https://cpw.cvlcollections.org/files/original/1caf370c06d6607e240ac7b36e0c08bc.pdf af6dd82d2c9b364c3aa04f1ecb2b3f4c PDF Text Text The research in this publication was partially or fully funded by Colorado Parks and Wildlife. Dan Prenzlow, Director, Colorado Parks and Wildlife • Parks and Wildlife Commission: Marvin McDaniel, Chair • Carrie Besnette Hauser, Vice-Chair Marie Haskett, Secretary • Taishya Adams • Betsy Blecha • Charles Garcia • Dallas May • Duke Phillips, IV • Luke B. Schafer • James Jay Tutchton • Eden Vardy �Reproductive success of mule deer in a natural gas development area Authors: Peterson, Mark E., Anderson, Charles R., Northrup, Joseph M., and Doherty, Paul F. Source: Wildlife Biology, 2017(4) Published By: Nordic Board for Wildlife Research URL: https://doi.org/10.2981/wlb.00341 BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses. Your use of this PDF, the BioOne Complete website, and all posted and associated content indicates your acceptance of BioOne’s Terms of Use, available at www.bioone.org/terms-of-use. Usage of BioOne Complete content is strictly limited to personal, educational, and non - commercial use. Commercial inquiries or rights and permissions requests should be directed to the individual publisher as copyright holder. BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research. Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use �Wildlife Biology 2017: wlb.00341 doi: 10.1111/wlb.00341 © 2017 The Authors. This is an Open Access article Subject Editor: Jennifer Forbey. Editor-in-Chief: Ilse Storch. Accepted 23 August 2017 Reproductive success of mule deer in a natural gas development area Mark E. Peterson, Charles R. Anderson, Jr., Joseph M. Northrup and Paul F. Doherty, Jr. M. E. Peterson (mark.peterson313@gmail.com),  J. M. Northrup and P. F. Doherty, Jr, Dept of Fish, Wildlife, and Conservation Biology, Colorado State Univ., Fort Collins, CO, USA. Present address for MEP: South Dakota Dept of Game, Fish and Parks, 4130 Adventure Trail, Rapid City, SD 57702, USA. Present address for JMN: Ontario Ministry of Natural Resources and Forestry, Wildlife Research and Monitoring Section, Peterborough, ON, Canada. – C. R. Anderson, Jr., Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA. 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. Natural gas development is increasing worldwide, causing concern over the potential impacts on wildlife and their habitat (Northrup and Wittemyer 2013). Impacts on mule deer Odocoileus hemionus population dynamics and their habitat are of particular interest in North America due to the recreational, social, and economic importance of deer as a game species (Sawyer et al. 2009, Northrup et al. 2015). While a number of studies have assessed the impacts of natural gas development on mule deer behavior (Sawyer et al. 2006, Northrup et al. 2015, 2016), the influencess on reproductive success have not been studied. Specifically, accurate estimates of pregnancy rates (i.e. proportion of adult females carrying 1 fetus), in utero fetal rates (i.e. the number of fetuses per pregnant female), and fetal survival rates (i.e. survival of fetuses to birth) are needed to quantify fawn recruitment and population dynamics (Bonenfant et al. 2005, DeCesare et al. 2012) Thus, we quantified reproductive success parameters to assess if and how natural gas development may influence mule deer populations. This work is licensed under the terms of a Creative Commons Attribution 4.0 International License (CC-BY) <http://creativecommons.org/licenses/by/4.0/>. The license permits use, distribution and reproduction in any medium, provided the original work is properly cited. The mechanism by which natural gas development may influence ungulate reproductive success is through direct and indirect habitat loss. Direct habitat loss results from construction of well pads, roads, compressor stations, and pipelines. Conversely, indirect habitat loss may result from activity and noise associated with increased human presence and development, which could lead to a zone of avoidance around development that is greater than the footprint itself. Past studies suggest that deer generally decrease time spent near roads (Webb et al. 2011, Lendrum et al. 2012) and well pads (Sawyer et al. 2006, 2009, Northrup et al. 2015), suggesting indirect habitat loss. In addition, development disturbances may cause stress, alter behavior and