Dual-Frame Lek Surveys for Estimating Greater Sage-Grouse Populations

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1 The Journal of Wildlife Management 82(8): ; 2018; DOI: /jwmg Research Article Dual-Frame Lek Surveys for Estimating Greater Sage-Grouse Populations JESSICA E. SHYVERS, 1 Department of Fish, Wildlife, and Conservation Biology; Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA BRETT L. WALKER, Colorado Parks and Wildlife, 711 Independent Ave., Grand Junction, CO 81505, USA BARRY R. NOON, Department of Fish, Wildlife, and Conservation Biology; Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA ABSTRACT Effective monitoring programs are important for ensuring proper management of wildlife populations, but they require substantial resources, effort, and funding. For this reason, managers often use indices of animal abundance. However, indices typically rely on untested assumptions and may be unreliable for estimating population size, status, and trend. Therefore, it is important to test the assumptions underlying indices and to evaluate more rigorous methods of population estimation. Counts of males at leks are used as an index to monitor populations of greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, throughout its range in the western United States and Canada. However, not all leks are known, and wildlife managers typically have no rigorous, quantitative estimates of the number of leks and males in any given population. Therefore, it remains unclear what proportion of leks and males are included in, or excluded from, lek-based population status assessments, trend analyses, and management decisions. We used dualframe surveys, in combination with occupancy analysis to adjust for imperfect detection, to estimate the number of active leks, the number of lekking males, and the proportion of active leks and lekking males counted on annual lek surveys over 3 consecutive breeding seasons in a small, low-density greater sage-grouse population in northwestern Colorado, USA. We estimated that annual lek surveys captured an average of 45 74% of active leks and 43 78% of lekking males each year. Our results suggest that many active leks remain unknown and annual counts fail to account for a substantial, but variable, proportion of the number of active leks and lekking males in the population in any given year. Managers need to recognize this potential source of bias in lek-count data and, if possible, account for it in trend analyses and management efforts. Increased cost likely precludes annual use of dual-frame lek surveys. However, we recommend using analytical methods that account for imperfect detection, mapping potential lek habitat to improve survey efficiency, and conducting dual-frame surveys over multiple years to better understand temporal variation in the proportion of active greater sage-grouse leks and lekking males being counted in specific populations of interest. Ó 2018 The Wildlife Society. KEY WORDS Centrocercus urophasianus, detection probability, dual-frame surveys, greater sage-grouse, lek, lek count, occupancy, population monitoring. Rigorous population monitoring is important for ensuring proper management of wildlife species, but it is challenging and requires considerable time and effort (Witmer 2005). Thus, wildlife managers often turn to population indices to estimate population size and trend. Indices measure variables thought to correlate with wildlife abundance rather than abundance (Caughley 1977), and they are typically less costly and easier to obtain than statistically rigorous population estimates. Indices can be based on a variety of metrics, including animal counts, vocalizations, sign (e.g., hair, feathers, scat, tracks), nests, or camera detections. Examples Received: 31 January 2018; Accepted: 7 June jessica.shyvers@state.co.us; elepaiyo@gmail.com of commonly used indices include avian point counts (Johnson 2008), pellet surveys for ungulates (Rowland et al. 1984, Fuller 1991), and lek counts for prairie grouse (Connelly et al. 2003, Johnson and Rowland 2007, Western Association of Fish and Wildlife Agencies [WAFWA] 2015). However, indices often rely on untested assumptions about their relationship with true population size (Witmer 2005) and they may only represent a subset of the population of interest (Johnson 2008). Assumptions of indices frequently include a constant probability of detection and that a constant proportion of the population is counted. For species of conservation concern, there is an important need to test these assumptions. The greater sage-grouse (Centrocercus urophasianus) has experienced historical population declines and substantial Shyvers et al. Dual-Frame Lek Surveys 1689

2 contraction of its pre-european settlement distribution (Connelly and Braun 1997, Connelly et al. 2004, Schroeder et al. 2004). These declines, in combination with ongoing habitat loss and human land use conflicts (Knick et al. 2003, Connelly et al. 2004, Holloran and Anderson 2005, Walker et al. 2007), have resulted in petitions for federal protection of the species under the Endangered Species Act (U.S. Fish and Wildlife Service [USFWS] 2010, 2015). The greater sage-grouse is often regarded as an umbrella species whose protection is likely to offer conservation benefits for other sagebrush-obligate species (Rowland et al. 2006). It also has cultural, recreational, educational, and economic value as a game species and for wildlife viewing (Colorado Greater Sage-Grouse Steering Committee [CGSSC] 2008). These factors, combined with the species still extensive range, spanning 11 states in the United States and 2 Canadian provinces (Schroeder et al. 2004), led to its status as a focal species for sagebrush ecosystem conservation in western North America. Ideally, management and conservation of greater sagegrouse should be based on accurate and precise estimates of population size and trend over time. Historically, sagegrouse populations have been monitored by counting males during the spring breeding season when they gather to display on traditional strutting grounds (i.e., leks; Patterson 1952). The seasonal high count of males at each lek is then used as an index