ungulates. Past research has also reported individual behavioral responses of elk exposed to recreational stimuli. However, an alternate approach to studying behavioral displacement would shift the focus away from individual animals and link elk distribution to specific geographic areas. One limitation to studying individual animals is that the presence or absence of unmarked animals within the study area is largely ignored. However, access management and land management planning decisions are intrinsically tied to geographic areas. Thus, knowledge about the presence, absence, and abundance of a species of interest is of great value to managers. STUDY AREAS This study is occurring in two study areas. The northern study area focuses on the Bear’s Ears elk herd between Craig and Steamboat Springs. Within the Bear’s Ears herd, the fine scale camera-based behavioral portion of this study is centered on the Routt County segment of the herd that uses the Elk River drainage near the community of Steamboat Springs. The Bear’s Ears 4 �study area will be sampled using 4 study units: Mad Creek, Buffalo Pass, Walton Rim, and Hwy 40/Ferndale. The northernmost Mad Creek study unit has few existing trails but has been identified as a potential site for future trail development. Immediately south of the Mad Creek study unit is the Buffalo Pass study unit. Extensive trail development in this study area occurred during the past 5-10 years and it is currently an important and key area for many trail-based recreational activities. Further south is the Walton Rim study unit. Bounded to the north by the Steamboat Springs ski area, Walton Rim currently has little or no recreational trail use and plans for future trail development in this unit currently do not exist. Finally, immediately south of Walton Rim falls the Hwy 40/Ferndale study unit. Currently the Hwy 40/Ferndale study unit has nominal trail development and use, but plans for future trail construction in this unit are being considered. With the exception of Walton Rim, all of the camera-based study units in the Bear’s Ears study area have the potential to experience extensive trail development and use. The southern study area is focused on the Avalanche Creek elk herd along the Roaring Fork River between Glenwood Springs and Aspen. Four camera-based study units in the Avalanche Creek study area have also been identified. The southernmost of these units is the Snowmass unit. This unit, managed by Pitkin County and White River National Forest, has existing trails but is managed with seasonal closures (low elevation trail closures in place until May 16th, and high elevation trail closures in place until June 21st) to protect elk wintering and calving areas. Near the Snowmass study unit (and also in the southern portion of this study area) is the Wildcat study unit. The Wildcat study unit is centered on private property and has nominal recreational trail use, allowing it to serve as a reference area. Further north and nearer the community of Carbondale are 2 additional study units. The eastern most of these additional 2 units is The Crown, which is managed by the Bureau of Land Management and has extensive recreational use, but also has winter closures for mechanized and motorized recreation. Immediately to the south and west of The Crown is the fourth study unit. This unit, Two Shoes Ranch, is privately managed and has little recreational use and minimal trail development. METHODS Camera Sampling — Recent development of non-invasive abundance estimation techniques provide opportunities to quantify species in finite areas over relatively short periods. Camera based Space-To-Event (STE) and Instantaneous Sampling (IS) methods provide tools to estimate abundance without expensive flight time (Moeller et al. 2018). An inherent property of these new techniques is that the scope of inference applies to geographic areas and not individual animals. We deployed remote field cameras (HP2X, Reconyx, Holmen, Wisconsin, USA) to estimate elk abundance and density within small geographic areas (<65 km2) and during short time frames (~3–4 weeks). We deployed cameras across 8 study units (4 within the Bear’s Ears study area and 4 within the Avalanche Creek study area). We designed grids composed of 1.6 km cells to overlay each study unit. Within each cell, we used generalized random-tessellation stratification (GRTS) sampling (Stevens and Olsen 2004, Kincaid and Olsen 2017) to select 2 coarse camera locations. We selected final camera site locations (<250 m of the randomly selected coarse locations) in the field with the specific objective of maximizing detection probability of elk. We deployed cameras during the spring and early summer seasons. We programmed the cameras to take pictures at 10-minute intervals throughout the day. 5 �Elk capture and handling — We captured adult female elk ≥2 years of age by helicopter netgunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns. We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. We outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 hrs. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. In December, we captured 6-month old elk calves by helicopter net-gunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2022, we collared 25 6-month old elk calves from the Avalanche Creek herd. The mean weight of calves was 109.9 kg (95% CI: 102.9-117.0 kg). During March 2023, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate of adult female elk was 93% (82-98%). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 7.9%. When late-winter IFBF values are <8-9% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summerautumn or winter nutrition (R. Cook, personal communication). During May-July 2023, we captured and collared 60 elk calves from the Avalanche Creek herd. During the summer of 2022, between 3.5–4.0 million photos were taken by the 238 cameras deployed across 8 study units. These photos are actively being archived. Automated 6 �photo recognition software is being developed, in collaboration with Colorado State University. Once this process is developed, it will be applied to these photos to expedite future analyses. Late during FY21–22, a collaborative effort with researchers from the USFS Rocky Mountain Research Station was initiated, to quantify human activity in our study areas. This activity, inferred from cell phone location data, will provide an index of human recreation. As part of this collaboration, efforts to disentangle different types of human activity from repeated location data will be made. SUMMARY From July 1, 2022 – June 30, 2023 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the fifth year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 40 pregnant females with GPS collars and VITs. We successfully captured and collared 60 newborn elk and 25 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. Field cameras were also successfully deployed. LITERATURE CITED Alldredge, M. 2016. Pilot study – elk recruitment and habitat use in Colorado. Program Narrative Study Plan, Colorado Parks and Wildlife, Fort Collins, CO, USA. Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Conner, M.M., G.C. White, and D.J. Freddy. 2001. Elk movement in response to early-season hunting in northwest Colorado. Journal of Wildlife Management 65:926–940. Cook, J. G., B. K. Johnson, R. C. Cook, R. A. Riggs, T. Delcurto, L. D. Bryant, and L. L. Irwin. 2004. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildlife Monographs 155:1–61. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Myers, S. M. Mccorquodale, D. J. Vales, L. L. Irwin, P. B. Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, and P. J. Miller. 2010. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880–896. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. McCorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, B. L. Murphie, K. A. Schoenecker, F. Geyer, P. B. Hall, R. D. Spencer, D. A. Immell, D. H. Jackson, B. L. Tiller, P. J. Miller, and L. Schmitz. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1–44. Durango Herald. 2018. Where have all the elk gone? Published 15 November 2018, accessed 16 November 2018 (https://durangoherald.com/articles/250613-where-have-all-the-elkgone). Eacker, D. R. 2015. Linking the effects of risk factors on annual calf survival to elk population dynamics in the Bitterroot Valley, Montana. M.S. thesis, University of Montana, Missoula, MT, USA. Eacker, D. R., M. Hebblewhite, K. M. Proffitt, B. S. Jimenez, M. S. Mitchell, and H. S. Robinson. 2016. Annual elk calf survival in a multiple carnivore system. Journal of Wildlife 7 �Management 80:1345–1359. Johnson, D. E. 1951. Biology of the elk calf, Cervus canadensis nelsoni. Journal of Wildlife Management 15:396–410. Kincaid, T.M., and A.R. Olsen. 2017. Spsurvey: spatial survey design and analysis. R package version 3.4. https://CRAN.R-roject.org/package=spsurvey. Lyon, L.J., and A.G. Christensen. 2002. Elk and land management in D.E. Toweill and J.W. Thomas, eds., North American Elk: ecology and management. Smithsonian Institute Press, Washington D.C., USA. Moeller, A.K., P.M. Lukacs, and J.S. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9:e02331. Phillips, G.E. and A.W. Alldredge. 2000. Reproductive success of elk following disturbance by humans during calving season. Journal of Wildlife Management 64:521–530. Shively, K.J., A.W. Alldredge, and G.E. Phillips. 2005. Elk reproductive response to removal of calving season disturbance by humans. Journal of Wildlife Management 69:1073–1080. State of Colorado, Colorado Statewide Comprehensive Outdoor Recreation Plan. 2019. https://cpw.state.co.us/Documents/Trails/SCORP/Final-Plan/2019-SCORP-Report.pdf Accessed 23 January 2019. Steamboat Pilot and Today. 2018. Newly formed group advocates to slow trail building in Routt National Forest to protect wildlife. Published 21 October 2018, accessed 16 November 2018 (https://www.steamboatpilot.com/news/newly-formed-group-advocates-toslow-trail-building-in-routt-national-forest-to-protect-wildlife/). Stevens, D.L., and A.R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262–278. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Outdoor Foundation. 2018. Outdoor Participation Report. https://outdoorindustry.org/resource/2018-outdoor-participation-report/. Accessed 26 September 2018. Vail Daily. 2018. Eagle County officials concerned by wildlife population declines. Published 14 September 2018, accessed 16 November 2018 (https://www.vaildaily.com/news/eaglecounty-officials-concerned-by-wildlife-population-declines/). Vieira, M.E.P., M.M. Conner, G.C. White, and D.J. Freddy. 2003. Effects of archery hunter numbers and opening dates on elk movement. Journal of Wildlife Management 67:717– 728. Wisdom, M.J., H.K. Preisler, L.M. Naylor, R.G. Anthony, B.K. Johnson, and M.M. Rowland, 2018. Elk responses to trail-based recreation on public forests. Forest Ecology and Management 411:223–233. Prepared by Eric J. Bergman, Wildlife Researcher 8 �Colorado Parks and Wildlife July 2023 – June 2024 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Colorado 3430 3002 2 : : : : Federal Aid Project No. W-242-R-8 : Parks and Wildlife Mammals Research Elk Conservation Response of Elk to Human Recreation at Multiple Scales: demographic shifts and behaviorally mediated fluctuations in local abundance Period Covered: July 1, 2023 – June 30, 2024 Authors: E.J. Bergman and N.D. Rayl All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT During the reporting period we focused on working with stakeholders and collaborators on research logistics, deploying field cameras, and capturing and collaring elk. Field efforts were centered on 3 objectives: 1) deploying/retrieving field cameras (i.e., cameras capable of timelapse and motion activation to estimate elk abundance), 2) capturing adult female elk, and collaring and outfitting pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior, and 3) capturing and collaring newborn and 6-month old elk calves to collect data on calf survival and cause-specific mortality. During spring (FY 23-24) and early summer (FY 24-25) of 2024 we deployed 238 cameras across 8 study units. Concurrently, data from the summer of 2023 data cards were retrieved. Downloading and cataloging of photos collected during the summer of 2023 occurred during late summer 2024. During FY 23-24, all photos collected during this study were archived on a DNR server. A CPW funded postdoctoral researcher at Colorado State University identified a work flow system for analyzing all unclassified photos. All photos from 2019 and 2020 were processed accordingly. We radio-collared 25 6-month-old elk calves in December 2023. In March 2024, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate was 90% and the mean ingesta-free body fat of adult females was 8.3%. During the 2024 calving season, we radio-collared 55 elk calves. 1 �WILDLIFE RESEARCH REPORT RESPONSE OF ELK TO HUMAN RECREATION AT MULTIPOLE SCALES: DEMOGRAPHIC SHIFTS AND BEHAVIORALLY MEDIATED FLUCTUATIONS IN LOCAL ABUNDANCE ERIC J. BERGMAN AND NATHANIEL D. RAYL PROJECT NARRATIVE OBJECTIVES This project has objectives on 2 scales. At the broad, elk herd-level scale, we will estimate pregnancy rates, calf survival rates, and cause-specific mortality rates to evaluate the importance of mortality sources for elk calf survival. More specifically, we will evaluate the influence of biotic (birth date, birth mass, gender, maternal body condition, habitat conditions), abiotic (previous and current weather conditions), and human-induced factors (i.e., relative exposure to recreational activities) on seasonal mortality risk of elk calves from birth to age 1 and on pregnancy rates of mature female elk. At the narrower geographic and temporal scale, we will use short-term (~3-4 weeks) changes in elk abundance within small study units (<65 km2) as a tool to evaluate the influence of human recreation on elk distribution. At this narrower scale, the primary objective is to evaluate the role that human recreation (e.g., hiking, mountain biking, horseback riding, trail running, hunting, etc.) has on the behavioral distribution of elk on spring calving, summer, and fall transition ranges. Coupled to the objective of detecting behaviorally influenced changes in abundance and density, we will evaluate the effectiveness of current recreational closures maintained by ski areas, counties, and federal land management agencies. SEGMENT OBJECTIVES 1. Work with personnel from CPW Areas 8 and 10, and private landowners on field research logistics. 2. Deploy field cameras (i.e., cameras capable of time-lapse) to estimate elk abundance. 3. Capture adult female elk, and collar and outfit pregnant females with vaginal implant transmitters (VITs) to collect data on elk demography, body condition, reproduction, and behavior. 4. Capture and collar newborn and 6-month old elk to collect data on calf survival and cause-specific sources of mortality. INTRODUCTION The role of outdoor recreation within the state of Colorado is difficult to overstate. According to Colorado's Statewide Comprehensive Outdoor Recreation Plan (SCORP), outdoor recreation contributes 511,000 jobs, $62.5 billion in economic output, and $9.4 billion in local, state, and federal tax revenue (State of Colorado 2019). Outdoor recreation includes multiple activities such as biking, camping, climbing, fishing, hiking, horseback riding, hunting, shooting, 2 �skiing and wildlife watching. The Outdoor Foundation (2018) estimates that in the United States, there are nearly 30 million hikers, just under 7 million mountain bikers, over 14 million hunters, and nearly 23 million wildlife watchers. While difficult to quantify, it is a reasonable assumption that many individual outdoor enthusiasts actively participate in more than one of these activities. Thus, the economies of Colorado, its counties, and its communities, rely on managing the landscape for a multitude of outdoor recreational opportunities. However, there is also evidence that human activities have an impact on wildlife. While trail-based recreation has the potential to impact many species, recent concerns in Colorado have focused on elk (Cervus canadensis; Durango Herald 2018, Steamboat Pilot and Today 2018, Vail Daily 2018). The sensitivity of elk to human presence and human activity has been a topic of interest for many decades. Preliminary research, focused on the effects of logging and vehicle use along road networks, provided consistent and clear evidence that elk-use declined in areas with high road densities, and as road use increased (Lyon and Christensen 2002). Similarly, research in Colorado evaluating the repeated displacement and disturbance of elk by people on foot provided evidence of suppressed recruitment rates following human disturbance (Phillips and Alldredge 2000, Shively et al. 2005). Experimental evaluation of the impact of hunter presence on elk movements and elk distribution has also occurred in Colorado (Conner et al. 2001, Vieira et al. 2003). This research demonstrated that the presence of hunters shifted elk off public lands and onto neighboring private lands. More recently, recreational trail use (all-terrain vehicle (ATV) riding, hiking, biking, and horseback riding) impacts on elk use of areas with trails was experimentally evaluated in Oregon (Wisdom et al. 2018), and reported that elk avoided areas with trails when recreationists of any type were present. Thus, regardless of human activity, behavioral displacement of elk by humans is well documented. In Colorado, increasing public concerns over human recreational use have coincided with declines in elk productivity, but a direct relationship to this activity in Colorado remains unaddressed. During FY 2016–2017, Colorado Parks and Wildlife (CPW) initiated a large-scale pilot study designed to evaluate pregnancy rates, elk calf survival, and causes of elk calf mortality (Alldredge 2016). At the onset, it was recognized that many factors contribute to suppression of pregnancy rates and calf survival. In addition to hunting, deteriorating habitat quality, habitat loss, and predation are key factors that may influence Colorado’s ungulate herds. Likewise, factors such as disease and competition may also play a role. Less clear, however, are the effects that human recreation may exert on the population dynamics of elk and other large ungulates. Past research has also reported individual behavioral responses of elk exposed to recreational stimuli (Phillips and Alldredge 2000, Shively et al. 2005, Wisdom et al. 2018). However, an alternate approach to studying behavioral displacement would shift the focus away from individual animals and link elk distribution to specific geographic areas. One limitation to studying individual animals is that the presence or absence of unmarked animals within the study area is largely ignored. However, access management and land management planning decisions are intrinsically tied to geographic areas. Thus, knowledge about the presence, absence, and abundance of a species of interest is of great value to managers. STUDY AREAS This study is occurring in two study areas. The northern study area focuses on the Bear’s Ears elk herd between Craig and Steamboat Springs. Within the Bear’s Ears herd, the fine scale camera-based behavioral portion of this study is centered on the Routt County segment of the 3 �herd that uses the Elk River drainage near the community of Steamboat Springs. The Bear’s Ears study area will be sampled using 4 study units: Mad Creek, Buffalo Pass, Walton Rim, and Hwy 40/Ferndale. The northernmost Mad Creek study unit has few existing trails but has been identified as a potential site for future trail development. Immediately south of the Mad Creek study unit is the Buffalo Pass study unit. Extensive trail development in this study area occurred during the past 5-10 years and it is currently an important and key area for many trail-based recreational activities. Further south is the Walton Rim study unit. Bounded to the north by the Steamboat Springs ski area, Walton Rim currently has little or no recreational trail use and plans for future trail development in this unit currently do not exist. Finally, immediately south of Walton Rim falls the Hwy 40/Ferndale study unit. Currently the Hwy 40/Ferndale study unit has nominal trail development and use, but plans for future trail construction in this unit are being considered. With the exception of Walton Rim, all of the camera-based study units in the Bear’s Ears study area have the potential to experience extensive trail development and use. The southern study area is focused on the Avalanche Creek elk herd along the Roaring Fork River between Glenwood Springs and Aspen. Four camera-based study units in the Avalanche Creek study area have also been identified. The southernmost of these units is the Snowmass unit. This unit, managed by Pitkin County and White River National Forest, has existing trails but is managed with seasonal closures (low elevation trail closures in place until May 16th, and high elevation trail closures in place until June 21st) to protect elk wintering and calving areas. Near the Snowmass study unit (and also in the southern portion of this study area) is the Wildcat study unit. The Wildcat study unit is centered on private property and has nominal recreational trail use, allowing it to serve as a reference area. Further north and nearer the community of Carbondale are 2 additional study units. The eastern most unit is The Crown, which is managed by the Bureau of Land Management and has extensive recreational use, but also has winter closures for mechanized and motorized recreation. Immediately to the south and west of The Crown is the Two Shoes Ranch, which is privately managed and has little recreational use and minimal trail development. METHODS Camera Sampling — Recent development of non-invasive abundance estimation techniques provide opportunities to quantify species in finite areas over relatively short periods. Camera based Space-To-Event (STE) and Instantaneous Sampling (IS) methods provide tools to estimate abundance without expensive flight time (Moeller et al. 2018). An inherent property of these new techniques is that the scope of inference applies to geographic areas and not individual animals. We deployed remote field cameras (HP2X, Reconyx, Holmen, Wisconsin, USA) to estimate elk abundance and density within small geographic areas (<65 km2) and during short time frames (~3–4 weeks). We deployed cameras across 8 study units (4 within the Bear’s Ears study area and 4 within the Avalanche Creek study area). We designed grids composed of 1.6 km cells to overlay each study unit. Within each cell, we used generalized random-tessellation stratification (GRTS) sampling (Stevens and Olsen 2004, Kincaid and Olsen 2017) to select 2 coarse camera locations. We selected final camera site locations (<250 m of the randomly selected coarse locations) in the field with the specific objective of maximizing detection probability of elk. We deployed cameras during the spring and early summer seasons. We programmed the cameras to take pictures at 10-minute intervals throughout the day. 4 �Elk capture and handling — We captured adult female elk ≥2 years of age by helicopter netgunning during late winter (March). During capture, we marked individuals with ear tags, collected a blood sample, and measured hind foot length, chest girth, and age based on tooth eruption and wear patterns (Quimby and Gaab 1957). We used a portable ultrasound machine to assess whether or not captured elk were pregnant, and estimated the percent of ingesta-free body fat (IFBF) following methods detailed in Cook et al. (2010). We verified non-pregnancies using pregnancy-specific protein B (PSPB) analysis of sampled blood. We outfitted pregnant elk with vaginal implant transmitters (VITs) and Global Positioning System (GPS) radio-collars that attempt to acquire a location every 2 hrs. We deployed VITs that use the satellite communication capabilities of the collar on the adult female to send a notification when the VIT is expelled, signifying a birth. In December, we captured 6-month old elk calves by helicopter net-gunning. During capture, we measured body mass, hind foot length, chest girth, and determined the gender of captured calves. We outfitted calves with expandable GPS radio-collars that were scheduled to drop off after 6 months. During all captures, we followed CPW’s animal care and use guidelines for capturing and handling elk (CPW ACUC #09-2008). Cause-specific mortality — Within 24 hours of detecting a mortality signal from an elk collar, we attempted to conduct a systematic field investigation to determine the cause of death. We searched the area surrounding kill sites for evidence of predator presence, including predator scats, tracks, and hair, or signs of a struggle (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). We examined elk carcasses for evidence of canine puncture wounds, subcutaneous hemorrhaging and bruising, aspirated blood in the mouth, nose, or trachea, claw or bite marks on the hide, cracked or chewed bones, and characteristic consumption patterns (Barber-Meyer et al. 2008, Eacker et al. 2016, Stonehouse et al. 2016). Nutritional condition of adult female elk — The body fat of lactating and non-lactating adult female elk can vary substantially, as lactating females are more sensitive than non-lactating females to their nutritional environment (Cook et al. 2004a, 2013). Therefore, it is difficult to interpret the body condition of adult female elk in late winter without knowing whether or not they experienced the energetic demands of lactation throughout the previous growing season (Cook et al. 2004a, 2013). RESULTS AND DISCUSSION In December 2023, we collared 25 6-month old elk calves from the Avalanche Creek herd. The mean weight of calves was 108.8 kg (95% CI: 101.0-116.6 kg). During March 2024, we radio-collared 40 pregnant elk and outfitted them with VITs. We estimated the pregnancy rate of adult female elk was 90% (95% CI: 78-96%). Elk populations experiencing good to excellent summer-autumn nutrition typically have pregnancy rates ≥90% (Cook et al. 2013). We estimated the mean IFBF of adult female elk to be 8.3%. When late-winter IFBF values are <89% for adult female elk that have lactated through the previous growing season, this suggests that there may be nutritional limitations, but it does not identify whether limitations are a result of summer-autumn or winter nutrition (R. Cook, personal communication). During May-July 2024, we captured and collared 55 elk calves from the Avalanche Creek herd. 5 �During the summer of 2023, approximately 4.0 million photos were taken by the 238 cameras deployed across 8 study units. These photos were uploaded into appropriate databases and are actively being archived on a DNR server. Automated photo recognition processes were applied to photos collected during 2019 and 2020. These processes reduce the number of photos requiring human evaluation by ~90%. It is expected that during FY 24-25 human evaluation of photos collected from 2019-2023 will be complete. SUMMARY From July 1, 2023 – June 30, 2024 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the fifth year of this study. We collected data on body condition and reproduction by capturing adult female elk, and we outfitted 40 pregnant females with GPS collars and VITs. We successfully captured and collared 55 newborn elk and 25 6-month old elk calves, meeting our sample size objectives, and allowing us to collect data on calf survival and cause-specific sources of mortality. Field cameras were also successfully maintained for the final summer of data collection. LITERATURE CITED Alldredge, M. 2016. Pilot study – elk recruitment and habitat use in Colorado. Program Narrative Study Plan, Colorado Parks and Wildlife, Fort Collins, CO, USA. Barber-Meyer, S. M., L. D. Mech, and P. J. White. 2008. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildlife Monographs 169:1–30. Conner, M.M., G.C. White, and D.J. Freddy. 2001. Elk movement in response to early-season hunting in northwest Colorado. Journal of Wildlife Management 65:926–940. Cook, J. G., B. K. Johnson, R. C. Cook, R. A. Riggs, T. Delcurto, L. D. Bryant, and L. L. Irwin. 2004. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildlife Monographs 155:1–61. Cook, R. C., J. G. Cook, T. R. Stephenson, W. L. Myers, S. M. Mccorquodale, D. J. Vales, L. L. Irwin, P. B. Hall, R. D. Spencer, S. L. Murphie, K. A. Schoenecker, and P. J. Miller. 2010. Revisions of rump fat and body scoring indices for deer, elk, and moose. Journal of Wildlife Management 74:880–896. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. McCorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, B. L. Murphie, K. A. Schoenecker, F. Geyer, P. B. Hall, R. D. Spencer, D. A. Immell, D. H. Jackson, B. L. Tiller, P. J. Miller, and L. Schmitz. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1–44. Durango Herald. 2018. Where have all the elk gone? Published 15 November 2018, accessed 16 November 2018 (https://durangoherald.com/articles/250613-where-have-all-the-elkgone). Eacker, D. R. 2015. Linking the effects of risk factors on annual calf survival to elk population dynamics in the Bitterroot Valley, Montana. M.S. thesis, University of Montana, Missoula, MT, USA. Eacker, D. R., M. Hebblewhite, K. M. Proffitt, B. S. Jimenez, M. S. Mitchell, and H. S. Robinson. 2016. Annual elk calf survival in a multiple carnivore system. Journal of Wildlife Management 80:1345–1359. 6 �Quimby, D.C., and J.E. Gaab. 1957. Mandibular dentition as an age indicator in Rocky Mountain elk. Journal of Wildlife Management 21:435-451. Johnson, D. E. 1951. Biology of the elk calf, Cervus canadensis nelsoni. Journal of Wildlife Management 15:396–410. Kincaid, T.M., and A.R. Olsen. 2017. Spsurvey: spatial survey design and analysis. R package version 3.4. https://CRAN.R-roject.org/package=spsurvey. Lyon, L.J., and A.G. Christensen. 2002. Elk and land management in D.E. Toweill and J.W. Thomas, eds., North American Elk: ecology and management. Smithsonian Institute Press, Washington D.C., USA. Moeller, A.K., P.M. Lukacs, and J.S. Horne. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9:e02331. Phillips, G.E. and A.W. Alldredge. 2000. Reproductive success of elk following disturbance by humans during calving season. Journal of Wildlife Management 64:521–530. Shively, K.J., A.W. Alldredge, and G.E. Phillips. 2005. Elk reproductive response to removal of calving season disturbance by humans. Journal of Wildlife Management 69:1073–1080. State of Colorado, Colorado Statewide Comprehensive Outdoor Recreation Plan. 2019. https://cpw.state.co.us/Documents/Trails/SCORP/Final-Plan/2019-SCORP-Report.pdf Accessed 23 January 2019. Steamboat Pilot and Today. 2018. Newly formed group advocates to slow trail building in Routt National Forest to protect wildlife. Published 21 October 2018, accessed 16 November 2018 (https://www.steamboatpilot.com/news/newly-formed-group-advocates-toslow-trail-building-in-routt-national-forest-to-protect-wildlife/). Stevens, D.L., and A.R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262–278. Stonehouse, K. F., C. R. J. Anderson, M. E. Peterson, and D. R. Collins. 2016. Approaches to field investigations of cause-specific mortality in mule deer (Odocoileus hemionus). Technical Publication Number 48, Colorado Parks and Wildlife, Fort Collins, CO, USA. Outdoor Foundation. 2018. Outdoor Participation Report. https://outdoorindustry.org/resource/2018-outdoor-participation-report/. Accessed 26 September 2018. Vail Daily. 2018. Eagle County officials concerned by wildlife population declines. Published 14 September 2018, accessed 16 November 2018 (https://www.vaildaily.com/news/eaglecounty-officials-concerned-by-wildlife-population-declines/). Vieira, M.E.P., M.M. Conner, G.C. White, and D.J. Freddy. 2003. Effects of archery hunter numbers and opening dates on elk movement. Journal of Wildlife Management 67:717– 728. Wisdom, M.J., H.K. Preisler, L.M. Naylor, R.G. Anthony, B.K. Johnson, and M.M. Rowland, 2018. Elk responses to trail-based recreation on public forests. Forest Ecology and Management 411:223–233. Prepared by Eric J. Bergman, Wildlife Researcher 7 � https://cpw.cvlcollections.org/files/original/9815eb0d52c429687679a2ba40a7f183.pdf cb86f6d1af74c21682ee29960f6f0c4d PDF Text Text Colorado Parks and Wildlife July 2022 – June 2023 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Colorado 3430 3003 Task No. 1 Federal Aid Project No. W-244-R-7 : Parks and Wildlife : Mammals Research : Predatory Mammals Conservation : Bobcat population density estimation: A pilot study : Period Covered: July 1, 2022 – June 30, 2023 Authors: S. Frank, J. Ivan, M. Vieira, and J. Runge All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Two bobcat study areas, one with low (Skull Creek) and another with high historical harvest (Piceance), were established in 2022. Camera trap grids were deployed in each 400 km2 study area, but only Piceance was complete and where the majority of capture efforts occurred, due to a severe winter season. Thirty-nine bobcat captures yielded 26 unique individual bobcats, 23 of which were collared (n = 19 in Piceance; n = 4 in Skull Creek). Population estimation using a mark-resight model was only possible in Piceance, where data were sufficient, and resulted in 34.62 bobcats (CV = 0.38, SE = 12.80) for the 400 km2 study area. Resights were regarded as low, potentially due to severe winter/reduced movement, suboptimal camera locations, and constrained trapping efforts (for marked individuals). Capture effort will be distributed and prioritized in hard-to-access winter areas prior to winter, more toward the center of the study area, and camera locations are being relocated to the best sites for resighting bobcats, as opposed to those areas best representing the habitat of a given grid cell. Altogether, these modifications will increase resights, provide more robust population estimates, and have a higher chance of estimating sex-specific densities that can be related to harvest statistics. �FINAL PERFORMANCE REPORT State: Colorado Project #: F22AF01995 Project Title: Predatory Mammals Conservation Period Covered: July 1, 2022 - June 30, 2023 (W-244-R-7) OBJECTIVES By June 30, 2023 1. Conduct mandatory checks on all bears and lions harvested in Colorado and maintain a database of all mandatory check data. Based on predicted license sales, historic participation and success rates, and on historic non-hunt mortality averages, we predict that about 1,900 black bear and about 600 mountain lion mortality records will be entered into our mortality database. 2. As part of the mandatory check process, collect tooth samples from harvested bears and lions to determine age and sex. This information will be used to provide insight into the age and sex structure of the population. Based on historic tooth collection rates, we predict that about 1,800 black bear teeth and about 550 mountain lion teeth will be processed for cementum age, and for black bear, female reproductive intervals determined. 3. Continue refinement of a human-bear / human-lion / human-wildlife conflict application that is accessible through multiple web based platforms (pc, tablet, smartphone). This is a multi-year project with initial roll-out of the low-code AppSheet version having occurred in April 2019 for bear/lion incidents and the fall of 2019 for bear/lion mortalities. The continuation of this project will involve both ongoing refinement and evaluation of the AppSheet application and continued scoping of the functional needs that agency staff will have for the database, structural planning, interaction with other agency platforms, and programming of a custom-designed application. Both the scoping of the custom application and ongoing evaluation of the low-code application will include assessing the flexibility of the platform to eventually include collection of other mandatory mortality data for other species (bobcats) and incident and conflict data beyond just bears and lions. 