habitat use, and decrease forage and habitat availability (Sawyer et al. 2006, Lendrum et al. 2012, Northrup et al. 2015). These processes all have the potential to influence body condition of maternal females by reducing available foraging habitat or by limiting their time spent foraging or causing increased energy expenditure (Frid and Dill 2002). Thus, reproductive success could be negatively influenced by development. We examined the influence of natural gas development and temporal (e.g. year) factors on reproductive success metrics of mule deer in the Piceance Basin, northwest Colorado, during 2012–2014. We estimated reproductive success metrics in areas with relatively high (0.04–0.90 well 1 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use �pads km–1) or low (0.00–0.10 well pads km–1) levels of natural gas development. Our objectives were to test hypotheses that reproductive success metrics would be lower in the high development areas than the low development areas and vary by year with increased precipitation influencing vegetation availability and quality. Our study provides the first insights into reproductive success of mule deer in a natural gas development area, which is helpful to comprehend mule deer population dynamics to assist in future development planning. Material and methods Study area During 2012–2014, we examined reproductive success metrics of migratory mule deer in the Piceance Basin, northwest Colorado. Our winter range study area included four study units in the Piceance Basin and are part of a larger research project (Anderson 2016). Deer occupied two winter range study units with relatively high levels of natural gas development (0.6–0.9 well pads km–1) and two winter range study units with relatively low levels of development (0.0–0.1 well pads km–1). We note that wells in our study area were directionally drilled from multiple well pads, which reduces the development density compared to coalbed methane and single-well drilling development. Winter range habitat was geographically diverse and comprised of two-needle pinyon pine Pinus edulis, Utah juniper Juniperus osteosperma, and mountain shrublands. Summer range study units included parts of Garfield, Moffat, Rio Blanco, and Routt counties in northwestern Colorado. Deer from winter range units with relatively high levels of natural gas development generally migrated to the Roan Plateau summer range (Lendrum et al. 2013) where deer potentially encountered natural gas development (0.04–0.06 well pads km–1). Hereafter we refer to deer inhabiting the winter and summer range study units with relatively high levels of development as being in the high development study areas. Deer from winter range units with relatively low levels of natural gas development generally migrated towards the Flat Tops Mountain Range summer range (Lendrum et al. 2013) where deer encountered minimal natural gas development (0.00–0.01 well pads km–1). Hereafter we refer to deer inhabiting the winter and summer range study units with relatively low levels of development as being in the low development study areas. Summer range habitat was dominated by Gambel oak Quercus gambelii, quaking aspen Populus tremuloides, two-needle pinyon pine, Utah juniper, and mountain shrublands. Shrublands included alderleaf mountain mahogany Cercocarpus montanus, antelope bitterbrush Purshia tridentate, big sagebrush Artemisia tridentate, mountain snowberry Symphoricarpos oreophilus, rubber rabbitbrush Ericameria nauseosa, and Utah serviceberry Amelanchier utahensis. Drainages bisected the study units and most of the primary drainage bottoms have been converted to irrigated, grass hay fields. Shrubs, forbs and grasses common to the area are listed in Bartmann (1983) and Bartmann et al. (1992). 