https://cpw.cvlcollections.org/files/original/e4c39b204c9c29d091c43e477e57c06a.pdf 89cfb5aa308ffeb2258f4cecee2da410 PDF Text Text Colorado Division of Parks and Wildlife September 2013-September 2014 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No.: Colorado 3420 0660 N/A Federal Aid Project No. N/A : : : : Division of Parks and Wildlife Avian Research Greater Sage-grouse Conservation Evaluation of alternative population monitoring strategies for greater sage-grouse (Centrocercus urophasianus) in the Parachute-Piceance-Roan population of northwestern Colorado Period Covered: September 1, 2013 – August 31, 2014 Author: B. L. Walker, CPW; J. S. Brauch, Colorado State University Personnel: B. Holmes, B. Petch, B. deVergie, CPW 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 Information MAY NOT BE PUBLISHED OR QUOTED without permission of the author.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 Robust estimates of population size and population trends provide the scientific basis for managers to make appropriate and defensible recommendations regarding land-use decisions, harvest regulations, and mitigation efforts for wildlife. When linked with environmental variables, robust monitoring programs also allow managers to examine wildlife responses to disease, land-use patterns, habitat treatments, weather, ecological succession, and disturbance. However, many wildlife monitoring programs continue to use untested population indices that may not provide reliable information on population status or trends. For this reason, it is essential to evaluate alternative approaches to population monitoring in terms of estimator precision, cost, practicality, and level of disturbance. Lek counts are the primary index used by state wildlife agencies to monitor changes in greater sage-grouse (Centrocercus urophasianus) abundance, but lek counts rely on untested assumptions about lek attendance, detectability, inter-lek movement, sex ratio, and proportion of leks counted. Given the availability of new methodological and statistical approaches to estimate wildlife populations, it is worth comparing the performance of lek counts against other monitoring methods. Dual-frame sampling of leks and noninvasive genetic mark-recapture are promising alternative for monitoring trends in sage-grouse populations. The purpose of this study is to evaluate and compare the reliability and efficiency of dualframe sampling, genetic mark-recapture, and standard lek counts for estimating population size and trend and to estimate sex ratio in the Parachute-Piceance-Roan population in northwest Colorado. We completed the third and final year of dual-frame sampling in spring 2014, surveying each of 59 list-frame and 104 area-frame cells 3 times. We recorded 28 active leks (7 new) in 24 list-frame cells, 1 new lek in an area-frame cell, and 2 other leks between cells. We marked 4 VHF females and banded 8 juveniles (3 females, 5 males) in fall 2013 (1 Sep-1 Dec 2013). We completed the second and final year of winter pellet sampling for genetic mark-recapture (Nov 2013-Mar 2014). We collected samples in 4 random sampling plots and at 140 additional incidental locations for a total of 1160 pellet and 29 feather samples. 1 �WILDLIFE RESEARCH REPORT EVALUATION OF ALTERNATIVE POPULATION MONITORING STRATEGIES FOR GREATER SAGE-GROUSE (Centrocercus urophasianus) IN THE PARACHUTE-PICEANCEROAN POPULATION OF NORTHWESTERN COLORADO BRETT L. WALKER AND JESSICA S. BRAUCH PROJECT OBJECTIVES 1. Estimate proportion of known leks, the average number of males attending known leks and the total number of male greater sage-grouse attending leks in the population during three consecutive lekking seasons using dual-frame sampling from helicopter. 2. Estimate population size using genetic mark-recapture during two consecutive fall/winter seasons. 3. Estimate sex ratio using genetic sampling during two consecutive fall/winter seasons. 4. Compare and contrast methods for estimating population size and evaluate the application of auxiliary data for improving estimations based on standard lek-count data. SEGMENT OBJECTIVES 1. 2. 3. 4. Conduct the third year of dual-frame sampling of leks from helicopter in April - May 2014. Capture and mark additional birds from 15 Jul–30 Nov 2013 to augment our marked sample. Conduct winter pellet sampling from Nov 2013 - Mar 2014 for genetic mark-recapture analyses. Monitor VHF-marked females to test to test assumptions of genetic mark-recapture analyses. INTRODUCTION Population monitoring programs are essential for the proper management of wildlife species. Well-designed monitoring strategies allow researchers to determine the status of species of interest; these are often keystone, umbrella, threatened or endangered, candidate, game, or invasive species. For candidate species under the Endangered Species Act, effective monitoring plays a critical role in determining their appropriate conservation status. Additionally, data from monitoring programs informs managers and allows for adjustment of land use strategies, federal or state legal status, hunting regulations, and mitigation plans in response to current population status and trend. Monitoring programs also allow investigators to identify key factors such as disease, human land use, or natural disturbances that influence populations. To provide the information needed to evaluate the status of a population and inform management decisions, researchers need to provide accurate and defensible estimates of population size and trend. Significant progress has been made in wildlife population estimation since the 1970s, driven by the practical need to estimate abundance and monitor populations over time (Burnham 2004). Advancements in methodology, statistical analysis and technology have been paramount in improving population size estimation and monitoring. Methods have expanded to include stratified and cluster sampling, mark-recapture, occupancy, dual-frame sampling, line intercept, adaptive cluster sampling, distance sampling, and indices from point or lek count data, among others. The development of technologies such as radio telemetry, satellite telemetry, global positioning systems (GPS), global information systems, genetic analysis and computer programs also represent major advances that contribute to improved monitoring strategies for wildlife species. Progress has also been made in the development of methods that reduce or eliminate disturbance to wildlife species, such as genetic markrecapture and track surveys. Additionally, innovations in the size and design of radio and satellite 2 �transmitters have reduced the impact on study animals, allowed researchers to evaluate habitat use and management in a greater variety of species, and reduced the cost of monitoring per individual. Despite recent progress, generating accurate and defensible estimates of population size and trends remains a key challenge