4. Evaluate relationships between bobcat density and sex/age composition with relative bobcat harvest (high versus low harvest). Results will be applied inform future bobcat harvest management. APPROACH Bear and Mountain Lion Mandatory Checks & Database Management Mandatory checks are performed by CPW personnel at all offices and in the field. Mandatory check information includes hunter and license information, as well as information about the �animal harvested (species, estimated age, sex, breeding status, harvest location, prior marks). As part of the mandatory check process, all bear and lion mortalities are marked with a numbered identification seal. The mandatory check process requires seals to be distributed to CPW personnel and information from the mandatory check forms to be entered into a central database, largely via the mortality application. This database is used to compile annual mortality estimates and to observe trends over time. A comprehensive training program has been initiated for CPW personnel that perform mandatory checks. Bear and Mountain Lion Tooth Analysis Tooth analysis can provide valuable information on the age and gender structure of bear and lion mortalities. Aging bears and lions based only on visual carcass inspection can have a high degree of error. Beginning in 2006, CPW began collecting premolar tooth samples, with the permission of hunters, as part of the bear and lion mandatory check process. However, a substantial percentage of hunters did not voluntarily allow a tooth to be extracted from their animal. In 2008, Wildlife Commission regulations required hunters to allow CPW to obtain a tooth sample. As a result, useable tooth samples are now collected from about 97% of hunterkilled bears and from 87% of hunter-killed lions (2018-2020 average). In addition to hunterharvested animals, tooth samples are collected from all bear and lion mortalities handled by CPW (e.g., vehicle mortalities, conflict mortalities) whenever possible. Tooth samples are submitted to a commercial laboratory for aging based on cementum annuli. In female bears it is also possible to use cementum deposition to identify years that females reared cubs. Because of the value of this information for management, CPW anticipates continuing bear and lion tooth collection and analysis as a routine part of the mandatory check process. Human-Wildlife Conflict Database and Recording Application Development CPW developed and deployed a low-code mobile application (AppSheets) to record information about the number, type, location, and date of human-bear and human-lion conflicts beginning in April 2019. This initial phase of use consisted of an evaluation to assess if this low-code application met the current needs for recording and tracking conflict incident information and if its use could be expanded to include bear/lion mandatory mortality check aspects described in Objective 1. During the fall of 2019, AppSheet was used statewide for bear/lion mandatory mortality data collection. Evaluation and integration of this data from this new source is currently underway. While AppSheet shows promise for most of the agency needs surrounding record-keeping of these data, an initial Request for Information and Request for Proposal scoping for development of a custom-designed application will continue. This application and database need to be secure because individual citizen’s and visitor’s identities will be contained within. The database will need geospatial and temporal update capabilities. The database will need to be accessible for data entry in real time or near-real time and from multiple platforms (PC, tablet, or smartphone devices). The database must also allow secure access for data analysis, plotting, and charting capabilities. Lastly the application and database will need to interact with other existing and future CPW software, applications and programs. Density and Sex/Age Composition Relative to Bobcat Harvest �We propose 2 400 km2 study areas of contiguous Colorado bobcat habitat that have different levels of bobcat harvest density (1 high harvest, 1 low harvest). Candidate study areas of high and low harvest will be identified through CPW harvest records. Within each of the 2 study areas, ~30 bobcats will be captured during September-November. Each animal will be uniquely marked with eartags and a GPS radiocollar. From early November (pre-harvest)–April (post-harvest), 80 remote trail cameras will be deployed per study area to provide pre- and post-hunt density estimates using marks (ear tags, GPS collars) to uniquely identify individual animals. Harvest rates on each study area will be estimated from the marked bobcats that are harvested and presented for mandatory inspection. During March, we will again capture bobcats to increase sample size and improve post-hunt density estimates. Spatially-explicit capture-recapture (SECR) models and spatial mark-resight estimators will be employed to estimate density, likely using both techniques in a “hybrid” fashion to improve precision. Given the cyclical nature of bobcat prey populations and the expected relationship with changes in bobcat density, we propose continuing this work over 5 years on each study area. The baseline sex/age composition of the pre-hunt population will be developed as bobcats are captured, and compared to sex/age composition data from harvested bobcats presented for mandatory inspection. We will evaluate how these changes in composition align with changes in density and how those compare to currently used predictors of change in population trend. GPS collars will provide habitat use, home range and survival estimates. This information will be useful to develop future population modeling approaches for bobcat management. Improving our understanding of bobcat populations in Colorado will provide us with a strong basis in supporting future management decisions and regulations. RESULTS 1. Bear and Mountain Lion Mandatory Checks & Database Management Mandatory checks and database management have been conducted as described in the foregoing section. The following table lists the number of checks conducted since this project began. The mortality database is available for managers to analyze gender, visual estimation of age, spatial and temporal mortality distribution, season, method of take for hunter harvest, and other non-hunt types of mortality. Year 2018-2019 2019-2020 2020-2021 2021-2022 2022-2023 Black Bear Mortality Mandatory Checks 1,620 1,792 2,210 2,003 2,171 Mountain Lion Mortality – Mandatory Checks 653 624 668 621 646 2. Bear and Mountain Lion Tooth Cementum Analysis Age and gender composition of hunter harvest and total mortality are used along with other �biological and social metrics to monitor the affect of hunting on population trajectory and human social parameters. Data is primarily analyzed at the DAU level to inform the management strategies in the 17 black bear DAUs and 8 mountain lion DAUs. In 2020, a new management plan for the West Slope of Colorado was developed which reduced the number of statewide lion DAUs down to 8. The number, type of samples, and basic age and gender data averages are displayed in the following tables. Year 2018-2019 2019-2020 2020-2021 2021-2022 2022-2023 Black Bear Tooth Sample Count 1,472 1,659 1,967 1,773 1,837 Mountain Lion Tooth Sample Count 531 594 585 587 581 Coarse scale analysis of black bear teeth show: the long-term age range of black bear in our mortality database is age class 0-30. Generally, black bears in non-hunt forms of mortality are equal, on average, to those in hunter harvest, but this is not annually consistent. Black Bear 2018-2019 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Female Average Age Primipatry Proportion in Harvest Proportion in Non-Hunt Mortality Male 4.6 4.6 0-27 62% 66% Female 6.4 5.4 0-30 4.6 38% 34% Black Bear 2019-2020 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Female Average Age Primipatry Proportion in Harvest Proportion in Non-Hunt Mortality Male 5.0 4.6 0-27 63% 74% Female 6.5 5.3 0-30 4.7 37% 26% Black Bear 2020-2021 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Female Average Age Primipatry Male 4.9 4.5 0-21 Female 6.4 6.1 0-26 4.5 �Proportion in Harvest Proportion in Non-Hunt Mortality 58% 70% 42% 30% Male 4.8 4.1 0-22 Female 6.1 5.5 0-29 4.8 36% 34% Black Bear 2021-2022 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Female Average Age Primipatry Proportion in Harvest Proportion in Non-Hunt Mortality Black Bear 2022-2023 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Female Average Age Primipatry Proportion in Harvest Proportion in Non-Hunt Mortality 64% 66% Male 4.6 4.4 0-24 58% 66% Female 6.2 6.6 0-26 4.7 42% 34% Coarse scale analysis of mountain lion teeth show: the long-term age range of lions in hunter harvest is age class 0-16 years. In non-hunt mortality the age range is age class 0-14 years. In contrast with bears, the age of lions in non-hunt mortality is generally younger than those in hunter harvest. Mountain Lion 2018-2019 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Proportion in Harvest Proportion in Non-Hunt Mortality Male 2.9 2.2 0-10 60% 51% Female 3.0 3.1 0-14 40% 49% Male 2.8 2.1 0-9 61% 43% Female 2.7 2.2 0-7 39% 57% Male 2.7 1.4 0-9 Female 2.7 2.1 0-10 Mountain Lion 2019-2020 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Proportion in Harvest Proportion in Non-Hunt Mortality Mountain Lion 2020-2021 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range �Proportion in Harvest Proportion in Non-Hunt Mortality 61% 48% 39% 52% Male 2.1 1.7 0-7 59% 47% Female 2.4 2.7 0-9 41% 53% Male 2.4 1.9 0-7 59% 45% Female 2.6 1.9 0-10 41% 55% Mountain Lion 2021-2022 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Proportion in Harvest Proportion in Non-Hunt Mortality Mountain Lion 2022-2023 Hunter Harvest Average Age Non-Hunt Mortality Average Age Age Range Proportion in Harvest Proportion in Non-Hunt Mortality 3. Human-Wildlife Conflict Database Development A low-code application platform was selected, purchased and developed by CPW staff for recording lion and bear incidents (sighting and conflicts). This mobile application was distributed to staff cell phones and computers and was put into statewide use for recording all reported incidents beginning April 1, 2019. Staff developed supporting reference materials for using the application and created training tools and webinars to inform staff on how to use this information. Total incidents reported annually for bears and lions are reported below. Year 2019 (partial year starting Apr) 2020 2021 2022 2023 (as of Sept 15, 2023) Total Black Bear Incidents Recorded 5,392 4,971 3,706 4,292 2,894 Total Mountain Lion Incidents Recorded 723 868 767 788 564 The internal CPW workgroup responsible for the bear/lion incident application has also developed and distributed an application for recording drug use and handling information for all immobilized wildlife. This workgroup has also developed and distributed an application for the entering mortality data from bears and lions. This application replaces the previous paper mandatory mortality form which was used to record data from all mortalities of bears and lions. This workgroup continues to meet and work on future steps in development of a more robust application platform that could integrate the three existing apps into multiple streams of currently-used agency data and software. 4. Density and Sex/Age Composition Relative to Bobcat Harvest A bobcat pilot study was conducted during the 2022-2023 field season for the purposes of honing capture strategies/methods in relation to data collected on cameras and from GPS-collared bobcats for �the purposes of population density estimation. Two study areas were selected, each 400 km2, with one depicting ‘high’ historical legal harvest (>2.55 bobcats/100km2; hereafter “Piceance”) and the other ‘low’ historical harvest (near null; hereafter “Skull Creek”). Due to a rare, severe winter season, snow depth precluded access to some of Piceance and most of Skull Creek. A camera trap grid of 100 cameras was successfully deployed in Piceance and only 37 of 100 cameras were deployed in Skull Creek. Bobcat captures were conducted from November 2022 through April 2023 in Piceance and from February through March 2023 in Skull Creek. Each study area below contains 100 2x2 km cells with camera locations shown with gray dots, live cage trap sites in yellow, successful live trap sites in red. Capture effort (trap nights) and success (captures), and the demographic break-down of bobcat �capture/collar success is shown below. Twenty-six unique bobcats were captured and 23 of those were collared. Overall, the sex ratio of captured and collared animals was male-biased, i.e. 27:12 and 17:9, respectively. Study area Piceance Skull Creek Total Study Area Trap Nights Number of Captures 1376 295 1671 34 5 39 Sex Female Piceance Male Skull Creek Female Male Total Number of Individuals Age Adult Sub-adult Adult Sub-adult Adult Subadult Adult Trap Nights / New Individual Capture 66 59 -- Trap Nights / All Captures 21 5 26 40 59 -- Captured New Individuals 5 4 21 4 2 1 2 39 4 2 12 3 2 1 2 26 Collared 4 1 12 2 2 0 2 23 There were 947 bobcat images, yielding 119 ‘hits’ or independent detections. Most detections were unmarked (n = 93), 8 were marked, and 17 were unknown whether marked or unmarked bobcats. Six of the 17 (35%) of the marked, collared bobcats were detected. We estimated bobcat population density via a mark-resight immigration-emigration mixed logit-normal model for Piceance, due to a lack of geographic closure. For Piceance, bobcat density was estimated for the beginning of the period from November 19, 2022 through April 23, 2023, as that corresponds to the last camera trap set-up at the beginning of the season and first SD card pick-up at the end of the season; demographic closure was violated during this period, due to legal harvest (n = 8), so the derived estimate corresponds to density at the beginning of the sampling period. Preliminary results yielded a bobcat density of 34.62 bobcats (CV = 0.38, SE = 12.80) for the 400 km2 Piceance study area, i.e., 8.41 bobcats/100 km2. Bobcat density estimation was not possible for Skull Creek, due to too few collared bobcats (n = 4) and detectors on the camera grid. We suspect that the Piceance density estimate is biased low, due to low number of bobcat resights, due to many marked animals occurring off-grid (spatially biased trapping effort), reduced movement during a severe winter, and mostly due to suboptimal camera locations. Camera locations were initially chosen based on representing the majority of habitat type and a good travel route/location for bobcat detection. In several cases, however, lower represented habitats likely provided better locations for detecting bobcats in a camera grid cell and/or bobcat behavior on the landscape was better understood from both experience and acquired GPS data. Animals captured closer to the study area edges were less likely to be resighted on camera sets (black bars below): �Although individuals with more GPS locations on-grid generally had higher resightability (red bars below), several marked bobcats were not resighted (black bars below), indicating that camera locations were potentially suboptimal: Findings from this pilot year are driving changes and modifications to capture strategy and camera trap locations. Capturing animals more toward the center of the study area and relocating camera locations toward ‘better’ bobcat microsites and possibly increasing scent lure refresh rates will yield higher resight rates and improve population density estimates for the two study areas. Moreover, increased sample size of marked individuals and resight rates are required to estimate sex ratio and to make harvestrelated associations with density within the study areas. PERSONNEL Mark Vieira Chuck Anderson Shane Frank Megan Sims CPW, Carnivore and Furbearer Program Coordinator CPW, Mammals Research Leader CPW, Wildlife Research Scientist CPW, Federal Aid Coordinator 970-472-4368 970-472-4335 970-646-2961 303-291-7622 � https://cpw.cvlcollections.org/files/original/1999fe70189d36896db00a004b3c7c06.pdf f1f28f515bd1396a565784d5645b54f1 PDF Text Text Colorado Parks and Wildlife July 2023 – June 2024 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No. Federal Aid Project No. Colorado 3430 3003 : Parks and Wildlife (CPW) : Mammals Research : Bobcat Population Density and Demographics 1 : Evaluation of bobcat population density and demographics across habitat types and harvest levels in Colorado W-244-R, Segment 8 : Period Covered: July 1, 2023 – June 30, 2024 Authors: S.C. Frank, J. Ivan, M. Vieira, J. Runge All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. ABSTRACT Bobcat (Lynx rufus) populations in Colorado are available for legal harvest each year. Data on bobcat population densities in Colorado are sparse, and Colorado Parks and Wildlife has assumed a bobcat density of 15 bobcats/100 km2 within bobcat core habitat. Colorado constituents have indicated that they desire a more robust understanding of the population-level impacts of harvest on bobcat populations. This study will address that demand by quantifying bobcat density and bobcat prey availability, particularly as it relates to human harvest. From July 1, 2023 – June 30, 2024, we focused on working with landowners and stakeholders to ensure continuation of the project and to enhance our abilities to maintain camera grids and capture bobcats. Field efforts were centered on several objectives: 1) set up the remaining 70% of cameras in the low harvest legacy study area 2) continue capturing bobcats, 3) estimate bobcat population density for both study areas, 4) sample prey species tissue (roadkill) for bobcat dietary analyses, and 5) conduct line transect of observing leporids and their tracks for distance sampling. We set up the remaining cameras, pulled/replaced SD cards from all cameras, and maintained the camera grid throughout the season. We captured 16 bobcats and (re)collared 14 and collected diet samples and morphometric data. A preliminary mark-resight population estimate was possible for the high harvest legacy study area from previous data collection (20222023), but photo image processing is in progress for 2023-2024 population density estimation for both study areas from data collected through April 2024. About 25% of the 800,000 images 14 �collected have been photo identified. Preliminary results on dietary analysis using stable isotopes are promising and more samples are required. We conducted ~50% (~100 km) of the target transect length (200 km) for distance sampling of leporids, but there were not enough live observations (N = 6) to fit a model. Pellet plots will be used as an index for leporid abundance in the future. EVALUATION OF BOBCAT POPULATION DENSITY AND DEMOGRAPHICS ACROSS HABITAT TYPES AND HARVEST LEVELS IN COLORADO SHANE C. FRANK, JAKE IVAN, MARK VIEIRA, JON RUNGE PROJECT NARRATIVE OBJECTIVES A background objective of this project is to set up two bobcat study areas similar in habitat type and topography, but differing in historical bobcat harvest legacy, i.e., one with low and another with high historical bobcat harvest. Within each of these study areas, objectives are to (1) estimate bobcat population density for before and after legal harvest, (2) estimate bobcat survival and cause-specific mortality for the study population(s), (3) determine bobcat sex-age composition of the study population(s), (4) determine bobcat diet for the study population(s), (5) estimate leporid (relative) prey abundance, (6) explore associations between both harvest demographics and leporid abundance with bobcat density to identify potential indicators of population change, and (6) extrapolate density from study areas to other comparable habitat/vegetation cover in Colorado. SEGMENT OBJECTIVES (1) Work with Area 6 personnel and maintain positive relationships with landowners, particularly in the Skull Creek study area where 25%+ of the land is private to ensure adequate access for bobcat captures and maintaining camera grids. (2) Set up the remaining 70 camera sets in the Skull Creek study area, pull SD cards from all of the previously deployed cameras (N = ~130) and move locations as necessary to access bobcat travel routes. (3) Capture, collar/mark, sample, measure bobcats following the ACUC capture/handling protocol and monitor/investigate cause-specific mortalities of the marked population. (4) Estimate population density for both Skull Creek/low harvest legacy and Piceance/high harvest legacy study areas. (5) Sample roadkill or potential bobcat prey for muscle tissue that will be applied to stable isotope analyses and inference of bobcat diet composition. (6) Perform line transects and document both live and track observations. 14 �INTRODUCTION Bobcat Management in Colorado Bobcat (Lynx rufus) populations in Colorado are available for legal harvest each year from December 1 until the end of February. As with all furbearer species that Colorado Parks and Wildlife (CPW) manages for sustainable harvest, management decisions are informed using the best available data. Currently, bobcats are harvested in Colorado without bag limits. However, data on bobcat population densities in Colorado are sparse and limited to point estimates derived during winter from 2 small (80 km2) study areas near the Uncompahgre Plateau and along the Front Range (Boulder) in 2009 and 2010, respectively (Lewis et al. 2015). Density estimates from those study areas ranged from 16-24 bobcats/100 km2. Beyond those temporally and geographically isolated estimates, population density estimates are not available for Colorado. In lieu of that, Colorado bobcat density is inferred from estimates in other States (California: 115153/100 km2, Lembeck 1978, Lembeck and Gould 1979; Oregon: 77/100 km2, Witmer and DeCalesta 1986; Arizona: 24-28/100 km2, Jones and Smith 1979, Lawhead 1984; Utah: 6.2/100 km2, Karpowitz 1981; Idaho: 5.4 adults/100 km2, Bailey 1974; Nevada: 20/100 km2, Golden 1982). Compared to these other study locations, bobcat habitat quality in Colorado is considered moderate. Colorado Parks and Wildlife has assumed a bobcat density of 15 bobcats/100 km2 within bobcat core habitat, i.e. constrained to <9,500 feet elevation and within 7 km distance to woodland and shrubland vegetation types (Figure 1a). This assumed density, when applied to the bobcat management area level (Fig. 1b) is most likely a conservative range, as it falls slightly below Lewis et al.’s (2015) estimates and is well under estimates from most other western States. Bobcat harvest management in Colorado is partially based on a 7-year study conducted in Idaho that examined how bobcat populations responded to exploitation (Knick 1987, 1990). Knick (1987, 1990) found that annual harvest had little impact on bobcat populations until it reached 20% of population size. Colorado Parks and Wildlife has not set a bag limit on bobcats, but considers a derived harvest rate acceptable statewide if it does not exceed 17% of assumed bobcat density (15/100 km2), i.e., 2.55 bobcats/100 km2 at the bobcat management area scale (Figure 1b). This threshold has not been exceeded in any of the bobcat management areas and ranged from 0.71 – 1.73 bobcat /100 km2 in 2016-2017 (Colorado Parks and Wildlife Furbearer Management Report). Furbearer harvest, including the legal regulated take of bobcat, has recently fallen under more intense scrutiny by some of CPW’s constituents. For example, continuation of harvest was challenged in recent citizen-proposed petitions to the Colorado Parks and Wildlife Commission (PWC), by Colorado Senate (Bill SB22-031), and more recently by a proposed ballot initiative that would ban harvest of bobcats and other wild felids. The basis for the past petitions and for the current ballot initiative include social, ethical and biological factors. Two criticisms of current bobcat management have focused on a lack of a robust population and density estimates for the State of Colorado and reliance on an inferred scientific basis for regulating harvest. More succinctly, CPW has not evaluated the relationships between bobcat density, demographics, and harvest specific to Colorado. These challenges to CPW’s bobcat management practices clearly indicate that constituents desire a more robust understanding of the population-level impacts of harvest on bobcat populations. This study will address that demand by quantifying bobcat density and bobcat prey availability, particularly as it relates to human harvest. 14 �STUDY AREAS Our selection of study areas was guided by several factors, including reported bobcat harvest from 2017-2019, key bobcat habitats of Colorado (State Wildlife Action Plan - SWAP 2015), topography (DEM), and logistical considerations. We identified study areas of 20 x 20 km2 with one of high (2.55-5 bobcats harvested annually/100 km2) and the other low historical (2017-2019) bobcat harvest (0-1 bobcats harvested annually/100 km2). Aside from historical bobcat harvest rates, the two study areas were chosen to be as similar as possible across other environmental factors. Bobcats select rocky outcrops, areas with access to water, and prefer cover and edges (Armstrong et al. 2011), all of which the interface between pinyon-juniper and sagebrush communities provide. Pinyon-juniper (Pinus edulis and Juniperus spp.) and sagebrush (Artemisia spp.) vegetation communities comprise approximately 90% of each study area. Pinyon-juniper (Pinus edulis-Juniperus spp.) communities are regarded as preferred bobcat habitat (Armstrong et al. 2011). In Colorado, pinyon-juniper is often mixed with sagebrush communities; the chosen study areas had a predominant mixture of both pinyon-juniper (4470%) and sagebrush (23-42%) habitat types. The high (south; ‘Piceance’) and low harvest (north; ‘Skull Creek’) study areas (Figure 2) fall within CPW game management units 22 and 10, respectively. Elevation ranges from ~5500 to 8600 ft in Skull Creek and from ~5900 to 7800 ft in the Piceance study area. Nearly 25% of Skull Creek is private land; ~5% private land exists in Piceance. The majority of land in each study area is managed by the Bureau of Land Management, with Skull Creek containing approximately 40 km2 of designated wilderness. Piceance has better road and trail access than Skull Creek. This project requires cooperation and coordination with CPW Biologists, Area Wildlife Managers, and District Wildlife Managers primarily in the northwest part of the state, i.e., Area 6, but also statewide for bobcat teeth and carcass collection from donated bobcat carcasses. Additionally, work occurs on lands managed by BLM, and coordination is ongoing with appropriate Wildlife Program Leads. METHODS Bobcat Live Captures Bobcats will be captured using box traps, and trapping will occur from September-April. The use of dogs and leg-hold traps may be utilized if live cage trapping success is insufficient. Capture methods including hound pursuit and leg-hold traps will follow CPW’s ACUC approved capture guidelines for felids (ACUC protocol #HG-001, Mountain Lion/Canada Lynx/Bobcat Capture and Handling Guidelines, 2015 update). We will set out bait and traps near travel routes and landscape features where bobcats often occur. We will bait, chemical, and visual attractants to lure bobcats to trapping sites and noted on camera, where traps will then be armed. Box traps will be our principal method of capture. We perform daily trap checks (~within 10a-12p) a maximum time-in-trap of 26 hours. We set traps to provide trapped animals with adequate protection from the elements and potential food until they can be released. Trapping is halted and cages locked closed in the case of inclement weather. Trapped bobcats will be chemically immobilized, weighed, and then marked with eartags, pit tags, and fitted with GPS collar (see Marking on capture form in Appendix II), not to exceed 5% of the animals body weight. GPS collars are programmed to record a position and cycle throughout the day. Collars are scheduled to drop off the animal after 104 weeks through an electronic release on the collar. Bobcats will be sexed, aged, measured (head, body, teeth, paws), and their reproductive status will be assessed 14 �(developed nipples, testicles present); samples will be taken (tissue biopsy, blood, hair, whisker – cut not pulled); and vitals (respiration, heart rate, temperature) and reflexes will be monitored at regular intervals throughout capture and handling (Appendix II) prior to receiving a reversal drug and being released. Bobcat Camera Deployment for Population Density Estimation via Traditional Mark-Resight In each study area (see “Location” below), an array of 100 Reconyx HP2X camera traps were deployed and maintained active year-round. Each 400 km2 study area was divided into 100 cells (2 x 2 km size), with each cell receiving one camera placed >200 m from cell boundaries and within favorable bobcat microhabitat (game trail, ridges, washes, rocky outcrops) to detect bobcats. Resightings of individually-identified bobcats and sightings of unmarked bobcats will be jointly used to estimate population density. Bobcats detected from cameras will be individually identified through a combination of eartag ID, collar ID, and potentially individual pelage markings (Heilbrun et al. 2003). Camera sets will have scent lure and visual attractants (e.g. flagging, feathers) to attract bobcats toward camera sets/viewsheds for image capture. Bobcat Cause-Specific Mortality and Survival Bobcat mortality will be monitored via GPS-collared animals as part of this study. A ‘mortality signal’ that is automatically sent via sms and email when collar-based accelerometer data suggest no movement has occurred for at least 8 consecutive hours. If a mortality signal is triggered, the VHF beacon will be used to locate the collar and the animal (carcass) if present. If the animal is dead, then a basic, in-field necropsy will be performed to ascertain cause-of-death and compile environmental information. Information on location, animal body condition, ectoparasite load, trauma (e.g. gunshot, bludgeon/road strike, predation), and other information specific to predation will be collected for assignment of mortality cause. If disease is suspected, e.g., no obvious trauma, then the carcass will be collected and sent to the CPW Health Lab for necropsy. Survival and cause-specific mortality will be estimated using a hierarchical multi-state model (Kéry and Schaub 2012). Most bobcats (Nexpected = ~120 over the course of the 5 year study) will be telemetered and monitored for approximately two years, but some dead, marked individuals can be recovered after telemetry equipment has been removed or fails (via eartags and/or pit tags). Due to mandatory seal checks, bobcat mortalities from legal harvest offer ‘perfect detection’ with or without GPS collars on the animal, whereas other sources of mortality (e.g. predation and disease) are not likely be detected unless occurring on telemetered animals. Recovery rate of individual fates is therefore different, depending on mortality source and telemetry performance, and if ignored will produce biased estimates of survival. We plan to use a combination of capture-recapture data (live trapping), telemetry (GPS positional ‘recaptures’), and dead recovery information from animals to create capture histories and fit a joint capturerecapture mark-recovery model, as depicted in Kéry and Schaub (2012). Bobcat Sex and Age Bobcat sex and age information are collected by CPW staff for both marked (via capture) and unmarked bobcats. Sex and age for unmarked bobcats are collected during mandatory seal checks at CPW field offices during and shortly following the end of the bobcat harvest season in February. Whether marked or unmarked, bobcat ages fall into either juvenile or adult classes. For 14 �the unmarked bobcats, harvest information data requests will be submitted to the Terrestrial Section at the end of each harvest season, following ample time to receive and process bobcat seal check information from each Area. Sex ratio of the study population can be determined in several ways, but challenging factors for bobcats include (1) the lack of sexually distinguishing characters that are observable on camera and (2) the high likelihood of sex-dependent probability of capture and resight rates. The lack of camera-based sexual dimorphism precludes the possibility of estimating sex ratio from unmarked animals alone. In this project, sex-specific population density estimates that account for sexspecific probabilities of detection/resight/recapture are the preferred method to derive sex ratios, but this is contingent upon having a representative amount of marked resights for both sexes and for all study areas. Alternatively, sex-specific survival rates can be used to derive sex ratios (Ancona et al. 2017), which will be a secondary approach, using the multistate model above. This approach is limited, however, as it relies on assumptions that are rarely met in wild populations: harvest probabilities of different age classes are equal, age structure remains constant over time, and recruitment rates do not differ between males and females (Ancona et al. 2017). Raw counts of harvest are generally avoided, but dead, recovered ‘recaptures’ of marked bobcats can be used to derive sex ratios, assuming that harvest is not sex-biased. This is not likely, particularly for trapping, where larger male pelts are generally preferred and selected, and females are sometimes released to enhance/sustain reproduction and population persistence. In other harvested bobcat populations, however, hunting is more selective of larger males compared to trapping (Allen et al. 2018). A review of Colorado’s harvest data suggests that hunting-based legal harvest methods for bobcats and not trapping per se is less biased and could be used as a third approach, but the magnitude of sex-bias by harvest type has not verified. Given the data, all possible approaches will be compared for corroboration of derived sex ratios, but we realize that none may be a rigorous estimate of the actual sex ratio in the study population. We may consider a non-invasive sample of collected bobcat scat using scent dogs and subsequent genetic analysis to identify sex, but this would require additional and/or reprioritized project funds. Bobcat Diet Bobcat diet will be assessed via stable isotope ratio (δ13C and δ15 N) analysis, i.e., information derived from GPS-marked bobcat samples, such as blood, hair, and whiskers, in combination with tissue samples taken from taxa that are potential prey. Tissue samples from prey taxa will be collected primarily via roadkill and by other means (e.g. grouse wings already collected by Area 6). Individual bobcat GPS locations will be analyzed respective to the same individual’s stable isotopic signatures (consumed prey), which will give insight into how bobcats use the landscape for prey acquisition. The availability of prey helps provide context for bobcat resource acquisition and selection, in addition to potentially helping explain spatial and temporal variation in population density (see Leporid Line Transects). The bobcat dietary work is a focus of a Masters project in cooperation with the University of Wyoming (UWyo). A graduate student will be co-supervised by the bobcat project leader, Shane Frank, and Assistant Professor Joe Holbrook (UWyo), and she will further develop the scope and methods addressing bobcat diet nearer to the start of her project in spring 2024. Leporid Line Transects and Distance Sampling We attempted to estimate leporid abundance in the study areas using distance sampling. Distance sampling allows for the estimation of animal abundance using the probability of 14 �detection from line transects, which decays as distance increases from the line. Crucial assumptions are that (1) there is 100% detection on the line (0m from the line) and (2) objects do not move (or are detected multiple times from the line). Leporids can flush, but they typically do so within close proximity, meaning the identification of original location is likely known, especially in snow. The number of detections per kilometer determines the sample size required to achieve desired precision for an abundance estimate (Buckland et al. 2015) . Leporids can range from as low as 0 animals to as high as 17 animals per kilometer transect (Fernandez-deSimon et al. 2011, van Strien et al. 2011, Robinson et al. 2014). We calculated optimal CVs by varying the number of animals detected/km and transect length and fixing variation (b) of animals detected per km (b = 3; Burnham et al. 1980) (Figure 6). RESULTS AND DISCUSSION Pilot Study Results Analyzed (November 2022 – April 2023) Between November 2022 and April 2023, CPW conducted a pilot study in two study areas. Due to stochastic and severe winter conditions in both study areas, particularly deep and persistent snow, the camera trap array in Piceance was fully deployed but only one third of the cameras were deployed in Skull Creek (Fig. 2, gray dots). Due to limited and remote access, capture efforts were similarly limited in Skull Creek. These conditions led to a higher level of capture effort and success in Piceance, (Fig. 1, more orange and blue dots in Piceance). Thirtynine bobcats were captured, consisting of 26 newly caught individuals and 13 recaptures. Seventeen bobcats were collared and ‘available’ for subsequent resighting in Piceance, whereas four were available in Skull Creek. The sex ratio (male:female) of captured and collared bobcats was male-skewed about 3:1 (n = 21) in Piceance, but female-skewed with 2:3 (n = 5) in Skull Creek. Most captured individuals were adults (n = 20 of 26), and most captured sub-adults (n = 5 of 6) were too small to collar safely, i.e., ≤15 lbs. (Table 1). There were no recorded mortalities of GPS-collared bobcats outside of legal harvest during this period. Bobcat density estimation was not possible for Skull Creek, due to small sample size (n = 4) and incomplete camera deployment (n = 37), and SD cards were not collected in April following two months of data collection; field efforts were prioritized toward Piceance. There were 947 bobcat images from Piceance camera traps, yielding 119 independent detections. Most detections were of unmarked individuals (n = 93), 8 were of marked individuals, and 17 were unknown whether marked or unmarked bobcats. Six of the 17 (35%) of the marked, collared bobcats were detected. We estimated the Piceance bobcat population density via a mark-resight immigration-emigration mixed logit-normal model (addressing lack of geographic closure). Bobcat density was estimated for the period from November 19, 2022 through April 23, 2023, corresponding to trap set and retrieval dates. Demographic closure was violated during this period, due to legal harvest (n = 8 with 2/8 marked), so the derived estimate corresponds to density at the beginning of the sampling period (McClintock 2014). A preliminary bobcat density of 34.62 bobcats (CV = 0.38, SE = 12.80) was estimated for the 400 km2 Piceance study area, i.e., 8.41 bobcats/100 km2. We suspect that the Piceance density estimate is biased low, due to many marked animals occurring off-grid (spatially biased trapping effort near study area borders), reduced movement during a severe winter, and due to suboptimal camera locations (low number of both marked/unmarked bobcat resights). Camera locations were primarily chosen to represent the dominant habitat type in a given cell and secondarily for anticipated preferred travel routes/locations to enhance bobcat detection. In several cases, however, lower 14 �represented habitats or edge habitats likely provided better locations for detecting bobcats in a camera grid cell and our understanding of bobcat spatial behavior improved from both experience and acquired GPS data. Bobcat captures and camera images (October 2023 – April 2024) We captured 16 bobcats (9 new and 7 recaptures) with 22 currently collared bobcats following harvest and previous captures/collaring. Trap effort was higher this season (~2700 trap nights vs ~1700 trap nights), but success was much less. We suspect that the mild onset of the winter, i.e., very little snow, allowed bobcats to take advantage of prey that is usually unavailable under snow, e.g., small rodents. We also suspect that bobcat movement was reduced during this period as well, as they were not as present at bait sites nor were interested in our roadkill baits like the previous year. For this reason, we implemented the use of hounds in a limited fashion. We released hounds on four occasions and had a successful (re)capture as a result in one instance. Given the proof of concept and in practice, we aim to utilize this capture method in the future to help increase capture effort and success. We successfully set up the rest of the camera grid in Skull Creek and collected SD cards from cameras that recorded images from October 2023 until April 2024 for both study areas. We have collected ~800,000 camera images for this period and have photo identified ~25% of them. A population density cannot be estimated from (April 2023-April 2024) until all bobcat images have been identified. We have collected ~24,000 GPS locations. Bobcat mortality rates are acquired from both GPS-collared and ear-tagged bobcats (N = 31; N = 5 ear-tag only) and cause-specific mortality will be estimated at the end of the 5-year study to maximize sample size or when study areas change and sample sizes will no longer increase. Altogether, we have newly captured and marked 26 males and 11 females, but only 31 have had collars deployed, due to size constraints. We have not estimated the sexspecific probabilities of detection on camera for each of these cohorts yet, as estimation depends on individual bobcat ID in camera images (in progress) and mark-resight density estimation. Leporid Line Transects (October 2023 – April 2024) We conducted ~50% of our targeted 200 km total transect length for leporid live observations and track counts. For each low and high harvest legacy study areas, we walked ~50 km length of transects. We only observed two jackrabbits for low harvest legacy and four cottontails for the high harvest legacy study area. Similarly, we observed more leporid tracks crossing the transect line(s) within the high harvest legacy study area (N = 471) compared to the low harvest legacy area (N = 186). Live observations of leporids was too low to fit a distance sampling model for abundance estimation and the field effort was substantial. We have therefore opted to use pellet plots around camera sets in the future to corroborate the use of leporid detection rates on cameras as an index of relative leporid abundance within and across study areas. Bobcat Diet In addition, blood, hair, and whisker samples have been taken from bobcats during capture, and roadkill tissue samples of potential bobcat prey have been collected for stable isotopic analysis. Preliminary analysis shows that bobcat inert hair and whisker tissues do differentiate from prey and specific prey taxa stable isotopes tend to cluster (Figure 3; e.g. ground squirrel/marmotini and mule deer/Odocoileus hemionus), but we need more prey samples to properly run mixture models and ‘assign’ dietary preference to individual bobcats. 14 �SUMMARY From July 1, 2023 – June 30, 2024 we successfully worked with private landowners and personnel from CPW to coordinate field research logistics and initiate the 2nd year of this study. We captured 16 bobcats, of which we newly collared 9 and replaced 4 collars; bobcats were otherwise too small for collars. We collected bobcat GPS locations, morphometrics, and took biological samples, including information on bobcat diet. Capture sample size objectives were likely not met this year, due to weather effects on capture success, but population density estimation relies on camera resight or detection rate as well, which is currently being estimated. Previous capture efforts and success might render a population density estimate possible. We have ~10 collared bobcats in each study area, which is ~the minimum number required. Despite the winter weather challenge in captures, it provides an opportunity to contrast bobcat diet between two capture seasons. We discovered that the transect method was not effective for population estimation of leporids using distance sampling, because we did not register enough live observations. Pellet plots will be used in the future to correlate it as a leporid abundance index with camera detection rates. We successfully set up the remaining camera sets in the Skull Creek study area and improved camera locations and successfully maintained both study area camera grids, enhancing our ability to estimate population size in subsequent periods. 14 �LITERATURE CITED Allen, M. L., N. M. Roberts, and T. R. Van Deelen. 2018. Hunter selection for larger and older male bobcats affects annual harvest demography. R Soc Open Sci 5:180668. Ancona, S., F. V. Denes, O. Kruger, T. Szekely, and S. R. Beissinger. 2017. Estimating adult sex ratios in nature. Philos Trans R Soc Lond B Biol Sci 372. Armstrong, D. M., J. P. Fitzgerald, and C. A. Meaney. 2011. Mammals of Colorado, Second Edition. University Press of Colorado. Bailey, T. N. 1974. Social Organization in a Bobcat Population. The Journal of Wildlife Management 38. Buckland, S. T., T. A. Marques, C. S. Oedekoven, and E. A. Rexstad. 2015. Distance Sampling: Methods and Applications. Pages 1 online resource (XV, 277 pages 280 illustrations, 224 illustrations in color in Methods in Statistical Ecology,. Springer International Publishing : Imprint: Springer,, Cham. Burnham, K. P., D. R. Anderson, and J. L. Laake. 1980. Estimation of density from line transect sampling of biological populations. Biometrical Journal 24. Fernandez-de-Simon, J., F. Díaz-Ruiz, F. Cirilli, F. S. Tortosa, R. Villafuerte, M. DelibesMateos, and P. Ferreras. 