of abundance (Connelly et al. 2003, WAFWA 2015). Lek counts are currently considered the only practical means of monitoring populations of lekbreeding grouse over large areas (Johnson and Rowland 2007), and they remain widely used by state and provincial wildlife agencies in the western United States and Canada (Connelly et al. 2004, WAFWA 2015). Managers also rely on lek-count data to estimate conservation status and viability of populations, identify changes in distribution, set harvest limits, measure the success of management efforts, generate and validate predictive habitat models, and monitor effects of environmental and anthropogenic stressors on populations (Schroeder et al. 2004; Walker et al. 2007; CGSSC 2008; Doherty et al. 2010a, b). Lek counts and locations are also used to help identify and prioritize sagegrouse habitat in state and range-wide conservation efforts (CGSSC 2008, Doherty et al. 2010c, Hagen 2011, Coates et al. 2014). Lek counts typically follow standardized protocols that specify season dates, recommended number of visits per lek per season, time of day, and weather conditions suitable for counting strutting males (Connelly et al. 2003). Standardized protocols are intended to maximize male counts, reduce bias due to temporal variation in male attendance, and increase the reliability of lek counts for documenting changes in relative abundance over time (Connelly et al. 2004, CGSSC 2008, WAFWA 2015). The use of standardized lek counts to estimate population size and trend relies on several untested assumptions and, as a result, their reliability has been questioned (Beck and Braun 1980, Applegate 2000, Walsh et al. 2004, Johnson and Rowland 2007, Walsh et al. 2010). It is generally accepted that lek counts provide an indication of relative differences in spatial or temporal abundance and are primarily useful for examining large-scale, long-term population trends (Connelly et al. 2004). However, this limitation creates challenges for establishing effective policies to manage and conserve populations at local scales, or in response to immediate concerns. Therefore, investigating the reliability of lek-count data for monitoring changes in greater sage-grouse populations, testing their underlying assumptions, and developing innovative methods to estimate population size are research priorities (Naugle and Walker 2007) and the focus of several recent studies (Baumgardt 2011, Blomberg et al. 2013, McCaffery et al. 2016, Baumgardt et al. 2017, Fremgen et al. 2017). A key assumption underlying the use of lek counts for monitoring is that the proportion of active leks that are counted during annual monitoring efforts is either relatively high (i.e., most if not all leks are known) or at least constant over time (Sedinger 2007, CGSSC 2008). This assumption has consequences for population assessment if the active leks that are known and counted differ from the true set of active leks in the population (which is typically unknown; Anderson 2001) or if the proportion of active leks known and counted varies annually. Although some agencies attempt to account for variation in the number of leks counted annually, either by adjusting lek count data to account for effort or by designating only certain leks for trend analysis, doing so may introduce other biases. For example, most newly discovered leks tend to be smaller, resulting in a negative bias in mean male counts over time (WAFWA 2015), and leks used to assess trend (i.e., trend leks) may not be representative of all leks in the population if based on convenience sampling, rather than a random sample (Anderson 2001). Furthermore, the use of lek counts and locations to prioritize areas for conservation assumes that the majority of leks are known and that count data reliably indicate relative abundance (Doherty et al. 2010c). However, if lek counts are biased, either because many lek locations are unknown (Coates et al. 2013) or because detectability is low or variable (Walsh et al. 2004), then areas prioritized for conservation based on lek data may be incomplete or inadequate. One potential method for testing these assumptions is known as a dual-frame survey (Hartley 1962). Dual-frame surveys can be used to estimate the number of active leks and lekking males in a population and therefore the proportion of leks and lekking males being counted by current survey efforts. In dual-frame methodologies, surveys are conducted within 2 sampling frames, the list frame (LF) and the area frame (AF; Hartley 1962). The LF is comprised of a list of known point locations (e.g., nests, Haines and Pollock 1998) that serve as LF sample units, whereas the AF is comprised of area-based sample units that are surveyed to locate new (i.e., previously undiscovered or newly formed) point locations. This approach allows each frame to effectively offset weaknesses of the other (Kott and Vogel 1995); the LF provides information on all known units of interest, and the AF provides information on the completeness of the LF. The method is most often used in public and business surveys 1690 The Journal of Wildlife Management 82(8)

3 (Hartley 1962), but to date the only published use of dualframe surveys to monitor wildlife populations has been to estimate numbers of active and successful bald eagle (Haliaeetus leucocephalus) nests (Haines and Pollock 1998, USFWS 2009). Dual-frame surveys are thought to be most useful for monitoring breeding populations for species that have highly visible and stable breeding locations (Haines and Pollock 1998), making them a potential candidate for monitoring male sage-grouse on leks. We investigated the use of dual-frame surveys for estimating greater sage-grouse abundance in a small, lowdensity population in northwestern Colorado over 3 consecutive spring lekking seasons from 2012 to Our objective was to estimate the number of active greater sage-grouse leks and lekking males in the population while accounting for variation in detection probability, and the proportion of active leks and lekking males in the population being counted during annual lek surveys. STUDY AREA We studied greater sage-grouse in the Parachute-Piceance- Roan (PPR) population in