for wildlife biologists and managers. This is particularly true for monitoring rare species and populations at low densities. Researchers investigating populations at low density face numerous challenges including: a lack of appropriate methods, greater susceptibility to estimation bias, issues with imperfect detectability, clustering of animals, and insufficient funding. As a result of these logistic challenges, investigators often turn to population indices to estimate abundance or monitor populations. While indices may be easier to obtain, they are often based on assumptions or unknown variables. Therefore, their relation to the true population may be unclear (Witmer 2005) or inaccurate. As a result, indices may be inadequate when accurate estimates of abundance and trend are required to determine proper management of a wildlife population. Recent declines in greater sage-grouse populations and substantial restriction of pre-settlement distribution of the species have been observed nationwide (Connelly and Braun 1997, Schroeder et al. 2004). These declines, in combination with habitat loss and human land use conflicts, have prompted repeated petitions for federal listing. In 2010, the species status was designated as warranted, but precluded under the Endangered Species Act (Leonard et al. 2000, Connelly et al. 2004, Schroeder et al. 2004, Braun et al. 2005, Aldridge et al. 2008, USFWS 2010). This decision by the U.S. Fish and Wildlife Service (USFWS) has created a critical need for accurate, defensible population estimations based on sound monitoring techniques. The development of innovative, yet practical methods for estimating populations of rare or cryptic animals, such as the sage-grouse, is essential to accurately determining population size, monitoring trends in abundance and instituting proper management practices. During the spring breeding season, male greater sage-grouse gather to display on traditional strutting grounds (Patterson 1952), known as leks. These leks offer a unique opportunity to observe and count individuals, particularly males. Historically, lek counts have been considered to be the best, if not the only, means for monitoring populations of lekking species and are currently used by state wildlife agencies throughout the western United States (Connelly et al. 2004). These counts are based on standard protocols (Patterson 1952) and are assumed to provide information on population trends (Fedy and Aldridge 2011). However, lek counts are subject to numerous sources of sampling bias and do not generate rigorous and defensible population estimates required for protection and management of species (Walsh et al. 2010). Standard state lek count indices, like those used by Colorado Parks and Wildlife (CPW), are based on seasonal high counts of males attending leks to estimate population size. The use of lek-count indices to estimate population size and trend rely on untested assumptions and do not account for spatial and temporal variation in detectability. Implicit in lek count indices are assumptions about the proportion of leks that are known and counted, the proportion of males that attend leks, the proportion of males on leks that are detected by observers, the frequency of inter-lek movements by males, and the sex ratio of the population. Each of these factors affects the accuracy of greater sage-grouse abundance estimates to an unknown degree. Therefore, there is a great need to either quantify these variables and adjust lek count index estimates accordingly or develop new methods for estimating population size and trends over time. Despite legitimate criticism, lek-count indices continue to be the primary means for monitoring changes in sage-grouse population size. Investigations into the reliability of lek-count data for monitoring changes in population size are a research priority for greater sage-grouse (Naugle and Walker 2007) and the development of alternative population monitoring methods is essential for greater sage-grouse population monitoring and management, both in the PPR population and range-wide. 3 �In recent years, attempts have been made to evaluate or improve lek count protocols for greater sage-grouse in order to generate more robust estimates of population size and trends. In addition to projects which evaluate the reliability of standard lek counts, alternative population estimation methods are being developed. These include survey methods that reduce disturbance to the species by employing non-invasive techniques. Recent advances in genetic mark-recapture using sources of DNA such as scat, feathers and hair have created new, innovative opportunities using mark-recapture theory (Lukacs and Burnham 2005a). Sampling of fecal DNA was first attempted in coyotes (Kohn et al. 1999) and has since been used in a variety of other species, including grizzly and brown bears (Mowat and Strobeck 2000, Boulanger et al. 2004, Bellemain et al. 2005), black bears (Coster et al. 2011), northern pike (Miller et al. 2001), northern goshawks (Bayard de Volo et al. 2005), Gunnison sage-grouse (Oyler-McCance, unpublished data) and humpback whales (Palsboll 1997). Genetic mark-recapture is a promising strategy for estimating and monitoring greater sage-grouse populations. This study will allow us to evaluate the efficacy of using novel techniques for estimating population size and observing trends in the small, isolated population of greater sage-grouse in northwest Colorado. These techniques will be conducted in the field, assessed for reliability of estimates and evaluated for feasibility in long-term population monitoring. These monitoring strategies will also be compared to traditional methods for monitoring sage-grouse populations (i.e. lek counts), focusing on the benefits and disadvantages of each. STUDY AREA The Parachute-Piceance-Roan (PPR) population of greater sage-grouse is located northwest of Rifle and southwest of Meeker in western Colorado (Fig. 1). It is recognized as one of six distinct populations in the state (CGSSC 2008). Occupied range in the PPR is characterized by high-elevation ridges and plateaus broken up by steep canyons and drainages. Vegetation is dominated by mountain big sagebrush (Artemisia tridentata vaseyana), mixed sagebrush-mountain shrub habitat and pinyon-juniper (Pinus edulis, Juniperus spp.) woodlands with occasional patches of aspen (Populus tremuloides). Mixed sagebrush-mountain shrub is primarily comprised of mountain big sagebrush and serviceberry (Amelanchier spp.) with Gambel oak (Quercus gambelii), snowberry (Symphoricarpus sp.), antelope bitterbrush (Purshia tridentata), mountain mahogany (Cercocarpus sp.), and wild rose (Rosa sp.). Greater sage-grouse are largely restricted to elevations from 7,000-9,000-ft. (PPR-GSGWG 2008). Approximately 35% of the occupied range is managed by state or federal agencies with the remaining 65% privately owned, primarily by energy