2011. Towards a standardized index of European rabbit abundance in Iberian Mediterranean habitats. European Journal of Wildlife Research 57:1091-1100. Golden, H. 1982. Bobcat populations and environmental relationships in northwestern Nevada. University of Nevada, Reno. Heilbrun, R. D., N. J. Silvy, M. E. Tewes, and M. J. Peterson. 2003. Using automatically triggered cameras to individually identify bobcats. Wildlife Society Bulletin 31:748-755. Jones, J. H., and N. S. Smith. 1979. Bobcat Density and Prey Selection in Central Arizona. The Journal of Wildlife Management 43. Karpowitz, J. T. 1981. Home ranges and movements of Utah bobcats with reference to habitat selection and prey base., Brigham Young University. Kéry, M., and M. Schaub. 2012. Bayesian population analysis using WinBUGS : a hierarchical perspective. 1st edition. Academic Press, Boston. Knick, S. T. 1987. Ecology of bobcats in southeastern Idaho. University of Montana. Knick, S. T. 1990. Ecology of Bobcats Relative to Exploitation and a Prey Decline in Southeastern Idaho. Wildlife Monographs 108:1-42. Lawhead, D. N. 1984. Bobcat Lynx rufus Home Range, Density and Habitat Preference in South-Central Arizona. The Southwestern Naturalist 29. Lembeck, M. 1978. Bobcat Study, San Diego County, California. State of California, The Resources Agencey, Department Fish and Game. Lembeck, M., and J. Gould, G.I. Dynamics of harvested and unharvested bobcat populations in California. National Wildlife Federation Science and Technology, 1979. Lewis, J. S., K. A. Logan, M. W. Alldredge, L. L. Bailey, S. VandeWoude, and K. R. Crooks. 2015. The effects of urbanization on population density, occupancy, and detection probability of wild felids. Ecol Appl 25:1880-1895. McClintock, B. T. 2014. Mark-resight models. in E. Cooch, and G. White, editors. Program MARK: a gentle introduction. Robinson, Q. H., D. Bustos, and G. W. Roemer. 2014. The application of occupancy modeling to evaluate intraguild predation in a model carnivore system. Ecology 95:3112-3123. 14 �SWAP, S. W. A. P.-. 2015. Colorado Parks and Wildlife. van Strien, A. J., J. J. A. Dekker, M. Straver, T. van der Meij, L. L. Soldaat, A. Ehrenburg, and E. van Loon. 2011. Occupancy dynamics of wild rabbits (Oryctolagus cuniculus) in the coastal dunes of the Netherlands with imperfect detection. Wildlife Research 38. Witmer, G. W., and D. S. deCalesta. 1986. Resource use by unexploited sympatric bobcats and coyotes in Oregon. Canadian Journal of Zoology 64:2333-2338. Prepared by Shane C. Frank, Wildlife Researcher FIGURES Figure 1. Bobcat Core Habitat (1a) and Bobcat Management Areas (1b). Colorado Parks and Wildlife’s core habitat model and map (1a) was developed to represent bobcat habitat (lavender) within the state. While bobcats may occur anywhere in the state, the core habitat model was developed to conservatively represent essential bobcat habitat. Core habitat was constrained to less than 9,500 feet elevation and to woodland and shrubland vegetation types identified in CPW’s Basinwide vegetation layer. Vegetation classifications were buffered to approximately 7 km distance in order to smooth boundaries. Colorado Parks and Wildlife’s geographical bobcat management structure is reflected by figure 1b. Bobcat management areas are depicted by uniformly colored polygons, CPW’s Management Regions are fall within the yellow borders, and CPW’s Game Management Units are reflected by black borders and numbered. 14 �Figure 2. Map depicting the Skull Creek or low harvest legacy study area (GMU 10) and Piceance or high harvest legacy study area (GMU 22) in northwest Colorado. Each study area is a 10 x 10 grid of 100, 2 x 2 km2 cells. Each 400 km2 grid has one camera per cell (not shown), which are used as detectors of collared/bobcats for mark-resight population estimation. Orange and blue dots signify trap sites for the 2022-2023 and 2023-2024 capture seasons, respectively. Red dots are successful capture sites, some of which repeated across seasons. Figure 3. Delta 15 Nitrogen (y-axis) and 13 Carbon stable isotope (x-axis) amounts for bobcat 14 �whisker, guard hair, and potential prey muscle tissues. Circles of the same color close together indicate clustering of prey items and implies differentiability from other prey items if there is separation between clusters. More prey tissue samples and stable isotope values will help create precise “envelopes” for prey differentiation. Bobcat guard hair (green open circles) and whiskers (lavender open circles) depict variation among individual bobcat diet and season (not shown here), indicating that bobcats might show variation in prey selection and use. 14 � https://cpw.cvlcollections.org/files/original/b73314202416fc330b7a222a396bb8b8.pdf 4f16bc31d3b82205b3c3f9be64241c7f https://cpw.cvlcollections.org/files/original/27c9847fd93c565485ea61db4d966dca.pdf d6ec97d4eddbc2c2d56e51a1c9bbe103 PDF Text Text ÿ ÿ ÿ ÿ 23435673ÿ965 ÿ67ÿ �474���ÿ ��������ÿ��������ÿ������ ÿ�!""��#ÿ ÿ �$%$&$ÿ�'%(ÿ")%*+),*%-ÿ*%ÿ�).),$&)ÿ ÿ /01234ÿ63701048ÿÿÿ9:;<ÿ=>ÿ?@=AB9:C0ÿD@>ÿ?@=Eÿ ÿ /12CF2GH;ÿIC70JK2LHK31J8ÿÿÿM12FÿN40;;>ÿM12FON40;;PJKHK0OF3O:JQÿ9HR0ÿI7HC>ÿ9HR0OI7HCPJKHK0OF3O:JQÿSF3KKÿ TH2K>ÿSF3KKOTH2KPJKHK0OF3O:Jÿÿÿ ÿ U44ÿ��35V6W�3ÿ�ÿWX�ÿY53Z�[Wÿ\VV65]ÿ�ÿY5�4�V�65]ÿ67ÿ\^Z�[WÿW3ÿ�\5WX�5ÿ�_64\6W�3 ÿ̀ÿ a�35V6W�3ÿbUcÿdefÿghÿ9igjaklhmÿenÿoiefhmÿp�WX3\WÿY�5V��3ÿ3�ÿWX�ÿY5�[�Y64ÿ 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W8f-)'^ÿ_ÿ8blf--'/-m(//ÿaÿbM0!6-c(0ÿaÿ4-"0^ÿ @�19dÿ �91ÿ ?9?eÿ 11ÿ W 8 f) ' ^ ÿ _ÿ 8 h 0 & M f' / ÿ a ÿ b M 0 ! 6 c ( 0 ÿ a ÿ 4" 0 ^ ÿ @�19�ÿ �98ÿ ?9?eÿ 11ÿ ÿ �]7�� ��3��3�4ÿ��65��567ÿ�6ÿ7�7��3�ÿ�6R�R33��ÿ3��57ÿ7��7���ÿ�6ÿ�56�7�ÿ�7�� >ÿR6773�4ÿ�7���>ÿ ��ÿ��ÿ7��7��ÿ�ÿ��7ÿS���SÿS3��76ÿ�ÿ8?1@ÿ�ÿ���76��9ÿ ÿ ÿ ÿ ÿ ÿ 11ÿ ÿ �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada Lynx Monitoring in Colorado Period Covered: July 1, 2018 − June 30, 2019 Principal Investigators: Eric Odell, Eric.Odell@state.co.us; Jake Ivan, Jake.Ivan@state.co.us; Scott Wait, Scott.Wait@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 1999−2006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success, and that the population of Canada lynx in the state was apparently viable and self-sustaining. In order to track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. During 2014−2019 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During 2018−2019 personnel from CPW and USFS completed the fifth year of monitoring work on this same sample. Specifically, 14 units were sampled via snow tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. The remaining 36 units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2018. Cameras were baited with visual attractants and scent lure to enhance detection of lynx living in the area. Cameras were retrieved during summer or fall 2019 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 510 km (317 mi) during snow tracking surveys and detected lynx at 6 units (Table 1). This represents a 5-year low in snow tracking effort and is due mostly to the record-setting snows experienced during the 2018–2019 winter. However, the mean distance surveyed per visit as well as the number of units with lynx remained similar to previous years. Surveyors collected more photos during 2018–2019 than in any other year. This was due in part to replacing snow tracking units with camera units in recent years, but mostly because many cameras were not retrieved until late summer or fall 2019 due to access issues related to the heavy snow pack. For the second year in a row we collected <50% of the number of lynx photos collected during the initial years of the monitoring effort, although the number of units with lynx returned to ‘normal’ after last year’s low (Table 2). Perhaps the abnormal snow patterns during the past few years (lack of snow in 2017–18, record snow in 2018–19) impacted our detection probability. Alternatively, lack of detections could have been due to the new lure (Caven’s 2 �Violator 7; Minnesota Trapline Products, https://www.minntrapprod.com/Bobcat-andLynx/products/829/) we used in 2017–2018 and 2018–19 after the lure we used previously (Pikauba; Luerres Forget’s Lures, http://www.leurresforget.com/product.php?id_product=15) became unavailable. Unfortunately, the changes in snow and lure are confounded, thus making it difficult to determine which factor resulted in fewer detections. We will use the same new lure in 2019–2020, which if accompanied by a normal snowfall, may allow us to retrospectively assess the lack of detections. Compared to previous years, we obtained new lynx detections at a camera unit near Table Mountain northwest of Creede and one north of Lemon Reservior. Also, we detected lynx again for only the second season at a unit west of Trujillo Meadows, near the New Mexico border. However, we failed to detect lynx in two units near Silverton that have had detections each winter since the inception of monitoring (Figure 1). Potential tracks were observed in each of these, but conditions were such that they could not be confirmed. An adult female with kittens was detected at cameras in a unit near Platoro Reservoir, thus documenting that at least some reproduction occurred in the study area. We used the R (R Development Core Team 2018) package ‘RMark’ (Laake 2018) to fit standard occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the probability of a unit being occupied (i.e., used) by lynx over the course of the winter (ψ), along with the probability of detecting a lynx (p) given that the unit was occupied. ‘Survey method’ and ‘year’ were treated as group variables so that we could, based on previous work, 1) allow detection probability to vary by survey method, 2) allow for detection probability for 2017–18 and/or 2018–19 to differ from other years due to abnormal snow or new lure, and 3) include a breeding season effect for detection at cameras (lynx tend to move more in late winter when they begin to breed, and thus should encounter cameras more often). We also considered a suite of covariates that could potentially explain variation in occupancy including proportion of the unit that was covered by spruce/fir forest, average years since bark beetle infestation, variability (standard deviation) in years since bark beetle infestation, proportion of the unit impacted by bark beetles, proportion of the unit that was burned during Summer 2013, and the number of photos of other species that could potentially impact presence of lynx (e.g., snowshoe hares as a food source, coyotes as potential competitors). We limited our model set by first setting a general structure for ψ while assessing fit of various combinations of variables expected to affect p. We then fixed the best-fitting structure for p, and assessed combinations of the covariates expected to influence ψ, allowing up to 2 of these covariates at a time, in addition to the covariates on detection. We included data from the pilot study (2010–11) as well as the first five years of monitoring (2014−2019) to maximize sharing of information across surveys. Since the inception of our monitoring program, the best-fitting model characterized occupancy as a function of 2 covariates: the proportion of the sample unit covered by spruce-fir forest and the number of photos of hares recorded at camera stations (Appendix 1). However, for the 2018–19 sampling year, the best fitting model characterized occupancy as a function of proportion of the sample unit covered by spruce-fir and by the number of cougar photos recorded at camera sites. The association with spruce-fir was positive, indicating that the probability of lynx use increased with more spruce-fir; the association with cougars was negative, indicating that probability of lynx use decreased with more photos of cougars. The second best model included bobcat photos in addition to spruce-fir; again lynx use was negatively associated with increased bobcat photos. Other covariates appeared in top models with spruce-fir, but addition of these covariates did not improve AICc scores beyond the model with spruce-fir only (Appendix 1). This phenomenon indicates that these other variables were not informative. Detection probability was relatively high for snow tracking surveys (p = 0.59, SE=0.05), and relatively low for camera surveys (p = 0.22, SE = 0.03) during December−February and April, although detection at cameras increased to 0.39 (SE = 0.07) during breeding season (March) as expected. We found a significant, negative effect on p during winters when Violator 7 was used as lure (p = 0.03, SE = 0.01 for December−February and April; p = 0.06, SE = 0.03 for breeding season), although it is unclear whether this drop in detection probability was due to abnormal snowpack or the alternate scent lure. We estimated that 31% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 12–60%) 3 �during 2018–19. Confidence intervals were quite large for the second year in a row, owing to the extra parameter needed to model the “Violator 7 effect and to the low, poorly estimated detection probability that resulted (Figure 2). The spatial distribution of lynx in the San Juans remained largely unchanged (Figure 1). LITERATURE CITED Ivan, J. S. 2013. Statewide Monitoring of Canada lynx in Colorado: Evaluation of Options. Pages 15-27 in Wildlife Research Report - Mammals. Colorado Parks and Wildlife., Fort Collins, CO, USA. http://cpw.state.co.us/learn/Pages/ResearchMammalsPubs.aspx Laake, J. L. 2018. Package 'RMark': R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplement:120-138. Table 1. Summary statistics from snow tracking effort. Season #Units Surveyed #Units with Lynx #Lynx Tracks #Genetic Samplesa Km Surveyed (Total) Mean Km Surveyed per Visit #CPW Personnel #USFS Personnel 2014–2015 24 8 13 10b 1,088 20.1 30 13 2015–2016 17 7 14 9c 987 21.9 23 6 2016–2017 16 8 13 7 d 703 18.0 20 8 2017–2018 14 7 9 3e 578 19.3 14 5 2018–2019 14 6 7 2 510 19.6 16 5 e Number of genetic samples (scat or hair) collected via backtracking putative lynx tracks b DNA analysis confirms that all samples collected from putative lynx tracks were lynx c DNA analysis confirms that 6 of 9 samples were lynx (1 coyote, 1 either mule deer or human, 1undetermined) d DNA analyses confirmed that 5 of 7 samples were lynx (1 coyote, 1 snowshoe hare) e DNA confirmation pending a Table 2. Summary statistics from camera effort. Season 2014–2015 #Units Surveyed 32 2015–2016 31 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx #CPW Personnel 46 #USFS Personnel 12 134,694 301 14 33 9 101,534 455 10 2016–2017 33 29 9 168,705 251 10 6 (5) 2017–2018 35 35 8 173,279 90 8 5 (4) 2018–2019 36 31 7 204,243 60 10 7 (5) a Number in parenthesis indicates units with lynx during the official survey period (Dec 1–Apr 30) 8 (7) 7 (6) 4 �a) b) Figure 1. Lynx monitoring results for a) the current sampling season (2018–2019) and b) the cumulative monitoring effort (2014–2019), San Juan Mountains, southwest Colorado. Colored units (n = 50) indicate those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 12 units in 2018−2019 and 23 units cumulatively since monitoring began in 2014−2015. 5 �1.0 0.9 -- 0.8 0.7 >, u C: m a. :::i u u 0 ·-~ 0.6 ti 0.5 0.40.3 0.2 0.1 - l I II lt -- -~ 0.0 I I I I I I I I I 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Figure 2. Model-averaged occupancy estimates and 95% confidence intervals for occupancy of Canada lynx in the San Juan Mountains, southwest Colorado. ‘Year’ indicates when the efforts were initiated (e.g., 2010−11, 2018−19). Appendix 1. Model selection results for lynx monitoring data collected in the San Juan Mountains, Colorado, 2010–2019. Rankings are based on Akaike’s Information Criterion adjusted for small sample size (AICc). Ten variables were considered as covariates to inform estimation of occupancy (ψ). The complete model set (n = 56) included all combinations of two, in addition to modeling detection (p) as a function of survey method, breeding season, and alternate lure used during the 2017–18 and 2018–19 seasons. Only the best 10 models are shown. Model AICc ∆AICc AICc Wts No. Par. a p(Best ) ψ (Cougar + Prop Spruce/Fir) 817.89 0 0.64 12 p(Best) ψ (Bobcat + Prop Spruce/Fir) 820.87 2.98 0.15 12 p(Best) ψ (Prop Spruce/Fir) 822.92 5.03 0.05 11 p(Best) ψ (Prop Burned + Prop Spruce/Fir) 824.14 6.26 0.03 12 p(Best) ψ (Coyote + Prop Spruce/Fir) 824.26 6.38 0.03 12 p(Best) ψ (Years Since Beetles + Prop Spruce/Fir) 824.46 6.57 0.02 12 p(Best) ψ (Fox + Proportion Spruce/Fir) 824.61 6.72 0.02 12 p(Best) ψ (Hare + Proportion Spruce/Fir) 825.03 7.14 0.02 12 p(Best) ψ (Prop Beetle + Prop Spruce/Fir) 825.06 7.17 0.02 12 p(Best) ψ (Variability Beetles + Prop Spruce/Fir) 825.08 7.19 0.02 12 a Best-fitting structure for detection probability included effects for survey method, breeding season, and an effect for the 2017–18 and 2018–19 survey seasons when Violator 7 was used for lure rather than Pikauba. 6 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada lynx monitoring in Colorado Period Covered: July 1, 2019 − June 30, 2020 Principal Investigators: Eric Odell, Eric.Odell@state.co.us; Jake Ivan, Jake.Ivan@state.co.us; Scott Wait, Scott.Wait@state.co.us; Morgan Hertel, Morgan.Hertel@state.co.us Personnel: Brad Weinmeister, Evan Phillips, Nate Seward, Brent Frankland All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 1999−2006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success, and that the population of Canada lynx in the state was apparently viable and self-sustaining. To track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. During 2014−2020 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During 2019−2020 personnel from CPW and USFS completed the sixth year of monitoring work on this same sample. Specifically, 14 units were sampled via snow tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. The remaining 36 units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2019. Cameras were baited with visual attractants and scent lure to enhance detection of lynx living in the area. Cameras were retrieved during summer or fall 2020 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 650 km during snow tracking surveys and detected lynx at 6 units (Table 1). These results are among the lowest recorded for the project, but mirror those recorded during the past 3 years (Table 1). Surveyors collected more than 3 times the photos during 2019–2020 than have been collected in any other year. This can be mostly attributed to the use of new, more sensitive cameras along with new, high capacity memory cards. However, for the third year in a row we collected <50% of the number of lynx photos taken during the initial years of the monitoring effort (Table 2). In fact, the 36 lynx photos collected during the 2019−20 season was the fewest recorded since the inception of the project. We initially considered at least 3 possible explanations for the lack of photos collected in recent years. First, we hypothesized that abnormal snow patterns (lack of snow in 2017–18, record snow in 2 �2018–19) could have impacted detection probability. Second, lack of detections could have been due to the new lure (Caven’s Violator 7; Minnesota Trapline Products, https://www.minntrapprod.com/Bobcatand-Lynx/products/829/) we used in 2017–18, 2018–19, and 2019−20 after the lure we used previously (Pikauba; Luerres Forget’s Lures, http://www.leurresforget.com/product.php?id_product=15) became unavailable. Finally, it could be that lynx have disappeared from a number of camera units. Unfortunately, the changes in snow and lure were confounded for a few years, thus making it difficult to determine which factor resulted in fewer detections. However, 2019−20 was a normal snow year, yet the number of lynx photos was still low. This indicates that abnormal snow was not the cause of the pattern we observed. Also, the number of snow tracking units with lynx has remained fairly steady throughout the project; we can think of no reason why snow track units would remain occupied while lynx blinked out of camera units, unless just by chance. Thus, we suggest that the new lure is less effective than the original. Fortunately the original formulation is again available and will be deployed for the 2020−21 survey. We plan to utilize this lure for the remainder of the survey efforts, provided it remains available. We obtained lynx detections for only the second time at a camera unit near Wolf Creek Pass. Lynx were again detected at Lizard Head Pass after no detections last year, and in all four snow tracking units along the Hwy 550 corridor after two of the four went without detections in 2018−19. However, we failed to detect lynx in at the Table Mountain Unit northwest of Creede, at Lemon Reservoir, at Little Squaw Creek west of Creede, and at Trujillo Meadows near the New Mexico border, where they had been detected the previous two seasons (Figure 1). We used the R (R Development Core Team 2018) package ‘RMark’ (Laake 2018) to fit multipleseason (i.e., “dynamic”) occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the derived probability of a unit being occupied (i.e., used) by lynx over the course of the winter (ψ), along with the probability of detecting a lynx (p) given that the unit was occupied, the probability a unit that was unused in one year was used the next (i.e., “local colonization”, γ), and the probability a used unit became unused from one year to the next (i.e., “local extinction”, ε). Based on previous work, we treated ‘survey method’ as a group variable so that we could allow p to vary by method. Additionally, we allowed p for 2017–18, 2018–19, and 2019–20 to differ from other years due to the new lure, and we included a breeding season effect for detection at cameras (lynx tend to move more in late winter when they begin to breed, and thus should encounter cameras more often). Also based on previous work, we specified initial ψ in the time series to be a function of the proportion of the unit that was covered by spruce/fir forest. We then allowed annual estimates of ε to be constant or a function of average years since bark beetle infestation, proportion of the unit impacted by bark beetles, proportion of the unit that was burned during Summer 2013, and the number of photos of other species that could potentially impact presence of lynx (e.g., snowshoe hares as a food source; coyotes, bobcats, foxes, and cougars as potential competitors). We allowed annual estimates of γ to be constant or a function of snowshoe hares. We limited our model set by first setting a general structure for ψ while assessing fit of various combinations of variables expected to affect p. We then fixed the best-fitting structure for p, and assessed combinations of the covariates expected to influence ε or γ, allowing up to 2 of these covariates at a time, in addition to the covariates on detection. We made inference from the best-fitting model as selected via Akaikie’s Information Criterion (AIC), adjusted for small sample size (Burnham and Anderson 2002). As has been the case since the inception of our monitoring program, the proportion of the sample unit covered by spruce-fir forest was positively associated with the initial occupancy estimate in the time series. Local colonization probability was estimated to be low (γ = 0.03, SE = 0.01 ) and constant; local extinction was also low, but in some years twice that of colonization (ε = 0.03 to 0.06, SE = 0.03 to 0.05). Furthermore, in all of the top models, ε was negatively (but weakly) associated with the number of coyote photos collected on the year indicating that the probability of extinction of a unit in any given year goes up as the index of coyote abundance goes down (Appendix 1). Local extinction was also significantly, positively associated with the number of fox photos in the top model, suggesting that extinction is more likely in units in which we detected fox more often. Other models for ε that performed better than a 3 �constant structure included a negative relationship with number of snowshoe hare photos (less likely to go extinct as hare index increases), a positive relationship with the number of bobcat photos (more likely to go extinct as bobcat index increases), and a positive association with proportion of a unit impacted by beetles. However, the hare, bobcat, and beetle models were not as well supported as those including coyotes and foxes. The five occupancy growth rates (λ) estimated between surveys were all near 1.0, indicating a stable distribution with little to no growth (Figure 2). Similar to previous years, detection probability was relatively high for snow tracking surveys (p = 0.59, SE=0.05), and relatively low for camera surveys (p = 0.23, SE = 0.04) during December−February and April, although detection at cameras increased to 0.34 (SE = 0.07) during breeding season (March) as expected. We found a significant, negative effect on p during winters when Violator 7 was used as lure (p = 0.08, SE = 0.02 for December−February and April; p = 0.13, SE = 0.05 for breeding season). We estimated that 29% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 15–43%) during 2019– 20 (Figure 2). The spatial distribution of lynx in the San Juans remained largely unchanged (Figure 1). Table 1. Summary statistics from snow tracking effort. Season 2014-2015 #Units Surveyed 24 #Units with Lynx 8 #Lynx Tracks 13 2015-2016 17 7 14 #Genetic Samplesa 10b 9c 13 7d 2016-2017 16 8 Km Surveyed (Total) 1,088 Mean Km Surveyed per Visit 20.1 #CPW Personnel 30 #USFS Personnel 13 987 21.9 23 6 703 18.0 20 8 2017-2018 14 7 9 3e 578 19.3 14 5 2018-2019 14 6 7 2e 510 19.6 16 5 10 2b 650 19.7 15 3 2019-2020 15 6 Number of genetic samples (scat or hair) collected via backtracking putative lynx tracks b DNA analysis confirms that all samples collected from putative lynx tracks were lynx c DNA analysis confirms that 6 of 9 samples were lynx (1 coyote, 1 either mule deer or human, 1undetermined) d DNA analyses confirmed that 5 of 7 samples were lynx (1 coyote, 1 snowshoe hare) e DNA analysis confirms 1 sample was lynx; remaining samples were not analyzed a Table 2. Summary statistics from camera effort. Season 2014-2015 #Units Surveyed 32 2015-2016 2016-2017 31 33 2017-2018 35 2018-2019 36 2019-2020 36 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx 8 7 6 5 6 4 134,694 101,534 168,705 173,279 204,243 701,724 301 455 251 90 59 36 14 10 10 8 9 4 4 #CPW Personnel 46 #USFS Personnel 12 33 29 9 9 35 8 31 7 29 6 �a) b) Figure 1. Lynx monitoring results for a) the current sampling season (2019–2020) and b) the cumulative monitoring effort (2014–2020), San Juan Mountains, southwest Colorado. Colored units (n = 50) depicted here are those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 11 units in 2019−2020 and 23 units cumulatively since monitoring began in 2014−2015. 5 �- 1.2 I I 1.0 0.9 0.8 - I I - 1.1 I - 1.0 - 0.9 - 0.8 >, u 0.7 - - 0. 7 0. ::l 0.6 - - 0.6 C rel u u 0 0.5 0.4 0. 3 0.2 0. 1 - - 0. 5 I I I I I I - 0.4 Cl '"' 0 ~ ,..... ~ :::0 QI ,..... (1) - 0.3 - 0.2 - 0. 1 0.0 - - 0.0 I I I 2014 2015 2016 I I I 201 7 2018 2019 Year Figure 2. Occupancy estimates (Ψ, filled circles, left axis) and annual growth rate (λ) in occupancy between surveys (open circles, right axis) for Canada lynx in the San Juan Mountains, southwest Colorado. ‘Year’ indicates when the efforts were initiated (e.g., winter 2014−15, winter 2019−20). Growth rates less than 1.0 indicate a decline in occupancy; those >1.0 indicate an increase. Literature Cited Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition. Springer, New York, New York, USA. Ivan, J. S. 2013. Statewide monitoring of Canada lynx in Colorado: evaluation of options. Pages 15–27 in Wildlife research report: Mammals. Colorado Parks and Wildlife., Fort Collins, USA. http://cpw.state.co.us/learn/Pages/ResearchMammalsPubs.aspx. Laake, J. L. 2018. Package “RMark”: R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. No Title. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplem:120–138. 6 �Appendix 1. Model selection results for lynx monitoring data collected in the San Juan Mountains, Colorado, 2014–2020. Rankings are based on Akaike’s Information Criterion adjusted for small sample size (AICc). Eight variables were considered as covariates to inform estimation of local extinction (ε); one was considered for local colonization (γ). The complete model set (n = 46) included all combinations of two of these covariates, in addition to modeling detection (p) as a function of survey method, breeding season, and alternate lure used during the 2017–18, 2018–19, and 2019–2020 seasons. Only the best 10 models are shown. Model ψ (Prop Spruce/Fir) ε (Coyote + Fox) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Coyote) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Coyote + PropBeetle) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Coyote + Hare) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Bobcat + Coyote) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (.) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Coyote + PropBurn) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (BKAvg + Coyote) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Cougar + Coyote) γ (.) p (Best) ψ (Prop Spruce/Fir) ε (Bobcat) γ (.) p (Best) AICc 574.54 576.43 576.50 576.61 577.17 578.01 578.12 578.21 578.30 578.50 ∆AICc 0.00 1.89 1.96 2.07 2.63 3.47 3.58 3.67 3.76 3.96 AICc Wts 0.19 0.08 0.07 0.07 0.05 0.03 0.03 0.03 0.03 0.03 No. Par. 10 9 10 10 10 8 10 10 10 9 Best-fitting structure for detection probability included effects for survey method, breeding season, and an effect for the 2017–18, 2018–19, and 2019–20 survey seasons when Violator 7 was used for lure rather than Pikauba. a 7 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada lynx monitoring in Colorado 2020 – 2021 Period Covered: July 1, 2020 − June 30, 2021 Principal Investigators: Eric Odell, Eric.Odell@state.co.us; Morgan Hertel, Morgan.Hertel@state.co.us; Jake Ivan, Jake.Ivan@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 1999−2006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success and that the population of Canada lynx in the state was apparently viable and self-sustaining. In order to track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. From 2014−2021 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During the 2020−2021 winter, personnel from CPW and USFS completed the seventh year of monitoring work on this same sample. Fourteen units were sampled via snow-tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (snow-covered paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. The remaining 36 units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2020. Cameras were lured with visual attractants and scent lure to enhance detection of lynx in the area. Cameras were retrieved during summer or fall 2021 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 744 km during snow tracking surveys and detected lynx at 7 units (Table 1). In 2020-21 surveyors collected more DNA samples than in previous years, likely because new environmental DNA (eDNA) sampling is more efficient to collect than the previous scat or hare sampling. As in 2019-20, significantly more photos were collected in 2020-21 than in the first 5 seasons of sampling. This can be mostly attributed to the use of new, more sensitive cameras along with new, highcapacity memory cards. However, for the fourth year in a row, we collected <50% of the number of lynx photos taken during the initial years of the monitoring effort (Table 2). In fact, the 36 lynx photos collected during the 2019-20 and 2020-21 seasons are the fewest recorded since the inception of the project. We initially considered at least 3 possible explanations for the lack of photos collected in recent years. First, we hypothesized that abnormal snow patterns (lack of snow in 2017–18, record snow in 2018–19) could have impacted detection probability. Second, lack of detections could have been due to 2 �the new lure (Caven’s Violator 7; Minnesota Trapline Products, https://www.minntrapprod.com/Bobcatand-Lynx/products/829/) we used in 2017–18, 2018–19, 2019-20, and 2020-21 after the lure we used previously (Pikauba; Luerres Forget’s Lures, http://www.leurresforget.com/product.php?id_product=15) became unavailable. Finally, it could be that lynx have disappeared from a number of camera units. Unfortunately, the changes in snow and lure were confounded for a few years, thus making it difficult to determine which factor resulted in fewer detections. However, 2019-20 and 2020-21 were normal snow years, yet the number of lynx photos was still low. This suggests that abnormal snow was not the cause of the pattern we observed. Also, the number of snow tracking units with lynx has remained fairly steady throughout the project; we can think of no reason why snow track units would remain occupied while lynx blinked out of camera units, unless just by chance. Thus, we suggest that the new lure is less effective than the original. Fortunately the original formulation, Pikauba, is again available and will be deployed for the 2021-22 survey. We plan to utilize this lure for the remainder of the survey efforts, provided it remains available. We obtained lynx detections for the first time in a unit near Mesa Mountain in the La Garitas. This detection represents the northernmost detection of lynx since surveys began. We also detected lynx for the first time in the unit that encompasses Fern Creek and lower Trout Creek west of Creede. This unit, however, is surrounded by other units where lynx have been detected several times previously. After a 1-year absence, lynx were again detected in the Barlow Creek Unit near Rico and the Pass Creek Unit near Wolf Creek Pass; lynx were not detected at the two units adjacent to Pass Creek, or at the southern Conejos Peak Unit after having been detected in all 3 last year (Figure 1). We used the R (R Development Core Team 2018) package ‘RMark’ (Laake 2018) to fit multipleseason (i.e., “dynamic”) occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the derived probability of a unit being occupied (i.e., used) by lynx over the course of the winter (ψ), along with the probability of detecting a lynx (p) given that the unit was occupied, the probability a unit that was unused in one year was used the next (i.e., “local colonization,” γ), and the probability a used unit became unused from one year to the next (i.e., “local extinction,” ε). For each model we fit for the analysis, we specified that the initial ψ in the time series should be a function of the proportion of the unit that is covered by spruce/fir forest – the single most important and consistent predictor of ψ in past analyses. For sake of comparison we fit a base model in which p was specified to be constant for the duration of the survey. Based on previous work, however, we considered several other structures for p we anticipated would fit better. We fit models that specified 1) p could vary by survey method (i.e., detection could be different for cameras compared to snowtracking), 2) p could be higher during breeding season when lynx tend to move more and are therefore more likely to be detected by track or at a camera, and 3) p for cameras deployed from 2017–21 could be different than p for other years due to the lure substitution. Additionally we fit a model in which the effect of breeding season was only allowed to act on cameras, not snowtracking. We allowed annual estimates of ε and γ to be different each year (i.e., assuming occupancy dynamics were not random but instead dependent on the year previous and the population is not at equilibrium), which allowed derived ψ to vary as freely as possible given the data. We used Akaike’s Information Criterion (AIC), adjusted for small sample size (Burnham and Anderson 2002) to identify the best-fitting model from this small set. Ultimately, we fit a linear model through the time series of ψ estimates to estimate the slope of the trend in occupancy through time. Ideally we would test other predictions of lynx occupancy to see, for instance, if colonization or extinction were influenced by bark beetles, fire, or the presence of competitors or prey species. However, we do not currently have enough data to test these predictions in addition to assessing trend, which is the highest priority. As has been the case since the inception of our monitoring program, the proportion of the sample unit covered by spruce-fir forest was significantly and positively associated with the initial occupancy estimate in the time series. Even though local colonization and extinction were allowed to vary freely from year to year, annual estimates were near zero and varied little (ε = 0.00–0.08; γ = 0.00–0.10). Accordingly, derived occupancy was relatively stable across years (ψ = 0.26–0.38). The slope of the trend 3 �in occupancy through time was slightly positive but not significantly different from zero (β = 0.017, SE = 0.01; Figure 2). These results suggests that future analyses may benefit from fitting models that hypothesize occupancy is at or near equilibrium and extinction/colonization are either Markovian (as modeled here) or possibly zero. Similar to previous years, detection probability was relatively high for snow tracking surveys (p = 0.69, SE = 0.06), lower for camera surveys (p = 0.23, SE = 0.03) using Pikauba, and lowest for camera surveys utilizing Violator 7 (p = 0.06, SE = 0.02). We estimated that 38% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 20–55%) during 2020–21 (Figure 2). The spatial distribution of lynx in the San Juan mountains remained largely unchanged (Figure 1). Table 1. Summary statistics from snow tracking effort. Lynx DNAb 8 6 Km Surveyed (Total) 884 987 Mean Km Surveyed per Visit 20.1 21.9 #CPW Personnelc 30 23 #USFS Personnelc 13 6 Season 2014–2015 2015–2016 #Units Surveyed 18 17 #Units with Lynx 7 7 2016–2017 16 8 13 7 5 703 18.0 20 8 2017–2018 14 7 9 3 1 578 19.3 14 5 2018–2019 14 6 8 2 1 510 19.6 16 5 2019–2020 14 7 11 3 2 640 19.4 15 3 2020–2021 15 9 14 12 7 790 18.8 17 3 #Lynx Tracks 12 14 #Genetic Samplesa 8 9 Number of genetic samples (scat, hair, or eDNA) collected via backtracking putative lynx tracks b Number of genetic samples that came back positive for Lynx c Number of staff that participate in the annual sampling effort a Table 2. Summary statistics from camera effort. Season 2014–2015 2015–2016 #Units Surveyed 31 31 2016–2017 33 2017–2018 35 2018–2019 35 2019–2020 36 2020–2021 35 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx 7 7 6 5 6 4 3 133,483 101,534 168,705 173,279 201,782 706,074 347,868 184 455 251 90 59 36 36 11 10 10 8 9 4 3 4 #CPW Personnel 46 33 #USFS Personnel 12 9 29 9 35 8 31 7 29 6 23 5 �a) b) Years With Lynx Detections 0 010 • 2') □ , 10 . .J - Zlo • l) - llo • 1) - • 10 • 1) - 510 • <) - •10 • 6) - 71o -7) Figure 1. Lynx monitoring results for a) the current sampling season (2020–2021) and b) the cumulative monitoring effort (2014–2021), San Juan Mountains, southwest Colorado. Colored units (n = 50) depicted here are those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 12 units in 2020−2021 and 24 units cumulatively since monitoring began in 2014−2015. 5 �1.0 0.9 0.8 0.7 >, 0.6- C. :::J 0.5- u C ro u u 0 0.4 0.3 0.2 0.1 0.0 I I I I I I I 2014 2015 2016 2017 2018 2019 2020 Figure 2. Occupancy estimates (Ψ) and trend (including 95%CI) for Canada lynx in the San Juan Mountains, southwest Colorado. ERRATA: We note here that some data in Tables 1 and 2, and Figure 1 are incongruent with reports issued for the previous two seasons. This was due to inadvertent removal of filters in our database that were originally set to exclude pilot data from report tables, figures, and input files. These filters have been restored. The cumulative tables and figures presented here are accurate and supersede discrepancies with previous reports. Literature Cited Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition. Springer, New York, New York, USA. Ivan, J. S. 2013. Statewide monitoring of Canada lynx in Colorado: evaluation of options. Pages 15–27 in Wildlife research report: Mammals. Colorado Parks and Wildlife., Fort Collins, USA. http://cpw.state.co.us/learn/Pages/ResearchMammalsPubs.aspx. Laake, J. L. 2018. Package “RMark”: R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. No Title. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplem:120–138. 