Rio Blanco and Garfield counties in northwestern Colorado in spring from 2012 to The 1,466-km 2 area is composed of a series of ridges and plateaus separated by drainages at elevations from 2,150 m to 2,750 m. Primary land uses included seasonal livestock grazing and big game hunting, and year-round natural gas development. At the time of our study, sage-grouse in the PPR represented approximately 4% of the males counted in Colorado (CGSSC 2008) and occupied mountain big sagebrush (Artemisia tridentata vaseyana) and mixed sagebrush-mountain shrub communities on ridges, plateaus, and the upper ends of drainages (Krager 1977, Hagen 1999, CGSSC 2008, Walker et al. 2016). Seasons in the PPR are characterized by cold winters with deep snowpack (x ¼ 155 cm/year; Colorado Climate Center data) and mild summers. Annual precipitation in the PPR increased with elevation from 41 cm to 64 cm (CGSSC 2008). Areas of suitable sage-grouse habitat were naturally fragmented and separated by steep drainages, cliffs, and patches of aspen (Populus tremuloides), conifers, and nonsagebrush shrubs. Other dominant fauna in the area included mule deer (Odocoileus hemionus), elk (Cervus elaphus), coyote (Canis latrans), black bear (Ursus americanus), common raven (Corvus corax), and dusky grouse (Dendragapus obscurus). Leks primarily occurred along the tops of sagebrush-dominated ridges, and distinct lek arenas separated by unsuitable areas were sometimes close together; the distance between nearest active leks (mean SE) was 1, m, 1, m, and 1, m, and minimum distance to nearest active lek was 175 m, 175 m, and 237 m, respectively, in 2012, 2013, and 2014 (Colorado Parks and Wildlife [CPW], unpublished data). Typical lek arenas included meadows, barren or grassy openings on ridges, areas of concentrated livestock use (i.e., clearings around stock ponds and mineral licks; Fig. 1), and occasionally disturbed areas such as well pads and pipeline cuts. Active leks in the PPR were typically small, ranging from 1 to 33 males (mean SE ¼ ), with annual total high counts of males from all leks counted ranging from 77 to 250 Figure 1. A typical lek arena in the Parachute-Piceance-Roan greater sagegrouse population, Colorado, USA, as seen from a helicopter in April Most leks are located in open areas on sagebrush-dominated ridges. males from 2005 to 2017 (CPW, unpublished data). Colorado Parks and Wildlife has attempted to survey all known leks with 1 male in the previous 5 years in the PPR 3 times/year, primarily from helicopter, since Although ground counts are occasionally used in parts of the study area, helicopters are required to conduct population-wide surveys because most leks are inaccessible from the ground for the majority of the lekking season. Leks with no males in the previous 5 years are also surveyed but less consistently than those with more recent strutting activity. METHODS We defined a lek as any open area, represented by a central point location, where 1 male had been observed strutting on 2 counts (i.e., observations) during the March May breeding season. This corresponds to CPW s definition of a lek for small populations (CGSSC 2008). We defined an active lek in any year as any lek with 1 male counted in that year and a lekking male as any male that attended, and therefore was available to be counted on, an active lek in any given year. We designated open areas where 1 male had been observed strutting only once as potentially active locations. Dual-Frame Helicopter Surveys We used an overlapping dual-frame survey methodology wherein point-based sample units in the LF (i.e., leks) occurred within area-based sample units (i.e., cells) in the AF (Haines and Pollock 1998). When data from known LF points are excluded from AF cells (i.e., unduplication; Haines and Pollock 1998, Otto and Sauer 2009), the remaining data can be used to estimate the number of unknown points not currently in the LF (i.e., unknown active leks). Unduplication allows estimators for the number of points and their associated variances in each frame to be independent and therefore, they can be combined to estimate the number of points (i.e., number of active leks) in the study area (Otto and Sauer 2009). These estimators are referred to as screening estimators because points included in the estimator for each frame are excluded, or screened, from the other frame (Hartley 1962, Haines and Pollock 1998). Shyvers et al. Dual-Frame Lek Surveys 1691

4 The list frame is point-based (i.e., the sample units are represented by a list of geographic coordinates, or locations in space). These units (e.g., nests, leks) are either active or inactive. The LF survey(s) estimates the proportion of units on the current list that are active. In contrast, the area frame is area-based and the sample units have a spatial extent (e.g., 1-km 2 grid cells). Surveys of area frame units record the number of active points within each unit (e.g., the number of active nests or active leks). We conducted dual-frame surveys during 3 consecutive spring breeding seasons from 2012 to Our focus was on estimating the number of active leks and lekking males, so we first compiled a list of leks and potentially active locations with 1 male counted in the previous 5 years (i.e., CPW s Active and Potentially Active/Unknown leks; CGSSC 2008, Parachute-Piceance-Roan Greater Sage-Grouse Work Group 2008) in the PPR from CPW s statewide lek database and used that as our initial LF in 2012 (Fig. 2). We included potentially active locations in the LF because some locations are not confirmed as leks until subsequent years. We then overlaid the study area (i.e., the AF) with a grid of 1-km 2 cells so the entire study area was captured within the AF cell grid and all AF cells were either partially or completely within the study area boundary. A 1-km 2 cell size was large enough to support 1 lek and small enough to efficiently survey from a helicopter. An advantage of dualframe surveys is that the AF can be stratified based on vegetative cover type, topography, slope, or other attributes (Haines and Pollock 1998). We divided the AF into 2 strata: AF 1 and AF 2.TheAF 1 stratum included all AF cells that overlapped 1 lek in