companies and ranches. Approximately 4% of the total male greater sagegrouse counted on known leks in Colorado are located in the PPR (CGSSC 2008, PPR-GSGWG 2008). The PPR population of greater sage-grouse was estimated to have approximately 340 total males in 2007 based on lek-count data (PPR-GSGWG 2008). This small population is experiencing substantial landscape changes, including energy development and pinyon-juniper encroachment into areas of formerly suitable sagebrush habitat (PPR-GSGWG 2008). The PPR population may be especially vulnerable due to its small size and imminent reductions in suitable habitat resulting from the ongoing changes in land use. However, reliable information on population size or trends is not currently available to accurately assess the level of risk to the population. The limited data available to estimate long-term trends in the PPR population include estimates of male population size based on state lek counts conducted by helicopter dating back to 2005 (CGSSC 2008). Unfortunately, the utility of lek count data for reliably estimating population size or monitoring trends in lekking species has been heavily criticized (Beck and Braun 1980, Applegate 2000, Walsh et al. 2004, Walsh et al. 2010) and there is currently no scientifically defensible population estimate available for the PPR (PPR-GSGWG 2008). 4 �METHODS Dual-Frame Sampling Dual-frame sampling of leks estimates the proportion of 1-km2 cells covering occupied range that contain one or more greater sage-grouse leks as well as the average number of males attending those leks. This information can be used to estimate: (1) the proportion of known leks, (2) the average number of males attending known leks, and (3) the population size of male greater sage-grouse attending leks. We will conduct dual-frame sampling of 1-km2 cells from helicopter during three consecutive spring lekking seasons in 2012, 2013, and 2014. We will survey for leks and count any leks found within two distinct sampling frames, the list frame and the area frame. The list frame consists of all 1-km2 cells known to contain an active lek. The area frame consists of a spatially balanced random selection of 1-km2 cells generated using a Reversed Randomized Quadrant-Recursive Raster (RRQRR; Theobald et al. 2007). Active leks are defined by Colorado Parks and Wildlife (CPW) as a location on the ground where one to two strutting males have been observed in two or more of the past five years. Leks of unknown status are those that have been recently identified and require a second year or observation before they can be defined as active. For the purposes of dual-frame sampling, unknown leks will be sampled as active leks. We will sample 39 1-km2 list-frame cells in the PPR population (excluding the “Magnolia” portion) known to contain 49 known active leks (as of February 2012) and approximately 100 area-frame cells (Fig. 1). Any leks newly discovered in 2012 or 2013 will be added to the list frame for sampling in a subsequent year. We investigated the statistical power to detect a 5%, 7.5% and 10% change in occupancy (the proportion of sample units containing one or more leks) based on lek activity observed during dual-frame sampling using simulations in Program Mark (White and Burnham 1999). We used input parameter values expected to represent the true population occupancy and anticipated sampling effort for the list and area frames. Capture histories were simulated based on input data (expected occupancy rate, detection probability and number of sample units surveyed) and analyzed in Program Mark to obtain standard errors (Runge et al. 2007). Power calculations were generated using Program R. Results indicate that with expected sampling effort (125-140 total 1-km2 cells per season), power to detect a minimum of 7.5% annual rate of decline in occupancy will be approximately 0.95 with 15 years of surveillance. All sampling will be conducted by helicopter. Helicopter sampling protocols are based on those developed by Dr. Paul Lukacs (formerly with Colorado Division of Wildlife, now at University of Montana) with slight modification to address logistical problems unique to the PPR. Surveys will be conducted from mid-April to early May, the primary lekking period for sage-grouse in the PPR, and from 30 minutes before local sunrise to 2 hours after sunrise in accordance with standard CPW lek-count protocols. Observers will count leks by circling the lek, scanning with binoculars, and recording the sex of all birds present. A minimum of three rounds of surveys and counts will be conducted in each cell sampled. This allows estimation of detectability of leks and adheres to standard state lek-count protocols that stipulate at least three counts per year. If leks are discovered incidentally while flying to or from surveyed cells, those lek locations will be recorded and used to improve the following year’s list frame. For area-frame cells, observers will survey the entire cell and count any newly discovered leks. A waypoint will be marked at the center of the lek and the location will be added to the list frame the following year. New leks located in area-frame cells will be sampled on any subsequent survey of those cells. Observers will survey list-frame cells the same way as area-frame cells, but observers will also check and, if birds are present, count all previously known lek locations. If new leks are located within list-frame cells, those leks will also be counted and a waypoint marked at the center of the lek. 5 �Dual-frame sampling data will be analyzed in an occupancy framework. Equations modified from Haines and Pollock (1998) will also be used to estimate population size using lek counts and the proportion of known vs. unknown leks. Genetic Mark-Recapture This project will use genetic mark-recapture methods to estimate population size. Birds will be captured, marked and have feathers sampled from July to October of each year within occupied range. Fecal pellets obtained from sampling in fall-winter will be genotyped to identify individual sage-grouse. While fecal pellets will predominantly consist of sagebrush DNA, traces of DNA from the intestinal walls of sage-grouse are transferred to the pellets and can be used to amplify microsatellites for genotyping. Sage-grouse pellets are expected to be at low density due to the apparently small population size of birds in the Piceance and the relatively large size of the study area (1,473 km2). In addition, unpredictable winter weather conditions, including snowfall and blowing snow, may obscure or bury tracks and pellets. In order to increase the number of samples collected, pellet samples will be obtained using several sampling strategies, including pellet collections from: (1) a random sampling scheme; (2) incidental locations of roost sites or pellets; and (3) back-tracking of incidentally located greater sagegrouse tracks in order to locate