6 �Appendix 1. Model selection results for lynx monitoring data collected in the San Juan Mountains, Colorado, 2014–2021. Rankings are based on Akaike’s Information Criterion adjusted for small sample size (AICc). We mostly sought to tease out best fitting models for detection, allowing constant detection (.), along with effects for survey type (ST), breeding season (B), substituting Violator 7 lure for Pikauba (V), and interactions to allow lure and breeding to act only on cameras. For these models we fixed the initial ψ to be a function of spruce-fir forest while local extinction (ε) and colonization (γ) were estimated annually to allow for non-equilibrium estimates in ψ that depended on previous year’s occupancy state. Post-hoc, we added tested for equilibrium conditions (ε (.) γ (.) ) or that occupancy from year to year was random ({ε = 1- γ}). Model ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+V+ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B+V+ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B+V+ST*B+ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B) ψ (Prop Spruce/Fir) ε (.) γ (.) p (.) ψ (Prop Spruce/Fir) ε (t) γ (t) p (.) ψ (Prop Spruce/Fir) {ε = 1- γ}p (1) 7 AICc 674.04 675.88 676.77 697.55 699.41 749.98 768.42 914.99 ∆AICc 0.00 1.85 2.74 23.52 25.38 75.95 94.38 240.95 AICc Wts 0.61 0.24 0.15 0.00 0.00 0.00 0.00 0.00 No. Par. 17 18 19 15 16 4 14 8 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada lynx monitoring in Colorado 2021 – 2022 Period Covered: July 1, 2021 − June 30, 2022 Principal Investigators: Eric Odell, Eric.Odell@state.co.us; Morgan Hertel, Morgan.Hertel@state.co.us; Jake Ivan, Jake.Ivan@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 1999−2006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success and that the population of Canada lynx in the state was apparently viable and self-sustaining. In order to track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. From 2014−2022 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During the 2021−2022 winter, personnel from CPW and USFS completed the eighth year of monitoring work on this same sample. Fourteen units were sampled via snow-tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (snow-covered paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. The remaining 36 units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2021. Cameras were lured with visual attractants and scent lure to enhance detection of lynx in the area. Cameras were retrieved during summer or fall 2022 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 692 km during snow tracking surveys and detected only 6 lynx tracks at 4 units, both all-time low for the program (Table 1). Significantly, more photos were collected in the past three seasons than in the first 5 seasons of sampling. This can be mostly attributed to the use of new, more sensitive cameras along with new, high-capacity memory cards. After four seasons (2017-2020) in which we collected the fewest lynx photos of any set of years on the project (<50% of the number of lynx photos taken during the initial years of the monitoring effort), the number of lynx photos collected this year rebounded substantially (Table 2). This substantiates our previous conclusions that the Violator7 lure (in use during those 4 season) was less effective than the Pikauba lure used this year and during the first 3 years of sampling. Pikauba will be utilized for the remainder of the survey efforts, provided it remains available. 8 �We obtained lynx detections in the La Garita Mountains north of Creede for first time in 5 years. Lynx were detected in the two units near Conejos Peak after having not been detected last year. Snowtracking surveys did not provide lynx detections in either the Mineral Creek or Molas Pass units near Silverton, nor at the Lime Creek unit south of Creede. This lack of detections is notable because these 3 units are among the most reliable for detecting lynx in the entire study area; each has provided lynx detections for 6–7 of the 8 years these areas have been surveyed (Figure 1). We used the R package (R Development Core Team 2018) ‘RMark’ (Laake 2018) to fit multipleseason (i.e., “dynamic”) occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the derived probability of a unit being occupied (i.e., used) by lynx over the course of the winter (ψ), along with the probability of detecting a lynx (p) given that the unit was occupied, the probability a unit that was unused in one year was used the next (i.e., “local colonization,” γ), and the probability a used unit became unused from one year to the next (i.e., “local extinction,” ε). For each model we fit for the analysis, we specified that the initial ψ in the time series should be a function of the proportion of the unit that is covered by spruce/fir forest – the single most important and consistent predictor of ψ in past analyses. For sake of comparison we fit a base model in which p was specified to be constant for the duration of the survey. However, based on previous work, we considered several other structures for p we anticipated would fit better. We fit models that specified 1) p could vary by survey method (i.e., detection could be different for cameras compared to snowtracking), 2) p could be higher during breeding season when lynx tend to move more and are therefore more likely to be detected by track or at a camera, and 3) p for cameras deployed from 2017–21 could be different than p for other years due to the lure substitution. Additionally we fit a model in which the effect of breeding season was only allowed to act on cameras, not snowtracking. We allowed annual estimates of ε and γ to be different each year (i.e., assuming occupancy dynamics were not random but instead dependent on the year previous and the population is not at equilibrium), which allowed derived ψ to vary as freely as possible given the data. We used Akaike’s Information Criterion (AIC), adjusted for small sample size (Burnham and Anderson 2002) to identify the best-fitting model from this small set. Ultimately, we fit a linear model through the time series of ψ estimates to estimate the slope of the trend in occupancy through time. Ideally we would test other predictions of lynx occupancy to see, for instance, if colonization or extinction were influenced by bark beetles, fire, or the presence of competitors or prey species. However, we do not currently have enough data to test these predictions in addition to assessing trend, which is the highest priority. As has been the case since the inception of our monitoring program, the proportion of the sample unit covered by spruce-fir forest was significantly and positively associated with the initial occupancy estimate in the time series. Even though local colonization and extinction were allowed to vary freely from year to year, annual estimates were near zero and varied little (ε = 0.00–0.08; γ = 0.00–0.10) up until the most recent season when extinction probability was high (ε = 0.40, SE = 0.15). Accordingly, derived occupancy was relatively stable across years (ψ = 0.26–0.35), but dropped to the lowest level observed to date this past season (ψ = 0.23, SE = 0.07). The slope of the trend in occupancy through time was zero (β = 0.001, SE = 0.01; Figure 2), indicating stability. Similar to previous years, detection probability was relatively high for snow tracking surveys (p = 0.65, SE = 0.06), lower for camera surveys (p = 0.22, SE = 0.03) using Pikauba, and lowest for camera surveys utilizing Violator 7 (p = 0.06, SE = 0.02). We estimated that 24% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 11–37%) during 2021–22 (Figure 2). The broad spatial distribution of lynx in the San Juan’s remained largely unchanged with the exception of no detection in 3 core snow tracking units where lynx are usually detected (Figure 1). 9 �Table 1. Summary statistics from snow tracking effort. Lynx DNAb 8 6 Km Surveyed (Total) 884 987 Mean Km Surveyed per Visit 20.1 21.9 #CPW Personnelc 30 23 #USFS Personnelc 13 6 18.0 20 8 Season 2014-2015 2015-2016 #Units Surveyed 18 17 #Units with Lynx 7 7 2016-2017 16 8 13 7 5 703 2017-2018 14 7 9 3 1 578 19.3 14 5 2018-2019 14 6 8 2 1 510 19.6 16 5 2019-2020 14 7 11 3 2 640 19.4 15 3 2020-2021 15 9 14 12 7 790 18.8 17 3 2021-2022 13 4 6 5 4 692 18.7 11 3 #Lynx Tracks 12 14 #Genetic Samplesa 8 9 Number of genetic samples (scat, hair, or eDNA) collected via backtracking putative lynx tracks b Number of genetic samples that came back positive for Lynx c Number of staff that participate in the annual effort a Table 2. Summary statistics from camera effort. Season 2014-2015 #Units Surveyed 31 2015-2016 31 2016-2017 33 2017-2018 35 2018-2019 35 2019-2020 36 2020-2021 2021-2022 35 35 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx 7 7 6 5 6 4 3 5 133,483 101,534 168,705 173,279 201,782 706,074 347,868 576,288 184 455 251 90 59 36 36 116 11 10 10 8 9 4 3 7 10 #CPW Personnel 46 #USFS Personnel 12 33 9 29 9 35 8 31 7 29 6 23 23 5 4 �a) b) Figure 1. Lynx monitoring results for a) the current sampling season (2021–2022) and b) the cumulative monitoring effort (2014–2022), San Juan Mountains, southwest Colorado. Colored units (n = 50) depicted here are those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 9 units in 2021−2022 and 25 units cumulatively since monitoring began in 2014−2015. 11 �1.00.90.80.7>, u ro 0.6- C C. :::J u u 0 0.50.40.30.20.1 0.0I I I I I I I I 2014 2015 2016 2017 2018 2019 2020 2021 Figure 2. Occupancy estimates (Ψ) and trend (including 95%CI) for Canada lynx in the San Juan Mountains, southwest Colorado. Literature Cited Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition. Springer, New York, New York, USA. Ivan, J. S. 2013. Statewide monitoring of Canada lynx in Colorado: evaluation of options. Pages 15–27 in Wildlife Research Report: Mammals. Colorado Parks and Wildlife, Fort Collins, USA. http://cpw.state.co.us/learn/Pages/ResearchMammalsPubs.aspx. Laake, J. L. 2018. Package “RMark”: R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. No Title. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplem:120–138. 12 �Appendix 1. Model selection results for lynx monitoring data collected in the San Juan Mountains, Colorado, 2014–2022. Rankings are based on Akaike’s Information Criterion adjusted for small sample size (AICc). We mostly sought to tease out best fitting models for detection, allowing constant detection (.), along with effects for survey type (ST), breeding season (B), substituting Violator 7 lure for Pikauba (V), and interactions to allow lure and breeding to act only on cameras. For these models we fixed the initial ψ to be a function of spruce-fir forest while local extinction (ε) and colonization (γ) were estimated annually to allow for non-equilibrium estimates in ψ that depended on previous year’s occupancy state. Post-hoc, we added tested for equilibrium conditions (ε (.) γ (.) ) or that occupancy from year to year was random ({ε = 1- γ}). Model ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+V+ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B+V+ST*B+ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B+V+ ST*V) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST) ψ (Prop Spruce/Fir) ε (t) γ (t) p (ST+B) ψ (Prop Spruce/Fir) ε (.) γ (.) p (.) ψ (Prop Spruce/Fir) ε (t) γ (t) p (.) ψ (Prop Spruce/Fir) {ε = 1- γ}p (.) 13 AICc 784.65 786.47 786.86 804.81 807.00 859.30 880.01 1038.81 ∆AICc 0.00 1.81 2.21 20.16 22.34 74.64 95.36 254.16 AICc Wts 0.58 0.23 0.19 0.00 0.00 0.00 0.00 0.00 No. Par. 19 21 20 17 18 4 16 9 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada lynx monitoring in Colorado 2022 – 2023 Period Covered: December 1, 2022 − April 30, 2023 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Tim Brtis; Lori McCurdy All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 1999−2006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success and that the population of Canada lynx in the state was apparently viable and self-sustaining. In order to track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. From 2014−2023 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During the 2022−2023 winter, personnel from CPW and USFS completed the ninth year of monitoring work on this same sample. Thirteen units were sampled via snow-tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (snow-covered paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. Thirty-five units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2022. Cameras were lured with visual attractants and scent lure to enhance detection of lynx in the area. Cameras were retrieved during summer or fall 2023 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 730 km during snow tracking surveys and detected 10 lynx tracks at 5 units (Table 1). This is a slight increase over the program-low of 6 tracks in 4 units observed in 2021–22. Lynx were detected via camera sampling in only one unit during the 2023–23 survey season, which is two fewer units than the previous program low for cameras, which was observed in 2020–21. Snow depths during the 2022–23 season were among the highest ever recorded and a number of cameras were buried for days to weeks, which could have resulted in fewer lynx detections. Also, after 9 seasons of sampling, perhaps resident individuals are developing fatigue to the lures used on the project. In response to the potential for lure fatigue, 117 cameras were passively (i.e., no lure) deployed along roads, trails, and other potential travel routes during fall 2023 in 16 camera units that have had lynx detections in the past. Deployments followed protocols established by (King et al. 2020) and (Anderson et al. 2023). These cameras will be retrieved in summer 2024. Detections at these deployments, and not at traditional camera stations in the same unit, would support the notion that lynx are exhibiting lure fatigue, and future 2 �sampling could switch to passive sampling to capture lynx moving along natural travel routes rather than luring them to a predetermined camera set. Given the program-low in snowtracking detections in 2021– 22, and program-low in camera detections this season (2022–23), it is also possible that lynx distribution declined sharply over the past two survey seasons, which would indicate a decline in the population as well. Lynx were once again detected during snowtrack surveys at Molas Pass and South Mineral, after having gone undetected there in 2021–22. Cameras picked up lynx near Wolf Creek Pass for only the 3rd time in 9 years of sampling, but failed to detect lynx at Rio Grand Reservoir, Lizard Head Pass, and Conejos Peak for only the 2nd or 3rd time since the monitoring program began (Figure 1). We used the R package (R Development Core Team 2018) ‘RMark’ (Laake 2018) to fit multipleseason (i.e., “dynamic”) occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the derived probability of a unit being occupied (ψ), or used, by lynx over the course of the winter, along with the probability of detecting a lynx (p) given that the unit was occupied, the probability a unit that was unused in one year was used the next (i.e., “local colonization,” γ), and the probability a used unit became unused from one year to the next (i.e., “local extinction,” ε). For each model we fit for the analysis, we specified that the initial ψ in the time series should be a function of the proportion of the unit that is covered by spruce/fir forest – the single most important and consistent predictor of ψ in past analyses. For sake of comparison we fit a base model in which p was specified to be constant for the duration of the survey. However, based on previous work, we considered several other structures for p we anticipated would fit better. We fit models that specified 1) p could vary by survey method (i.e., detection could be different for cameras compared to snowtracking), 2) p could be higher during breeding season when lynx tend to move more and are therefore more likely to be detected by track or at a camera, and 3) p for cameras deployed from 2017–21 could be different than p for other years due to the lure substitution. Additionally we fit a model in which the effect of breeding season was only allowed to act on cameras, not snowtracking. We allowed annual estimates of ε and γ to be different each year (i.e., assuming occupancy dynamics were not random but instead dependent on the year previous and the population is not at equilibrium), which allowed derived ψ to vary as freely as possible given the data. We used Akaike’s Information Criterion (AIC), adjusted for small sample size (Burnham and Anderson 2002) to identify the best-fitting model from this small set. Ultimately, we fit a linear model through the time series of ψ estimates to estimate the slope of the trend in occupancy through time. Ideally we would test other predictions of lynx occupancy to see, for instance, if colonization or extinction were influenced by bark beetles, fire, or the presence of competitors or prey species. However, we do not currently have enough data to test these predictions in addition to assessing trend, which is the highest priority. As has been the case since the inception of our monitoring program, the proportion of the sample unit covered by spruce-fir forest was positively associated with the initial occupancy estimate in the time series. Even though local colonization and extinction were allowed to vary freely from year to year, annual estimates were near zero and varied little (ε = 0.00–0.11; γ = 0.00–0.10) up until the most recent 2 seasons when extinction probability was high (ε21–22 = 0.36, SE = 0.18; ε22–23 = 0.73, SE = 0.17). Accordingly, derived occupancy was relatively stable across years (ψ = 0.25–0.34), but dropped to the lowest level observed to date this past season (ψ = 0.11, SE = 0.05). The slope of the trend in occupancy through time was slightly negative but not statistically different from zero (β = -0.007, SE = 0.01; Figure 2). Similar to previous years, detection probability was relatively high for snow tracking surveys (p = 0.65, SE = 0.06), lower for camera surveys (p = 0.22, SE = 0.03) using Pikauba, and lowest for camera surveys utilizing Violator 7 (p = 0.06, SE = 0.02). We estimated that 11% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 2–20%) during 2022–23 (Figure 2). 3 �Table 1. Summary statistics from snow tracking effort. Lynx DNAb 8 Km Surveyed (Total) 884 Mean Km Surveyed per Visit 20.1 #CPW Personnelc 30 #USFS Personnelc 13 Season 2014-2015 #Units Surveyed 18 #Units with Lynx 7 2015-2016 17 7 14 9 6 987 21.9 23 6 2016-2017 16 8 13 7 5 703 18.0 20 8 2017-2018 14 7 9 3 1 578 19.3 14 5 2018-2019 14 6 8 2 1 510 19.6 16 5 2019-2020 14 7 11 3 2 640 19.4 15 3 2020-2021 15 9 14 12 7 790 18.8 17 3 2021-2022 13 4 6 5 4 692 18.7 11 3 2022-2023 15 5 10 9 7 730 18.3 15 2 #Lynx Tracks 12 #Genetic Samplesa 8 Number of genetic samples (scat, hair, or eDNA) collected via backtracking putative lynx tracks b Number of genetic samples that came back positive for Lynx c Number of staff that participate in the annual effort a Table 2. Summary statistics from camera effort. Season 2014-2015 #Units Surveyed 31 2015-2016 31 2016-2017 33 2017-2018 35 2018-2019 35 2019-2020 36 2020-2021 35 2021-2022 35 2022-2023 35 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx 7 7 6 5 6 4 3 5 1 133,483 101,534 168,705 173,279 201,782 706,074 347,868 576,288 531,083 184 455 251 90 59 36 36 116 4 11 10 10 8 9 4 3 7 1 4 #CPW Personnel 46 #USFS Personnel 12 33 9 29 9 35 8 31 7 29 6 23 5 23 4 31 3 �a) b) Figure 1. Lynx monitoring results for a) the current sampling season (2022–2023) and b) the cumulative monitoring effort (2014–2023), San Juan Mountains, southwest Colorado. Colored units (n = 50) depicted here are those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 6 units in 2022−2023 and 25 units cumulatively since monitoring began in 2014−2015. 5 �1.00.9 0.8 0.7- > u 0.6 - <ti 0.. ::::, 0.5 - 0 0.4- C: u u 0.3 0.2 0.1 0.0I I I I I I I I I 2014 2015 2016 2017 2018 2019 2020 2021 202;1 Figure 2. Occupancy estimates (Ψ) and trend (including 95% CI) for Canada lynx in the San Juan Mountains, southwest Colorado. LITERATURE CITED Anderson, A. K., J. S. Waller, and D. H. Thornton. 2023. Canada lynx occupancy and density in Glacier National Park. Journal of Wildlife Management e22383:1–24. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. Springer, New York, New York, USA. Ivan, J. S. 2013. Statewide monitoring of Canada lynx in Colorado: evaluation of options. Pages 15–27 in Wildlife research report - mammals. Colorado Parks and Wildlife., Fort Collins, Colorado, USA. http://cpw.state.co.us/learn/Pages/ResearchMammalsPubs.aspx. King, T. W., C. Vynne, D. Miller, S. Fisher, S. Fitkin, J. Rohrer, J. I. Ransom, and D. Thornton. 2020. Will lynx lose their edge? Canada lynx occupancy in Washington. Journal of Wildlife Management 84:705–725. Laake, J. L. 2018. Package “RMark”: R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. No Title. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplem:S120–S138. 6 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Canada lynx monitoring in Colorado 2023 – 2024 Period Covered: December 1, 2023  December 30, 2024 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Tim Brtis All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. In an effort to restore a viable population of Canada lynx (Lynx canadensis) to the southern portion of their former range, 218 individuals were reintroduced into Colorado from 19992006. In 2010, the Colorado Division of Wildlife (now Colorado Parks and Wildlife [CPW]) determined that the reintroduction effort met all benchmarks of success and that the population of Canada lynx in the state was apparently viable and self-sustaining. In order to track the persistence of this new population and thus determine the long-term success of the reintroduction, a minimally-invasive, statewide monitoring program is required. From 2014−2024 CPW initiated a portion of the statewide monitoring scheme described in Ivan (2013) by completing surveys in a random sample of monitoring units (n = 50) from the San Juan Mountains in southwest Colorado (n = 179 total units; Figure 1). During the 2023−2024 winter, personnel from CPW and USFS completed the tenth year of monitoring work on this same sample. Fifteen units were sampled via snow-tracking surveys conducted between December 1 and March 31. On each of 1–3 independent occasions, survey crews searched roadways (snow-covered paved roads and logging roads) and trails for lynx tracks. Crews searched the maximum linear distance of roads possible within each survey unit given safety and logistical constraints. Each survey covered a minimum of 10 linear kilometers (6.2 miles) distributed across at least 2 quadrants of the unit. Thirty-five units could not be surveyed via snow tracking. Instead, survey crews deployed 4 passive infrared motion cameras in each of these units during fall 2023. Cameras were lured with visual attractants and scent lure to enhance detection of lynx in the area. Cameras were retrieved during summer or fall 2024 and all photos were archived and viewed by at least 2 observers to determine species present in each. Camera data were then binned such that each of 10 15-day periods from December 1 through April 30 was considered an ‘occasion,’ and any photo of a lynx obtained during a 15-day period was considered a ‘detection’ during that occasion. Surveyors covered 826 km during snow tracking surveys and detected 11 lynx tracks at 6 units (Table 1). This is considered a rebound from the program-low of 6 tracks in 4 units observed in 2021–22. Lynx were detected via camera sampling in 3 units during the 2023–24 survey season, which also represents a rebound from the previous program low (1 unit) for cameras, which was observed in 2022– 23. In response to program low in camera detections during the 2022-23 winter, and our thinking that a potential explanation could be fatigue to the lured camera sets in use for nearly a decade, 117 cameras were passively (i.e., no lure) deployed along roads, trails, and other potential travel routes during fall 2023 in 16 camera units that have had lynx detections in the past. Deployments followed protocols established by (King et al. 2020) and (Anderson et al. 2023). These cameras were retrieved in summer 2024. During the usual analysis period we recorded 384 lynx detections at 11 units, 35 cameras. That is, the passive sampling scheme produced lynx detection in more units and cameras than at any point during the decade-long sampling using traditional lured sets. It also produced more lynx photos than all but one 2 �year during that same timeframe. Coupled with the increased efficiency and reduced cost of deploying passive sets, the monitoring program will transition to this methodology in coming years. Lynx were once again detected in the upper Rio Grande Reservoir and Conejos Peak areas, after having gone undetected there in 2022–23. However, no lynx were detected near Lizard Head Pass or west of Lake City for the second year in a row (Figure 1). We used the R package (R Development Core Team 2018) ‘RMark’ (Laake 2018) to fit multipleseason (i.e., “dynamic”) occupancy models (MacKenzie et al. 2006) to our survey data using program MARK (White and Burnham 1999). Thus, we estimated the derived probability of a unit being occupied (), or used, by lynx over the course of the winter, along with the probability of detecting a lynx (p) given that the unit was occupied, the probability a unit that was unused in one year was used the next (i.e., “local colonization,” ), and the probability a used unit became unused from one year to the next (i.e., “local extinction,” ). For each model we fit for the analysis, we specified that the initial  in the time series should be a function of the proportion of the unit that is covered by spruce/fir forest – the single most important and consistent predictor of  in past analyses. For sake of comparison we fit a base model in which p was specified to be constant for the duration of the survey. However, based on previous work, we considered several other structures for p we anticipated would fit better. We fit models that specified 1) p could vary by survey method (i.e., detection could be different for cameras compared to snowtracking), 2) p could be higher during breeding season when lynx tend to move more and are therefore more likely to be detected by track or at a camera, and 3) p for cameras deployed from 2017–21 could be different than p for other years due to a lure substitution. Additionally we fit a model in which the effect of breeding season was only allowed to act on cameras, not snowtracking. We allowed annual estimates of  and  to be different each year (i.e., assuming occupancy dynamics were not random but instead dependent on the year previous and the population is not at equilibrium), which allowed derived  to vary as freely as possible given the data. We used Akaike’s Information Criterion (AIC), adjusted for small sample size (Burnham and Anderson 2002) to identify the best-fitting model from this small set. Ultimately, we fit a linear model through the time series of  estimates to estimate the slope of the trend in occupancy through time. Ideally we would test other predictions of lynx occupancy to see, for instance, if colonization or extinction were influenced by bark beetles, fire, or the presence of competitors or prey species. However, we do not currently have enough data to test these predictions in addition to assessing trend, which is the highest priority. As has been the case since the inception of our monitoring program, the proportion of the sample unit covered by spruce-fir forest was positively associated with the initial occupancy estimate in the time series. Even though local colonization and extinction were allowed to vary freely from year to year, annual estimates were near zero and varied little ( = 0.00–0.11;  = 0.00–0.8) except for the the interval between the 2021–22 and 2022–23 seasons when extinction probability was high (21–22 = 0.35, SE = 0.14). Accordingly, derived occupancy was relatively stable across years ( = 0.25–0.31), but dropped to a program low the past two winters ( ≈ 0.17, SE = 0.05). The slope of the trend in occupancy through time was slightly negative but not statistically different from zero ( = -0.008, SE = 0.01; Figure 2). Similar to previous years, detection probability was relatively high for snow tracking surveys (p = 0.60, SE = 0.05), lower for camera surveys (p = 0.21, SE = 0.02) using Pikauba, and lowest for camera surveys utilizing Violator 7 (p = 0.07, SE = 0.02). We estimated that 17% of the sample units in the San Juan’s were occupied by lynx (95% confidence interval: 6–27%) during 2023–24 (Figure 2). 3 �Table 1. Summary statistics from snow tracking effort. Lynx DNAb 8 Km Surveyed (Total) 884 Mean Km Surveyed per Visit 20.1 #CPW Personnelc 30 #USFS Personnelc 13 Season 2014-2015 #Units Surveyed 18 #Units with Lynx 7 2015-2016 17 7 14 9 6 987 21.9 23 6 2016-2017 16 8 13 7 5 703 18.0 20 8 2017-2018 14 7 9 3 1 578 19.3 14 5 2018-2019 14 6 8 2 1 510 19.6 16 5 2019-2020 14 7 11 3 2 640 19.4 15 3 2020-2021 15 9 14 12 7 790 18.8 17 3 2021-2022 13 4 6 5 4 692 18.7 11 3 2022-2023 15 5 10 9 7 730 18.3 15 2 2023-2024 15 6 11 10 6 826 19.7 14 3 #Lynx Tracks 12 #Genetic Samplesa 8 Number of genetic samples (scat, hair, or eDNA) collected via backtracking putative lynx tracks b Number of genetic samples that came back positive for lynx c Number of staff that participate in the annual effort a Table 2. Summary statistics from camera effort. Season 2014-2015 #Units Surveyed 31 2015-2016 31 2016-2017 33 2017-2018 35 2018-2019 35 2019-2020 36 2020-2021 35 2021-2022 35 2022-2023 35 2023-2024 35 #Units With Lynx #Photos (Total) #Photos (Lynx) #Cameras With Lynx 7 7 6 5 6 4 3 5 1 3 133,483 101,534 168,705 173,279 201,782 706,074 347,868 576,288 531,083 601,371 184 455 251 90 59 36 36 116 4 336 11 10 10 8 9 4 3 7 1 4 4 #CPW Personnel 46 #USFS Personnel 12 33 9 29 9 35 8 31 7 29 6 23 5 23 4 31 3 24 3 �a) b) Figure 1. Lynx monitoring results for a) the current sampling season (2023–2024) and b) the cumulative monitoring effort (2014–2024), San Juan Mountains, southwest Colorado. Colored units (n = 50) depicted here are those selected at random from the population of units (n = 179) encompassing lynx habitat in the San Juan Mountains. Lynx were detected in 8 units in 2023−2024 and 25 units cumulatively since monitoring began in 2014−2015. 5 �1.00.90.80.7- > u 0.6- RJ 0.. ::::, 0.5- 0 0.4- C u u 0.30.20.10.0I I I I I I I I I I 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Figure 2. Occupancy estimates () and trend (including 95%CI for each) for Canada lynx in the San Juan Mountains, southwest Colorado. Literature Cited Anderson, A. K., J. S. Waller, and D. H. Thornton. 2023. Canada lynx occupancy and density in Glacier National Park. Journal of Wildlife Management e22383:1–24. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition. Springer, New York, New York, USA. Ivan, J. S. 2013. Statewide monitoring of Canada lynx in Colorado: evaluation of options. Pages 15–27 in Wildlife Research Report - Mammals. Colorado Parks and Wildlife., Fort Collins, Colorado, USA.https://spl.cde.state.co.us/artemis/nrserials/nr616internet/nr616201213internet.pdf. King, T. W., C. Vynne, D. Miller, S. Fisher, S. Fitkin, J. Rohrer, J. I. Ransom, and D. Thornton. 2020. Will lynx lose their edge? Canada lynx occupancy in Washington. Journal of Wildlife Management 84:705–725. Laake, J. L. 2018. Package “RMark”: R Code for Mark Analysis. Version 2.2.5. https://cran.rproject.org/web/packages/RMark/RMark.pdf. MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Oxford, United Kingdom. R Development Core Team. 2018. No Title. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46 Supplem:120–138. 6 � https://cpw.cvlcollections.org/files/original/810c8d21f39c8922d072fc63f5696df1.pdf 527ce74442b45924ff49391ec9954940 PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Evaluation of accelerometer collars and methods development for domestic cattle Period Covered: January 1, 2023-December 31, 2023 Principal Investigators: Ellen Brandell, ellen.brandell@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Livestock production is an important component of Colorado’s economy (University of Arkansas accessed 2023, Bureau of Land Management accessed 2023), as well as ingrained in the state’s culture and heritage – cattle production in particular. Colorado citizens are concerned about the effects of reestablishing gray wolves (Canis lupus) on livestock (Niemiec et al. 2022), and given the geographic constraints of CRS 33-2-105.8 (Colorado General Assembly 2020, CPW 2023) and suitable wolf habitat in Colorado (Ditmer et al. 2022), wolves and livestock will spatially overlap in western Colorado. Wolves may affect livestock both directly and indirectly; direct effects include depredation, which has already occurred in the state. Indirect effects, such as increased stress or vigilance behavior, are much more difficult to observe and quantify. Indirect effects of wolves on cattle have been documented in other western states or laboratory experiments, such as decreased weight gain (Ramler et al. 2014) and increased stress (Cooke et al. 2013). However, these negative effects are not ubiquitous across studies, and the majority of published literature on this topic lacks a mechanistic understanding. For example, cattle movement rates (Laporte et al. 2010, Bailey et al. 2018) and physiology (Cooke et al. 2013) in response to wolf presence have been studied, but unless changes in movement rates or physiology have direct implications for weight gain, pregnancy rates, or animal health, it might not be important to a producer or impact the operation’s economics. In a future research project, we aim to link cattle behavior and movement in response to wolf presence to cattle stress levels, weight gain, and pregnancy rates. Quantifying the mechanisms of changes in cattle stress, weight gain, and pregnancy rates is critical for identifying whether a causal relationship exists between wolf exposure and cattle responses, the magnitude of this effect, and subsequent consequences for producers’ bottom line. However, before we can launch a research project, we need to test the field equipment and develop data collection methods. In spring 2023, we began a methods testing project to evaluate GPS and accelerometer collars on beef cattle. We had three goals of this methods testing project: (1) assess proper fit of GPS/accelerometer collars on both adult female cows and calves throughout the grazing season; (2) develop methods to calibrate accelerometer data to common cattle behaviors; (3) test field equipment, and improve equipment as needed. We outfitted 20 cows with collars in May and June 2023. More specifically, we collared and monitored 10 cow-calf pairs from two cattle operations (one in Northeast Colorado, one in Northwest Colorado). Cow-calf pairs are of interest as calves are the most vulnerable to predation. Data collection ranged from approximately 1-5 months while cattle were grazing on allotments (e.g., USFS, BLM). We obtained a high-quality visual observation of all collared animals at least twice per month, and often multiple times a week. Visual observations were obtained by CPW staff, the livestock owner, or ranch personnel. Animal condition and collar fit was assessed visually, and with associated photos and video 36 �where possible. We used this information to determine if collars needed to be periodically adjusted. Calf collars had a section of elastic to allow for growth in between adjustments. Accelerometers collect triaxial data (x, y, and z axes) 8 times per second (8 Hz). Accelerometers have been used on cattle and other grazing species to identify behaviors and quantify time budgets (Riaboff et al. 2020, Riaboff et al. 2022). We will create time budgets by specifying cattle behaviors such as feeding, resting, ruminating, moving, acting vigilant, and grooming. We will calibrate cattle behavior by performing focal follows, where an individual cow or calf is observed for a predetermined amount of time (20 minutes), and the timing of different behaviors is recorded (Riaboff et al. 2022). One adult female cow per operation was outfitted with a camera collar as well to provide constant behavioral validation data. The observation data is compared with the triaxial data patterns, and unique data patterns are labeled as specific behaviors using machine learning algorithms (Riaboff et al. 2020, Riaboff et al. 2022). Collars will also collect geospatial data at short, regular intervals to calculate distance moved and movement rates (Bailey et al. 2018). We are currently organizing and analyzing these data. Experiences from this methods testing project will help guide equipment decisions, data collection methods, and fieldwork as we develop a larger-scale research project focusing on indirect effects of predators on livestock. Literature Cited Bailey, D. W., M. G. Trotter, C. W. Knight, and M. G. Thomas. 2018. Use of GPS tracking collars and accelerometers for rangeland livestock production research. Translational Animal Science 2:81-88. Bureau of Land Management. Colorado rangeland management and grazing. <https://www.blm.gov/programs/natural-resources/rangeland-and-grazing/rangeland-health/colorado>. Accessed 2023. Clark, P. E., D. E. Johnson, L. L. Larson, M. Louhaichi, T. Roland, and J. Williams. 2017. Effects of wolf presence on daily travel distance of range cattle. Rangeland ecology & management 70:657-665. Colorado General Assembly. 2020 Colorado ballot analysis, proposition 114, reintroduction and management of gray wolves. <https://leg.colorado.gov/ballots/reintroduction-and-management-graywolves>. Accessed 2023. Colorado Parks and Wildlife. 2023. Colorado wolf restoration and management plan. Denver, USA. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. Mccorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, and B. L. Murphie. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1-45. Ditmer, M. A., G. Wittemyer, S. W. Breck, and K. R. Crooks. 2022. Defining ecological and socially suitable habitat for the reintroduction of an apex predator. Global Ecology and Conservation 38:e02192. Laporte, I., T. B. Muhly, J. A. Pitt, M. Alexander, and M. Musiani. 2010. Effects of wolves on elk and cattle behaviors: implications for livestock production and wolf conservation. PLoS One 5:e11954. Niemiec, R., R. E. Berl, M. Gonzalez, T. Teel, J. Salerno, S. Breck, C. Camara, M. Collins, C. Schultz, and D. Hoag. 2022. Rapid changes in public perception toward a conservation initiative. Conservation Science and Practice 4:e12632. Ramler, J. P., M. Hebblewhite, D. Kellenberg, and C. Sime. 2014. Crying wolf? A spatial analysis of wolf location and depredations on calf weight. American Journal of Agricultural Economics 96:631-656. Riaboff, L., S. Couvreur, A. Madouasse, M. Roig-Pons, S. Aubin, P. Massabie, A. Chauvin, N. Bédère, and G. Plantier. 2020. Use of predicted behavior from accelerometer data combined with GPS data to explore the relationship between dairy cow behavior and pasture characteristics. Sensors 20:4741. Riaboff, L., L. Shalloo, A. F. Smeaton, S. Couvreur, A. Madouasse, and M. T. Keane. 2022. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Computers and Electronics in Agriculture 192:106610. 37 �University of Arkansas Division of Agriculture Research & Extension. Economic impact of agriculture. <https://economic-impact-of-ag.uada.edu/colorado/>. Accessed 2023. 