the LF, and the AF 2 stratum consisted of all remaining cells in the study area (i.e., those that did not overlap an LF lek). We used the reversed randomized quadrant-recursive raster (RRQRR) tool in ArcGIS (Theobald et al. 2007) to select a spatially balanced random sample of AF 2 cells with equal inclusion probabilities each year. We updated the LF, AF 1, and AF 2 prior to surveys in 2013 and 2014 to account for new leks discovered the previous year (Fig. 2). Each sampling unit in the LF and AF 1 had an inclusion probability of 1.0 because we surveyed all LF points and all AF 1 cells each year. We surveyed the maximum possible number of AF 2 cells each year within the constraints imposed by flight logistics and survey protocols. The number of AF 2 cells surveyed was based on an a priori power analysis conducted in Program MARK (White and Burnham 1999, Shyvers 2017). We searched for and counted strutting males from helicopter at all leks in the LF, in all AF 1 cells, and in a sample of AF 2 cells on 3 survey occasions (on average once every 6 days) each year. We conducted surveys between 17 April and 4 May to coincide with peak male attendance in the PPR (CPW, unpublished data). Surveys followed CPW s standardized lek count protocols that restrict counts to 0.5 hours before sunrise to 1.5 hours after sunrise on days with little or no precipitation and wind speeds <15 mph. We divided the study area into 5 sections with pre-determined flight paths to maximize survey efficiency and minimize flight distance. We made consecutive surveys on each route Figure 2. Sample units for dual-frame lek survey sampling frames in the Parachute-Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, in spring 2012 (top), 2013 (middle), and 2014 (bottom). in the opposite direction to reduce the influence of time of day on counts. We did not survey areas obviously unsuitable for leks (e.g., cliffs, dense shrubs on steep hillsides, aspen stands, conifer forest). We attempted to avoid disturbing strutting males during helicopter counts as much as possible. Observations of animals in this study followed protocols approved by a CPW and Colorado State University interinstitutional agreement (ACUC# ) and CPW s 1692 The Journal of Wildlife Management 82(8)

5 guidelines and protocols for conducting standardized lek counts. Analysis Sampling of potential lek habitat. We developed and used a potential lek habitat model to refine our dual-frame estimates following the conclusion of field work. During dual-frame surveys, we found that many AF2 cells had vegetation and topography unsuitable for sage-grouse leks. In 2015, CPW completed and validated a resource selection model of greater sage-grouse breeding habitat for the PPR (Walker et al. 2016). We used this information to exclude AF2 cells from our analysis that had little or no potential to support a lek to increase the accuracy and precision of dualframe survey estimates. Leks represent a subset of breeding habitat, and, in the PPR, leks only occur on ridges with relatively gentle slopes. We used mapped boundaries of known lek arenas to determine minimum thresholds for breeding resource selection function scores, topographic position index, and slope, and then applied those thresholds across the study area to develop a potential lek habitat layer (Fig. 3). We then identified the minimum proportion of potential lek habitat in an AF2 cell known to support a lek and, prior to analysis, removed all AF2 cells, both surveyed and unsurveyed, that fell below that minimum threshold. Adjusting for imperfect detectability. We used Occupancy Estimation with Detection <1 models in Program MARK (White and Burnham 1999) to estimate the true proportion of LF leks that were active and the per-visit probability of detecting an active lek in the LF (^p LF ) and in each AF stratum (^p AF 1, ^p AF 2 ). Our estimation methods assumed constant within-season occupancy of each sample unit, and independence of sample units. In Program MARK, we used the attribute groups option to distinguish the 3 frames (strata) and estimated detectability separately for each survey year. We fit 5 models, estimating occupancy by group (frame) and allowing detection probability to vary by time (survey occasion) and group in each year s analysis. Data from LF surveys are used to estimate a proportion (i.e., the proportion of leks in the LF that are active in year t), so MARK analyses can be used to directly estimate this proportion equivalent to an occupancy rate (MacKenzie et al. 2006). In contrast, AF sample units are used to estimate a count the number of active leks in the sample unit and MARK analyses are used to estimate the detectability of leks. The MARK analyses also estimate the proportion of AF cells occupied (i.e., that support 1 active lek), but those estimates are not used in calculations (Appendix A, available online in Supporting Information). Given annual estimates of detection probability, we were able to adjust the count of the number of active leks detected in each area frame for any active leks present but not detected. We used model averaging across all models based on Akaike s Information Criterion values adjusted for small sample size (AICc) to obtain parameter estimates in Program MARK. We were unable to estimate ^p AF 2 because of the low incidence of leks in sampled AF2 cells, so we assumed that detection probabilities of leks in AF1 and AF2 were equal and set ^p AF1 ¼ ^p AF2 for all group-varying models. We think this was a tenable assumption because lek size (i.e., number of males) is a major factor influencing detectability (WAFWA 2015) and all newly discovered AF leks during the study were small (<10 males) and should have had similar detection probabilities. Figure 3. Predicted potential lek habitat within the current (2016) occupied range boundary for the Parachute-Piceance-Roan greater sage-grouse population in northwestern Colorado, USA. Shyvers et al. Dual-Frame Lek Surveys 1693