2 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use Adult female capture and handling During December 2011–2013, adult (2.5 years old) female mule deer were captured in each of the four winter range study units using helicopter net gunning (Webb et al. 2008, Jacques et al. 2009). Deer were blindfolded, hobbled, chemically sedated with 0.5 mg kg–1 of Midazolam and 0.25 mg kg–1 of Azaperone given intramuscularly and ferried to a central handling location. We fit each captured deer with a global positioning system (GPS) radio collar (Model G2110D, Advanced Telemetry Systems). During early March 2012–2014, radio-collared adult females were recaptured on winter ranges using helicopter net gunning. We performed transabdominal ultrasonography (SonoVet 2000, Universal Medical Systems) to determine pregnancy status and number of in utero fetuses (Stephenson et al. 1995, Bishop et al. 2007). If an adult female was pregnant, we inserted a vaginal implant transmitter (VIT; Model M3930, Advanced Telemetry Systems) following VIT insertion procedures described in detail by Bishop et al. (2011) and Peterson (2016). Helicopter net gunning and vaginal implant transmitters have been used without influencing reproductive success and are effective methods to capture females and neonates (Carstensen et al. 2003, Bishop et al. 2009, Monteith et al. 2014). Adult female monitoring and neonate capture During parturition (late May–mid-July), we checked for radio collar and VIT signals daily from a fixed-wing aircraft. When we detected a fast pulse rate (80 beats min–1) signifying parturition, ground crews used radio telemetry to simultaneously locate the radio-collared female and expelled VIT. Ground crews then searched for neonates around (400 m) the female and VIT for up to 1 h. If neonate(s) documented in utero were not captured during the initial attempt, crews located the female on the next two days and searched near the female to locate neonates. Ground crews attempted to determine the fate of each female’s fetus(es) documented in March as live or stillborn neonates, including scavenged remains. Unless evidence suggested a neonate was born alive at a birth site (e.g. milk in the abomasum), crews classified a dead neonate as stillborn. We submitted stillborn neonates to the Colorado Parks and Wildlife’s Health Laboratory (Fort Collins, CO) for necropsy to confirm that a neonate had died before birth. During 2012 and 2013, ground crews captured neonates in the high and low development study areas. In 2014, crews captured neonates predominantly in the high development study areas and sporadically in the low development study areas because VIT photo sensors malfunctioned. We focused our effort in the high development areas during 2014 because it was logistically more difficult to monitor deer from the ground and capture neonates in the low development areas due to geographic separation among birth sites. Each captured neonate was blindfolded and sexed. All individuals who handled neonates wore latex gloves to minimize transfer of human scent. All capture, handling, radio collaring, and VIT insertion procedures were approved by the Institutional Animal Care and Use Committee at Colorado Parks and Wildlife (protocol no. 17-2008 and no. 01-2012). �Statistical methods We modeled pregnancy and fetal rates of adult females as a function of winter range study area and year using PROC LOGISTIC and PROC MIXED (e.g. generalized linear models) in SAS (SAS Inst.), respectively. We modeled fetal survival from March to birth as a function of study area and year using PROC NLMIXED in SAS and a joint-likelihood described in Bishop et al. (2008). We were not able to determine fate of all fetuses detected in utero because neonates were challenging to detect and some VITs malfunctioned, thus we used the joint-likelihood with six nuisance parameters (relative to our interests in this paper) to estimate fetal survival probability (S1). The six nuisance parameters are neonatal survival probability from birth to 5 days old (S2), the probability of detecting a neonatal fawn 1 day old given that field crews conducted a search 1 day after birth (p1), the probability of detecting a neonatal fawn 1 day old given that crews conducted a search 1 day after birth (p2), the probability of detecting a stillborn fetus when a VIT was not expelled at a birth site (r), the probability of locating a radio-collared adult female and searching for her neonate(s) 1 day after birth (a), and the probability a VIT was expelled at a birth site (b). We modeled S2 as constant or as a function of study area to account for survival differences between areas. We modeled p1, p2, a and b as constant or as a function of study area and year to account for temporal differences in detection probabilities. We constrained r to be constant because crews did not locate stillborns without the aid of a VIT during some years and in some study areas, thus we could not separately estimate r. We assumed fetal survival data were not overdispersed based on the recommendation of