roost sites and pellets. Roosting behavior of greater sage-grouse allows collection of high-quality pellet samples. Sagegrouse roosting at night (and often also during the middle of the day) typically remain at a single location on the ground, regularly dropping fecal pellets. This results in condensed piles of pellets referred to as “roost piles” (Patterson 1952) (Fig. 2). Greater sage-grouse are a gregarious species with both males and hens forming flocks, particularly in the non-breeding or winter months (Patterson 1952). The average flock size of the PPR population is estimated at five to six birds with flocks as large as 24 birds observed (CPW, unpublished data). In the winter months when snow is abundant and temperatures often remain below freezing, fecal DNA is expected to remain viable for several days, particularly for those pellets concealed in roost piles and protected from sun and desiccation. In addition to the collection and analysis of fecal pellets, feather samples from captured birds will also be collected and used as a source of DNA for individual identification. A related project currently involves the capture of male greater sage-grouse in the PPR and the attachment of 30 g rump-mounted, solar-powered GPS PTT satellite transmitters (Northstar Science and Technology, King George, VA). Up to 35 GPS transmitters per year will be attached to males during 2012 and 2013. We will capture and attach 22 g battery-powered VHF necklace collars equipped with mortality sensors (Advanced Telemetry Systems, Isanti, MN) to an equal number of hens (up to 35 per year) in 2012 and 2013. Capture and marking of greater sage-grouse in the PPR serves two purposes. First, feathers collected during capture will be genotyped to identify (or “mark”) individual birds and the resulting capture data will constitute the first mark-recapture occasion. The addition of this initial capture occasion will increase precision of population abundance estimates generated from mark-recapture data based on the sampling of fecal pellets. Second, systematic monitoring of marked birds with radio-collars will allow us to assess the assumption of demographic and geographic closure of the population (i.e. no death or emigration) during sampling periods, a crucial assumption for the use of closed mark-recapture models. Random transects will be generated using a spatially balanced (GRTS) sample design (e.g. Reversed Randomized Quadrant-Recursive Raster (RRQRR) (Theobald et al. 2007)). Spatially balanced samples allow for more complete coverage of the study area while increasing the probability of sampling clustered individuals, such as winter flocks of sage-grouse. Approximately 65% of the study area is privately owned (mostly by energy companies or private ranches). Spatially balanced sampling is likely to be advantageous as it will avoid clustering of random points in locations where it may be difficult to gain 6 �access for sampling. Spatially balanced sampling will also allow greater coverage of the study area and should aid in reducing heterogeneity in detection probability of individual sage-grouse. Stratification of random samples may be achieved using RRQRR with the incorporation of relative probability of use maps developed for greater sage-grouse in the PPR (Walker 2010). The number of random plots to be sampled will ultimately be constrained by funding and logistics. Sampling plots will be surveyed for roost piles or other evidence of grouse, particularly tracks in the snow. Fecal piles identified at roost sites or along tracks will be sampled with roost sites being the preferred source for pellet sampling whenever they are available. Roost sites or pellets encountered outside of the random sampling scheme will also be included as incidental sampling locations. When tracks from flocks or individual birds are encountered, they will be followed in an attempt to locate a roost site for sampling. If a roost piles are unavailable, pellets will be sampled along the tracks. The location of each roost pile will be clearly marked by a staked flag to facilitate sampling. Following an initial search, a 30-meter buffer surrounding roost piles will be searched for additional piles and the buffer reset around the new location until no additional piles are identified within the buffer. This search strategy was developed during the 2011 pilot work conducted at roost site locations near Hiawatha, CO and was designed to maximize pellet detection. At each roost pile, a total of four to five pellets will be collected with a focus on pellets in the best condition (i.e. least exposed or least desiccated). Caecal piles will not be sampled and pellets having contact with caecum will be avoided. When sampling pellets from tracks, the number of birds present in the flock will be counted and an attempt made to collect several pellets from each individual. Pellet samples will be placed in sterile Whirlpak® bags with a single FTA® desiccant pouch, sealed, labeled for individual identification and stored on ice or snow until they can be transferred to a 20°F non-frost-free freezer at the Little Hills SWA bunkhouse). Pellets will be later transported on dry ice to the USGS Fort Collins Science Center Molecular Ecology Laboratory for DNA extraction. Capture, Handling, Transmitter Attachment, and Feather Sampling We plan to capture adult and yearling female greater sage-grouse in July-November in each year. All captured females will be sexed, aged, weighed, and fitted with individually-numbered, aluminum leg bands (size 20) and will have a 22-g, necklace-style, battery-powered VHF transmitter attached (Model A4060, Advanced Telemetry Systems, Isanti, MN). VHF transmitters have a 4-hour mortality switch, a guaranteed life of 15 months, and a range of several miles both from the ground and from the air (depending on terrain and radio age). Transmitters from birds that die may be recovered, cleaned, refurbished and redeployed as necessary to maintain sample sizes. Crews will capture females using CODA net launchers (Giesen et al. 1982), night-time spotlighting and hoop-netting (Wakkinen et al. 1992), walk-in traps modified for sage-grouse (Schroeder and Braun 1991), Super Talon® net guns (Advanced Weapons Technology, La Quinta, CA), MagNet® net guns (Wildlife Capture Services, Flagstaff, AZ), or throw nets, all of which have been approved for capture in this population. The trapping effort will be distributed across the population so that it is proportional to and representative of the amount of local breeding habitat present as identified in preliminary seasonal habitat models (Walker 2010). Otherwise, capture and handling methods will follow standard CPW operating procedures established for sage-grouse. The decision whether injured birds will either be released or euthanized will be made in the field rather than transporting birds back to Fort Collins. No known rehabilitators in western Colorado currently have the facilities to care for wild, injured sage-grouse. Feather samples will be collected from each captured bird following modified protocols based on those previously used by the USGS Fort Collins Science Center Molecular Ecology Laboratory (MEL) for collection of Gunnison sage-grouse feathers for DNA analysis. 