6 We excluded count data for potentially active lek locations from all analyses (unless those locations were later confirmed as leks by subsequent observations) to avoid including data from temporary or aberrant strutting locations. We also excluded new leks discovered incidentally during dual-frame survey flights (i.e., while traveling between sampling units) from occupancy analysis the first year we discovered them. This was necessary because these leks occurred outside sampled units and were typically surveyed <3 times in the first year. However, we included high male counts at incidental leks when calculating average males per AF lek to improve point estimates of the number of lekking males at AF leks. Estimating the number of leks. We used Hartley s screening estimator (Hartley 1962, Haines and Pollock 1998), modified to account for imperfect detectability, to estimate the total number of active leks in the population and its associated variance. We used encounter history data from multiple survey occasions to estimate per-occasion detection probability and adjusted the estimate of the proportion of leks to account for imperfect detectability. Following Haines and Pollock (1998), an initial estimate of the number of active leks in the LF would have been computed as ^t LF ¼ N LF y LF, where N LF is the number of leks in the current LF, and y LF ¼ðn 0 =n LF Þ is the proportion of LF leks that had lekking males (n 0 ) divided by the number of leks surveyed from the LF (n LF ). However, this estimator is unbiased only when detection probability p ¼ 1 as assumed in Haines and Pollock (1998). After adjusting for imperfect detectability using per-occasion estimates of detection probability (^p iðlfþ ), the number of active leks in the LF is computed as ^t LF ¼ N LF y LF, where y LF ¼ðn 0=n LF Þ=^p LF and ^p Yk LF ¼ 1 ð1 ^p iðlfþ Þ based on k surveys. Note that y LF i¼1 is equivalent to an estimate of an occupancy rate (Hoeting et al. 2000, MacKenzie et al. 2006). The number of leks in each AF frame j (j ¼ 1, 2) would have been computed as ^t AFj ¼ N AFj y AFj, where y AFj is the sample mean number of active leks detected in AF j cells, and N AFj is the total number of 1-km 2 cells in AF j. Accounting for imperfect detectability, the number of leks is computed as ^t AFj ¼ N AF j y AFj, where ^p ^p AF j ¼ 1 Yk ð1 ^p iðafj ÞÞ based on AF j i¼1 k survey occasions (Thompson 2012). We computed the number of leks in the study area ( ^T ) using the combined estimator ^T ¼ ^t LF þ ^t AF1 þ ^t AF2. Finally, we estimated the proportion of leks that would have been counted on traditional annual lek surveys by dividing the number of leks detected in the LF by ^t LF. Estimating the number of lekking males. We used the within-year maximum number of males detected over 3 survey occasions to estimate the mean number of males per lek across all leks in the study area. Based on separate estimates by frame, we computed the number of lekking males (M)as ^M ¼ ^t LF x LF þ ^t AF1 x AF1 þ ^t AF2 x AF2, where x j is the sample mean number of lekking males in frame j based on the high male count. Because we assumed no false positive detections, we truncated lower confidence intervals for the number of leks and lekking males to the minimum number observed during our surveys. Finally, we calculated the proportion of lekking males that would have been observed on traditional annual lek surveys using the high count of males at each lek summed across all LF leks divided by our estimate of the total number of lekking males across all 3 frames. We present details of our dual-frame estimates, including equations used to compute variances, and link these to the original set of estimators developed by Haines and Pollock (1998; Appendix A). Paired helicopter and ground counts. We compared data from simultaneous ground and helicopter counts to test whether observers in helicopters detected the same number of males as those on the ground. We obtained ground count data from CPW (collected as part of a separate research project) and calculated the difference between the male count recorded for leks by the observer in the helicopter against the high male count recorded by an observer in a truck or on foot during the same 5-minute interval that the helicopter surveyed the lek. We also reviewed the ground observer s notes to determine if helicopters were noted to have influenced male counts. RESULTS We surveyed LF points, AF 1 cells, and AF 2 cells each year. The number of unsurveyed AF 2 cells in each year ranged from out of available cells (after excluding AF 2 cells with unsuitable areas). Each year, we detected the most active leks in the LF, the second most in AF 1, and the fewest in AF 2. The number of active leks detected per sampling frame was similar across years with active leks detected in the LF, 3 4 in AF 1, and 0 1 in AF 2. The percentage of LF leks active in one year but not the next (i.e., between-year turnover) was 26.9% (14/52) from 2012 to 2013 and 25.0% (16/64) from 2013 to Active leks in the LF had no birds detected on 50% of 66, 33.3% of 51, and 31.9% of 69 dual-frame survey occasions in 2012, 2013, and 2014, respectively. We excluded data from only 1 potentially active location (with a single strutting male) from our analysis because the location was never confirmed as a lek. We discovered 21 new leks on dual-frame surveys over 3 years, including those found in the AF and incidentally while flying between survey units (Table 1). We discovered 3 4 AF 1 cells with 1 new lek and 1 AF 2 cell with 1 new lek in both 2012 and 2014, and found no new leks in AF 2 cells in 2013 (Table 2). The mean number of new leks detected per active AF cell, for combined years, was 1.1 for AF1 and 1.0 for AF2. In addition to new leks discovered during dualframe surveys, CPW discovered 9 additional new leks during other field research, so 30 new leks were discovered in the PPR during the 3 years of our study. The number of males counted on dual-frame surveys, based on the within-year maximum number of males detected at each lek across all 3 occasions, was similar in 2012 and The Journal of Wildlife Management 82(8)