Bishop et al. (2008). Lastly, we fit the same model set for reproductive success metrics as Bishop et al. (2009), except age class, and that we hypothesized would influence reproductive success. We used Akaike’s information criterion adjusted for small sample size (AICc), ΔAICc, and AICc weights (wi) for model selection (Burnham and Anderson 2002). We used model averaging to obtain model-averaged parameter estimates when more than one model was within 2 AICc units of the top-ranked model (Burnham and Anderson 2002). Table A1). Pregnancy rate for all adult females during the study was 0.948 (SE = 0.012). Pregnancy rate for females in the high and low development areas was 0.953 (SE = 0.016) and 0.942 (SE = 0.018), respectively. A model indicating constant fetal rates across years ranked highest (wi = 0.766; Supplementary material Appendix 2 Table A2). We found minimal support for a study area effect on fetal rates (Supplementary material Appendix 2 Table A2). Fetal rate for all adult females during the study was 1.877 (SE = 0.029) and most females produced twins (0.819; Supplementary material Appendix 3 Table A3). In utero fetal rate for females in the high and low development areas was 1.849 (SE = 0.037) and 1.908 (SE = 0.044), respectively. The most parsimonious model for fetal survival from March until birth included an interaction between study areas and year (wi = 0.714; Supplementary material Appendix 4 Table A4). The same model for fetal survival, but without the study area variable had little support (ΔAICc = 17.598, wi  0.0014). Fetal survival was higher in the low development areas than the high development areas in 2012, whereas we found no difference in 2013 and 2014 (Fig. 1). The probability of detecting a neonatal fawn 1 day old ranged from 0.554 (SE = 0.051) in 2012 to 0.412 (SE = 0.053) in 2013. The probability of detecting a neonatal fawn 1 day and 5 days old increased each year from 0.333 (SE = 0.073) in 2012 to 0.5850 (SE = 0.134) in 2014. In the high and low development areas, respectively, females produced eight (11%) and zero stillborn fetuses in 2012, eight (12%) and three (4%) stillborns in 2013 and zero and zero stillborns in 2014. Discussion We found pregnancy and in utero fetal rates were high, showed little variation across years, and were similar in the high versus low development areas. Bishop et al. (2009) found no difference in pregnancy and in utero fetal rates when examining the effects of supplemental nutrition treatments versus a control group in a different area of Colorado 1.00 We documented pregnancy status of 346 adult females, of which 204 produced 383 fetuses (31, 167, and 6 females with 1, 2 or 3 fetus(es), respectively). Seventeen females were not pregnant and we were unable to determine accurate fetal counts for 127 females for various reasons (e.g. denied access to private property, VIT malfunctioned) and we excluded these females from the fetal analyses. Ultimately, we documented sex of 195 fetuses (99 males and 96 females). Fetal survival rate Results 0.80 0.60 0.40 0.20 0.00 2012 2013 2014 Year Reproductive success metrics A model indicating constant pregnancy rates across years ranked highest (wi = 0.776; Supplementary material Appendix 1 Table A1). We found minimal support for a study area effect on pregnancy rates (Supplementary material Appendix 1 High development Low development Figure 1. Model-averaged estimates of fetal survival ( 95% CI) of mule deer fetuses from March until birth in the high and low development study areas in the Piceance Basin, northwest Colorado, 2012–2014. 3 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use �and Anderson (2016) found no difference in body condition of adult females between the high and low development areas in our study area. Pregnancy and fetal rates were high in each area and in the upper range of previous estimates (0.860–1.000 and 1.650–2.010, respectively) across Colorado (Andelt et al. 2004, Bishop et al. 2009). Of note, deer abundance is trending upward in the Piceance Basin (Anderson 2016) after a decline during the 1990s (White and Bartmann 1998) and could be partly explained by high fetal rates coupled with fawn recruitment, which has largely been driven by relatively high overwinter fawn survival (Anderson 2016). Ultimately, high pregnancy and fetal rates seem to be the norm for deer despite a wide range of spatial and temporal differences across populations (Bishop et al. 2009, Hurley et al. 2011, Monteith et al. 2014) including our study area with natural gas development. Fetal survival from March until birth was higher in the low development areas than the high development areas, suggesting an influence of development, although survival varied annually. However, fetal survival rates exceeded previous estimates (0.747–0.983) measured on the Uncompahgre Plateau, southwest Colorado (Bishop et al. 2009). Annual variation in fetal survival could be the result of an interaction between environmental conditions and development. Annual variation in precipitation may alter the onset of spring green-up (Pettorelli et al. 2005), which can influence maternal condition (Parker et al. 2009) and possibly reduce fetal survival. Increased precipitation in arid environments is linked to forage availability (Derner et al. 2008) and quality and growth of forbs (Marshal et al. 2005), thus drought conditions may reduce forage availability and/or quality below levels needed for growth of fetuses (Parker et al. 2009). Access to high quality forage is necessary to meet the energetic demands of the last trimester when most fetal growth occurs (Armstrong 1950). Precipitation during the third trimester (1 April – 15 June) was lower in 2012 (4 cm) than 2013 (12 cm) and 2014 (12 cm), suggesting reduced forage availability and growth of forbs, which may have contributed to lower fetal survival particularly in the high development areas during 2012. Further, dry weather likely reduced forage availability which may have been exacerbated by habitat loss and fragmentation associated with development, possibly contributing to lower fetal survival in the high development areas during 2012. Of note, eight stillborn neonates were produced in the high development areas versus zero in the low development areas during 2012. Stillborn fetuses were mostly small and lightweight suggesting reduced forage availability and quality contributed to increased stillborns (Verme 1969) and consequently decreased fetal survival in the high development areas during 2012. However, active natural gas development activity was minimal during our study because most wells were in production (a phase that is characterized by less human activity and construction than the more active drilling phase), thus the influence of development could be stronger with increased development intensity and associated disturbances. Overall, development coupled with extreme environmental conditions (e.g. drought) may have contributed to lower fetal survival we observed during 2012. The probability of detecting a neonate 1 day old was low, but was highest in 2012 and similar in 2013 and 2014 4 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use because neonates were challenging to detect and some VITs malfunctioned particularly in 2014. The probability of detecting a neonate 1 day and 5 days old was also low, but increased from 2012 to 2014. Many VITs failed in 2014 providing minimal assistance in detecting neonates at birth sites, thus contributing to higher detection of older neonates as mothers and presumably neonates move farther from VITs and birth sites as they age (Vore and Schmidt 2001, Long et al. 2009). Estimating reproductive success metrics from marked adult females is helpful to understand fawn recruitment and population dynamics of ungulates (Bonenfant et al. 2005). Our results suggest managers should not be concerned with the influences of natural gas development on pregnancy and fetal rates under existing development conditions during this study. However, we suggest that future research should be conducted in areas with increased development intensity to further evaluate the influence of natural gas development on pregnancy and fetal rates. Contrarily, managers should be concerned with the potential influences of development on fetal survival as our results suggest fetal survival was lower during 2012 from increased stillbirths in the high development areas when drought conditions also were present. If development indeed caused an increase in stillbirths during extreme environmental conditions, fetal survival could be increased if forage availability and quality is improved. Thus, developers and wildlife managers should continue to collaborate during development planning to avoid important habitats during critical time periods (e.g. parturition) and consider habitat treatments (e.g. hydro-ax, roller chopping, and seeding) and/or reclamation plans to improve forage availability and quality (Johnston and Chapman 2014, Stephens et al. 2016) to enhance fetal survival and possibly fawn recruitment. Future research should focus on attempting to more precisely identify the mechanism underlying the documented differences in fetal survival. Acknowledgements – We thank S. Bard, N. Bellerose, A. Burleson, E. Cato, A. Collier, D. Collins, J. DeCoste, S. Eno, B. Frankland, T. Gettelman, M. Grode, A. Groves, C. Harty, T. Jenkins, A. Jones, D. Lewis, J. Lewis, H. MacIntyre, J. Matijas, M. Melham, S. Nagy, B. Panting, J. Peterson, E. Sawa, R. Schilowsky, J. Simpson, K. Stonehouse, M. Trump, B. Tycz, C. Wait and personnel from CPW Area 6 for their assistance with field work. L. Wolfe, C. Bishop, E. Bergman, D. Finley, M. Fisher and S. Wilson at Colorado Parks and Wildlife (CPW) assisted with handling deer, ultrasounds and insertion of VITs. Fixed wing pilots L. Gepfert (CPW) and L. Coulter (Coulter Aviation, Meeker, CO, USA) provided assistance with aerial telemetry flights and Quicksilver Air assisted with deer captures. C. Bishop, J. Forbey, K. Logan, A. Maki, P. Meiman, B. Walker and G. Wittemyer provided constructive reviews that greatly improved earlier drafts of the manuscript. Funding – Funding for this research was provided by Exxon Mobil Production/XTO Energy, Colorado Parks and Wildlife (CPW), Federal Aid in Wildlife Restoration, Colorado State University, the Boone and Crockett Club, the Colorado Chapter of the Wildlife Society, EnCana Corporation, Williams/WPX Energy, Marathon Oil Corporation, and Shell Exploration. Permits – All capture, handling, radio collaring, and VIT insertion procedures were approved by the Institutional Animal Care and Use Committee at Colorado Parks and Wildlife (protocol no. 17-2008 and no. 01-2012). �References Andelt, W. F. et al. 2004. Long-term trends in mule deer pregnancy and fetal rates in Colorado. – J. Wildl. Manage. 68: 542–549. Anderson, C. R., Jr 2016. Population performance of Piceance mule deer in response to natural gas resource extraction and mitigation efforts to address human activity and habitat degradation. – Federal Aid in Wildlife Restoration Job Progress Report W-185-R. – Colorado Parks and Wildlife, Fort Collins, CO. Armstrong, R. A. 1950. Fetal development of the northern white-tailed deer (Odocoileus virginianus borealis Miller). – Am. Midl. Nat. 43: 650–666. Bartmann, R. M. 1983. Composition and quality of mule deer diets on pinyon-juniper winter range, Colorado. – J. Range. Manage. 36: 534–541. Bartmann, R. M. et al. 1992. Compensatory mortality in a Colorado mule deer population. – Wildl. Monogr. 121: 1–39. Bishop, C. J. et al. 2007. Using vaginal implant transmitters to aid in capture of mule deer neonates. – J. Wildl. Manage. 71: 945–954. Bishop, C. J. et al. 2008. Evaluating dependence among mule deer siblings in fetal and neonatal survival analyses. – J. Wildl. Manage. 72: 1085–1093. Bishop, C. J. et al. 2009. Effect of enhanced nutrition on mule deer population rate of change. – Wildl. Monogr. 172: 1–28. Bishop, C. J. et al. 2011. Effectiveness of a redesigned vaginal implant transmitter in mule deer. – J. Wildl. Manage. 75: 1797–1806. Bonenfant, C. et al. 2005. Can we use the young: female ratio to infer ungulate population dynamics? An empirical test using red deer Cervus elaphus as a model. – J. Appl. Ecol. 42: 361–370. Burnham, K. P. and Anderson, D. R. 2002. Model selection and multimodel inference a practical information-theoretic approach. – Springer. Carstensen, M. et al. 2003. Using doe behavior and vaginalimplant transmitters to capture neonate white-tailed deer in north–central Minnesota. – Wildl. Soc. Bull. 31: 634–641. DeCesare, N. J. et al. 2012. Estimating ungulate recruitment and growth rates using age ratios. – J. Wildl. Manage. 76: 144–153. Derner, J. D. et al. 2008. Functional group and species responses to precipitation in three semi-arid rangeland ecosystems. – Arid Land Res. Manage. 22: 81–92. Frid, A. and Dill, L. 2002. Human-caused disturbance stimuli as a form of predation risk. – Conserv. Ecol. 6: art11. Hurley, M. A. et al. 2011. Demographic response of mule deer to experimental reduction of coyotes and mountain lions in southeastern Idaho. – Wildl. Monogr. 178: 1–33. Jacques, C. N. et al. 2009. Evaluating ungulate mortality associated with helicopter net-gun captures in the northern Great Plains. – J. Wildl. Manage. 