38 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Evaluation of accelerometer collars and methods development for domestic cattle Period Covered: January 1, 2024-December 31, 2024 Principal Investigator: Ellen Brandell, ellen.brandell@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Livestock production is an important component of Colorado’s economy (University of Arkansas, Bureau of Land Management), as well as ingrained in the state’s culture and heritage– cattle production in particular. Colorado citizens are concerned about the effects of re-establishing gray wolves (Canis lupus) on livestock (Niemiec et al. 2022), and given the geographic constraints of CRS 33-2-105.8 (Colorado General Assembly 2020, CPW 2023) and suitable wolf habitat in Colorado (Ditmer et al. 2022), wolves and livestock will spatially overlap in western Colorado. Wolves may affect livestock both directly and indirectly; direct effects include depredation, which has already occurred in the state. Indirect effects, such as increased stress or vigilance behavior, are much more difficult to observe and quantify. Indirect effects of wolves on cattle have been documented in other western states or laboratory experiments, such as decreased weight gain (Ramler et al. 2014) and increased stress (Cooke et al. 2013). However, these negative effects are not ubiquitous across studies, and the majority of published literature on this topic lacks a mechanistic understanding. For example, cattle movement rates (Laporte et al. 2010, Bailey et al. 2018) and physiology (Cooke et al. 2013) in response to wolf presence have been studied, but unless changes in movement rates or physiology have direct implications for weight gain, pregnancy rates, or animal health, it might not be important to a producer or impact the operation’s economics. In a future research project, we aim to link cattle behavior and movement in response to wolf presence to cattle stress levels, weight gain, and pregnancy rates. Quantifying the mechanisms of changes in cattle stress, weight gain, and pregnancy rates is critical for identifying whether a causal relationship exists between wolf exposure and cattle responses, the magnitude of this effect, and subsequent consequences for producers’ bottom line. However, before we can launch a research project, we need to test the field equipment and develop data collection methods. In spring 2023, we began a methods testing project is to evaluate GPS and accelerometer collars on beef cattle. We had three goals of this methods testing project: (1) Assess proper fit of GPS/accelerometer collars on both adult female cows and calves throughout the grazing season; (2) develop methods to calibrate accelerometer data to common cattle behaviors; and (3) test field equipment, and improve equipment as needed. We applied what we learned in 2023 to a 2024 field season where we refined our data collection protocols. We outfitted 20 cows with collars in May and June 2023. More specifically, we collared and monitored 10 cow-calf pairs from two cattle operations (one in Northeast Colorado, one in Northwest Colorado). Cow-calf pairs are of interest as calves are the most vulnerable to predation. In May 2024, we outfitted collars on 8 cows from one operation in Northwest Colorado. Data collection ranged from approximately 1-5 months while cattle were grazing on allotments (e.g., USFS, BLM) or privately owned pastures. We obtained a high-quality visual observation of all collared animals at least twice per month, and often multiple times a week. Visual observations were obtained by CPW staff, the livestock owner, or ranch personnel. Animal condition and collar fit was assessed visually, and with associated photos and 33 �video where possible. We used this information to determine if collars needed to be periodically adjusted. Calf collars had a section of elastic to allow for growth in between adjustments. Accelerometers collected triaxial data (x, y, and z axes) 8 times per second (8 Hz). Accelerometers have been used on cattle and other grazing species to identify behaviors and quantify time budgets (Riaboff et al. 2020, Riaboff et al. 2022). We will create time budgets by specifying cattle behaviors such as feeding, resting, ruminating, moving, acting vigilant, and grooming. We will calibrate cattle behavior by correlating observer behavior with accelerometer data. We performed focal follows, where an individual cow or calf is observed for a predetermined amount of time (20 minutes), and recorded the timing of different behaviors (Riaboff et al. 2022). In 2023, one adult female cow per operation was outfitted with a camera collar as well to provide constant behavioral validation data. Currently, we are assessing the observation data and labeling as specific behaviors; the next step is to use machine learning algorithms to correspond these behaviors with triaxial data patterns (Riaboff et al. 2020, Riaboff et al. 2022). Collars also collected geospatial data at short, regular intervals (5 minutes), which will be used to calculate distance moved and movement rates (Bailey et al. 2018). We are currently organizing and cleaning these locational data. Experiences from this methods testing project will help guide equipment decisions, data collection methods, and fieldwork as we develop a larger-scale research project focusing on indirect effects of predators on livestock. Data collected in 2023 and 2024 should be adequate to develop time budgets and behavioral models. We plan to analyze these data before moving forward with a full research project. The timeline for this research is also dependent on wolf activity in the state and partnerships with livestock producers, and therefore we will be adaptable moving forward. Literature Cited: Bailey, D. W., M. G. Trotter, C. W. Knight, and M. G. Thomas. 2018. Use of GPS tracking collars and accelerometers for rangeland livestock production research. Translational Animal Science 2:81-88. Bureau of Land Management. Colorado rangeland management and grazing. <https://www.blm.gov/programs/natural-resources/rangeland-and-grazing/rangelandhealth/colorado>. Accessed 2023. Clark, P. E., D. E. Johnson, L. L. Larson, M. Louhaichi, T. Roland, and J. Williams. 2017. Effects of wolf presence on daily travel distance of range cattle. Rangeland ecology & management 70:657-665. Colorado General Assembly. 2020 Colorado ballot analysis, proposition 114, reintroduction and management of gray wolves. <https://leg.colorado.gov/ballots/reintroduction-and-management-graywolves>. Accessed 2023. Colorado Parks and Wildlife. 2023. Colorado wolf restoration and management plan. Denver, USA. Cook, R. C., J. G. Cook, D. J. Vales, B. K. Johnson, S. M. Mccorquodale, L. A. Shipley, R. A. Riggs, L. L. Irwin, S. L. Murphie, and B. L. Murphie. 2013. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildlife Monographs 184:1-45. Ditmer, M. A., G. Wittemyer, S. W. Breck, and K. R. Crooks. 2022. Defining ecological and socially suitable habitat for the reintroduction of an apex predator. Global Ecology and Conservation 38:e02192. Laporte, I., T. B. Muhly, J. A. Pitt, M. Alexander, and M. Musiani. 2010. Effects of wolves on elk and cattle behaviors: implications for livestock production and wolf conservation. PLoS One 5:e11954. Niemiec, R., R. E. Berl, M. Gonzalez, T. Teel, J. Salerno, S. Breck, C. Camara, M. Collins, C. Schultz, and D. Hoag. 2022. Rapid changes in public perception toward a conservation initiative. Conservation Science and Practice 4:e12632. Ramler, J. P., M. Hebblewhite, D. Kellenberg, and C. Sime. 2014. Crying wolf? A spatial analysis of wolf location and depredations on calf weight. American Journal of Agricultural Economics 96:631-656. 34 �Riaboff, L., S. Couvreur, A. Madouasse, M. Roig-Pons, S. Aubin, P. Massabie, A. Chauvin, N. Bédère, and G. Plantier. 2020. Use of predicted behavior from accelerometer data combined with GPS data to explore the relationship between dairy cow behavior and pasture characteristics. Sensors 20:4741. Riaboff, L., L. Shalloo, A. F. Smeaton, S. Couvreur, A. Madouasse, and M. T. Keane. 2022. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Computers and Electronics in Agriculture 192:106610. University of Arkansas Division of Agriculture Research & Extension. Economic impact of agriculture. <https://economic-impact-of-ag.uada.edu/colorado/>. 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1979-1998 Treatment Year 1999-2018 345678ÿÿÿ 8�ÿ����ÿ����ÿ�8��8�������ÿ����8�ÿ�ÿ8��ÿ!"#$%ÿ%&'#ÿ(ÿ)*+*,#)#+%ÿ*-%./.%&ÿ(ÿ-!)'0#%.!+ÿ ÿ 1*%#ÿ-!)2.+*%.!+ÿÿ3���8�ÿ�4�8�ÿ4��4��8ÿ��8ÿ�8�ÿ�8��8�������ÿ4�7ÿ��8ÿ6�� �58�ÿ4�78��ÿ�5�8ÿ4�7ÿ ����74���ÿ��ÿ� �58�8��ÿ��4���ÿÿ6��58���8ÿ�4�8ÿ�����ÿ78ÿ778�5ÿ� �58�ÿ4�ÿ6�878�ÿ4��4��8ÿ ��8784�78ÿ��ÿ�6��ÿ�����ÿ9878ÿ����8�ÿÿ:��8ÿ��8ÿ8;�78�8ÿ�6��487ÿ�8�4��8�ÿ<5ÿÿ�4�5�8ÿ���ÿ4�ÿ��8ÿ���58���8ÿ �4�88ÿ878�=58�ÿ��8�ÿÿ>�4�ÿ4�ÿ��8ÿ��4��ÿ8��4� �8ÿ�87478�ÿ47��ÿ��ÿ�4ÿ��8ÿ��ÿ4�ÿ���ÿ�����8�4��ÿ��8ÿ<4�8ÿ 4���6�4�5ÿ���ÿ47��ÿÿ�4�5�8ÿ����ÿ4�ÿ9�4��ÿ��8ÿ78758ÿ�8��8�ÿ��6��ÿ9 �ÿ�ÿ�7�87ÿ�4ÿ� 5�4�6�8ÿ�7587ÿ���ÿ �59�878ÿ8��8ÿ98ÿ����8�ÿÿ>�8�8ÿ��ÿ�88�ÿ�6��8��ÿ�ÿ�78�ÿ78ÿ���ÿ���65��ÿ��ÿ78��ÿ�45�ÿ�8��4�48�ÿ4�ÿ 7875ÿ���ÿ��87�6��ÿÿ>�8ÿ��ÿ��4��ÿ94��ÿ����4�8�ÿ����7�ÿ877�7ÿ4�ÿ��8ÿ8��4� �8ÿ44ÿ��4�ÿ���8��4�ÿ�6��487ÿ4�ÿ ��4��8�ÿ ÿ ÿ ÿ 12ÿ ÿ �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: July 1, 2018  June 30, 2019 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Eric Newkirk, Eric.Newkirk@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is important because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production and management response obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here we describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine and spruce-fir systems in Colorado. To select forest stands for sampling, we first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team, we then grouped relevant forest management activities (stratum 2) into 4 broad categories: even-aged management, uneven-aged management, thinning, and unmanaged controls. We wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, we took the additional step of binning the date of the most recent management activity into 2-decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). We then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that we sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so we simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so we did not sample that combination, leaving a total of n = 105 stands sampled (Figure 1). During summer 2018, we established n = 50 1-m2 permanent circular plots within each of the n = 105 stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they were established. These same plots were re-visited and re-counted during summer 2019. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval, meaning that inference 2 �from future years will be based on 7 stands within each combination, or n = 128 total stands (note that this total also reflects a handful of stands that were re-classified based field observations, along with new stands that were brought into the sample in 2019 to replace those that were reclassed). Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, we focused the current analysis on the 2019 data from previously cleared plots. For each forest type × management activity combination, we plotted mean pellet counts against “year since activity,” then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have twice the relative hare density of unmanaged lodgepole pine forests. For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition, but in spruce-fir systems estimated peak densities approach, but never match, the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole), immediately depresses hare densities, but densities are estimated to slowly recover through time in nearly linear fashion, reaching their maximum 45-55 years after the treatment. As with the even-aged treatment, maximum hare density after thinning in lodgpole systems is estimated to be higher than the control condition, whereas in spruce-fir systems, the maximum hare density matches that of the control sites. Uneven-aged management of spruce-fir forests results in a similar snowshoe hare trajectory as that observed in thinned spruce-fir forests. Note the two outliers on the right side of the even-aged lodgepole panel. These “high density” sites are represent even-aged lodgepole stands that happen to be surrounded by high quality spruce-fir forest on at least two sides. Thus, the high relative hare density observed at these sites may be due to the quality habitat in adjacent stands rather than by the quality of the sampled stands themselves. While we left them on the figure for transparency, we excluded them when fitting the curve as they appear to be true outliers. Also note that in some cases, 95% CIs are relatively large and overlap the control reference line in some panels. Thus, even though the fitted lines indicate the relationships discussed above, evidence for some of these patterns is moderate or weak. In future years, each panel will include cleared plot data from 6 additional sites, and each site will have data from multiple years (i.e., repeated measures). Both phenomena will greatly improve sample sizes, diminish the role of a few outlying data points, and tighten up our estimate, and corresponding inference, regarding the response of snowshoe hare density to forest management through time. Literature Cited: Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U. S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 3 �Figure 1. Location of all stands (n = 105) resampled for snowshoe hare pellets, June-September 2019. Unmanaged Q) 0 0.. Cl) .._. Uneven-aged Thinned 12 12 12 12 ·5. 10 10 10 10 Q) 8 C .._. Even-aged 0 Cl. Q) C) "O 0 ....J Q) 6 6 4 0 0 , 60 Q) 0 0 10 20 30 40 50 60 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0.. C ro Q) ~ .... .;: 12 12 12 12 10 10 10 10 I Q) (.) ....::JCl. 8 6 Cl) 0 0 , 60 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 4 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: July 1, 2019 − June 30, 2020 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Eric Newkirk, Eric.Newkirk@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is important because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here we describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine (Pinus contorta) and spruce-fir (Picea engelmannii – Abies lasiocarpa) systems in Colorado. To select forest stands for sampling, we first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team, we then grouped relevant forest management activities (stratum 2) into 4 broad catetories: even-aged management, uneven-aged management, thinning, and unmanaged controls. We wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, we took the additional step of binning the date of the most recent management activity into 2-decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). We then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that we sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so we simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so we did not sample that combination (Figure 1). During summer 2018, we established n = 50 1-m2 permanent circular plots within each of the stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they established them. These same plots were re-visited and re-counted during summers 2019 and 2020. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval during 2019. Additionally, a handful of stands visited in 2019 and 2020 were re-classified or tossed based on field 7 �observations and new stands were sampled in their place by pulling the next one from the spatially balanced list. Currently, then inference is based on n = 130 total stands. Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, we focused the current analysis on the 2019 and 2020 data from previously cleared plots. For each forest type × management activity combination, we plotted mean pellet counts against “year since activity”, then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have twice the relative hare density of unmanaged lodgepole pine forests (Figure 2). For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition. However, in spruce-fir systems the estimated fitted line is flatter and peak densities fell well short of the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole), immediately depresses hare densities. In spruce-fir stands, densities were estimated to slowly recover through time in nearly linear fashion. However, they follow a peaked response in lodgepole pine, similar to the response to even-aged management. Uneven-aged management of spruce-fir forests results in immediate depression of relative hare density, which then recovers back to pre-treatment levels approximately 30 years after the treatment. Note the outlier on the right side of the even-aged lodgepole panel. This “high density” site is an even-aged lodgepole stand that happens to be surrounded by high quality spruce-fir forest on at least two sides. Thus, the high relative hare density observed at this site may be due to the quality habitat in adjacent stands rather than by the quality of the sampled stand itself. While we left the point on the figure for transparency, we excluded it when fitting the curve as it appears to be a true outlier (including it “flattens” the curve somewhat such that it crosses the control line at about 55 years). Literature Cited: Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U. S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 8 �* Control Treatment Figure 1. Location of all stands (n = 130) resampled for snowshoe hare pellets, June-September 2020. Unmanaged Even-aged Uneven-aged Thinned (1) C ·a. (1) +-' 0 - a.. Cl) +-' 0 4 0.. (1) Cl "C 2 0 _J 0 Q) 0 >b0 Q) 0 10 20 30 40 50 bO 0 10 20 30 40 50 bO 0 10 20 30 40 50 b0 0 10 20 30 40 50 b0 a.. C .... co Q) ..:::I ~ :::, ....0.. (1) (.) 4 4 (/) 0 0 >b0 0 10 20 30 40 50 0 b0 10 20 30 40 50 b0 Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 9 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: July 1, 2019 − June 30, 2020 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Eric Newkirk, Eric.Newkirk@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is important because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here we describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine (Pinus contorta) and spruce-fir (Picea engelmannii – Abies lasiocarpa) systems in Colorado. To select forest stands for sampling, we first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team, we then grouped relevant forest management activities (stratum 2) into 4 broad categories: even-aged management, uneven-aged management, thinning, and unmanaged controls. We wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, we took the additional step of binning the date of the most recent management activity into 2-decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). We then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that we sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so we simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so we did not sample that combination (Figure 1). During summer 2018, we established n = 50 1-m2 permanent circular plots within each of the stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they established them. These same plots were re-visited and re-counted during summers 2019 and 2020. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval during 2019. Also, a handful of stands visited in 2019 and 2020 were re-classified or tossed because ground-truthing revealed 8 �they did not actually fit in the stratum for which they were selected. New stands were sampled in their place by pulling the next one from the spatially balanced list. Similarly, a handful more stands were replaced during the 2021 field season, and 12 new stands were selected to replace those that burned during the 2020 fire season. Currently, inference is based on n = 130 total stands. Finally, in 2021, we sampled vegetation metrics in each stand that will hopefully account for the considerable noise we have observed (highly variable results for some strata) and allow us to better assess the effects of the treatments themselves. This vegetation sampling will be completed during the 2022 field season. Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, we focused the current analysis on the 2019-21 data from previously cleared plots. For each forest type × management activity combination, we plotted mean pellet counts against “year since activity”, then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have twice the relative hare density of unmanaged lodgepole pine forests (Figure 2). For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition. However, in spruce-fir systems the estimated fitted line is flatter and peak densities fell well short of the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole), immediately depresses hare densities. In spruce-fir stands, densities were estimated to slowly recover through time in nearly linear fashion. However, they follow a peaked response in lodgepole pine, similar to the response to even-aged management. Uneven-aged management of spruce-fir forests results in immediate depression of relative hare density, which then recovers back to pre-treatment levels approximately 30 years after the treatment. Note the outlier on the right side of the even-aged lodgepole panel (Figure 2). This “high density” site is an even-aged lodgepole stand that happens to be surrounded by high quality spruce-fir forest on at least two sides. Thus, the high relative hare density observed at this site may be due to the quality habitat in adjacent stands rather than by the quality of the sampled stand itself. While we left the point on the figure for transparency, we excluded it when fitting the curve as it appears to be a true outlier (including it “flattens” the curve somewhat such that it crosses the control line at about 55 years). Literature Cited: Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U. S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 9 �Figure 1. Location of all stands (n = 130) resampled for snowshoe hare pellets, June-September 2020. Unmanaged Q) 10 Uneven-aged Even-aged 10 C ·a. ....... 0 0.... en ....... 8 Q) 0 C. Q) 4 4 0 ...J 0 >60 0.... ~ 6 Cl Q) co Q) 6 "tJ Q) C Thinned 10 10 10 0 0 10 20 JO 40 50 60 0 10 10 .... .;:::: 8 8 Q) (.) 6 6 4 4 10 20 JO 40 50 60 0 10 20 JO 40 50 60 10 I ....::::J C. . Cf) - _,,,__ - .... ~ - - •• - _ "'J i:.. - - - -~~~ -a1.• ~ •t· ~~t -' ~t=:.}: 4 0 , 60 0 0 10 20 JO 40 50 60 0 10 20 JO 40 50 60 0 10 20 JO 40 50 Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 10 60 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: July 1, 2021 − June 30, 2022 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; Eric Newkirk, Eric.Newkirk@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is imperative because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here we describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine (Pinus contorta) and spruce-fir (Picea engelmannii – Abies lasiocarpa) systems in Colorado. To select forest stands for sampling, we first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team and USFS Rocky Mountain Research Station, we then grouped relevant forest management activities (stratum 2) into 4 broad categories: even-aged management, uneven-aged management, thinning, and unmanaged controls. We wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, we took the additional step of binning the date of the most recent management activity into 2decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). We then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that we sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so we simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so we did not sample that combination (Figure 1). During summer 2018, we established n = 50 1-m2 permanent circular plots within each of the stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they established them. These same plots were re-visited and re-counted during summers 2019 and 2020. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval during 2019. Also, a 14 �handful of stands visited in 2019 and 2020 were re-classified or excluded because ground-truthing revealed they did not actually fit in the stratum for which they were selected. New stands were sampled in their place by pulling the next one from the spatially balanced list. Similarly, a handful more stands were replaced during the 2021 field season, and 12 new stands were selected to replace those that burned during the 2020 fire season. Currently, inference is based on n = 130 total stands. Finally, in 2021 and 2022, we sampled vegetation metrics in each stand that will hopefully account for the considerable noise we have observed (highly variable results for some strata) and allow us to better assess the effects of the treatments themselves. Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, we focused the current analysis on the 2019–22 data from previously cleared plots. For each forest type × management activity combination, we plotted mean pellet counts against “year since activity,” then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have twice the relative hare density of unmanaged lodgepole pine forests (Figure 2). For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition. However, in spruce-fir systems the estimated fitted line is flatter and peak densities fell short of the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole) immediately depresses hare densities. In spruce-fir stands, densities were estimated to slowly recover through time in nearly linear fashion. However, they follow a peaked response in lodgepole pine, similar to the response to even-aged management. Uneven-aged management of spruce-fir forests results in immediate depression of relative hare density, which then recovers back to pre-treatment levels approximately 30 years after the treatment. Literature Cited: Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U. S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 15 �I '-·---··-- * Control Treatment Figure 1. Location of all stands (n = 130) resampled for snowshoe hare pellets, June-September 2022. Unmanaged Even-aged Uneven-aged Thinned Q) ... -... 0 Q_ (/) C ·o.. Q) 0 c.. Q) 0) 2 -0 0 .J 0 (].) (].) >60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 >60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Q_ C cu (].) ~ .... .;:::: (I) (.) ::::, a. Cl) Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 16 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: January 1, 2022 − December 31, 2023 Principal Investigators: Jake Ivan, Jake.Ivan@state.co.us; All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is imperative because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here I describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine (Pinus contorta) and spruce-fir (Picea engelmannii – Abies lasiocarpa) systems in Colorado. To select forest stands for sampling, I first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team and USFS Rocky Mountain Research Station, I then grouped relevant forest management activities (stratum 2) into 4 broad categories: even-aged management, uneven-aged management, thinning, and unmanaged controls. I wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, I took the additional step of binning the date of the most recent management activity into 2decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). I then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that I sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so I simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so I did not sample that combination (Figure 1). During summer 2018, I established n = 50 1-m2 permanent circular plots within each of the stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they established them. These same plots were re-visited and re-counted during summers 2019 and 2023. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval during 2019. In 2021 and 2022, we sampled vegetation metrics in each stand to help account for extraneous noise in the data and allow us 7 �to better assess the effects of the treatments themselves. A handful of initially selected stands were reclassified or excluded during 2019–2022 because ground-truthing and/or vegetation metrics revealed they did not actually fit in the stratum for which they were selected. New stands were sampled in their place by pulling the next one from the spatially balanced list. Similarly, 12 new stands were selected to replace those that burned during the 2020 fire season. Currently, inference is based on n = 130 total stands. Finally, prior to the 2023 field season, I computed the sampling variance of the pellet count for each time interval within each treatment. We sampled additional stands in the 3 most variable bins in an effort to reduce variability and improve our understanding of snowshoe hare response to these treatments. Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, I focused the current analysis on the 2019–23 data from previously cleared plots. For each forest type × management activity combination, I plotted mean pellet counts against “year since activity,” then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have more than twice the relative hare density of unmanaged lodgepole pine forests (Figure 2). For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again (lodgepole systems) or leveling off (sprue-fir systems) 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition. However, in spruce-fir systems the estimated fitted line is flatter and peak densities fell short of the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole) immediately depresses hare densities. In spruce-fir stands, densities were estimated to slowly recover through time in nearly linear fashion. However, they follow a peaked response in lodgepole pine, similar to the response to even-aged management. Uneven-aged management of spruce-fir forests results in immediate depression of relative hare density, which then recovers back to pre-treatment levels approximately 40 years after the treatment. Literature Cited Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U.S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 8 �Figure 1. Location of all stands (n = 130) resampled for snowshoe hare pellets, June-September 2023. Unmanaged Even-aged Uneven-aged Thinned Q) C ·a. .....0 0a. >60 -.D 0 10 20 JO 40 50 60 0 10 20 JO 40 50 60 0 10 20 JO 40 50 60 >60 0 10 20 JO 40 50 60 0 10 2') JO 40 50 60 0 10 20 JO 40 50 60 Q) a.. .....Q) Cl) Q) 2 Ol "O 0 ...J Q) a.. C cu Q) ~ '-- ~ Q) <..) ::, a. (f) Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 9 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Influence of forest management on snowshoe hare density in lodgepole and spruce-fir systems in Colorado Period Covered: January 1, 2024  December 31, 2024 Principal Investigator: Jake Ivan, Jake.Ivan@state.co.us; All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Understanding and monitoring snowshoe hare (Lepus americanus) density in Colorado is imperative because hares comprise 70% of the diet of the state-endangered, federally threatened Canada lynx (Lynx canadensis; U.S. Fish and Wildlife Service 2000, Ivan and Shenk 2016). Forest management is an important driver of snowshoe hare density, and all National Forests in Colorado are required to include management direction aimed at conservation of Canada lynx and snowshoe hare as per the Southern Rockies Lynx Amendment (SRLA; https://www.fs.usda.gov/detail/r2/landmanagement/ planning/?cid= stelprdb5356865). At the same time, Forests in the Region are compelled to meet timber production obligations. Such activities may depress snowshoe hare density, improve it, or have mixed effects dependent on the specific activity and the time elapsed since that activity was initiated. Here we describe a sampling scheme to assess impacts of common forest management techniques on snowshoe hare density in both lodgepole pine (Pinus contorta) and spruce-fir (Picea engelmannii – Abies lasiocarpa) systems in Colorado. To select forest stands for sampling, we first used U. S. Forest Service (USFS) spatial data to delineate all spruce-fir and lodgepole pine stands (stratum 1) on USFS land in Colorado, and identified all of the management activities that have occurred in each stand over time. With consultation from the USFS Region 2 Lynx-Silviculture Team and USFS Rocky Mountain Research Station, we then grouped relevant forest management activities (stratum 2) into 4 broad categories: even-aged management, uneven-aged management, thinning, and unmanaged controls. We wanted to assess both the immediate and long-term impacts of management on hare densities. Therefore, when selecting stands for sampling, we took the additional step of binning the date of the most recent management activity into 2-decade intervals (i.e., 0-20, 20-40, and 40-60 years before 2018). We then selected a spatially balanced random sample of 5 stands within each combination of forest type × management activity × time interval. This design ensured that we sampled the complete gradient of time since implementation for each management activity of interest in each forest type of interest. There is no notion of “completion date” for unmanaged controls, so we simply sampled 10 randomly selected stands from this combination. Also, uneven-aged lodgepole pine treatments are rare, so we did not sample that combination (Figure 1). During summer 2018, we established n = 50 1-m2 permanent circular plots within each of the stands selected for sampling. Plot locations within each stand were selected in a spatially balanced, random fashion. Technicians cleared and counted snowshoe hare pellets in each plot as they established them. These same plots were re-visited and re-counted during summers 2019 through 2024. In addition to sampling the previously cleared plots from 2018, technicians were able to install plots at 2 more replicate sites for each combination of forest type × management activity × time interval during 2019. In 2021 and 2022, we sampled vegetation metrics in each stand to help account for extraneous noise in the data and 7 �allow us to better assess the effects of the treatments themselves. A handful of initially selected stands were re-classified or excluded during 2019–2023 because ground-truthing and/or vegetation metrics revealed they did not actually fit in the stratum for which they were selected. New stands were sampled in their place by pulling the next one from the spatially balanced list. Similarly, 12 new stands were selected to replace those that burned during the 2020 fire season. Currently, inference is based on n = 137 total stands. Finally, prior to the 2023 field season, we computed the sampling variance of the pellet count for each time interval within each treatment. We sampled additional stands in the 3 most variable bins in an effort to reduce variability and improve our understanding of snowshoe hare response to these treatments. Pellet information from cleared plots is more accurate than that from uncleared plots because uncleared plots usually include pellet accumulation across several years (Hodges and Mills 2008). The degree to which previous years are represented can depend on local weather conditions, site conditions at the plot, and variability in actual snowshoe hare density over previous winters. Data from cleared plots necessarily reflects hare activity from the previous 12 months, and tracks true density more closely. Therefore, we focused the current analysis on the 2019–24 data from previously cleared plots. For each forest type × management activity combination, we plotted mean pellet counts against “year since activity,” then fit a curve (e.g., quadratic function) through the data (Figure 2). Results from this preliminary analysis suggest that on average the highest snowshoe hare densities typically occur in unmanaged spruce-fir forests, and that unmanaged spruce-fir forests are estimated to have more than twice the relative hare density of unmanaged lodgepole pine forests (Figure 2). For both forest types, the fitted line suggests that even-aged management (e.g., clearcutting), immediately depresses relative hare density to near zero, but density rebounds and peaks 20-40 years after management before declining again (lodgepole systems) or leveling off (sprue-fir systems) 40-60 years after. Estimated peak hare densities after even-aged management in lodgepole systems tend to be higher than the control condition. However, in spruce-fir systems the estimated fitted line is flatter and peak densities fell short of the control condition. In both forest types, thinning (which often occurs 20-40 years after stands undergo even-aged management, especially in lodgepole) immediately depresses hare densities. In spruce-fir stands, densities were estimated to slowly recover through time in nearly linear fashion. However, they follow a peaked response in lodgepole pine, similar to the response to even-aged management. Uneven-aged management of spruce-fir forests results in immediate depression of relative hare density, which then recovers back to pre-treatment levels and beyond approximately 40 years after the treatment. The final season of field sampling for this project was summer 2024. Literature Cited: Hodges, K. E., and L. S. Mills. 2008. Designing fecal pellet surveys for snowshoe hares. Forest Ecology and Management 256:1918-1926. Ivan, J. S., and T. M. Shenk. 2016. Winter diet and hunting success of Canada lynx in Colorado. The Journal of Wildlife Management 80:1049-1058. U.S. Fish and Wildlife Service. 2000. Endangered and threatened wildlife and plants: determination of threatened status for the contiguous U. S. distinct population segment of the Canada lynx and related rule, final rule. Federal Register 65:16052–16086. 8 �0 D,n<>U<Jr t.•toon,I 0 Cheyenne Fort Collins Monum<1nt 0 • .- • OBoulder 0enver 0 0 CD 0 GrandJ~ Greeley Co l or;,do • 0 Colorado Springs Cu, t uri(, N•(,on,I SiteType ~,ot,o,,Ar~, 0 • Control 0 Treatment 0 6) (J 0 F¥mi 0 on Figure 1. Location of all stands (n = 137) resampled for snowshoe hare pellets, June-August 2024. Unmanaged Even-aged Uneven-aged Thinned Q) -0 0... Cl) C ·a. Q) 0 c.. Q) O> --0 0 ....J 0 Q) Q) >60 0 10 20 30 40 50 60 , 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0... C ,_ ro Q) ..-:::: ~ ::::, (I) (.) Q_ Cf) 0 0 Years Since Treatment Figure 2. Fitted quadratic function (white line) and 95% CI (shaded polygon) relating pellet counts (i.e., relative snowshoe hare density) to time elapsed since treatment for each forest type × management activity combination. Dotted lines indicate the mean pellets/plot for the unmanaged controls for each forest type. 9 � https://cpw.cvlcollections.org/files/original/8124a0f56f0fab2f6e7f9239e84b8cdf.pdf 4021cf56b1eab46c95dabea77aa38359 PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Mule deer population response to cougar population manipulation Period Covered: January 1, 2023 – December 31, 2023 Principal Investigators: Mat Alldredge, mat.alldredge@state.co.us; Allen Vitt, allen.vitt@state.co.us; Bryan Lamont, bryan.lamont@state.co.us; Ty Woodward, tyrel.woodward@state.co.us; Jamin Grigg, jamin.grigg@state.co.us; Chuck Anderson, chuck.anderson@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The adopted Colorado mule deer (Odocoileus hemionus) strategy identified predation as one of the potential factors limiting Colorado mule deer populations. Since the adoption of the mule deer strategy by the Colorado Parks and Wildlife (CPW) Commission, members of the CPW Leadership Team developed a plan to implement the strategy. To inform predator harvest and management decisions, staff examined existing data sets related to predator and deer relationships. In June 2015, CPW personnel from the SE Region, Terrestrial, and Research branches met to explore the concept for a project that examines how deer demographic parameters may change following cougar population suppression. Deer Data Analysis Unit (DAU) D-16 had experienced significant deer mortality from cougars. This study initiated in 2017 in D-16 and the adjacent D-34 as a manipulative study to examine the effects of cougar predation on mule deer and simultaneously examine the effects of cougar harvest on the cougar population. To assess the effect of management manipulations, it was necessary to develop an experimental framework including a control and treatment study area. Otherwise, the magnitude of the effect would be unknown as other limiting factors fluctuate. D-34 is an adjacent mule deer DAU to the south of D-16, which has a similar mule deer population size and habitat. Using D-16 and D-34 in a crossover design allowed for the manipulation of a potential limiting factor for mule deer population growth or survival and examine similarities in the response as the control and treatment are switched between the areas. The study's first objective was to assess the impact of cougar predation on mule deer survival and determine if this impact could be manipulated by altering cougar densities. The second objective was to assess how this manipulation would affect the cougar population in terms of intraspecific mortality and human conflict. The manipulation involved increasing cougar harvest in D-16 for the first 3 years of the study and then reducing harvest to a low level for the following 6 years and doing the reverse in D-34 with a reduced harvest for the first 6 years and increased harvest in the last 3 years. During this time we would monitor deer mortality from cougars, measure cougar density, and assess intraspecific cougar mortality and cougar/human conflict in both study areas. To date, deer survival has been relatively high (86% average doe survival D-16 and D-34; 64% average winter fawn survival D-16; 84% average winter fawn survival D-34) in both study areas across years and deer mortality associated with cougars has been low (5.6% does D-16; 7.2% does D-34; 4.2% fawns D-16; 2.1% fawns D-34). Because deer survival was relatively high in the area and mortality associated with cougars was relatively low during the first 6 years of the study, we stopped investigating the impact of cougar predation on deer survival. The remaining treatment was to increase cougar harvest in D34, which presumably would increase deer survival. However, it was decided that it would not be 33 �possible to measure an effect if it did occur with relatively high deer survival evident during the period of low cougar harvest/relatively high cougar density. Graduate student, Annie Hart, at Colorado State University is continuing her Master’s project examining the deer data. The first part of her project examines how variation in natural forage abundance influences mule deer selection of agricultural resources. The other part of her project will model adult and juvenile survival to help understand the costs and benefits of migration. This is using a state uncertainty modeling approach to estimate survival of migrant and resident fawns, which incorporates the survival of individuals that die before their movement strategy is classified. The cougar population component of the study is continuing with assessing impacts of cougar harvest in D-16 and D-34. We continue to estimate cougar density in both study areas and are monitoring intraspecific effects and cougar/human conflict. As this continues, we will maintain a low cougar harvest (quota of 12) in D-16 but need to increase the cougar quota in D-34. The quota in D-34 had been reduced to 15 since the study started, but we proposed an increase in the quota to 35 cougars to start in the 20232024 hunting season, which was approved by the CPW Wildlife Commission in 2023. During the study we have captured and collared 108 cougars in D-16 and 120 in D-34. Last year we captured 11 in D-16 and 20 in D-34. The higher captures in D-34 were related to increased sample size requirements for the cougar survey in D-34 that year. Over the last couple of years collars have been failing sooner than expected, presumably because collar batteries are not lasting as long as they used to. To date, we have completed 3 density estimates in each D-16 and D-34 with preliminary estimates ranging from 2.7 to 3.1 independent cougars per 100 km2. This does not account for any cougars that may have been harvested prior to the initiation of the survey each year. We have not detected a significant change in density relative to changes in harvest quotas or achieved harvest. In 2023 the density estimate was conducted in D-34. Cougar mortality has been relatively low throughout the study, with the majority of this attributable to hunting mortality. Other sources of mortality include disease, intraspecific killing, human conflict removal and unknown. Intraspecific mortality has ranged from 1 to 2 incidences yearly in D-16 and 1 to 3 in D-34 for collared cougars. Cougar/human conflict is variable between years and study areas. This conflict may include livestock depredation, pet depredation, being in unacceptable locations, or aggressive behaviors toward humans. We show conflict rates from 2000-2023 (Figure 1) which shows the variability across time. There may also be variability in these data from how it was reported and recorded, most notably the switch to an electronic/online approach of the conflict app in 2019. D-34 had some of the highest conflict, especially in 2021 and 2023, but historical conflict rates also had occasional high years as well. 34 �Figure 1: Number of human/cougar conflicts in DAUs D-16 and D-34 by year. This does not include sightings. 