7 Table 1. Summary of new leks detected in area-frame (AF) strata by year in the Parachute-Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, yr total AF a 1 (dual-frame surveys) AF 2 (dual-frame surveys) Incidental b (dual-frame surveys) Total (dual-frame surveys) Total (all CPW surveys combined) c a AF 1 sampling frame cells contain 1 previously known lek; AF 2 cells do not contain a previously known lek. b Active leks detected in AF 2 during dual-frame surveys while travelling between sample units. c Includes leks discovered by other Colorado Parks and Wildlife (CPW) monitoring activities, including ground and additional helicopter surveys. but increased by more than a factor of 2 in 2014 largely because of a substantial increase in the mean high male count per active lek in 2014 (Table 2). The number of males per new lek for combined years ranged from 1 to 9 with a mean of (n ¼ 21). The best-supported model varied among years, but groupvarying and constant (.) detection probability (p) models received the most support given our data (Table 3). Modelaveraged estimates of the proportion of known leks in the LF that were active (c LF, ), and model-averaged estimates of the proportion of occupied cells in AF1 (c AF1, ) and AF2 (c AF2, ) varied by year (Table 4). Model-averaged per-occasion detection probability estimates for leks ranged from among sampling frames and were relatively constant across years in the AF (Table 4). Detection probability estimates in the LF were lowest in 2012 (^p LF ¼ ) and similar to those in the AF that year (^p AF ¼ ; Table 4). Mean modelaveraged detection probabilities across occasions for each sampling frame were 0.40, 0.62, and 0.64 in the LF and 0.44, 0.42, and 0.42 for AF 1 and AF 2 in 2012, 2013, and 2014, respectively. Estimates of p (the probability of detecting an active lek at least once across 3 occasions during a season) were lowest in the first year (2012) in the LF at 0.78, then Table 2. Total sampling units with 1 active lek detected, summed high male count (HMC) across 3 visits, and mean HMC per active lek in the list frame (LF) and in area frame (AF) strata in the Parachute-Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, Units with 1 Mean HMC a /active active lek HMC a lek Frame LF b AF AF Total a Includes counts at incidental leks. b LF sampling frame points represent previously known leks; AF 1 sampling frame cells contain 1 previously known lek; AF 2 cells do not contain a previously known lek. increased to 0.95 in 2013 and 2014; estimates for p in the AF varied from 0.80 to 0.82 across years (Fig. 4). Estimates of the proportion of the number of active leks in the population that would have been counted on annual surveys of LF leks (i.e., those with 1 male counted in the previous 5 years) were 0.45 in 2012, 0.74 in 2013, and 0.45 in 2014, and the estimated proportion of lekking males that would have been counted was 0.43, 0.78, and 0.57, respectively, for the 3 years (Table 5). Based on point estimates, we observed extensive among-year variation in the estimated number of active leks (range ¼ 23 51) and number of lekking males (range ¼ ), but the precision of these estimates was low and yearly confidence intervals overlapped extensively (Table 5). On average, helicopter crews counted the same number of males as ground crews. The mean difference in high male count between helicopter and ground observers was (n ¼ 65; 95% CI ¼ 0.53 to 0.25). Ground observers noted that males occasionally flushed off the lek in response to the helicopter before they could be counted by the helicopter crew. However, helicopter crews also occasionally counted more males than ground crews when males were obscured by vegetation or otherwise out of view of ground observers. DISCUSSION We estimated the number of active leks and lekking males in the PPR greater sage-grouse population using dual-frame survey estimators from Haines and Pollock (1998) with 2 key differences: we conducted repeat surveys of sample units to estimate detection probability and account for imperfect detection of active leks, and we stratified AF cells into 2 frames based on the presence of known leks from the LF. We estimated that, if annual lek surveys in this population only monitored leks with 1 male in the past 5 years, only 45 74% of active leks and 43 78% of lekking males would have been represented in lek-count data across years. If we assume that the number of active leks and lekking males in each year could be as low as the lower 95% confidence limit of our estimates, those proportions would still only be 81 85% of active leks and 70 83% of lekking males across years. This in turn suggests that unknown active leks exist in the PPR, a finding supported by the discovery of 30 new leks during this study. We suspect that an unknown proportion of these new leks (and lekking males) represent a previously uncounted portion of the population based on distance from known active leks. State wildlife agencies have conducted intensive search efforts and discovered hundreds of new greater sagegrouse leks range-wide in recent decades (WAFWA 2015). In conjunction with our findings, these data support the idea that populations often have previously undiscovered leks. Moreover, it is likely that some new leks form each year, albeit at an unknown rate. Regardless, failure to account for a substantial but variable number of previously unknown or newly formed leks each year, even if they support fewer males per lek than known leks (this study, WAFWA 2015), may result in substantial underestimation of the abundance of lekking males and affect assessments of population size, Shyvers et al. Dual-Frame Lek Surveys 1695