73: 1282–1291. Johnston, D. B. and Chapman, P. L. 2014. Rough surface and high-forb seed mix promote ecological restoration of simulated well pads. – Invas. Plant Sci. Manage. 7: 408–424. Lendrum, P. E. et al. 2012. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. – Ecosphere 3: art82. Lendrum, P. E. et al. 2013. Migrating mule deer: effects of anthropogenically altered landscapes. – PLoS One 8: e64548. Long, R. A. et al. 2009. Resource selection and movements by female mule deer Odocoileus hemionus: effects of reproductive stage. – Wildl. Biol. 15: 288–298. Marshal, J. P. et al. 2005. Rainfall, temperature, and forage dynamics affect nutritional quality of desert mule deer forage. – Rangeland Ecol. Manage. 58: 360–365. Monteith, K. L. et al. 2014. Life-history characteristics of mule deer: effects of nutrition in a variable environment. – Wildl. Monogr. 186: 1–62. Northrup, J. M. and Wittemyer, G. 2013. Characterising the impacts of emerging energy development on wildlife, with an eye towards mitigation. – Ecol. Lett. 16: 112–125. Northrup, J. M. et al. 2015. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. – Global Change Biol. 21: 3961–3970. Northrup, J. M. et al. 2016. Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. – Divers. Distrib. 22: 547–557. Parker, K. L. et al. 2009. Nutrition integrates environmental responses of ungulates. – Funct. Ecol. 23: 57–69. Peterson, M. E. 2016. Reproductive success, habitat selection, and neonatal mule deer mortality in a natural gas development area. – PhD thesis, Colorado State Univ., Fort Collins, CO. Pettorelli, N. et al. 2005. The relative role of winter and spring conditions: linking climate and landscape-scale plant phenology to alpine reindeer body mass. – Biol. Lett. 1: 24–26. Sawyer, H. et al. 2006. Winter habitat selection of mule deer before and during development of a natural gas field. – J. Wildl. Manage. 70: 396–403. Sawyer, H. et al. 2009. Influence of well pad activity on winter habitat selection patterns of mule deer. – J. Wildl. Manage. 73: 1052–1061. Stephens, G. J. et al. 2016. Understory responses to mechanical treatment of pinyon-juniper in northwestern Colorado. – Rangeland Ecol. Manage. 69: 351–359. Stephenson, T. R. et al. 1995. Diagnosis of pregnancy and twinning in moose by by ultrasonography and serum assay. – Alces 31: 167–172. Verme, L. J. 1969. Reproductive patterns of white-tailed deer related to nutritional plane. – J. Wildl. Manage. 33: 881–887. Vore, J. M. and Schmidt, E. M. 2001. Movements of female elk during calving season in northwest Montana. – Wildl. Soc. Bull. 29: 720–725. Webb, S. L. et al. 2008. Assessing the helicopter and net gun as a capture technique for white-tailed deer. – J. Wildl. Manage. 72: 310–314. Webb, S. L. et al. 2011. Using pellet groups to assess response of elk and deer to roads and energy development. – Wildl. Biol. Practice 7: 32–40. White, G. C. and Bartmann, R. M. 1998. Effect of density reduction on overwinter survival of free-ranging mule deer fawns. – J. Wildl. Manage. 62: 214–225. Supplementary material (available online as Appendix wlb-00341 at  www.wildlifebiology.org/appendix/wlb00341 ). Appendix 1–4. 5 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 30 Jun 2021 Terms of Use: https://bioone.org/terms-of-use � https://cpw.cvlcollections.org/files/original/d337c425d563af8918bb252984587810.pdf 6da6d8d6bbd12a1509d07bebcc80c49f PDF Text Text Wildlife Biology WLB-00341 Peterson, M. E., Anderson Jr, C. R., Northrup, J. M. and Doherty Jr, P. F. 0000. Reproductive success of mule deer in a natural development area. – Wildlife Biology 2017: wlb.00341. Appendix 1 Table A1. Model selection results for pregnancy rate of mule deer during early March in the Piceance Basin, northwest Colorado 2012–2014. ΔAICcb wi c Intercept Study area Year Study area + year 0.000 3.788 4.779 6.601 0.776 0.117 0.071 0.029 1 2 3 4 0.500 0.529 0.563 0.592 Study area × year 9.265 0.001 6 0.613 Modela Kd AUCe a Variable definition: study area signifies the high and low development winter range. b ΔAICc is difference in Akaike’s information criterion adjusted for small sample size from the top-ranked model (AICc = 141.465). c wi is AICc model weight. d K is the number of parameters in the model. e AUC is area under receiver operating characteristics curve (i.e. goodness of fit). 