7 �Sample Analysis DNA extraction and microsatellite analysis will be conducted using protocols developed by the MEL and demonstrated to be reliable for genotyping DNA from fecal pellets of the Gunnison sage-grouse (Centrocercus minimus) (Oyler-McCance and St. John, unpublished report). Protocols used for genotyping Gunnison sage-grouse from fecal pellets will be equivalent to those used for greater sagegrouse. DNA extraction will be performed using the QIAmp DNA stool mini kit (Qiagen, Germantown, MD) following protocols for “Isolation of DNA from stool for human DNA analysis” with a slight modification that decreases the final elution volume to 60 ul. Polymerase Chain Reaction (PCR) for amplification of DNA will be performed using a 2-step, pre-amplification method (Piggot et al. 2004) based on primer recipes and thermal profiles currently used by the MEL for genotyping from fecal samples of sage-grouse (pers. comm. Sara Oyler-McCance and Jennifer Fike, USGS). Microsatellite analysis will focus on loci previously identified by the USGS laboratory as reliable for use in identifying individual sage-grouse. Genetic analysis of feather samples will be performed using similar methods developed by the MEL for use in genotyping individual sage-grouse. A major challenge for researchers conducting genetic mark-recapture studies using non-invasive samples such as feces is the potential for genotyping error. Non-invasively collected samples are often characterized by low quantity or quality of DNA (Broquet et al. 2007), which may be highly variable among samples (Miquel et al. 2006). Problems facing analysis of these samples include amplification failure, allelic dropout and mutation during amplification (Lukacs and Burnham 2005a), each of which may result in genotyping error and violation of a critical assumption of closed models that “marks” are correctly identified and recorded. Lukacs and Burnham (2005b) showed that genotyping error may result in biased abundance estimates from closed mark-recapture models. To address these concerns, actions will be taken in the field and laboratory to reduce genotyping error and estimate the rate at which it occurs. Throughout this project, special care in the collection and storage of samples will be taken to prevent contamination and maintain sample integrity. In the laboratory, genotyping error rates can be greatly decreased or eliminated with proper training of personnel, careful protocols, the systemization and automation of methods and the use of a reliable set of microsatellite loci (Paetkau 2003). In addition to these measures, each pellet sample will be analyzed twice to monitor for and estimate rates of genotyping error. Sample pairs that fail to match (indicating that potential genotyping error has occurred) will be resampled. Additionally, occasional inclusion of blind duplicate samples will be employed to validate the accuracy of laboratory methods. Data Analysis All greater sage-grouse in the study population possess a unique genetic fingerprint and are therefore inherently marked. DNA from fecal pellets will be genotyped, referenced to unique individuals in the population and used to generate encounter histories for those individuals. Encounter history data will be analyzed using closed mark-recapture models in program MARK (White and Burnham 1999). Analysis using closed capture models requires that fecal pellets be sampled across several unique temporal occasions and that closed model assumptions (i.e. demographic and geographic closure, no mark loss or misidentification) are satisfied. Power Analysis Simulations were performed in program MARK, using the Closed Captures and Full Closed Captures with Heterogeneity data types to estimate the sampling effort required to achieve acceptable levels of precision and bias in abundance estimates. Simulations of 500 repetitions were run using a range of probable values for true population size (N), detection probability (p), heterogeneity mixtures (pi), and the number of sampling occasions. Results from these simulations indicate that, in the absence of individual capture heterogeneity, at least 10% of individuals (p=0.1) in the population should be encountered during each sampling occasion for a minimum of four to five occasions to obtain abundance 8 �estimates with acceptable accuracy (i.e. CV < 0.15 and 95% coverage of N). These results were used, in combination with current expectations for population size and considerations for sampling with replacement, to compute cost estimates for the project reported in the budget section of this proposal. Simulations also indicated that heterogeneity in detection probabilities may make it difficult to obtain unbiased estimates of abundance when using closed mark-recapture models. As a result, emphasis will be placed on sampling strategies that reduce heterogeneity in encountering pellets of individual sage grouse. These strategies include the use of spatially balanced random sampling (RRQRR) to improve sampling coverage of the study area and generation of a unique set of random sampling transects for each occasion to reduce the chance of repeatedly encountering individual birds with fidelity to certain locations. Sex Ratio Sex ratios of greater sage-grouse populations have been estimated in several states. However, the majority of data used for these estimates were obtained from hunter harvest efforts such as wing-barrel programs (Connelly et al. 2011) which may have bias due to hunter behavior or preference. Sex ratio of the PPR greater sage-grouse population will be estimated using genetic samples from the genetic markrecapture component of this project that allows us to determine sex of individual birds. Sex ratio will be estimated using data from two sources of DNA (fecal pellets and/or feathers) collected during sampling efforts for Objective 2. The genetic data obtained from this project will also provide an opportunity to investigate variation in sex composition of greater sage-grouse winter flocks. Method Comparison Following the conclusion of the sampling periods and data analysis, a comparison of key population estimation methods investigated in this study will be conducted. Population size estimates from dual-frame sampling, genetic mark-recapture and standard state lek count techniques will be compared. Factors, including variance of population size estimates, cost, practicality of methods, and disturbance to birds associated with each method will be evaluated and recommendations made regarding continued monitoring of the species, both in the PPR and range-wide. Additionally, we