8 Table 3. Program MARK model summaries by year for dual-frame survey analysis of lek occupancy (c) and detectability (p) by sampling frame (g) and sampling occasion (t) in the Parachute-Piceance-Roan greater sage-grouse population in Colorado, USA, Year and model a AIC c DAIC c AIC c weight Model likelihood Number of parameters Deviance 2012 p(.), c(g) p(g), c(g) b p(t), c(g) p(g þ t), c(g) b p(g t), c(g) b p(g), c(g) b p(.), c(g) p(g þ t), c(g) b p(t), c(g) p(g t), c(g) b p(g), c(g) b p(.), c(g) p(g þ t), c(g) b p(t), c(g) p(g t), c(g) b a Akaike s Information Criterion values adjusted for small sample size. b For all models where p varied by sampling frame, p(g), p(g þ t), and p(g t), we set p AF1 ¼ p AF2.AF 1 ¼ sample cells that contain 1 previously known lek. AF 2 ¼ sample cells that do not contain a previously known lek. likelihood of persistence, conservation status, and trend. Although abundance estimates are required for inferences regarding population status and persistence, estimates of trend do not require enumerating all leks or lekking males if sampled leks are representative of unsampled (i.e., unknown) leks. However, in populations where unknown leks exist, leks sampled non-randomly may not be representative of the population (Anderson 2001). Estimating detection probability allowed us to account for variation in factors known to affect counts of males on leks, such as lek size, environmental conditions, male behavior, presence of females, and daily, seasonal, and age-related lek attendance (Baumgardt 2011, Fremgen 2014, and WAFWA 2015). Our finding of mean per-occasion lek detection probabilities 0.64 reinforces previous authors recommendations to conduct multiple counts per year when using lekcount data to monitor populations with <50 active leks (Connelly et al. 2003, Fedy and Aldridge 2011, Blomberg et al. 2013) because it substantially increases the probability of detecting males on an active lek at least once during the survey period. To our knowledge, ours are the first reported estimates of per-occasion detection probability for greater sage-grouse leks. Unfortunately, it is unclear how our detection probability estimates compare to other greater sage-grouse populations because of a lack of comparable data. McCaffery et al. (2016) used N-mixture models to estimate per-occasion detection probability of males over a 13-year period in Montana, but these models have been discounted by some (Barker et al. 2018) and our estimates of detection probability are for leks rather than males. Other studies have reported detectability of marked males on leks (Johnson and Rowland 2007, Baumgardt 2011, Blomberg et al. 2013, Fremgen et al. 2016), mean annual lek attendance (Blomberg et al. 2013), or daily lek attendance rates (Fremgen 2014) rather than detectability of leks. Additional dual-frame lek survey studies, especially in larger populations with less fragmented habitat, are needed to Table 4. Model-averaged estimates standard errors (95% CIs) of the proportion of list-frame (LF) leks that were active (c LF ), proportion of area-frame (AF) cells in each stratum containing an active lek (c AF1, c AF2 ), and detection probability (p) by sampling frame and survey occasion (Occ 1 3) in the Parachute- Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, We present occupancy estimates for AF strata for reference only, they are not used in estimators ( ) ( ) ( ) c AF ( ) ( ) ( ) c AF ( ) ( ) ( p LFOcc ( ) ( ) ( ) p LFOcc ( ) ( ) ( ) p LFOcc ( ) ( ) ( ) p AFOcc ( ) ( ) ( ) p AFOcc ( ) ( ) ( ) p AFOcc ( ) ( ) ( ) c LF a a LF sampling frame points represent previously known leks; AF 1 sampling frame cells contain 1 previously known lek; AF 2 cells do not contain a previously known lek The Journal of Wildlife Management 82(8)

9 Figure 4. Estimated probability of detecting 1 male at a list frame lek or 1 active lek in an area frame cell at least once across 3 survey occasions (p ), with 95% confidence intervals, from dual-frame lek surveys in the Parachute- Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, in spring determine whether detection probability of leks in the PPR is comparable with other populations. There are 2 considerations for detection in lek monitoring: detection of active leks on the landscape, and detection of individual males on active leks. Our estimates of the number of active leks reflect both these sources of uncertainty. The first source of uncertainty was addressed by sampling from known, previously active leks (the LF) and previously undetected or newly active leks (the AF). The second source of uncertainty was addressed by conducting multiple surveys and estimating detectability (i.e., the probability of detecting 1 male at an active lek; MacKenzie et al. 2006, Blomberg et al. 2013). Although we did not estimate the detectability of individual males on leks, those values are generally high (Fremgen et al. 2016) and, based on our data, similar between helicopter and ground counts. However, nondetection of males on leks, and possibly disturbance from helicopters, may have led to a small negative bias in our estimates of lek detection probabilities. The result would be a small underestimation of the abundance of leks and lekking males in the PPR population. Additional data on variation in lek attendance, detectability of individual males on leks, and inter-lek movement of males are needed to determine the relative contributions of availability and detectability to overall detection probability. In addition to investigating detection of leks, future combinations of dual-frame with other approaches, such as N-mixture models (Royle 2004, McCaffery et al. 2016, Burkhalter et al. 2018), doubleobserver counts (Riddle et al. 2010, Fremgen et al. 2016), or mark-resight models (Blomberg et al. 2013, Fremgen et al. 2016) could provide information on detectability of individual males and further improve estimates. We pooled data for AF 1 and AF 2 to estimate detection probabilities by setting ^p AF1 equal to ^p AF2 in each of our models that allowed detection probability to vary with sampling frame. This was necessary because we detected few leks in sampled AF 2 cells, resulting in insufficient data to estimate ^p AF2. This may be a limitation of dual-frame lek surveys when monitoring either low-density or well-studied populations where few new leks are likely to be discovered. Because dual-frame estimators are sensitive to sparse data in the AF, and we detected no new leks in AF 2 in 2013, we may have underestimated the number of active leks in AF 2 and in the population that year. To address this problem, we recommend that future dual-frame lek survey efforts delineate and only sample from potential lek (or breeding) habitat within the AF to maximize survey efficiency and minimize estimator variance. Our estimates of the proportion of active leks and lekking males counted on annual lek surveys may have been slightly higher if all inactive and historic leks (as defined by CPW) had also been