1 �Appendix 2 Table A2. Model selection results for in utero fetal rate of mule deer during early March in the Piceance Basin, northwest Colorado, 2012–2014. ΔAICcb wi c Kd Intercept Study area Year Study area + year 0.000 2.817 6.134 8.877 0.766 0.187 0.036 0.009 1 2 3 4 Study area × year 11.813 0.003 6 Modela a Variable definitions: study area signifies the high and low development winter range. b ΔAICc is difference in Akaike’s information criterion adjusted for small sample size from the top-ranked model (AICc = 220.317). c wi is AICc model weight. d K is the number of parameters in the model. 2 �Appendix 3 Table A3. In utero fetal count of mule deer documented during early March in the Piceance Basin, northwest Colorado, 2012–2014. Study area and in utero fetal count High development [no. fetus(es)] Low development [no. fetus(es)] Year 1 2 3 1 2 3 2012 2013 2013 Total 9 3 5 17 31 32 25 88 1 0 0 1 8 6 0 14 31 32 16 79 3 2 0 5 3 �Appendix 4 Table A4. Model selection results for fetal survival of mule deer from March until birth in the Piceance Basin, northwest Colorado, 2012–2014.OnlymodelswithanAICcweight≥0.005are shown. Modela S1(study area × year) S2(year) p1(year) p2(year) r(.) a(year) b(year) S1(study area × year) S2(.) p1(year) p2(.) r(.) a(year) b(year) S1(study area × year) S2(.) p1(year) p2(year) r(.) a(year) b(year) S1(study area × year) S2(.) p1(year) p2(year) r(.) a(year) b(year) S1(study area × year) S2(study area) p1(year) p2(year) r(.) a(year) b(year) S1(study area × year) S2(.) p1(.) p2(year) r(.) a(year) b(year) S1(study area) S2(.) p1(year) p2(year) r(.) a(year) b(year) S1(study area × year) S2(.) p1(year) p2(study area × year) r(.) a(study area × year) b(study area × year) S1(study area × year) S2(.) p1(year) p2(year) r(.) a(study area × year) b(study area × year) ΔAICcb wi c Kd 0.000 0.714 22 4.104 4.604 6.504 0.092 0.712 0.028 18 18 20 6.328 0.030 21 6.504 8.126 0.028 0.012 18 16 9.535 0.006 29 9.689 0.006 26 a Parameter S1 is fetal survival probability. All other model parameters are nuisance parameters: S2 is neonatal survival probability from birth to 5 days old, p1 is the probability of detecting a neonate ≤1 day old given that field crews conducted a search ≤1 day after birth, p2 is the probability of detecting a neonate >1 day old given that crews conducted a search >1 day after birth, r is the probability of detecting a stillborn fetus when a vaginal implant transmitter (VIT) was not expelled at a birth site, a is the probability of locating a radio-collared adult female and searching for her neonate(s) ≤1 day after birth, and b is the probability a VIT was expelled at a birth site. Study area signifies the high and low development winter range. b ΔAICc is difference in Akaike’s information criterion adjusted for small sample size from the top-ranked model (AICc = 1617.406). c wi is AICc model weight. d K is the number of parameters in the model. 4 � 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 Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Reproductive success of mule deer in a natural gas development area Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/" target="_blank" rel="noreferrer noopener">In Copyright - Non-Commercial Use Permitted</a> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noreferrer noopener">Creative Commons Attribution 4.0 International License (CC-BY)</a> Date Created Date of creation of the resource. 2017-10-18 Subject The topic of the resource Natural gas development <em>Odocoileus hemionus</em> Survival Description An account of the resource 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 <em>Odocoileus hemionus</em> 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, directional drilling to minimize pad disturbance density to increase fetal survival in developed areas. Creator An entity primarily responsible for making the resource Peterson, Mark E. Anderson Jr, Charles R. Northrup, Joseph M. Doherty Jr, Paul F. Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Wildlife Biology Extent The size or duration of the resource. 5 pages Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. 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 4:1-5. <a href="https://doi.org/10.2981/wlb.00341" target="_blank" rel="noreferrer noopener">https://doi.org/10.2981/wlb.00341</a> Format The file format, physical medium, or dimensions of the resource application/pdf Type The nature or genre of the resource Article