35 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Mule deer population response to cougar population manipulation Period Covered: January 1, 2024 – December 31, 2024 Principal Investigators: Mat Alldredge, mat.alldredge@state.co.us; Allen Vitt, allen.vitt@state.co.us; Bryan Lamont, bryan.lamont@state.co.us; Ty Woodward, tyrel.woodward@state.co.us; Jamin Grigg, jamin.grigg@state.co.us; Chuck Anderson, chuck.anderson@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The adopted Colorado mule deer (Odocoileus hemionus) strategy identified predation as one of the potential factors limiting Colorado mule deer populations. Since the adoption of the mule deer strategy by the Colorado Parks and Wildlife (CPW) Commission, members of the CPW Leadership Team developed a plan to implement the strategy. To inform predator harvest and management decisions, staff examined existing data sets related to predator and deer relationships. In June 2015, CPW personnel from the SE Region, Terrestrial, and Research branches met to explore the concept for a project that examines how deer demographic parameters may change following cougar population suppression. Deer Data Analysis Unit (DAU) D-16 had experienced significant deer mortality from cougars. This study initiated in 2017 in D-16 and the adjacent D-34 as a manipulative study to examine the effects of cougar predation on mule deer and simultaneously examine the effects of cougar harvest on the cougar population. To assess the effect of management manipulations, it was necessary to develop an experimental framework including a control and treatment study area. Otherwise, the magnitude of the effect would be unknown as other limiting factors fluctuate. D-34 is an adjacent mule deer DAU to the south of D-16, which has a similar mule deer population size and habitat. Using D-16 and D-34 in a crossover design allowed for the manipulation of a potential limiting factor for mule deer population growth or survival and examine similarities in the response as the control and treatment are switched between the areas. The study's first objective was to assess the impact of cougar predation on mule deer survival and determine if this impact could be manipulated by altering cougar densities. The second objective was to assess how this manipulation would affect the cougar population in terms of intraspecific mortality and human conflict. The manipulation involved increasing cougar harvest in D-16 for the first 3 years of the study and then reducing harvest to a low level for the following 6 years and doing the reverse in D-34 with reduced harvest for the first 6 years and increased harvest in the last 3 years. During this time we would monitor deer mortality from cougars, measure cougar density, and assess intraspecific cougar mortality and cougar/human conflict in both study areas. To date, deer survival has been relatively high (86% average doe survival D-16 and D-34; 64% average winter fawn survival D-16; 84% average winter fawn survival D-34) in both study areas across years and deer mortality associated with cougars has been low (5.6% does D-16; 7.2% does D-34; 4.2% fawns D-16; 2.1% fawns D-34). Because deer survival was relatively high in the area and mortality associated with cougars was relatively low during the first 6 years of the study, we stopped investigating the impact of cougar predation on deer survival. The remaining treatment was to increase cougar harvest in D34, which presumably would increase deer survival. However, it was decided that it would not be 30 �possible to measure an effect if it did occur with relatively high deer survival evident during the period of low cougar harvest/relatively high cougar density. Graduate student, Annie Hart, at Colorado State University finished her Master’s project examining the deer data. The first part of her project examined how variation in natural forage abundance influenced mule deer selection of agricultural resources. The other part of her project modeled adult and juvenile survival to help understand the costs and benefits of migration. This used a state uncertainty modeling approach to estimate survival of migrant and resident fawns, which incorporates the survival of individuals that die before their movement strategy is classified. The cougar population component of the study is continuing with assessing impacts of cougar harvest in D-16 and D-34. We continue to estimate cougar density in both study areas and are monitoring intraspecific effects and cougar/human conflict. As this continues, we will maintain a low cougar harvest (quota of 12) in D-16 but need to increase the cougar quota in D-34. The quota in D-34 had been reduced to 15 since the study started, but we increased the quota to 35 cougars to start in the 2023-2024 hunting season, which was approved by the CPW Wildlife Commission in 2023 and will continue through the 2025-2026 hunting season. This total harvest of 35 was achieved in the 2023-2024 hunting season. During the study we have captured and collared 124 cougars in D-16 and 129 in D-34. Last year we captured 11 in D-16 and 20 in D-34. The higher captures in D-34 were related to increased sample size requirements for the cougar survey in D-34. Over the last two years collars have been failing sooner than expected, presumably because collar batteries are not lasting as long as they used to. To date, we have completed 4 density estimates in each D16 and D34 with preliminary estimates ranging from 2.7 to 3.1 independent cougars per 100 km2. This does not account for any cougars that may have been harvested prior to the initiation of the survey each year. We have not detected a significant change in density relative to changes in harvest quotas or achieved harvest. In 2024 the density estimate was conducted in D16, which is the final estimate for this area. Cougar mortality has been relatively low throughout the study, with the majority of this attributable to hunting mortality. Other sources of mortality include disease, intraspecific killing, human conflict removal and unknown. Intraspecific mortality has ranged from 1 to 2 incidences yearly in D16 and 1 to 3 in D34 for collared cougars. Cougar/human conflict is variable between years and study areas. This conflict may include livestock depredation, pet depredation, being in unacceptable locations, or aggressive behaviors toward humans. We show conflict rates from 2000-2023 (Figure 1) which shows the variability across time. There may also be variability in these data from how it was reported and recorded, most notably the switch to an electronic/online approach of the conflict app in 2019. D34 had some of the highest conflict, especially in 2021 and 2023, but historical conflict rates also had occasional high years as well. 31 �Figure 1: Number of human/cougar conflicts in DAUs D-16 and D-34 by year. This does not include sightings. 32 � https://cpw.cvlcollections.org/files/original/9d613a275f41913af3c5a7d0fa2c99bc.pdf 31ae0f3cd88548f7c461daf4a339e5b0 PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Pilot evaluation of prey distribution and moose recruitment following exposure to wolf predation risk in North Park, Colorado Period Covered: January 1, 2022 – December 31, 2022 Principal Investigators: Eric Bergman, eric.bergman@state.co.us; Ellen Brandell, ellen.brandell@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. During November 2020, Colorado voters passed Proposition 114 (subsequently codified as Colorado Revised Statue 33-2-105.8), which directed Colorado Parks and Wildlife (CPW) and the CPW Wildlife Commission to develop a gray wolf (Canis lupus) reintroduction and management plan for Colorado by the end of 2023. Wolves are a native species to Colorado and prior to westward European expansion they occurred throughout the Rocky Mountains and into Colorado’s eastern plains (Feldhamer et al. 2003). Since the 1940s, wolf presence in Colorado has been sporadic (Warren 1942, Lechleitner 1969, Armstrong et al. 2011). Beginning in the early 2000s, CPW documented occasional wolf presence in Colorado (Colorado Parks and Wildlife 2021), primarily in North Park. During the summer of 2021, a pack comprised of 2 adults and 6 pups was observed. Between dispersal and reproduction of wolves from neighboring states and reintroductions mandated by Colorado Revised Statute 33-2-105.8, wolves will become a consistent feature on Colorado’s landscape, and specifically in North Park. The return of wolves to Colorado’s landscape has already generated interest in future research projects. Between the 1940s and present day, and largely in the absence of wolves, Colorado’s ungulate prey populations (i.e., elk (Cervus americanus), mule deer (Odocoileus hemionus), and moose (Alces alces)) adapted to many changes. These changes included successional change in vegetation, increases and reductions in competition with other native herbivores and livestock, novel diseases, predation from mountain lions (Felis concolor), black bears (Ursus americanus), and coyotes (Canis latrans), but also increased human activity, human disturbance, and large increases in human infrastructure. Moose experienced deliberate management transplants between the late 1970s (Denney 1976) and mid-2000s. By 2022, Colorado’s moose population was estimated to be 3,000–3,500 animals (Colorado Parks and Wildlife, unpublished data). Similarly, during the 1940s it was believed there were 45,000 elk in Colorado (Swift 1945) and population growth during the next 6–7 decades led to a peak of ~300,000 animals during the late 1990s and early 2000s (CPW, unpublished data). This research is generally focused on predator-prey dynamics and how wolves will influence wild prey. Specifically, this research will measure prey survival, productivity, and distribution. To supplement survival and spatial data collected from moose during 2013–2019 (Bergman 2022), we initiated capture and collaring efforts of cow and calf moose during the winter of 2021–2022. These efforts demonstrated that moose calf abundance and subsequent moose calf density in North Park were insufficient to accommodate the necessary sample size for the initial study design of this project. Historically modeled estimates for the North Park moose herd suggest it is comprised of 600–800 animals. Sex and age distribution data from this herd simultaneously indicate there are ~70 bulls/100 cows and ~52 calves/100 cows, thereby lending evidence that there are ~140–190 calves in North Park. However, it is likely that >50% of these calves reside on private lands during winter, making their access for capture purposes 23 �logistically difficult. Accordingly, there are likely only ~70–95 calves available on public land, of which CPW would need to capture 65%-85% to meet sample size requirements. Capturing such a large proportions of this calf population is both logistically and financially difficult and preliminary efforts in North Park provided evidence that it would be infeasible to capture 60 moose calves each winter. However, capture efforts of cow moose between 2013–2019 (Bergman 2022) and again during the winter of 2021–2022 provided evidence of adequate densities to accommodate robust capturing and collaring efforts, thereby presenting alternative opportunities to estimate calf survival. Advancements in satellite collar technology make it feasible for researchers to attain location data from moose that were collected only a few hours earlier. When coupled with VHF capabilities, researchers have the ability to quickly relocate and observe animals. For the purposes of this study, this technology will allow researchers to observe cow moose, but also observe if cow moose are accompanied by a calf (<12 months old). Repeated observations of cows and calves in this manner, and gathered at key points in time, will allow researchers to approximate calf survival by quantifying the decay in calf/cow ratios from birth to the yearling age class (Lukacs et al. 2004). While these data will not provide causespecific calf mortality estimates, they will improve population models that inform moose ecology and harvest management decision making for the North Park moose herd. To implement this alternative approach to estimating calf survival, a total of 80 cow moose will be collared in North Park. Approximately 65 additional collars will be deployed during winter of 2022– 2023. Collars will be deployed in a spatially balanced manner, with 40 collars on both the northern and southern halves of North Park. To expand this research to include additional prey species, 40 cow elk will be captured and collared during the winter of 2022–2023. Once available for observation, these elk will serve as sentinel animals that will allow researchers to quantify group size behavior, spatial distribution, and habitat use, relative to any known wolf activity. Data collected from cow moose during 2022 did not deviate from data collected during 2013– 2019. Between 2012–2022 survival of cow moose ranged from 91.2%–94.8%. During the same period, pregnancy rates of moose ranged from 54.8%–88.0. Literature Cited: Armstrong, D. M., J. P. Fitzgerald, and C. A. Meaney. 2011. Mammals of Colorado (2nd Edition). University Press of Colorado, Boulder, USA. Bergman, E. J. 2022. Incorporation of moose life history traits, nutritional status, and browse characteristics in Shiras moose management in Colorado. Federal Aid in Wildlife Restoration Annual Report W-245-R4, Ft. Collins, CO USA. Denney, R. N. 1976. A Proposal for the Reintroduction of Moose into Colorado. Colorado Parks and Wildlife, Ft. Collins, USA. Feldhamer, G.A., B.C. Thompson, and J.A. Chapman. 2003. Wild mammals of North America: biology, management, and conservation. Johns Hopkins University Press, Baltimore, MD, USA. Lechleitner, R. R. 1969. Wild mammals of Colorado: their appearance, habits, distribution, and abundance. Pruett Publishing, Boulder, CO, USA. Lukacs, P. M., V. J. Dreitz, F. L. Knopf, and K. P. Burnham. 2004. Estimating survival probabilities of unmarked dependent young when detection is imperfect. Condor 106:926–931. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114–119. Warren, E. R. 1942. The mammals of Colorado: their habits and distribution (2nd Edition). University of Oklahoma Press, Norma, USA. 24 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Pilot evaluation of prey distribution and moose recruitment following exposure to wolf predation risk in North Park, Colorado Period Covered: January 1, 2023 – December 31, 2023 Principal Investigators: Eric Bergman, eric.bergman@state.co.us; Ellen Brandell, ellen.brandell@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. During November 2020, Colorado voters passed Proposition 114 (subsequently codified as Colorado Revised Statue 33-2-105.8), which directed Colorado Parks and Wildlife (CPW) and the CPW Wildlife Commission to develop a gray wolf (Canis lupus) reintroduction and management plan for Colorado by the end of 2023 (CPW 2023). Wolves are a native species to Colorado and prior to westward European expansion they occurred throughout the Rocky Mountains and into Colorado’s eastern plains (Feldhamer et al. 2003). Since the 1940s, wolf presence in Colorado has been sporadic (Warren 1942, Lechleitner 1969, Armstrong et al. 2011, CPW 2023). Beginning in the early 2000s, CPW documented occasional wolf presence in Colorado (Colorado Parks and Wildlife 2021), primarily in North Park. During the summer of 2021, a pack comprised of 2 adults and 6 pups was observed in North Park. In December 2023, CPW introduced 10 wolves into the state from Oregon, fulfilling the December 31, 2023 deadline set in CRS 33-2-105.8. Between immigration, reintroduction, and reproduction, wolves will become a consistent feature on Colorado’s landscape, and specifically in North Park. The return of wolves to Colorado’s landscape has already generated interest in future research projects. Between the 1940s and present day, and largely in the absence of wolves, Colorado’s ungulate prey populations (i.e., elk (Cervus americanus), mule deer (Odocoileus hemionus), and moose (Alces alces) adapted to many changes. These changes included successional change in vegetation, increases and reductions in competition with other native herbivores and livestock, novel diseases, predation from mountain lions (Puma concolor), black bears (Ursus americanus), bobcats (Lynx rufus) and coyotes (Canis latrans), but also increased human activity, human disturbance, and large increases in human infrastructure. Moose experienced deliberate management transplants between the late 1970s (Denney 1976) and mid-2000s. By 2022, Colorado’s moose population was estimated to be 3,000–3,500 animals (CPW, unpublished data). Similarly, during the 1940s it was believed there were 45,000 elk in Colorado (Swift 1945) and population growth during the next 6–7 decades led to a peak of ~300,000 animals during the late 1990s and early 2000s (CPW, unpublished data). This research is generally focused on predator-prey dynamics and how wolves will influence wild prey. Specifically, this research will measure prey survival, productivity, and distribution. To supplement survival and spatial data collected from moose during 2013–2019 (Bergman 2022), we initiated capture and collaring efforts of cow and calf moose during the winter of 2021–2022. These efforts demonstrated that moose calf abundance and subsequent moose calf density in North Park were insufficient to accommodate the necessary sample size for the initial study design of this project. Historically modeled estimates for the North Park moose herd suggest it is comprised of 600–800 animals. Sex and age distribution data from this herd simultaneously indicate there are ~70 bulls/100 cows and ~52 calves/100 cows, thereby lending evidence that there are ~140–190 moose calves in North Park. However, it is likely 11 �that >50% of these calves reside on private lands during winter, making their access for capture purposes logistically difficult. Accordingly, there are likely only ~70–95 calves available on public land, of which CPW would need to capture 65%-85% to meet sample size requirements. Capturing such a large proportions of this calf population is both logistically and financially difficult and preliminary efforts in North Park provided evidence that it would be infeasible to capture 60 moose calves each winter. However, capture efforts of cow moose between 2013–2019 (Bergman 2022) and again during the winter of 2021–2022 provided evidence of adequate densities to accommodate robust capturing and collaring efforts, thereby presenting alternative opportunities to estimate calf survival. Advancements in satellite collar technology make it feasible for researchers to attain location data from moose that were collected only a few hours earlier. When coupled with VHF capabilities, researchers have the ability to quickly relocate and observe animals. For the purposes of this study, this technology will allow researchers to observe cow moose, but also observe if cow moose are accompanied by a calf (<12 months old). Repeated observations of cows and calves in this manner, and gathered at key points in time, will allow researchers to approximate calf survival by quantifying the decay in calf/cow ratios from birth to the yearling age class (Lukacs et al. 2004). While these data will not provide causespecific calf mortality estimates, they will improve population models that inform moose ecology and harvest management decision making for the North Park moose herd. To implement this alternative approach to estimating calf survival, a total of 80 cow moose will be collared in North Park. In addition to the previously collared moose, 65 moose were collared for the first time in February 2023. Collars were be deployed in a spatially balanced manner, with approximately 40 collars on both the northern and southern halves of North Park. Calf-at-heel surveys were conducted in June and December 2023. 92% and 71% of moose with active collars were observed in the June and December surveys, respectively. Preliminary calf-at-heel ratios were 0.63 and 0.43 calves/cow during the first two surveys. Further analysis and estimation of monthly and annual calf survival rates will be done in the future when all data have been collected. There was some collar failure over the year, which effectively reduces sample size due to inability to locate collared moose during surveys. We plan to collar an additional 5–10 moose in the winter 2023–2024 to meet our desired sample size for calf-at-heel surveys. Data collected from cow moose during 2022 did not deviate from data collected during 2013–2019. Between 2012–2022 survival of cow moose ranged from 91.2%–94.8%. During the same period, pregnancy rates of moose ranged from 54.8%–88.0%. To expand this research to include additional prey species, 40 cow elk were collared in February 2023. These elk will serve as sentinel animals that will allow researchers to quantify group size behavior, spatial distribution, and habitat use, relative to any known wolf activity. To collect these data, we aimed to obtain aerial visual observations of all collared elk on a monthly basis and record the habitat type they occurred in and the size of the elk group they resided in. In addition to estimating group size from the air, we took photographs, allowing us to count elk in groups. We conducted seven aerial surveys from March to December, 2023, and located 50% of collared elk per flight on average. This resulted in 9–19 unique elk groups observed per survey. We will continue approximately monthly elk surveys in addition to the continual locational data collection on GPS collars. Six collared elk died over the year, therefore we plan to collar elk in the winter 2023–2024 to retain our desired sample size of 40 elk. Literature Cited Armstrong, D. M., J. P. Fitzgerald, and C. A. Meaney. 2011. Mammals of Colorado, 2nd ed. University Press of Colorado, Boulder, USA. Colorado Parks and Wildlife. 2023. Colorado wolf restoration and management plan. Denver, USA. Denney, R. N. 1976. A proposal for the reintroduction of moose into Colorado. Colorado Division of Wildlife planning document. 12 �Feldhamer, G. A., B. C. Thompson, and J. A. Chapman. 2003. Wild mammals of North America: biology, management, and conservation. JHU Press, Baltimore, Maryland, USA. Lechleitner, R. R. 1969. Wild mammals of Colorado: their appearance, habits, distribution, and abundance. Pruett Publ. Co., Boulder, Colorado, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114–119. Warren, E. R. 1942. The mammals of Colorado: their habits and distribution, 2nd (revised) ed. Univ. Oklahoma Press, Norman, USA. 13 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Pilot evaluation of prey distribution and moose recruitment following exposure to wolf predation risk in North Park, Colorado Period Covered: January 1, 2024 – December 31, 2024 Principal Investigator: Ellen Brandell, ellen.brandell@state.co.us All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. During November 2020, Colorado voters passed Proposition 114 (subsequently codified as Colorado Revised Statue 33-2-105.8), which directed Colorado Parks and Wildlife (CPW) and the CPW Wildlife Commission to develop a gray wolf (Canis lupus) reintroduction and management plan for Colorado by the end of 2023 (CPW 2023). Wolves are a native species to Colorado and prior to westward European expansion they occurred throughout the Rocky Mountains and into Colorado’s eastern plains (Feldhamer et al. 2003). Since the 1940s, wolf presence in Colorado has been sporadic (Warren 1942, Lechleitner 1969, Armstrong et al. 2011, CPW 2023). Beginning in the early 2000s, CPW documented occasional wolf presence in Colorado (Colorado Parks and Wildlife 2021), primarily in North Park. During the summer of 2021, a pack comprised of 2 adults and 6 pups was observed in North Park, demonstrating the first wolf reproduction in Colorado in nearly 80 years. In December 2023, CPW introduced 10 wolves into the state from Oregon, fulfilling the December 31, 2023 deadline set in CRS 33-2-105.8. CPW continued their efforts by introducing 15 wolves from British Columbia into Colorado in January 2025. Between immigration, reintroduction, and reproduction, wolves will become a consistent feature on Colorado’s landscape, and specifically in North Park. The return of wolves to Colorado’s landscape has generated interest in future research projects. Between the 1940s and present day, and largely in the absence of wolves, Colorado’s ungulate prey populations (i.e., elk (Cervus americanus), mule deer (Odocoileus hemionus), and moose (Alces alces)) adapted to many changes. These changes included successional change in vegetation, increases and reductions in competition with other native herbivores and livestock, novel diseases, predation from mountain lions (Puma concolor), black bears (Ursus americanus), and coyotes (Canis latrans), but also increased human activity, human disturbance, and large increases in human infrastructure. Moose experienced deliberate management transplants between the late 1970s (Denney 1976) and mid-2000s. By 2022, Colorado’s moose population was estimated to be 3,000–3,500 animals (CPW, unpublished data). Similarly, during the 1940s it was believed there were 45,000 elk in Colorado (Swift 1945) and population growth during the next 6–7 decades led to a peak of ~300,000 animals during the late 1990s and early 2000s (CPW, unpublished data). This research is generally focused on predator-prey dynamics and how wolves will influence wild prey. Specifically, this research will measure prey survival, productivity, and behavior. To supplement survival and spatial data collected from moose during 2013–2019 (Bergman 2022), we initiated capture and collaring efforts of cow and calf moose during the winter of 2021–2022. These efforts demonstrated that moose calf abundance and subsequent moose calf density in North Park were insufficient to accommodate the necessary sample size for the initial study design of this project. Historically modeled estimates for the North Park moose herd suggest it is comprised of 600–800 animals. Sex and age distribution data from this herd simultaneously indicate there are ~70 bulls/100 cows and ~52 calves/100 11 �cows, thereby suggesting that there are ~140–190 calves in North Park. However, it is likely that >50% of these calves reside on private lands during winter, making their access for capture purposes logistically difficult. Accordingly, there are likely only ~70–95 calves available on public land, of which CPW would need to capture 65%-85% to meet sample size requirements. Capturing such a large proportion of this calf population is both logistically and financially difficult, and preliminary efforts in North Park provided evidence that it would be infeasible to capture 60 moose calves each winter. However, capture efforts of cow moose from 2013–2019 (Bergman 2022), and again during the winter of 2021–2022, provided evidence of adequate densities to accommodate robust capturing and collaring efforts, thereby presenting alternative opportunities to estimate calf survival. Advancements in satellite collar technology make it feasible for researchers to attain location data from moose that were collected only a few hours earlier. When coupled with VHF capabilities, researchers have the ability to quickly relocate and observe animals. For the purposes of this study, this technology will allow researchers to observe cow moose, but also observe if cow moose are accompanied by a calf (<12 months old). Repeated observations of cows and calves in this manner, and gathered at key points in time, will allow researchers to approximate calf survival by quantifying the decay in calf/cow ratios from birth to the yearling age class (Lukacs et al. 2004). While these data will not provide causespecific calf mortality estimates, they will improve population models that inform moose ecology and harvest management decision making for the North Park moose herd. To implement this alternative approach to estimating calf survival, we planned to capture and collar a total of 80 cow moose in North Park. In addition to the previously collared moose, 65 moose were collared for the first time in February 2023. Collars were be deployed in a spatially balanced manner, with approximately 40 collars on both the northern and southern halves of North Park. Three calf-at-heel surveys are to be conducted per biological year during June, December, and April; this allows for calculation of survival post-parturition, prior to their first winter, and at nearly one-year old. Calf-at-heel surveys were conducted for the 2023 biological year in June, December, and May, as well as for the 2024 biological year in June and December so far (Table 1). In each survey, cows may not have been located due to dense cover, animal movement from last known GPS location, inaccessible terrain, or collar malfunction. Over time, sample size decreased due to collar failures and harvest. We collared six additional moose in March 2024 to bolster sample size. Further analysis and estimation of monthly and annual calf survival rates will be done in the future when data collection is complete. Thus far, data collected from cow moose during 2023 and 2024 did not deviate from data collected during 2013–2019. From 2012–2022, survival of cow moose ranged from 91.2%–94.8%. During the same period, pregnancy rates of moose ranged from 54.8%–88.0%. Table 1. Preliminary summary of calf-at-heel surveys. Cows observed is reported as a proportion and number. Calf:cow ratios are unadjusted and should not be interpreted as survival. Biological Year 2023 2023 2023 2024 2024 Month June December May June December Cows Observed 0.93 (n = 53) 0.71 (n = 37) 0.86 (n = 51) 0.82 (n = 46) 0.90 (n = 44) Calf:Cow Ratio 0.60 0.43 0.30 0.54 0.48 To expand this research to include additional prey species, 40 cow elk were collared in February 2023. These elk will serve as sentinel animals that will allow researchers to quantify group size behavior, spatial distribution, and habitat use, relative to any known wolf activity. Collars were be deployed in a spatially balanced manner, with approximately 20 collars on both the northern and southern halves of North Park. Six additional elk were collared in March 2024 to maintain our sample size following harvest. 12 �To collect these data, we aimed to obtain aerial visual observations of all collared elk on a monthly basis and record the habitat type and the elk group size. In addition to estimating group size from the air, we took photographs, allowing us to count elk in groups. We conducted sixteen aerial surveys from March 2023 to December 2024 (7 in 2023, 9 in 2024), and located 58% of collared elk per flight on average. This resulted in an average of 13.13 unique elk groups observed per survey. We will continue approximately monthly elk surveys in addition to the continual locational data collection on GPS collars. Literature Cited: Armstrong, D. M., J. P. Fitzgerald, and C. A. Meaney. 2011. Mammals of Colorado (2nd Edition). University Press of Colorado, Boulder, USA. Colorado Parks and Wildlife. 2023. Colorado wolf restoration and management plan. Denver,USA. Denney, R. N. 1976. A proposal for the reintroduction of moose into Colorado. Colorado Parks and Wildlife, Ft. Collins, USA. Feldhamer, G. A., B. C. Thompson, and J. A. Chapman. 2003. Wild mammals of North America: biology, management, and conservation. Johns Hopkins University Press, Baltimore, MD, USA. Lechleitner, R. R. 1969. Wild mammals of Colorado: their appearance, habits, distribution, and abundance. Pruett Publishing Company, Boulder, Colorado, USA. Swift, L. W. 1945. A partial history of the elk herds of Colorado. Journal of Mammalogy 26:114–119. Warren, E. R. 1942. The Mammals of Colorado: their habits and distribution (2nd Edition). University of Oklahoma Press, Norma, USA. 13 � https://cpw.cvlcollections.org/files/original/3288fd6b782bb14ed47c8124c74f9a93.pdf 6bcf6ec0bfc2d05255c6d5d48d990e45 PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Plant and mule deer responses to pinyon-juniper removal by three mechanical methods (follow-up to: Examining the effectiveness of mechanical treatments as a restoration technique for mule deer habitat) Period Covered: July 1, 2020 – June 30, 2021 Principal Investigators: Danielle Johnston (Danielle.bilyeu@state.co.us), Chuck Anderson (chuck.anderson@state.co.us) Personnel: C. Bishop, D. Collins, K. Kain, S. VanNortwick, B. deVergie, D. Finley, L. Gepfert, T. Knowles, B. Petch, J. Rivale, Z. Swennes, M. Way, CPW; L. Belmonte, E. Hollowed, BLM; M. Paschke, G. Stephens, B. Wolk, J. Northrup, B. Gerber, G. Wittemyer, Colorado State University; L. Coulter, Coulter Aviation. Project support received from Federal Aid in Wildlife Restoration, Colorado Mule Deer Association, Colorado Mule Deer Foundation, Muley Fanatic Foundation, Colorado State Severance Tax Fund, Caerus Oil and Gas LLC, EnCana Corp., ExxonMobil Production Co./XTO Energy, Marathon Oil Corp., Shell Petroleum, and WPX Energy. All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. Land managers in western North America often reverse succession by removing pinyon (Pinus spp.) and juniper (Juniperus spp.) trees to reduce fire risk and/or increase forage for wildlife or livestock (Monaco and Gunnell 2020). Because prescribed fire is risky, mechanical methods such as chaining, rollerchopping, and mastication are often used (Figure 1). Mechanical methods differ in cost and in the size of woody debris produced, and may also differ in plant and animal responses. We implemented a randomized, complete-block, split-plot experiment in December 2011 in the Piceance Basin, northwestern Colorado, USA, to compare chaining, rollerchopping, mastication and control (whole plots, n = 7) and to explore seeding (subplot) interactions (Figure 2). We assessed plants 1, 2, 5, and 6 years post-treatment, and mule deer (Odocoileus hemionus) response via GPS locations 3-8 years post-treatment. Early results were published previously (Stephens et al. 2016); this effort combines follow-up vegetation data with mule deer responses. By 2016, treated plots had 3-5 times higher perennial grass cover and ~10 times higher cheatgrass (Bromus tectorum) cover than controls (Figure 3). Rollerchopped plots had both the highest annual species cover, and when seeded, also the highest density of bitterbrush (Purshia tridentata), a nutritious shrub for mule deer (Figure 4). Winter deer GPS point detections in chained and rollerchopped plots were almost twice as high as control (P < 0.001), while detections in masticated plots were about 20% higher than control (P ≤ 0.042; Figure 5). Deer detections appear related to a combination of relative hiding cover, resulting from residual woody debris, and winter forage availability. Masticated plots received higher bitterbrush use during summer/fall than chained or rollerchopped plots (P < 0.05; Figure 6). This may have made masticated plots less attractive the following winter, as ungulates tend to browse the most palatable plants and plant parts first (Armstrong and Macdonald 1992). Rollerchopped and chained plots appeared to provide the best combination of mule deer cover and winter forage, but mastication, applied leaving dispersed security cover, may be a viable option where invasive species concerns exist. 17 �Literature Cited: Armstrong, H. M., and A. J. Macdonald. 1992. Tests of different methods for measuring and estimating utilization rate of heather (Calluna-Vulgaris) by vertebrate herbivores. Journal of Applied Ecology 29:285-294. Monaco, T. A., and K. L. Gunnell. 2020. Understory vegetation change following woodland reduction varies by plant community type and seeding status: a region-wide assessment of ecological benefits and risks. Plants 9:1113. Stephens, G. J., D. B. Johnston, J. L. Jonas, and M. W. Paschke. 2016. Understory responses to mechanical treatment of pinyon-juniper in northwestern Colorado. Rangeland Ecology & Management 69:351-359. Figure 1. Equipment, residual structure, and vegetation response 9 years post-treatment for a) chaining, b) rollerchopping, and c) mastication. 18 �Figure 2. Location of tree removal and control plots within north and south Magnolia winter range study areas in the Piceance Basin, Rio Blanco County, Colorado, USA. 19 �Figure 3. Percent cover of A) snowberry, B) perennial grasses, C) exotic annual forbs, and D) cheatgrass 1-6 years following implementation of 3 pinyon and juniper removal methods, unseeded subplots only. Points not sharing letters are significantly different at α = 0.05 for within-year contrasts between treatments. Error bars = 95% CIs. 20 �Figure 4. 2017 bitterbrush density within seeded (solid outline) and unseeded (dashed outline) subplots 6 years after implementation of 3 pinyon and juniper removal methods: CON (control), MAST (masticated), CHAIN (chained, and ROLLER (rollerchopped). Star indicates a significant contrast between seeded and unseeded subplots at α = 0.05. Error bars = 95% CIs. Figure 5. Mule deer GPS locations (points/ha) in winter over a 5-year period in control plots and plots treated to remove pinyon and juniper trees by 3 different methods: CON (control), MAST (masticated), CHAIN (chained), and ROLLER (rollerchopped). Bars not sharing letters are significantly different at α = 0.056. Error bars = 95% CIs. A B Figure 6. Percent of current year growth removed by herbivory during the growing season for A) bitterbrush and B) serviceberry 6 years following implementation of 3 pinyon and juniper removal methods: CON (control), MAST (masticated), CHAIN (chained), and ROLLER (rollerchopped), unseeded subplots only. Bars not sharing letters are significantly different at α = 0.05. Error bars = 95% CIs. 21 � https://cpw.cvlcollections.org/files/original/4474d900fa0731d88287db2b5c1c361b.pdf 9bffce4249b4c61b36b5330cb49f4a47 PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Spatiotemporal effects of human recreation on elk behavior: an assessment within critical time stages Period Covered: July 1, 2019-June 30, 2020 Principal Investigators: Nathaniel Rayl, nathaniel.rayl@state.co.us; Eric Bergman, eric.bergman@state.co.us; Joe Holbrook, Joe.Holbrook@uwyo.edu All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The influence of recreational disturbance on ungulate populations is of particular interest to wildlife managers in Colorado, as there is growing concern about its potential impacts within the state. Currently, the western United States is experiencing some of the highest rates of human population growth in the country, with growth in rural and exurban areas frequently outpacing growth in urban areas. Additionally, participation in outdoor recreation is also increasing. In Colorado, the number of individuals participating in recreational activities, and the associated demand for recreational opportunities, appear to be increasing. Understanding potential impacts of recreational activity on elk spatial ecology in Colorado is critical for guiding management actions, as altered movements may result in reduced foraging time and higher energetic costs, which may decrease fitness. We are studying elk from the resident portion of the Bear’s Ears elk herd (DAU E-2) in Colorado to determine potential impacts of recreational activities on this population (Fig. 1). This research project is a collaboration between Colorado Parks and Wildlife (CPW) and the Haub School of Environment and Natural Resources at the University of Wyoming, and will form the basis of an M.S. thesis for a graduate student enrolled at the Haub School. In January 2020, we collared 30 adult female elk from the resident portion of the Bear's Ears elk herd on U.S. Forest Service (USFS) land near Steamboat Springs. The estimated pregnancy rate was 93% (95% CI: 79-98%). This spring, summer, and fall we will be deploying trail counters and cameras at trailheads in the study area, and handing out GPS units to recreationists to quantify human recreation on the landscape and evaluate how elk respond to recreationists. 