will discuss the efficacy and potential consequences of employing these methods to estimate greater sage-grouse population size and/or determine trends in population size. We will also discuss opportunities for the improvement of lek count-based population estimations through the use of supplemental population information. Sex ratio, inter-lek movements of male sage-grouse and the proportion of known versus unknown leks will be estimated by this project. Related research being conducted in the Piceance by Dr. Brett Walker will additionally provide estimates of male lek attendance rates and detectability. Combined efforts from the two studies will generate estimated values the five unknown variables which are lacking, or assumed, in traditional state lek count estimations. RESULTS AND DISCUSSION Dual-frame Sampling We surveyed 59 list-frame and 104 area-frame cells (163 cells total) for greater sage-grouse leks from helicopters (Bell 47 Soloy and Robinson Raven R44) from one-half hour before sunrise to two hours after sunrise three times each from April 17 to May 3, 2014 on three rounds of five flights (15 flights total). During these flights, we confirmed 28 active leks (including 7 new leks) in 24 list-frame cells and 1 new active lek in an area-frame cell. Two other leks were found in transit between cells for a total of 10 new leks recorded during spring 2014 dual-frame sampling flights. Capture and Monitoring 9 �Field crews captured and marked 4 additional VHF females (adult or yearling) and banded and released 8 juveniles (5 males, 3 females) from 1 September 2013 through 30 November 2013. Field crews then tracked 24 VHF females and an additional 9 non-juvenile (adult or yearling) male greater sagegrouse marked with GPS transmitters (as part of Dr. Brett Walker’s GPS male project) from November 2013 - March 2014 to test closure assumptions for mark-recapture analyses. Genetic Mark-recapture The second and final year of pellet sampling for genetic mark-recapture analysis occurred from November 2013 through early March 2014, with samples collected in 4 random sampling plots and at 140 additional incidental locations for a total of 1160 pellet and 29 feather samples. Lack of access to portions of the study area again in winter 2013-2014 limited the geographic extent of sampling. Genetic samples are currently being analyzed at the USGS laboratory in Fort Collins. LITERATURE CITED Aldridge, C. L., S. E., Nielsen, H. L. Beyer, M. S. Boyce, J. W. Connelly, S. T. Knick, and M. A. Schroeder. 2008. Range-wide patterns of greater sage-grouse persistence. Diversity and Distributions. 14: 983-994. Applegate, R. D. 2000. Use and misuse of prairie chicken lek surveys. Wildlife Society Bulletin. 28: 457248. Bayard de Volo, S., R. T. Reynolds, J. R. Topinka, B. May, and M. F. Antolin. 2005. Population genetics and genotyping for mark-recapture studies of northern goshawks (Accipiter gentilis) on the Kaibab Plateau, Arizona. Journal of Raptor Research. 39(3): 286-295. Beck, T. D. I. and C. E. Braun. 1980. The strutting ground count: variation, traditionalism, management needs. Proceedings of the Western Association of Fish and Wildlife Agencies 60: 558-566. Bellemain, E., J. E. Swenson, D. Tallmon, S. Brunberg, and P. Taberlet. 2005. Estimating population size of elusive animals with DNA from hunter-collected feces: four methods for brown bears. Conservation Biology. 19(1): 150-161. Boulanger, J., B. N. McLellan, J. G. Woods, M. F. Proctor, and C. Strobeck. 2004. Sampling design and bias in DNA-based capture-mark-recapture populations and density estimates of grizzly bears. Journal of Wildlife Management. 68(3): 457-469. Braun, C. E., J. W. Connelly, and M. A. Schroeder. 2005. Seasonal habitat requirements for sage-grouse: spring, summer, fall, and winter. USDA Forest Service Proceedings RMRS-P-38. Broquet, T., N. Menard, and E. Petit. 2007. Noninvasive population genetics: a review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conservation Genetics. 8: 249-260. Burnham, K. P. 2004. Foreword. Pages xi – xiii in Thompson, W.L. (Editor). 2004. Sampling rare or elusive species: concepts, designs and techniques for estimating population parameters. Island Press, Washington, D.C. Colorado Greater Sage-Grouse Steering Committee (CGSSC). 2008. Colorado greater sage-grouse conservation plan. Colorado Division of Wildlife, Denver, USA. Connelly, J. W., and C. E. Braun. 1997. Long-term changes in sage grouse Centrocercus urophasianus populations in western North America. Wildlife Biology. 3: 229-234. Connelly, J.W., C. A. Hagen, and M. A. Schroeder. 2011. Characteristics and dynamics of greater sagegrouse populations. Pages 53-67 in Knick, S.T. and Connelly, J.W. (editors). 2011. Greater SageGrouse: Ecology and Conservation of a Landscape Species and Its Habitats. Studies in Avian Biology Series Vol. 38. University of California Press, Berkeley, USA. Connelly, J. W., S. T. Knick, M. A. Schroeder, and S. J. Stiver. 2004. Conservation assessment of greater sage-grouse and sagebrush habitats. Western Association of Fish and Wildlife Agencies, Cheyenne, Wyo. 10 �Coster, S. S., A. I. Kovach, P. J. Pekins, A. B. Cooper, and A. Timmins. 2011. Genetic mark-recapture population estimations in black bears and issues of scale. Journal of Wildlife Management. 75(5): 1128-1136. Fedy, B. C., and C. L. Aldridge. 2011. The importance of within-year repeated counts and the influence of scale on long-term monitoring of sage-grouse. Journal of Wildlife Management 75:1022-1033. Haines, D. E., and K. H. Pollock. 1998. Estimating the number of active and successful bald eagle nests: an application of the dual frame method. Environmental and Ecological Statistics. 5: 245-256. Kohn, M. H., E. C. York, D. A. Kamradt, G. Haught, R. M. Sauvajot, and R. K. Wayne. 1999. Estimating population size by genotyping faeces. Proceedings of the Royal Society B. 266: 657-663. Leonard, K. M., K. P. Reese, and J. W. Connelly. 2000. Distribution, movements and habitats of sage grouse Centrocercus urophasianus on the Upper Snake River Plain of Idaho: changes from the 1950s to the 1990s. Wildlife Biology. 6(4): 265-270. Lukacs, P. M., and K. P. Burnham. 2005a. Review of capture-recapture methods applicable to noninvasive genetic sampling. Molecular Ecology. 14: 3909-3919. Lukacs, P. M., and K. P. Burnham. 2005b. Research Notes: Estimation population size from DNA-based closed capture-recapture data incorporating genotyping error. Miller, L. M., L. Kallemeyn, and W. Senanan. 2001. Spawning-site and natal-site fidelity by northern pike in a large lake: mark-recapture and genetic evidence. Transactions of the American Fisheries Society. 130(2): 307-316. Miquel, C., E. Bellemain, C. Poillot, J. Bessiere, A. Durand, and P. Taberlet. 2006. Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. Molecular Ecology Notes. 6: 985-988. Mowat, G., and C. Strobeck. 2011. Estimating population size of grizzly bears using hair capture, DNA profiling, and mark-recapture analysis. The Journal of Wildlife Management. 64 (1): 183-193. Naugle, D. E., and B. L. Walker. 2007. A collaborative vision for integrated monitoring of greater sagegrouse populations. Pages 57-62 in Reese, K.P. and Bowyer (editors). Monitoring populations of greater sage-grouse: proceedings of a symposium at Idaho State University. College of Natural Resources Experiment Station Bulletin 88. Moscow, Idaho. Oyler-McCance, S. J., and J. St. John. (Unpublished Report). Final report for “Pilot study to assess the effectiveness of DNA extraction from Gunnison sage-grouse feces for use in population estimation studies.” Unpublished Report. Paetkau, D. 2003. An empirical exploration of data quality in DNA-based population inventories. Molecular Ecology. 12: 1375-1387. Palsboll, P. J., J. Allen, M. Berube, P. J. Clapham, T. P. Feddersen, P. S. Hammond, R. R. Hudson, H. Jorgensen, S. Katona, A. H. Larsen, F. Larsen, J. Lien, D. K. Mattila, J. Sigurjonsson, R. Sears, T. Smith, R. Sponer, P. Stevick, and N. Oien. 1997. Genetic tagging of humpback whales. Nature. 388: 767-769. Parachute-Piceance-Roan Greater Sage-Grouse Work Group (PPR-GSGWG). 2008. Parachute-PiceanceRoan (PPR) greater sage-grouse conservation plan. Colorado Division of Wildlife, Denver, USA. Patterson, R. L. 1952. The sage grouse in Wyoming. Sage, Denver, Colo. Piggot, M. P., E. Bellemain, P. Taberlet, and A. C. Taylor. 2004. A multiplex pre-amplification method that significantly improves microsatellite amplification and error rates for faecal DNA in limiting conditions. Conservation Genetics. 5: 417-420. Runge, J. P., J. E. Hines, and J. D. Nichols. 2007. Estimating species-specific survival and movement when species identification is uncertain. Ecology. 88(2): 282-288. Schroeder, M. A., C. L. Aldridge, A. D. Apa, J. R. Bohne, C. E. Braun, S. D. Bunnell, J. W. Connelly, P. A. Deibert, S. C. Gardner, M. A. Hilliard, G. D. Kobriger, and C. W. McCarthy. 2004. Distribution of Sage-grouse in North America. Condor 106:363-376. Theobald, D. M., D. L. Stevens Jr., D. White, N. S. Urquhart, A. R. Olsen, and J. B. Norman. 2007. Using GIS to generate spatially balanced random survey designs for natural resource applications. Environmental Management. 40: 134-146. 11 �United States Fish and Wildlife Service (USFWS). 2010. 12-month finding for petitions to list the greater sage-grouse (Centrocercus urophasianus) as threatened or endangered. Federal Register 75(55): 13909-14014. Walsh, D. P., J. R. Stiver, G. C. White, T. E. Remington, and A. D. Apa. 2010. Population estimation techniques for lekking species. Journal of Wildlife Management. 74(7): 1607-1613. Walsh, D. P., G. C. White, T. E. Remington, and D. C. Bowden. 2004. Evaluation of the lek-count index for greater sage-grouse. Wildlife Society Bulletin. 32(1): 56-68. White, G. C., and K. P. Burnham. 1999. Program Mark: survival estimation from populations of marked animals. Bird Study. 46: 120-139. Witmer, G. W. 2005. Wildlife population monitoring: some practical considerations. Wildlife Research. 32: 259-263. 12 �FIGURES Figure 1. The Parachute-Piceance-Roan population study area showing Colorado Parks and Wildlife’s greater sage-grouse occupied range boundary (as of February 2012) as well as known greater sage-grouse leks (as of February 2014) that were either active (within the past five years), inactive (inactive in the past 5 years), historic (inactive for at least the past 10 years), or newly discovered in 2012, 2013, or 2014. The map also shows list-frame (yellow squares) and area-frame (blue squares) 1-km2 cells used for dual-frame sampling in April-May 2014. Seven of the 15 newly discovered leks in spring 2012, 7 of the 8 newly discovered leks in spring 2013, and 5 of the 14 newly discovered leks in spring 2014 were found on dualframe sampling flights, either within sampled cells or incidentally in transit between cells. Note that newly discovered leks are assigned “Unknown” status in CPW’s statewide database and only considered potential lek sites until they are confirmed to have strutting males again in a subsequent year. 13 �Figure 2. Greater sage-grouse roost location in the snow with a roost pile and cecal droppings. 14 � https://cpw.cvlcollections.org/files/original/8fb249d872e3d3ff8f59bbec85421518.pdf e8de6343223d4ab473a9df46d16eb010 PDF Text Text Colorado Division of Parks and Wildlife September 2014-September 2015 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No.: Colorado 3420 0660 N/A Federal Aid Project No. N/A : : : : Division of Parks and Wildlife Avian Research Greater Sage-grouse Conservation Evaluation of alternative population monitoring strategies for greater sage-grouse (Centrocercus urophasianus) in the Parachute-Piceance-Roan population of northwestern Colorado Period Covered: September 1, 2014 – August 31, 2015 Author: B. L. Walker, CPW; J. S. Brauch, Colorado State University Personnel: B. Holmes, B. Petch, W. deVergie, J. T. Romatzke 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. EXTENDED ABSTRACT Robust estimates of population size and population trends provide the scientific basis for managers to make appropriate and defensible recommendations regarding land-use decisions, harvest regulations, and mitigation efforts for wildlife. When linked with environmental variables, robust monitoring programs also allow managers to examine wildlife responses to various stressors. However, many wildlife monitoring programs continue to use untested population indices that may not provide reliable information on population status or trends. For this reason, it is essential to evaluate alternative approaches to population monitoring in terms of estimator precision, cost, practicality, and level of disturbance. Lek counts are the primary index used by state wildlife agencies to monitor changes in greater sage-grouse (Centrocercus urophasianus) abundance, but current lek-count monitoring relies on untested assumptions about lek attendance, detectability, inter-lek movement, sex ratio, and proportion of leks counted. Given the availability of new methodological and statistical approaches to estimate wildlife populations, it is worth comparing the performance of current lek-count approaches against other monitoring methods. Dual-frame sampling of leks by helicopter and non-invasive genetic mark-recapture analyses based on winter pellet sampling are promising alternative for monitoring trends in sage-grouse populations. The purpose of this study is to evaluate and compare the reliability and efficiency of dualframe sampling, genetic mark-recapture, and standard lek counts for estimating population size and trend and to estimate sex ratio in the Parachute-Piceance-Roan population in northwest Colorado. All field data collection for this project was completed in May 2014. We are using occupancy modeling to account for imperfect detectability of leks in each frame. The dual-frame analysis is in progress. All pellet samples have been analyzed to derive genetic data. Some pellet samples are currently being re-run because they had missing data for some alleles. We will analyze genetic data (including sex ratio) once final data are available using both traditional genetic mark-recapture and spatial genetic mark-recapture models to estimate sex-specific abundance in each of the two winters. 