25 �DENVER • Colorado 5 10 Figure 1. Routt National Forest study area located in northwest Colorado, USA. 26 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Spatiotemporal effects of human recreation on elk behavior: an assessment within critical time stages Period Covered: July 1, 2020 − June 30, 2021 Principal Investigators: Nathaniel Rayl, nathaniel.rayl@state.co.us; Eric Bergman, eric.bergman@state.co.us; Joe Holbrook, Joe.Holbrook@uwyo.edu All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The influence of recreational disturbance on ungulate populations is of particular interest to wildlife managers in Colorado, as there is growing concern about its potential impacts within the state. Currently, the western United States is experiencing some of the highest rates of human population growth in the country, with growth in rural and exurban areas frequently outpacing growth in urban areas. Additionally, participation in outdoor recreation is also increasing. In Colorado, the number of individuals participating in recreational activities, and the associated demand for recreational opportunities, appear to be increasing. Understanding potential impacts of recreational activity on elk spatial ecology in Colorado is critical for guiding management actions, as altered movements may result in reduced foraging time and higher energetic costs, which may decrease fitness. We are studying elk from the resident portion of the Bear’s Ears elk herd (DAU E-2) in Colorado to determine potential impacts of recreational activities on this population. This research project is a collaboration between Colorado Parks and Wildlife (CPW) and the Haub School of Environment and Natural Resources at the University of Wyoming, and forms the basis of an M.S. thesis for a graduate student enrolled at the Haub School. In January 2020 and January 2021, we collared 30 and 26 adult female elk, respectively, from the resident portion of the Bear's Ears elk herd on U.S. Forest Service (USFS) land near Steamboat Springs. In both years, the estimated pregnancy rate was 93% (95% CI: 79-98%). From May-October 2020 we deployed trail counters at 22 trailheads in the Routt National Forest (Figure 1). We recorded roughly 100,000 people departing and returning from these trailheads. Among individual trailheads, we documented average daily traffic counts ranging from 2-325 people (Figure 2). Most traffic was recorded on weekends with noticeable lulls in traffic frequency observed during weekdays. During the 2021 field season, we again deployed trail counters at the 22 trailheads, and also added additional trail counters at 1-km intervals along each trail for up to 5-km from the trailhead. These additional trail counters are being deployed on a rotating basis to sample each trail. Data collected from these additional trail counters will provide an estimate of the decay of traffic along trails. During the 2020 and 2021 field season, we distributed handheld GPSs to recreationists (hikers, bikers, hunters) to record detailed tracks of human use within this trail system (Figure 3). In 2020, we collected over 100 GPS tracks. GPS tracks from recreationists and hunters will allow us to better quantify human recreation on the landscape and evaluate how elk respond to recreationists. 32 �Figure 1. Routt National Forest study area located in northwest Colorado, USA. 33 �Figure 2. Daily trends in trailhead traffic documented with trail counters from June through October 2020, excluding Fish Creek Falls, Mad Creek, and Red Dirt trailheads, which received average daily counts >200. Figure 3. GPS track (blue) recorded from recreational mountain biker on trail system (white) in August 2020. Note the off-trail use near Long Lake. 34 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Spatiotemporal effects of human recreation on elk behavior: an assessment within critical time stages Period Covered: January 1, 2022 – December 31, 2022 Principal Investigators: Nathaniel Rayl, nathaniel.rayl@state.co.us; Eric Bergman, eric.bergman@state.co.us; Joe Holbrook, Joe.Holbrook@uwyo.edu All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The influence of recreational disturbance on ungulate populations is of particular interest to wildlife managers in Colorado, as there is growing concern about its potential impacts within the state. Currently, the western United States is experiencing some of the highest rates of human population growth in the country, with growth in rural and exurban areas frequently outpacing growth in urban areas. Additionally, participation in outdoor recreation is also increasing. In Colorado, the number of individuals participating in recreational activities, and the associated demand for recreational opportunities, appear to be increasing. Understanding potential impacts of recreational activity on elk spatial ecology in Colorado is critical for guiding management actions, as altered movements may result in reduced foraging time and higher energetic costs, which may decrease fitness. We are studying elk from the resident portion (i.e., non-migratory) of the Bear’s Ears elk herd (DAU E-2) in Colorado to determine potential impacts of recreational activities on this population. This research project is a collaboration between Colorado Parks and Wildlife (CPW) and the Haub School of Environment and Natural Resources at the University of Wyoming, and forms the basis of an M.S. thesis for a graduate student enrolled at the Haub School. In January 2020 and January 2021, we collared 30 and 26 adult female elk, respectively, from the resident portion of the Bear's Ears elk herd on U.S. Forest Service (USFS) land near Steamboat Springs. We estimated pregnancy rates of 93% (95% CI: 79-98%) in 2020 and 96% (95% CI: 81-100%) in 2021. From May-October 2020 we deployed trail counters at 22 trailheads in the Routt National Forest (Figure 1). We recorded roughly 100,000 people departing and returning from these trailheads. Among individual trailheads, we documented average daily traffic counts ranging from 2-325 people (Figure 2). Most traffic was recorded on weekends with noticeable lulls in traffic frequency observed during weekdays. During the 2021 field season, we again deployed trail counters at the 22 trailheads, and also added additional trail counters at 1-km intervals along each trail for up to 5-km from the trailhead. These additional trail counters are being deployed on a rotating basis to sample each trail. Data collected from these additional trail counters will provide an estimate of the decay of traffic along trails. During the 2020 and 2021 field season, we distributed handheld GPSs to recreationists (hikers, bikers, hunters) to record detailed tracks of human use within this trail system (Figure 3). In 2020, we collected over 100 GPS tracks. These tracks from recreationists and hunters will allow us to better quantify human recreation on the landscape and evaluate how elk respond to recreationists. 33 �Figure 1. Routt National Forest study area located in northwest Colorado, USA. 34 �Figure 2. Daily trends in trailhead traffic documented with trail counters from June through October 2020, excluding Fish Creek Falls, Mad Creek, and Red Dirt trailheads, which received average daily counts >200. Figure 3. GPS track (blue) recorded from recreational mountain biker on trail system (white) in August 2020. Note the off-trail use near Long Lake. 35 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Spatiotemporal effects of human recreation on elk behavior: an assessment within critical time stages Period Covered: January 1, 2023-December 31, 2023 Principal Investigators: Nathaniel Rayl, nathaniel.rayl@state.co.us; Eric Bergman, eric.bergman@state.co.us; Joe Holbrook, Joe.Holbrook@uwyo.edu All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The influence of recreational disturbance on ungulate populations is of particular interest to wildlife managers in Colorado, as there is growing concern about its potential impacts within the state. Currently, the western United States is experiencing some of the highest rates of human population growth in the country, with growth in rural and exurban areas frequently outpacing growth in urban areas. Additionally, participation in outdoor recreation is also increasing. In Colorado, the number of individuals participating in recreational activities, and the associated demand for recreational opportunities, appear to be increasing. Understanding potential impacts of recreational activity on elk spatial ecology in Colorado is critical for guiding management actions, as altered movements may result in reduced foraging time and higher energetic costs, which may decrease fitness. We are studying elk from the resident portion of the Bear’s Ears elk herd (DAU E-2) in Colorado to determine potential impacts of recreational activities on this population. This research project is a collaboration between Colorado Parks and Wildlife (CPW) and the Haub School of Environment and Natural Resources at the University of Wyoming, and forms the basis of an M.S. thesis for a graduate student (Eric VanNatta, also CPW Area 10 Terrestrial Biologist) enrolled at the Haub School. In January 2020 and January 2021, we collared 30 and 26 adult female elk, respectively, from the resident portion of the Bear's Ears elk herd on U.S. Forest Service (USFS) land near Steamboat Springs. We estimated pregnancy rates of 93% (95% CI: 79-98%) in 2020 and 96% (95% CI: 81-100%) in 2021. From May-October 2020 we deployed trail counters at 22 trailheads in the Routt National Forest (Fig. 1). We recorded roughly 100,000 people departing and returning from these trailheads. Among individual trailheads, we documented average daily traffic counts ranging from 2-325 people (Fig. 2). Most traffic was recorded on weekends with noticeable lulls in traffic frequency observed during weekdays. During the 2021 field season, we again deployed trail counters at the 22 trailheads, and also added additional trail counters at 1-km intervals along each trail for up to 5-km from the trailhead. These additional trail counters are being deployed on a rotating basis to sample each trail. Data collected from these additional trail counters will provide an estimate of the decay of traffic along trails. During the 2020 and 2021 field season, we distributed handheld GPSs to recreationists (hikers, bikers, hunters) to record detailed tracks of human use within this trail system (Fig. 3). In 2020, we collected over 100 GPS tracks. These tracks from recreationists and hunters will allow us to better quantify human recreation on the landscape and evaluate how elk respond to recreationists. In fall 2023, Eric VanNatta successfully completed and defended his M.S. proposal at the University of Wyoming and finished processing and cleaning the trail counter dataset. 22 �Figure 1. Routt National Forest study area located in northwest Colorado, USA. 23 �Figure 2. Daily trends in trailhead traffic documented with trail counters from June through October 2020, excluding Fish Creek Falls, Mad Creek, and Red Dirt trailheads, which received average daily counts >200. Figure 3. GPS track (blue) recorded from recreational mountain biker on trail system (white) in August 2020. Note the off-trail use near Long Lake. 24 �Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Spatiotemporal effects of human recreation on elk behavior: an assessment within critical time stages Period Covered: January 1, 2024-December 31, 2024 Principal Investigators: Nathaniel Rayl, nathaniel.rayl@state.co.us; Eric Bergman, eric.bergman@state.co.us; Joe Holbrook, Joe.Holbrook@uwyo.edu All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. By providing this summary, CPW does not intend to waive its rights under the Colorado Open Records Act, including CPW’s right to maintain the confidentiality of ongoing research projects. CRS § 24-72-204. The influence of recreational disturbance on ungulate populations is of particular interest to wildlife managers in Colorado, as there is growing concern about its potential impacts within the state. Currently, the western United States is experiencing some of the highest rates of human population growth in the country, with growth in rural and exurban areas frequently outpacing growth in urban areas. Additionally, participation in outdoor recreation is also increasing. In Colorado, the number of individuals participating in recreational activities, and the associated demand for recreational opportunities, appear to be increasing. Understanding potential impacts of recreational activity on elk spatial ecology in Colorado is critical for guiding management actions, as altered movements may result in reduced foraging time and higher energetic costs, which may decrease fitness. We are studying elk from the resident portion of the Bear’s Ears elk herd (DAU E-2) in Colorado to determine potential impacts of recreational activities on this population. This research project is a collaboration between Colorado Parks and Wildlife (CPW) and the Haub School of Environment and Natural Resources at the University of Wyoming, and forms the basis of an M.S. thesis for a graduate student (Eric VanNatta, also CPW Area 10 Terrestrial Biologist) enrolled at the Haub School. In January 2020 and January 2021, we collared 30 and 26 adult female elk, respectively, from the resident portion of the Bear's Ears elk herd on U.S. Forest Service (USFS) land near Steamboat Springs. We estimated pregnancy rates of 93% (95% CI: 79-98%) in 2020 and 96% (95% CI: 81-100%) in 2021. From May-October 2020 we deployed trail counters at 22 trailheads in the Routt National Forest (Figure 1). We recorded roughly 100,000 people departing and returning from these trailheads. Among individual trailheads, we documented average daily traffic counts ranging from 2-325 people (Figure 2). Most traffic was recorded on weekends with noticeable lulls in traffic frequency observed during weekdays. During the 2021 field season, we again deployed trail counters at the 22 trailheads, and also added additional trail counters at 1-km intervals along each trail for up to 5-km from the trailhead. These additional trail counters are being deployed on a rotating basis to sample each trail. Data collected from these additional trail counters will provide an estimate of the decay of traffic along trails. During the 2020 and 2021 field season, we distributed handheld GPSs to recreationists (hikers, bikers, hunters) to record detailed tracks of human use within this trail system (Figure 3). In 2020, we collected over 100 GPS tracks. These tracks from recreationists and hunters will allow us to better quantify human recreation on the landscape and evaluate how elk respond to recreationists. In fall 2023, Eric VanNatta successfully completed and defended his M.S. proposal at the University of Wyoming and finished processing and cleaning the trail counter dataset. In 2024, Eric worked on analyses for the first chapter of his thesis, which should be completed in early 2025. 22 �Figure 1. Routt National Forest study area located in northwest Colorado, USA. 23 �Figure 2. Daily trends in trailhead traffic documented with trail counters from June through October 2020, excluding Fish Creek Falls, Mad Creek, and Red Dirt trailheads, which received average daily counts >200. Figure 3. GPS track (blue) recorded from recreational mountain biker on trail system (white) in August 2020. Note the off-trail use near Long Lake. 24 � https://cpw.cvlcollections.org/files/original/a9c3a8e3f306c3f5788b2b0aeb8f3c7b.pdf 710515316eedcaa81e674255860be4db PDF Text Text Colorado Parks and Wildlife WILDLIFE RESEARCH PROJECT SUMMARY Predator community effects pilot study: browsing of upland shrubs in Middle Park, Colorado Period Covered: January 1, 2025 – December 31, 2025 Authors: Danielle B. Johnston, Michael Peyton, and Lauren Brandt Principal Investigators: Danielle B. Johnston (Habitat Researcher, CPW), Ellen Brandell (Wildlife Researcher, CPW), Brian Gerber (Research Ecologist, USGS and Assistant Unit Leader (Colorado Cooperative Fish and Wildlife Research Unit) Project Collaborators: Michael Peyton (Postdoctoral Researcher, CPW); Terrestrial and Field Operations Staff, Area 8; Terrestrial and Field Operations Staff, Area 9; Anthony Vorster, Research Scientist, Colorado State University; Nicholas Young, Research Scientist, Colorado State University All information in this report is preliminary and subject to further evaluation. Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author. Manipulation of these data beyond that contained in this report is discouraged. ABSTRACT Effects of predator addition or removal on ecosystem processes are potentially numerous but sometimes subtle. Possible impacts of wolf reintroduction to Colorado ecosystems include changes in prey abundance and/or behavior and indirect effects on plant communities mediated by shifts in browsing pressure. Existing CPW efforts to quantify deer and elk space use and abundance in monitoring areas will offer an opportunity to tie deer and elk responses to wolf space use. In addition, we are proposing to examine the impact of browsing pressure changes on upland shrub communities in Middle Park and the upper Colorado River valley (GMUs 15, 18, 181, 27, 28, 35, 36, 361, 37, 371). Changes in browsing pressure may occur in the near term (1-10 years) due to changes in wolf space use, may occur over a longer time span (10-20 years) as ungulate populations respond numerically to wolf presence, both may occur, or neither. Upland shrub communities include deciduous shrubs such as serviceberry, bitterbrush, and mountain mahogany as well as sagebrush, all of which provide critical winter forage for deer and elk. Shrub seed production and recruitment, which are important for long-term shrub community sustainability, may be sensitive to browsing pressure changes. A study design capable of detecting browsing pressure changes, and the impact of those changes on shrub growth and reproduction, must consider landscape variability and the timescale over which we can reasonably detect changes. We seek to design a cost-efficient study with robust statistical power. To do so, we plan to address three components over a two year period. These components include: 1) improving shrub community maps; 2) determining sample size and design via data simulation; and 3) developing efficient field methods and protocols to �measure shrub productivity, browsing pressure, seed production, and recruitment. We made progress on each of these goals in 2025. Leveraging year end FY25 funds and in collaboration with the NASA DEVELOP internship program, we created new maps of vegetation in deer and elk winter concentration areas in the Middle Park and Upper Colorado River Valley areas. This involved ground-truthing vegetation at 481 locations, 120 with a full protocol with complete plant cover data, and 361 with a quick protocol consisting of cover estimates. The NASA DEVELOP team used these points to train a model based on imagery from Landsat 8-9, Sentinel-2, radar, and lidar. This resulted in a map with overall accuracy of 83% which included detailed classifications such as serviceberry, mountain mahogany, and bitterbrush. To determine sample size and design, we employed a postdoctoral researcher, Michael Peyton, to simulate how shrub cover may respond to changes in browsing pressure. Michael’s simulation incorporates many of the important factors that influence shrub growth, such as soil moisture, fire, and grazing. The simulation represents shrub communities in the landscape of the Middle Park and Upper Colorado River Valley study site in silico (i.e. by computer simulations), parameterized by available environmental data from remote sensing and field sources. The model is initialized using aboveground biomass estimates from across the study site and computes annual biomass change based on major system drivers. Specifically, shrub biomass accumulation is computed from the contribution of soil moisture, soil nitrogen, and their interaction to annual productivity, while biomass is removed through fire, cattle impacts, browsing, and maintenance respiration. These gain and loss terms are informed by established relationships in the published literature for shrubs in this system. Uncertainty and process noise are incorporated in simulation outputs by iteratively testing model behavior across plausible parameter ranges for these relationships. Finally, the simulation tests differences in sample size, stratification approaches, and statistical methods to provide estimates of detection probability under different design choices. These outputs will help guide our sampling approach and provide a defensible basis for determining where and how to prioritize sampling effort. To develop efficient field methods, we completed tasks related to shrub aging, seed production method development, plot setup, and browsing pressure estimation. The ability to accurately age young shrubs is critical for our ability to assess recruitment. In 2025 we used root crown ring counts to age 75 shrubs. 40 of these were of known ages, having come from prior experiments, and this allowed us to assess our ability to age shrubs by various methods. We were able to determine actual age from ring counts with good accuracy (R2 = 0.88), and root crown diameter approximated ring-estimated age with reasonable accuracy (R2 = 0.66). For seed production, we counted seeds on 19 shrubs, documented shrub density classes with photographs, and assessed our ability to visually estimate density class. We completed 6 practice plots combining estimates of shrub density, productivity, seed production, and recruitment, which allowed us to streamline our protocols. Finally, we constructed grazing cages around 26 shrubs in early fall 2025 to prepare them for a browsing pressure method development study. In early 2026, we will select locations for permanent shrub sampling plots based on our maps of shrub communities and the results of the simulation analysis. In spring of 2026, we will continue our browsing pressure methods study and refine methods for seed production, productivity, and recruitment through the summer and fall. We will establish as many of the permanent plots as possible and begin taking data to be used in long-term assessments. We also plan to submit a manuscript concerning the sample size estimation process. �BACKGROUND Gray wolves (Canis lupus) are re-establishing in Colorado, joining mountain lions (Puma concolor) and black bears (Ursus americanus) as key predators of ungulates. This re-establishment is expected to cause community effects, defined as direct or indirect changes to vegetation, prey, or other predators. The complexity of Colorado's ecosystems—characterized by wide ranges in elevation, plant communities, hunting practices, and land use—makes conclusively demonstrating a classical trophic cascade unlikely. Trophic cascades fundamentally involve changes in one trophic level causing changes in two or more lower trophic levels. While these are dramatic in simple, discrete systems such as tide pools (Paine 1980), complex communities are often buffered by numerous species interactions (Brice et al. 2022). We focus instead on wolf community effects, which encompass a wide range of potential interactions, including competition with other predators and alterations to the movement, habitat selection, diet, and/or density of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) as well as impacts to vegetation. A key community effect we aim to evaluate is the impact of wolves on elk and mule deer space use. We will capitalize on existing Colorado Parks and Wildlife (CPW) monitoring efforts, which maintain Global Positioning System (GPS) collars on ungulates within five existing Mule Deer Intensive Monitoring Areas and five new, spatially overlapping Elk Intensive Monitoring Areas. This provides extensive data on movement, space use patterns, and survival, and will be supplemented by new efforts to estimate elk abundance. This coordination of efforts is intended to require minimal additional budget over the next few years. The main focus of this pilot phase is the impact of wolves on upland shrub communities, a critical forage resource for ungulates. We specifically select upland deciduous shrubs, such as serviceberry (Amelanchier alnifolia), mountain mahogany (Cercocarpus montanus), and bitterbrush (Purshia tridentata), along with sagebrush (Artemisia tridentata). We have strategically chosen to focus on these upland shrubs instead of more commonly studied species like willow or aspen. This decision is based on several ecological and management objectives. First, unlike willow and aspen, the impact of browsing on upland shrubs has received very little scientific attention in the context of top-down predator effects. By focusing here, we aim to fill a significant gap in Rocky Mountain ecology. Secondly, serviceberry, mountain mahogany, and bitterbrush are critical, highvalue forage species that receive heavy ungulate browsing on winter ranges in Colorado. They constitute a large fraction of winter diet, particularly for mule deer (Bartmann 1983), and increasing their productivity is a common CPW management objective. Although sagebrush is less preferred, it remains an important part of mule deer winter diet (Richens 1967, Carpenter et al. 1979), and CPW is actively involved in its restoration (Tarbox et al. 2025). Lastly, deciduous upland shrubs may be vulnerable to climate changes such as increased drought or fire. As upland plants, they are dependent on precipitation for water and therefore may be more susceptible to drought than willows, which rely on stream or ground water. Furthermore, while aspen often benefits from fire, upland shrubs often decline following fire �(Updike et al. 1992). Investigating how browsing pressure changes interact with these climate-driven vulnerabilities is crucial for future management. Wolves can influence browsing pressure on upland deciduous shrubs through two types of indirect effects: ● ● Behaviorally-Mediated Indirect Effects (BMIE): Occur when wolves change prey behavior (e.g., space use, increased vigilance), which then drives reduced browsing in certain areas. ○ Timeline: Changes could occur relatively quickly (1–10 years) once wolves are establish and introduce spatially heterogeneous predation risk. ○ Pattern: Responses may be finer-scale and uncertain, as they are influenced by both topdown (predator) and bottom-up (forage preference, site fidelity) processes. Ungulates may adopt vigilance, altered diel patterns, and/or group size changes instead of abandoning preferred areas (Mao et al. 2005, Liley and Creel 2008, Kohl et al. 2018). Density-Mediated Indirect Effects (DMIE): Occur when wolves reduce prey density (population size), which drives reduced browsing across the landscape. ○ Timeline: Requires a longer timescale (10–20 years) for wolf populations to reach a size that significantly reduces elk and/or deer density. ○ Pattern: Expected to be straightforward: lower prey density leads to decreased landscapescale browsing pressure. Given the complexities of BMIEs and the wide-ranging nature of wolves and ungulates, DMIEs are likely the dominant driver of long-term plant community response (Schmitz et al. 2004). Our proposed long-term study will be designed to detect changes mediated by both mechanisms, requiring a significant time commitment and resources. To ensure feasibility, statistical power, and cost-effectiveness for a long-term study, we are conducting a two-year pilot study focused on elk and mule deer winter ranges in Middle Park and the upper Colorado River valley (GMUs 15, 18, 181, 27, 28, 35, 36, 361, 37, 371), areas with current wolf activity and co-occurring elk and deer monitoring efforts. The pilot study focuses on three key goals: 1. Mapping: Develop an accurate spatial map of the extent of focal plant communities. 2. Sample Size Estimation: Perform a simulation exercise to determine the feasibility of detecting effects on shrubs from browsing pressure changes of varying magnitude and extent and to identify critical covariates to measure (e.g. soil moisture). 3. Field Methods Development: Identify the most efficient field methodology for measuring browsing pressure, recruitment, and growth of shrubs. Determine efficient ways to measure critical covariates. The overall goal is to inform a robust, low-annual-cost observational study that can be sustained by internal CPW funding to monitor wolf community effects over the necessary extended timeframe. �Ultimately, we hope to relate vegetation responses to predator and prey data to address broader hypotheses, such as those outlined below. Note that these hypotheses may change based on feasibility as revealed in our pilot study 1: ● Are wolves causing changes in upland deciduous shrub and sagebrush communities? ○ ○ Behaviorally-mediated indirect effects ■ H1a: (Bottom-up processes dominate). Browsing pressure and wolf space use metrics will be either uncorrelated or positively correlated. Height gain, cover, seed production, and recruitment of shrubs (or temporal changes in those metrics) will be uncorrelated or negatively correlated with wolf space use metrics. ■ H1b: (Top-down processes dominate). Browsing pressure and wolf space use metrics will be negatively correlated. Height gain, cover, seed production, and recruitment of shrubs (or temporal changes in those metrics) will be positively correlated with wolf space use metrics. Density-mediated indirect effects ■ ● H2: Browsing pressure will lessen with increasing wolf density. Height gain, cover, seed production, and recruitment of shrubs will increase with increasing wolf density. Is ungulate use limiting to upland deciduous shrubs and sagebrush? ○ ○ Behavioral direct effects ■ H3: Browsing pressure will be positively correlated with elk and mule deer space use. ■ H4a: (Bottom-up processes dominate) Height gain, cover, seed production, and recruitment of shrubs will be positively correlated with elk and mule deer space use. ■ H4b: (Top-down processes dominate) Height gain, cover, seed production, and recruitment of shrubs will be negatively correlated with elk and mule deer space use. Density direct effects ■ H5: Browsing pressure will be positively correlated with elk and mule deer density. Height gain, cover, seed production, and recruitment of shrubs will be negatively correlated with elk and mule deer density. Here, we separate hypotheses related to space use versus density. We define space use as a measure of time spent formulated from GPS data, such as a probability density of wolf GPS locations. Space use has a spatial resolution relevant to individual vegetation sampling locations, and a temporal resolution at the season level or finer. We define density as population at the level of our entire study area with a temporal resolution of one year. 1 �Importantly, we are not proposing to examine wolf-prey relationships in this pilot study, but nonetheless, these hypotheses are important for guiding our work. 2025 ACTIVITIES In 2025, we made progress on all three of our goals for the pilot study: shrub mapping, sample size determination via data simulation, and field methods development. The first step to support these processes was precisely defining our study area. Study area definition We chose to work in ungulate winter range because this is where we expect a reduction in browsing pressure to be most likely to benefit plants. We defined winter range in Middle Park and the Upper Colorado River Valley based on species activity mapping (SAM) layers. We consulted with CPW personnel knowledgeable about the layers (Chuck Anderson, Dani Neumann, Jon Runge) and considered the spatial extent of Winter Range, Winter Concentration Area, and Severe Winter Range for both elk and mule deer. We concluded that Winter Concentration Areas were the most likely to receive winter browsing pressure from both elk and mule deer in most years. We then explored the outputs from intersecting versus merging the Winter Concentration Area layers for species (elk and mule deer) and for SAM mapping years (2014, 2018, and 2023), preferring to intersect the layers to more easily design a study that captures both elk and mule deer responses. To ensure that we are capturing the majority of upland shrub communities experiencing winter browsing pressure, we merged SAM layers across the 3 years for each species before intersecting them, and then buffered the result by 1 km in order to connect polygons that were near each other and produce a more cohesive final layer. Shrub mapping CPW biologists consistently report that nationwide remotely-sensed products, such as LANDFIRE, do a poor job of mapping deciduous upland shrub communities in Colorado. Accurate maps of the shrub communities we wish to sample are required for assessing landscape scale responses to changes in browsing pressure. In 2025, we addressed this problem by leveraging year-end FY 25 funds and a program free to us, the NASA DEVELOP remote sensing internship program. In June 2025, we captured plant community data at 481 points within our study area, as defined above. Locations for sampling were selected on public land, and within 1.6 km of roads. We collected data at 125 randomly selected points and 356 opportunistic points. To increase our likelihood of sufficiently sampling within upland deciduous shrub communities, which are rare on the landscape, we selected half of the random points from the Rocky Mountain Lower Montane-Foothill Shrubland classification from SWReGAP, the most accurate pre-existing classification we had identified based on local field knowledge. The remainder were selected without regard to prior remote sensing classification. We verified with the NASA DEVELOP team manager, Tony Vorster, that the spatial extent of our sampling locations were sufficient to support modeling to the entire study area, as they encompassed the range of elevations and shrub communities of interest. At 120 randomly selected points, we collected cover data for a 5m radius plot in two ways: 1) quick ocular assessment of cover of the three most dominant plant groups visible from above, categorized by groupings of interest (serviceberry, bitterbrush, mountain mahogany, perennial grass, perennial forb, �pinyon, juniper, aspen, rabbitbrush, sagebrush, snowberry, riparian shrub, riparian tree, other shrubs); and 2) complete line-point-intercept data taken for all canopy layers, to species, in a circle-and-crosshairs pattern. At the remainder of the points, we collected cover data only by quick ocular assessments. At all points, we collected categorical, qualitative estimates of shrub browsing pressure, which was used to initialize our sample size estimation data simulation (see following section). All data was taken with Survey 123 and sent weekly to the NASA DEVELOP team, who consisted of Ashley Bañuelos, Erin Burke, Scott Mohan, and Sheyla Rios Galeano. The NASA DEVELOP team found that our quick assessments related to the LPI data by R2 of 0.64 to 0.87 for our shrub species of interest. They used the quick assessments in a cluster analysis to define plant community associations, keeping in mind our interest in distinguishing between upland shrub community types. Next, they modeled these vegetation classifications across our study area using imagery from Landsat 8-9, Sentinel-2, Radar, and Lidar (Figure 1). Figure 1. Procedure used by the NASA DEVELOP interns to map shrub communities in ungulate winter range in Middle Park and the Upper Colorado River Valley. Image credit: Ashley Bañuelos, Erin Burke, Scott Mohan, and Sheyla Rios Galeano. We are still awaiting the finished report from NASA DEVELOP, but we have received a map and accuracy assessments of each classification. The map based on LANDSAT had slightly higher overall accuracy than the SENTINEL map, but the SENTINEL map has a finer resolution. Accuracy for serviceberry was satisfactory, with 83% user accuracy (83% of pixels labeled as serviceberry are actually dominated by serviceberry) and 85% producer accuracy (pixels labeled as serviceberry capture 85% of pixels actually dominated by serviceberry). However, accuracy for bitterbrush was less satisfactory, with 100% user accuracy but only 25% producer accuracy, indicating that many points dominated by bitterbrush were not captured by the model (Table 1). �Table 1. Classifications modeled by the NASA DEVELOP intern team for ungulate winter ranges in Middle Park and the Upper Colorado River Valley, with accuracy for LANDSAT-based and SENTINEL-based models. 43 PREDICTORS NO TERRAIN VARIABLES #ID Class LANDSAT Producer Accuracy SENTINEL User Accuracy Area (sq miles) Percent cover Producer Accuracy User Accuracy Area (sq miles) Percent over 1 Rabbitbrush and Snowberry 0.63 0.83 42.53 6.1% 0.75 0.83 20.73 3.0% 2 Bitterbrush 0.42 1 0.15 0.0% 0.25 1 0.12 0.0% 3 Mountain Mahogany 0.63 0.95 4.84 0.7% 0.56 0.86 9.67 1.4% 4 Perennial Grass /Sagebrush 0.99 0.77 419.68 60.0% 0.97 0.79 404.04 58.4% 5 Aspen /Gambel Oak 0.47 0.90 5.66 0.8% 0.42 0.62 44.15 6.4% 6 Other Conifer 0.86 0.83 67.41 9.6% 0.84 0.88 88.72 12.8% 7 Serviceberry 0.67 0.94 12.29 1.8% 0.85 0.83 24.54 3.5% 8 Pinyon Juniper 0.9 0.83 42.85 6.1% 0.81 0.64 34.64 5.0% 9 Mixed Riparian Shrub and Tree 0.33 1 1.91 0.3% 0.33 1 4.68 0.7% 10 Water 0.93 1 5.91 0.8% 0.90 1 5.16 0.7% 11 Agriculture 0.97 0.9 67.15 9.6% 0.94 0.88 32.90 4.8% 12 Developed/ Bare Ground 0.77 0.9 22.91 3.3% TOTAL OF OBS 614 699.87 0.63 0.88 614 18.13 692.06 OVERALL ACCURACY (GT DATA) 0.83 0.82 OUT OF BAG ERROR 0.39 0.37 2.6% �This may be because bitterbrush is often intermixed with many other species, including sagebrush, serviceberry, mountain mahogany, and pinyon. In practice, it was very difficult to find locations that had bitterbrush cover exceeding 25%. Even so, the model will be a useful starting point to guide long-term study site selection, as it provides a finer-scale and more shrub-focused classification than previously available (Figure 2). Figure 2. Modeled vegetation classification map for ungulate winter ranges in Middle Park and the Upper Colorado River Valley, based on 10m2 pixel resolution Sentinel imagery. Sample size estimation: simulation Approach Determining sample sizes and study duration is dependent on the number of indirect effects we are interested in measuring (i.e., response variables), the number of variables we are interested in quantifying or controlling for (i.e., explanatory variables), and the strength of their interactions. We started to address this by conducting data simulations, informed by pilot field data, remotely sensed data products, and peer-reviewed literature, which explore what magnitude of effects are feasible to detect under different design decisions. Brian Gerber (USGS Colorado Cooperative Fish and Wildlife Research Unit) led this component with the help of a post-doctorate researcher, Michael Peyton, whom we hired through Colorado State University with allocated FY25 and FY26 Mammals Research funds. Our study design must be capable of accounting for (via study design or modeling) the factors that can impact shrub productivity and biomass change. To determine how to parameterize the simulation, we first identified the primary drivers influencing shrub dynamics in Middle Park. We selected soil moisture, soil nitrogen, fire, cattle use, and ungulate browsing as drivers to be explicitly modelled in the simulation, while other factors – which may have minor contributions to biomass change – were modelled as process noise. �We simulated the study site as a collection of 20 m x 20 m cells encompassing the extent of the shrub community in Middle Park. We leveraged the NASA DEVELOP shrub map, masked by vegetation communities identified as upland shrublands, to determine the operational study area. Using this sampling frame, we extracted data from remotely sensed and publicly available sources capturing aboveground biomass (NASA GEDI), soil moisture (ERA5-LAND), soil nitrogen (SoilGrids), burn probability (Colorado Wildfire Risk Assessment), and cattle allotment jurisdictions (BLM and USFS grazing allotment boundaries). Raw data sources or probability distributions capturing these data were used to initialize conditions and project the annual variability of major drivers across cells, when applicable. This simulation is not spatially explicit; we determined that modelling this variation without spatial structure was sufficient for our primary objective of evaluating sample design and detection power. Methods The simulation proceeds in annual timesteps, with biomass gains and losses computed as a function of measured relationships among major drivers from published, peer-reviewed literature. Estimates of these relationships vary among studies, so each relationship is designed to be modelled across multiple plausible levels to capture the range of variation estimated in the literature. All processes outlined below are modelled with process noise informed by the literature, ensuring the contribution of other drivers not explicitly incorporated into the model are indirectly captured in the spatial and temporal variability across cells. First, annual biomass gain is computed through the effect of soil moisture, soil nitrogen, and their interaction on annual primary productivity. These gains are added to the existing crop of standing biomass on a per-cell basis. Next, fire is simulated by using per-cell burn probability to project and identify burned cells during this timestep. Cells identified as burned incur biomass losses based on probability distributions derived from the literature on measured losses due to fire events in this ecosystem. Post-fire trajectories of biomass development differ from the dynamics observed in unburned areas, but estimates differ widely based on a variety of factors, including pre-burn site conditions, existing vegetation, and founder effects. Moreover, fire can alter abiotic conditions such as soil moisture and nitrogen, directly influencing factors important for productivity. Due to the uncertainty involved in the effects of fire on these factors collectively, we modelled postfire trajectories directly, using a stepwiselinear form with no growth immediately following fire initialization followed by recovery. Both the recovery trajectory and the years of recovery (before returning to pre-fire conditions) were allowed to vary across the range of estimates found in the literature. Next, the effect of cattle on shrub biomass is computed by a two step process: first, the probability of cattle use is applied within allotment cells, then the grazing effect is applied on cells selected that year. Estimates of actual use are not available by public record nor are collected by government agencies, so estimates of usage probability come from personal communication with the Bureau of Land Management and the US Forest Service, with appropriate noise to encapsulate uncertainty. Estimates of the effect of cattle on shrub productivity range widely, with most sources reporting some losses due to herbivory but some sources reporting minor gains due to a reduction in grass competition. We therefore parameterized the effects of cattle to range from small, positive change in growth to progressively larger losses, informed by the literature. Effects were scaled to the current year’s productivity but applied to standing biomass, allowing effects that exceeded annual productivity projections to impact existing biomass. �Finally, the model applies winter browsing pressure from ungulates, which come from estimates of losses in the field. Similarly to the effects of cattle, these effects are scaled to the current year’s productivity, but are applied to standing biomass and can exceed annual productivity. Mule deer and elk show some degree of site fidelity across years, which we captured by assigning cells to none, low, medium, or high browsing pressure categories but allowed significant variation in year-to-year proportional losses within each category (except ‘none’) to reflect temporal variability in site use. Browsing intensity at a site depends heavily on the composition of shrubs within a vegetation community and their palatability. To determine the distribution of browsing pressure categories across vegetation communities, we used field estimates of browsing pressure (categorized similarly as none, low, medium, or high) to understand differences in browsing pressure across the vegetation communities identified by NASA DEVELOP. We detected two distinct distributions of browsing intensity categories across vegetation communities using PERMANOVA, which we collapsed into two vegetation ecotypes broadly consisting of shared composition. We identified a generally less-extensively browsed, Sagebrushdominated system (hereafter, Sagebrush community), and a more heavily-browsed system abundant with palatable shrubs (e.g. bitterbrush, mountain mahogany, serviceberry; hereafter, Mtn. Shrub community). Using these community distinctions, we identified the proportion of the landscape at the study site consisting of the Sagebrush community (94.2%) and the Mtn. Shrub community (5.8%) to apply our field-derived measurements of browsing pressure, and randomly assigned each cell a vegetation community based on these proportions. Then, for each cell, we applied a browsing intensity category (none, low, medium, or high) based on probability distributions of these categories specific to the cell’s assigned vegetation community. Thus, vegetation communities here represent differences in initial browsing conditions, with the Sagebrush community representing sites dominated by less palatable vegetation, and Mtn. Shrub communities representing those with more palatable species. Respiration costs are an important constraint on growth and maximum biomass. We modelled this dynamic as a loss of standing biomass based on the initial biomass at the current timestep. This loss function follows a well-established power law, where respiration losses are proportional to the ¾ power of standing biomass. While the exponent of this power law is well-studied, the constant used to scale standing biomass to biomass loss from respiration is not. Therefore, we tested a range of values for this constant to determine which value allows us to best approximate the observed values of standing biomass in Middle Park. We proceeded using 0.3, with the option of adjusting as new information becomes available. Respiration losses are calculated from initial biomass after annual productivity is added to the standing crop, but applied after losses from fire, cattle, and ungulate browsing. Impacts of predator space-use and density on ungulate density and behavior are extremely variable and contingent, and current data do not support parameterizing wolf - ungulate interactions. Rather than explicitly modelling predator-prey dynamics, we instead impose a change in browsing pressure directly as a shift in annual herbivory losses. This allows for the testing of sample design across a range of plausible scenarios while avoiding the additional uncertainty from poorly constrained parameters. We first allowed the simulation to run for 30 annual timesteps (i.e. 30 years) to allow initial conditions to equilibrate, then applied a shift in browsing pressure which effectively subtracted the proportion of annual productivity removed through ungulate browsing by 0.1 (low), 0.3 (medium), or 0.5 (high). �We used shrub cover as the primary metric for detecting vegetation change to align with commonly used monitoring protocols. We converted standing biomass to cover using a saturating Michaelis-Menton curve from the literature, with process noise to reflect variation in canopy architecture. Sample plots were randomly assigned within vegetation communities at the beginning of each simulation run and sampled each year to represent permanent plots. Each plot is equivalent to a single 20 m x 20 m cell. Random noise was incorporated to reflect sampling error for sampled cover and sampled browsing pressure. The spatial extent of wolf indirect effects on vegetation is unknown and difficult to predict. To test how the spatial extent of predator impacts influences detection, and to capture across the range of uncertainty, we modelled four scenarios corresponding to differing breadths of browsing pressure change: local (10% of the landscape), regional (30% of the landscape), landscape (60% of the landscape), and all (100% of the landscape). Within each of these scenarios, we applied low, medium, and high browsing pressure shifts to run a total of 12 scenarios corresponding to differences in the spatial extent and intensity of predator impacts on browsing. Across these scenarios, we tested four sample sizes reflecting the sampling effort plausible for each field season: 30, 60, 90, and 130 sample plots within each vegetation community for a total of 60, 120, 189, and 260 samples across both Sagebrush and Mt. Shrub communities. Thus, we tested across 48 scenarios to understand detection with varying sampling effort. To evaluate how stratified sampling influences detection probability, we ran each set of 48 scenarios under five sampling schema: stratification by soil moisture, soil nitrogen, initial browsing pressure, and cattle allotments, plus an unstratified design within each vegetation community. We used a simple allocation structure with an equal number of plots within each stratum. For continuous variables (soil moisture and soil nitrogen), we defined strata as low, medium, and high by splitting the data into three groups of equal size. For categorical variables (initial browsing pressure category and cattle allotments), strata corresponded to the existing categories. Comparing detection performance across these sampling schemes allowed quantification of the effect of stratification on our ability to detect changes. Detection probability depends not only on sample design but also on the statistical methods used to evaluate vegetation change. To compare alternative statistical methods, we implemented four statistical tests representing different approaches to test for the impact of wolves on shrub dynamics. Because each test was applied to 1000 simulation iterations for each of the 48 scenarios, we used streamlined implementations of each statistical approach that retain their core structure while keeping computation tractable. For the first three tests, we compared measurements from a five-year window immediately before wolf establishment to a five-year window following a 10-year equilibration period after establishment. We also fit a fourth model that compared temporal trends in cover across the pre- and postestablishment periods. First, we used a simple paired t-test on plot-level mean cover in the pre- and post-establishment windows, representing a naive test for a step change in vegetation. Second, we fit a mixed-effects model comparing cover across pre- and post-establishment periods, with period as a fixed effect and plot as a random effect. For scenarios with stratified sampling, we also included a fixed effect for stratum. This test represented a modelling approach that explicitly accounts for repeated measurements and stratification, when applicable. Third, we used a piecewise Structural Equation Model (SEM) which required two conditions for detection: (1) a mixed-effects model testing the effect of pre- vs post-establishment period �on browsing pressure, and (2) a mixed-effects model testing the effect of browsing pressure on shrub cover while also including establishment period as a covariate to capture other changes within each period. Both models included plot as a random effect. Together, these models provide an explicit test of a tri-trophic cascade by establishing both the link between browsing pressure (representing ungulate population density) and wolf establishment, and the link between browsing pressure and shrub cover. Finally, we fit a mixed effect model to test differences in the temporal trend between the five-year window immediately before establishment and a five year window immediately after establishment. Cover was modelled as a function of time, establishment period, and their interaction, with a plot-level random effect. Detection occurred when the time x period interaction was significant, indicating a temporal slope change between the pre- and post-establishment period. Results Detection probability was strongly influenced by both spatial scale and the magnitude of the browsing pressure shift (Figure 3). �Figure 3. Detection probability for the Mtn. Shrub community across four alternative statistical approaches. Rows show four different statistical tests used to detect an indirect effect of wolf establishment on shrub cover: (a) simple paired t-test on plot-level mean cover pre- vs. postestablishment, (b) mixed-effects model comparing pre- and post-establishment periods, (c) piecewise Structural Equation Model (SEM) testing both (i) the effect of establishment on browsing pressure and (ii) the effect of browsing on cover, and (d) mixed-effects model testing a change in temporal slope in the pre- and post-establishment periods. Columns show differences in the spatial extent of establishment, with local (10% of the landscape), regional (30% of the landscape), landscape (60% of the landscape), and all (100% of the landscape) from left to right. Lines indicate the magnitude of the shift in browsing pressure, with blue lines showing a low shift (0.1), yellow lines showing a medium shift (0.3), and green lights showing a high shift (0.5). The y axis shows detection probability, computed as the percentage of simulation iterations (n = 1000) where a shift was detected using the given statistical approach. The x axis shows variation in sample size. Each point represents a single simulation run for a given combination of spatial scale, sample size, browsing shift magnitude, and statistical test. At the local scale (10%), all four statistical approaches had low power, and differences in detection among browsing shifts were small. As spatial scale increased to regional (30%) and landscape (60%) extents, detection improved and the magnitude of the browsing-pressure shift became more important for detection probability. While moderate (0.3) and high (0.5) shifts in browsing pressure met our target detection (~ 80% power) at landscape scales, low (0.1) shifts remained difficult to detect across methods. Detection improved with sample size, but gains generally diminished beyond ~ 60 plots for most combinations of scale, shift magnitude, and statistical test. This pattern suggests that a design with �no fewer than 60 plots can achieve sufficient power given the trade-off between power and sampling effort. We also tracked the proportion of plots that burn over a 35-year period, which indicated that ~ 7% of plots will likely burn over the course of the monitoring effort. Factoring in this expected loss, we tentatively suggest targeting ~ 70 - 80 plots to maintain effective sample sizes using our current models. This estimate may be refined as the simulation work continues. Of the first three tests, the simple t-test provided the lowest rate of detection, followed by the design-aware mixed-effects modelling approach. These differences were most pronounced at the regional scale with moderate to high shifts in browsing pressure. Piecewise SEM showed the greatest promise, providing detection that approached or exceeded our target power at the regional scale (but see sensitivity analysis results below). The final test comparing temporal trends in cover demonstrated the lowest detection rate across all methods, suggesting detecting slope changes may be difficult under a simple regression framework. Detecting temporal trends may require more sophisticated time-series approaches in practice, which were not implemented here due to computational constraints. Comparisons between stratified and unstratified sampling under our equal-allocation design showed only modest differences in detection across methods. We plan to continue testing the potential benefits of stratification using a stratified sampling estimator that explicitly accounts for heterogeneity in variance to determine if they will provide additional gains in detection. These tests of detection were conducted under a common set of baseline simulation settings for all scenarios. However, key processes in the real system are uncertain, and published estimates for many relationships span a broad range. To evaluate how this uncertainty may influence our conclusions about detection probability, we conducted a sensitivity analysis on major model drivers and relationships. We varied parameters controlling the strength of soil moisture, soil nitrogen, and their interaction on productivity, fire-related biomass loss and recovery time, the frequency of allotment use and the magnitude of cattle impacts, respiration costs (i.e. the respiration constant), and sampling error for cover and browsing pressure. For each parameter, we varied across each categorical distinction (or three plausible values for continuous variables) while holding all other parameters at their baseline settings under an unstratified sample design. Sample size, the magnitude of the shift in browsing pressure following establishment, and the spatial extent of establishment were held constant across simulation runs. For each parameter setting, we recomputed detection probabilities and compared them to the baseline, allowing us to identify which assumptions most strongly influence detection and which conclusions are robust to uncertainty in model structure. The sensitivity analysis showed detection probability was most strongly influenced by sampling error in cover and browsing pressure (Figure 4). �Figure 4. Sensitivity of detection probability to simulation parameters for the design-aware mixedeffects modelling approach (a) and the piecewise SEM (b). The y-axis shows the absolute change in detection probability when simulation parameters are varied across their low, medium, and high settings relative to a common baseline configuration where all categorical parameters are set at their medium values. Variables include the strength of the relationships between productivity and soil moisture (SM_level), the strength of the relationship between productivity and soil N (N_level), the strength of the soil moisture and N interaction on productivity (SM_N_level), the amount of variance in productivity attributed to soil moisture, N, and process noise (var_level), the amount of variance attributed to the soil moisture x N interaction (int_level), the slope of the post-fire biomass accumulation trajectories (pf_level), the number of post-fire recovery years (rec_years), the effect of cattle on standing biomass (cattle_level), cattle allotment use probability (cattle_use), sample noise for shrub cover (cover_sd), sample noise for browsing intensity (sample_browse_sd), and the maintenance respiration cost constant (resp_constant). Colors indicate sampling between both communities with equal sample size (All; red), sampling only within the Mtn Shrub community (Mt_Shrub; green), and sampling only within the Sagebrush community (Sagebrush; blue). Bars show the range of |Δ % detection| values across all scenarios compared to baseline conditions, with larger values indicating greater influence of simulation parameters on detection probability. Note differences between panels in the y-axis scale. �Increases in observation error for cover substantially reduced detection across all four statistical approaches, emphasizing that accurate, precise, and repeatable field estimates of cover are critical for detecting indirect effects of wolves on vegetation. Piecewise SEM performance was particularly sensitive to sampling error in browsing pressure and the ability to accurately track changes over time; performance sharply declined when estimates of browsing pressure included substantial error. In practice, measurements of indirect effects are often evaluated using both browsing metrics and independent estimates of ungulate density and space-use. This finding underscores the importance of investing sampling effort in these measurements, and suggest that combining estimates of browsing pressure and ungulate data may help to constrain the effect of noise on detection, especially for mechanistic tests of indirect effects. Field methods development Shrub recruitment Heavy ungulate browsing can create a ‘regeneration debt’ in woody plants, whereby recruitment of new individuals is insufficient to replace mature individuals, resulting in shifts in plant community composition (Miller et al. 2023). Seedlings of bitterbrush, mountain mahogany, and serviceberry can be killed by ungulate browsing, even though mature plants are quite resilient to browsing (Shepard 1971, Paschke et al. 2003). Thus, regeneration is an important and sensitive metric with respect to browsing pressure changes. Our proposal included quantifying juvenile shrub density as a metric of regeneration. We have decided that juvenile shrub density, while interesting, is not sufficient to understand regeneration processes. This is because juvenile shrub density could decrease for either good reasons (juveniles grow to become adults) or for bad reasons (juveniles die). Probabilities for juvenile emergence, survival, and transition to the mature class would be much more informative. This requires tagged individuals. Thus, we developed methods for tagging and relocating juveniles in 2025. We placed permanently marked center points for each plot and recorded polar coordinates for each tagged individual (Figure 5). �Figure 5. 5 gallon bucket lids permanently marking plot centers allow us to precisely align a device for measuring and relocating juvenile shrubs via polar coordinates. Our proposal included ancillary work to ensure that we could consistently and reliably classify juveniles, including testing methods for aging young shrubs. We assessed our ability to age shrubs via bud scar counts, ring counts at the root crown, and root crown diameter by starting with 40 shrubs of known age harvested from decommissioned prior experiments or purchased from local nurseries. Shrubs can be aged using ring counts in a similar manner to trees, if the counts are performed at the point of germination, which is identified as the lowest part of the plant containing pith (Telewski 1993). We sectioned the shrubs in 1cm increments, sanded the sections with progressively finer sandpaper, and used dissecting scopes to identify the correct section (Cooper et al. 2003). Observers without knowledge of the shrub’s actual age then counted the rings. A regression between actual age and ring count had an R2 of 0.88. For ages over 2, ring counts tended to underestimate age, likely due to missing rings occurring during drought years. Therefore our ring count estimates should be regarded as minimum ages (Figure 6). �Figure 6. Relationship between root crown diameter and age (as estimated by ring counts at the root crown) for a lumped sample of bitterbrush (circles), mountain mahogany (triangles), sagebrush (squares), and serviceberry (crosses). Next, we harvested 34 bitterbrush, mountain mahogany, serviceberry, and sagebrush shrubs of unknown ages, counted bud scars, measured root crown diameter, and then sectioned them and counted rings. Bud scar counts proved a very unreliable estimate of age, with an R2 of only 0.13 when regressed versus ring-estimated age. Plants of ages 5, 7, 10, and 24 had only 2 bud scars, implying that shrubs often lose entire leaders. Diameter at root crown performed better, with an R2 of 0.66 when regressed versus ring-estimated age. Species were lumped for this analysis (Figure 6). We decided to use root crown diameter as our basis for classifying juveniles, and we considered that shrubs having survived at least 4 years would have passed the most vulnerable stage of higher seedling mortality. Our model estimates that shrubs with root crown diameter of 3mm have a ringestimated age of 4.2 (3.5, 4.8) years, therefore we plan to define juveniles as having a diameter of 3mm or less. As serviceberry plants produce suckers, we excavated several small serviceberry plants to develop criteria for distinguishing suckers from juveniles based on the tapering of the tap root (Figure 7). �Figure 7. Serviceberry suckers (a) can be distinguished from serviceberry juveniles (b) by whether or not the tap root tapers in diameter with depth. We ultimately created a scheme of 5 different age and size related classifications that are relevant to shrub origin, survival, and contribution to forage resources: baby, sucker, juvenile, dwarf, and mature. Table 2 describes these classifications, why they matter, and what type of monitoring we plan to perform. Some criteria may undergo further refinement. Table 2. Shrub recruitment classes to be monitored. Name Approx Age Criteria Relevance 2025 and Planned monitoring baby Less than 1 year Stem diameter less than 1mm. Easily identifiable. Starting point for recruitment Counted, but not tagged; tagging not practical due to small size and high mortality. juvenile Less than about 4 years Stem diameter less than 3mm (Cutoff of 5mm was used in 2025), tapering by at least a third within 7cm of the ground surface. If serviceberry, more than 1 m from canopy of mature serviceberry. Canopy area less than 200 cm2. Stage which may contribute to future recruitment, but vulnerable to mortality. Survival may be sensitive to browsing pressure. Counted and tagged so individual fate can be determined. sucker Less than about 4 Stem diameter less than 3mm (Cutoff of 5mm was Stage which may contribute to future recruitment. Likely to be Counted in 2025. Plan to begin tagging in 2026 so �Name 2025 and Planned monitoring Approx Age Criteria Relevance years used in 2025), NOT tapering by at least a third within 7cm of the ground surface. If serviceberry, more than 1 m from canopy of mature serviceberry. Canopy area less than 200 cm2. less vulnerable to mortality, with individual fate can be survival less sensitive to determined. browsing pressure, than juveniles. dwarf More than about 4 Stem diameter more than 3mm (Cutoff of 5mm was years used in 2025). If serviceberry, more than 1 m from canopy of mature serviceberry. Canopy area less than 200 cm2. Stage which is less vulnerable to mortality than juveniles or suckers, but which does not yet provide meaningful forage resources. Transition to mature plants may be sensitive to browsing pressure. Counted in 2025. Plan to begin tagging in 2026 so individual fate can be determined. Mature More than about 4 Canopy area more than 200 cm2. Stem diameter years unlikely to be less than 3mm. Stage which is less vulnerable to mortality than juveniles or suckers and provides meaningful contribution to forage resources and seed production. 8 randomly selected individuals per species per plot to be tagged in 2026 for mortality estimates, seed production, productivity, canopy area, and browsing pressure Plot-scale sampling strategy In spring 2025, we discussed temporal aspects of data collection and plot spacing. We considered the merits of different temporal strategies for collecting plot-scale data, such as measuring all plots every year, half the plots every other year, or a third of the plots every three years. After considering our future desire to relate plot-scale vegetation data to animal space use and density, we decided that taking data at every plot in every year is necessary. We also considered how far plots should be spaced in order for each plot to be considered independent with regards to ungulate movement, and decided that vegetation plots should be spaced by a minimum of 400m. In late summer 2025, we completed practice plots in 6 locations to try out different sampling strategies for tagging immature shrubs as well as for gathering data on mature shrubs. Our initial strategy was to measure all shrubs within a defined area, as this would allow estimates to be calculated on a perarea basis from our measurements. We assessed several plot areas/shapes (80m2 circle, 40m2 hourglass or pinwheel, 20m2 thin pinwheel). For immature shrubs, allowing a flexible plot size/shape for each species in each location was successful, allowing us to complete our surveys within about 30 minutes per plot. In some cases, �however, we tagged fewer than 10 plants per species. In 2026, we may survey for immature shrubs over a plot radius of 7m rather than 5m. For mature shrubs, flexible plot area allowed us to gather data more efficiently, but even so, each survey took 1-3 hours to complete. Choosing the most appropriate plot area/shape for each species at each location proved difficult, and we ended up with excessive numbers of measurements for certain species, particularly sagebrush. We are interested in height, seed production, productivity, canopy area, and density for mature shrubs. In 2025 we measured all parameters for all shrubs within our defined area. In 2026, we propose to randomly select 8 mature shrubs per species per plot for seed production, productivity, canopy area, and height measurements. We will tag these shrubs and collect polar coordinates to facilitate relocation over time, similar to the immature shrub survey. In this way, we will obtain data on mature plant mortality probability. This approach does not directly provide a measure of shrub density or total cover, nor does it provide productivity or seed production on a per-area basis. We will be able to get estimates of these parameters by coupling our measurements on marked plants with line-point intercept (LPI) measurements of percent cover taken at the plot scale in late summer. We will collect LPI in a crosshairs pattern centered on a permanent plot marker, allowing us to remeasure exactly the same transects each year, and providing a complete picture of the plant community, including herbaceous plants. The marked plants will allow us to calculate productivity and seed production per unit of shrub canopy area, and the LPI data will provide a measure of shrub canopy area at the plot level. We can also obtain a rough estimate of shrub density by dividing plot-level shrub area estimated by LPI by average shrub canopy area estimated from marked plants. Shrub seed production Seed production is the starting point for regeneration and essential to plant community persistence. Serviceberry, mountain mahogany, and bitterbrush produce flowers and seeds on wood that grew the prior growing season. If most or all of the current year’s growth is browsed, very little seed will be produced (Hormay 1943, Clements and Young 2001). Browsing can also reduce sagebrush seed production due to energetic constraints (Wambolt and Sherwood 1999). Thus, seed production is a metric of interest that may be sensitive to browsing pressure changes. There is no established, reproducible method for efficiently estimating bitterbrush, serviceberry, and mountain mahogany seed production. Seeds can be collected in traps or grates, but this method requires infrastructure to be left in the field and multiple site visits per year (Clements and Young 2001). For big sagebrush, inflorescence length correlates well with seed production per inflorescence (Landeen et al. 2017), but methods to estimate inflorescence density are still needed. In 2025, we began to develop seed production methods for bitterbrush, serviceberry, mountain mahogany, and sagebrush by correlating ocular estimates of seed density classes with actual numbers of seeds (for bitterbrush, serviceberry, and mountain mahogany) or numbers of inflorescences (for sagebrush). To do this, we photographed shrubs, estimated their seed density class on a scale of 0-10, and measured their canopy area. For smaller or more sparsely seeded shrubs, we then picked and counted all seeds on the shrub. For larger or more densely seeded shrubs, we randomly selected three 0.03m2 vertical columns of the shrub canopy. We then photographed each of these three subsamples, estimated the seed density class of each, and then picked and counted the seeds within them. Our naive estimates of seed �density class correlated with actual seed density by an R2 of 0.56 for sagebrush, 0.57 for mountain mahogany, and 0.70 for bitterbrush. We were unable to test our accuracy for serviceberry, due to very poor seed production in 2025. We arrayed our photographs in order of actual seed density to create training materials to improve our future ocular estimates of seed density classes. In 2026, we will continue this work by including serviceberry plants and estimating and counting samples of higher seed density for the other species. We will also determine if seed density estimates are more accurate when made at the scale of the whole plant or when a few subsamples are selected and then averaged. To do this, we will complete photographs, estimates, and counts at the scale of whole plants and at the scale of subsamples for the same shrubs. Shrub productivity Shrub productivity is similar to shrub seed production and browsing pressure in that standardized, efficient methods have not been developed. Shrub productivity is important to measure as it is known to be predictive of herbivore use. In addition, shrub productivity is a function of many site-level variables, including precipitation, soil quality, aspect, and competition. Our modeling exercises may reveal that it will be more efficient to measure shrub productivity directly, rather than model or collect data on each of the site-level factors that contribute to it (Kauffman et al. 2010). For trees, site productivity can be measured by measuring changes in trunk diameter from one year to the next. Shrubs do not offer such a handy, discrete way of measuring annual growth, as they consist of multiple stems of differing ages with the palatable current-year shoots scattered in a complex three-dimensional distribution(Rutherford 1979). The gold standard is to quantify all current-year growth by either clipping and weighing all current-year shoots, or by measuring many shoots and then applying allometric equations (Johnston et al. 2007). Both of these options are too time-consuming for our needs. In 2025 we attempted a method relating the diameters of the largest two shoot diameters per shrub to current year growth biomass. We utilized data from prior experiments to assess the method and found that it failed to predict biomass adequately (R2 of 0.18 - 0.32). In 2026 we will compile detailed shrub allometry data from additional prior experiments and use machine learning to determine the minimum number of measurements needed to assess biomass with accuracy of at least R2 0.65. In late summer, we will test the resulting method by sampling off plot and clipping all current-year biomass from vertical columns of shrubs with defined volume. Shrub browsing pressure We plan to assess browsing pressure by trained ocular estimates of the percentage of current-year biomass removed (Johnston and Anderson 2023). This method has proven informative for landscape patterns, but requires well-trained observers, and has yet to be calibrated against weighed biomass to ensure accuracy for upland deciduous shrubs. If proven reliable, this method would be preferable to alternative methods which suffer from various limitations. For instance, the percentage of browsed shoots can be an informative metric for low to moderate browsing pressures, but above ~40% biomass removal it becomes inaccurate because a single bite in a larger diameter stem might represent more biomass removed than multiple bites taken from higher on the plant (Armstrong and Macdonald 1992). Browsing history by growth form is informative, �but only reliable for species that grow taller than herbivores can reach; smaller statured plants, and those growing in poor conditions, can look stunted or clubby even in the absence of herbivory (Keigley 1998). Comparing shoot-level biomass estimates pre- versus post-browsing is accurate, but is extremely time consuming and requires marked plants(Bilyeu et al. 2007, 2008). In fall 2025, we constructed grazing cages around 25 shrubs which we will use for a clipping study in early spring 2026 to test our ability to make accurate ocular estimates. We will use two observers. Observer 1 will select a target percentage of prior (2025) growth to remove, e.g. 25%, and then attempt to clip that percentage in a pattern similar to that of an ungulate (i.e. larger and more accessible shoots clipped preferentially), retaining the clipped portion. Photographs will be taken both before and after clipping. Observer 2, kept blind from the percentage chosen by Observer 1, will then estimate the percentage removed. Next, we will clip the remaining current annual growth on the protected plant, retaining the clipped material. After drying and weighing the two clipped components, we will calculate true percentages of biomass removed and then compare these to the estimates made by Observer 2. We will likely move the grazing cages to nearby plants in order to repeat this process in fall 2026/spring 2027. If this method seems viable, we will use the photographs to create training materials to ensure accuracy over time. Browsing pressure estimates will be made in spring in order to capture prior winter browsing activity. In May 2026, we will also begin making browsing pressure estimates on tagged individuals at each of our permanent locations. Because we will be simultaneously developing our methods and tagging individuals, we likely will not be able to complete measurements at all sites. Covariate data In 2025, we made progress on how to quantify three potentially important covariates at plot scale: soil moisture, soil nutrients, and cattle use. We identified soil moisture probes that include on-board dataloggers and will be suitable to quantify plot-level soil moisture at an economical price. We purchased 60 of these with FY25 year-end funds. We also identified cost-effective soil nutrient probes and purchased these as well. Soil nutrient and moisture probes will be kept in storage until we have selected permanent sites for our long-term study. We noted cattle use by presence/absence of cattle fecal piles and tracks within study plots, and this data was used to help parameterize the sample size simulation. 2026 PLANNED ACTIVITIES In early 2026, we plan to use the results of the sample size simulation and estimated fieldwork times to make an informed decision about sample size. We will decide how to distribute plots within our study area, making use of the shrub community maps provided by the NASA DEVELOP interns. If necessary we will reduce our study scope to stay within a feasible budget, which we judge to be the amount of fieldwork 2 technicians could complete in six to seven weeks. We will begin our clipping study for browsing pressure method development and begin taking browsing pressure measurements on permanent plots in April to May. We will continue our efforts to develop seed production and productivity metrics in July. In August, we will continue setting up permanent plots and take data on shrub recruitment, seed production, productivity, and cover. �We will produce a manuscript describing our process for the sample size data simulation and including a metanalysis of how prior community effects studies have selected sample size. LITERATURE CITED Armstrong, H. M., and A. J. Macdonald. 1992. 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Plant-Based Techniques for Determining Available Browse and Browse Utilization: A Review. Botanical Review 45:203–228. Schmitz, O. J., V. Krivan, and O. Ovadia. 2004. Trophic cascades: the primacy of trait-mediated indirect interactions. Ecology Letters 7:153–163. Shepard, H. R. 1971. Effects of clipping on key browse species in Southwestern Colorado, Technical Publication Number 28. L. E. Yeager, editor. Game Range Investigations, Colorado Division of Game, Fish, and Parks, Denver, Co. Tarbox, B. C., N. J. Van Lanen, A. P. Monroe, and C. L. Aldridge. 2025. Tiered spatial conservation prioritizations for sagebrush ecosystems in northwest Colorado. U.S. Geological Survey data release. Telewski, F. W. 1993. Determining the germination date of woody plants: a proposed method for locating the root/shoot interface. Tree-ring bulletin 53. Updike, D. R., E. R. Loft, and F. A. Hall. 1992. Wildfires on big sagebrush/antelope bitterbrush range in northeastern California: implications for deer populations. Biological Conservation 59:277. Wambolt, C. L., and H. W. Sherwood. 1999. Sagebrush response to ungulate browsing in Yellowstone. Journal of Range Management 52:363–369. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Mammals research pdfs Description An account of the resource Pdfs of mammals research projects by title . Pdfs are linked from this page. Includes federal aid progress reports and non federal aid progress reports. If reports cover more than one year they are combined into one pdf. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. 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