1 � https://cpw.cvlcollections.org/files/original/42688b410af54449851e8788153faa50.pdf 73d17af0bc8876d0b2c54acaee8d88c2 PDF Text Text Colorado Division of Parks and Wildlife September 2015-September 2016 WILDLIFE RESEARCH REPORT State of: Cost Center: Work Package: Task No.: Colorado 3420 0660 N/A Federal Aid Project No. N/A : : : : Division of Parks and Wildlife Avian Research Greater Sage-grouse Conservation Evaluation of alternative population monitoring strategies for greater sage-grouse (Centrocercus urophasianus) in the Parachute-Piceance-Roan population of northwestern Colorado Period Covered: September 1, 2015 – August 31, 2016 Author: B. L. Walker, CPW; J. S. Brauch, Colorado State University Personnel: B. Holmes, B. Petch, W. deVergie, J. T. Romatzke 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. EXTENDED ABSTRACT Robust estimates of population size and population trends provide the scientific basis for managers to make appropriate and defensible recommendations regarding land-use decisions, harvest regulations, and mitigation efforts for wildlife. When linked with environmental variables, robust monitoring programs also allow managers to examine wildlife responses to various stressors. However, many wildlife monitoring programs continue to use population indices that may or may not meet the assumptions required to accurately estimate population abundance or trends. For this reason, it is essential to evaluate alternative approaches to population monitoring in terms of estimator precision, cost, practicality, and level of disturbance. Lek counts are the primary index used by state wildlife agencies to monitor changes in greater sage-grouse (Centrocercus urophasianus) abundance over time, but they rely on untested assumptions about lek attendance, detectability, inter-lek movement, sex ratio, and proportion of leks counted. Given the availability of new methodological and statistical approaches to estimate abundance, it is worth comparing the performance of current lek-count approaches against other potential monitoring methods. Dual-frame sampling of leks by helicopter and non-invasive genetic mark-recapture analyses based on winter pellet sampling are two promising alternatives for monitoring trends in sage-grouse populations. The purpose of this study was to evaluate and compare the reliability and efficiency of dual-frame sampling of leks, genetic mark-recapture, and standard lek counts for estimating population size, trend, and sex ratio in the ParachutePiceance-Roan (PPR) population in northwest Colorado. All field data collection for this project was completed in May 2014. For the dual-frame lek sampling analysis, we used occupancy modeling to account for imperfect detectability of leks in each sampling frame. We are currently 1 �finishing that analysis and preparing a manuscript for publication. Preliminary dual-frame results suggest that there are a substantial number of unknown leks in the population, a conclusion supported by the discovery of 44 new leks from 2012-2016 (25 of which have been confirmed active in ≥ 2 years). Dual-frame analysis also suggests that different proportions of occupied leks are counted in each year and this variation may bias estimates of population status, abundance, or trends based on lek-count data. For the genetic mark-recapture (GMR) analysis, we completed genetic analysis of all feather and pellet samples and used microsatellite data for six loci plus one sex locus to generate capture histories for individual birds. We conducted GMR analysis with closed population models in Program MARK to obtain preliminary abundance and sex ratio estimates for the PPR population over two consecutive winter seasons (2012-2013 and 20132014). These preliminary results suggest a between-year population change similar in direction and magnitude to that reported in CPW lek-count data (summed maximum male count across leks). However, genetic data also indicated substantial variation in sex ratio between years. Annual variation in sex ratio may be an additional source of bias for estimates of population status, abundance, and trends based on lek-count data that rely on a constant sex ratio assumption. 2 � 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 Reports 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 Evaluation of alternative population monitoring strategies for greater sage-grouse (<em>Centrocercus urophasianus</em>) in the Parachute-Piceance-Roan population of northwestern Colorado Description An account of the resource Robust estimates of population size and population trends provide the scientific basis for managers to make appropriate and defensible recommendations regarding land-use decisions, harvest regulations, and mitigation efforts for wildlife. When linked with environmental variables, robust monitoring programs also allow managers to examine wildlife responses to disease, land-use patterns, habitat treatments, weather, ecological succession, and disturbance. However, many wildlife monitoring programs continue to use untested population indices that may not provide reliable information on population status or trends. For this reason, it is essential to evaluate alternative approaches to population monitoring in terms of estimator precision, cost, practicality, and level of disturbance. Lek counts are the primary index used by state wildlife agencies to monitor changes in greater sage-grouse (<em>Centrocercus urophasianus</em>) abundance, but lek counts rely on untested assumptions about lek attendance, detectability, inter-lek movement, sex ratio, and proportion of leks counted. Given the availability of new methodological and statistical approaches to estimate wildlife populations, it is worth comparing the performance of lek counts against other monitoring methods. Creator An entity primarily responsible for making the resource Walker, Brett L. Brauch, J. S. Subject The topic of the resource Greater sage-grouse <em>Centrocercus urophasianus</em> Parachute-Piceance-Roan (PPR) region Wildlife management Northwestern Colorado Extent The size or duration of the resource. various pages Date Created Date of creation of the resource. 2014-2016 Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/NoC-NC/1.0/">No Copyright - Non-Commercial Use Only</a> Type The nature or genre of the resource Text Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Cost Center 3420 Avian Research. Work Package 0660 Greater Sage-grouse conservation