included in our LF. We excluded these leks from the LF because of the absence of males in the previous 5 years. However, 3 of 46 inactive and historic leks that we excluded from our initial LF in 2012 (6.5%) were later found to have lekking males during our study. Although inclusion of these locations in our LF would have resulted in slightly fewer newly discovered leks, they represent a small proportion of the number of new active leks discovered and do not change our conclusion that a substantial proportion of active leks were unknown in this population. Table 5. Total number of detected and estimated active leks, proportion of active leks known and counted, and total estimated number of males attending leks (with 95% CIs) in the list frame (LF) and in area frame (AF) strata from dual-frame lek surveys in the Parachute-Piceance-Roan greater sage-grouse population in northwestern Colorado, USA, We truncated lower confidence intervals for the number of active leks and lekking males to the minimum number observed in dual-frame sampling units Number or proportion 95% CI Number or proportion 95% CI Number or proportion 95% CI Number of active leks counted (LF a ) Number of males counted (LF) Estimated number of active leks (LF) Estimated number of active leks (AF 1 ) Estimated number of active leks (AF 2 ) Estimated total number of active leks Estimated total number of lekking males Estimated proportion of active leks counted Estimated proportion of lekking males counted a LF sampling frame points represent previously known leks; AF 1 sampling frame cells contain 1 previously known lek; AF 2 cells do not contain a previously known lek. Shyvers et al. Dual-Frame Lek Surveys 1697

10 The number of new leks we discovered may also be related to increasing population size. High male count data from CPW suggest that male abundance generally increased from 2010 to a 13-year high in 2014 (CPW, unpublished data). Although we interpret this information cautiously (CPW considers high male count data an untested index of relative abundance), the relatively large number of new leks we located from 2012 to 2014 may be in part related to formation of new leks (including satellite leks) as the population increased. It is possible that dual-frame surveys conducted during periods of population decline or cyclic lows in abundance could reach the opposite conclusion, that most leks are already known and counted every year. However, CPW also discovered 8 additional new leks during an apparent downturn in abundance in the 3 years after our study concluded ( ; CPW, unpublished data). If active lek locations in a population are relatively stable, conducting dual-frame surveys across multiple years should result in a larger LF each year and eventually include almost all existing active leks. However, compiling a complete inventory of existing leks may not be possible in populations where the location or status (e.g., active, inactive) of leks is non-static, either because new leks are established or existing leks become unoccupied over time. In these cases, the LF represents a moving target. That may be the case in the PPR, where the list of known leks increased in size annually during our study but was still incomplete after 3 years of intensive survey effort. Although dual-frame lek surveys may more quickly produce a nearly complete LF in well-studied populations, populations with unstable dynamics, perhaps because of small population size or changes in land use, likely will not. In dual-frame surveys, sampling frames can be stratified based on attributes of interest to increase sampling efficiency (Haines and Pollock 1998). We divided the AF into 2 strata based on proximity to existing leks. Our use of 2 AF strata was justified because the majority of new leks in the PPR (85%) were discovered within the same 1-km 2 cell as known LF leks, and we consistently found more new leks in AF 1 cells than in AF 2 cells each year. We suspect this is because natural habitat fragmentation in the PPR limits available breeding habitat (Walker et al. 2016) and leks are clustered within those areas. Stratification of the AF may be especially important in populations where potential breeding habitat is limited or in years of high abundance when satellite leks may form near existing leks (Dalke et al. 1963, Connelly et al. 2003). Another advantage of dual-frame lek surveys is that they are compatible with, and easily incorporated into, existing lek-count monitoring efforts. Standardized lek count protocols typically stipulate multiple counts at all, or a subset of, known leks (i.e., equivalent to the LF) during a specified survey period (Connelly et al. 2003), and count protocols for dual-frame lek surveys and standardized lek counts are identical. Dual-frame surveys do, however, require additional effort and funding to define, stratify, and survey for leks within the AF. In the PPR, performing a complete survey of leks in the LF was possible because of the small size of the study area and the manageable number of known active leks. However, this may not be possible in areas where leks are far apart or too numerous to count in a short period of time given finite resources (e.g., funding, helicopters). In this case, the dual-frame sampling design and variance estimators can be adjusted to accommodate surveys of a random subset of known leks (Appendix A). In our study, surveying the AF increased the total cost to approximately 2.5 times that of annual helicopter lek surveys alone (from $4,071 to $10,177/ survey occasion/year), which likely precludes using dual-frame lek surveys for annual monitoring. Dual-frame estimators rely on 2 assumptions: within-year occupancy of sample units does not change, and sample units are independent. Violation of these assumptions would result in underestimation of detection probability, overestimation of the number of active leks and lekking males, and underestimation of the proportion of leks known and counted (Kendall et al. 2013). We surveyed during the peak period of male lek attendance to minimize the influence of changes in within-year availability and detectability, and we minimized the duration of the survey period (18 days) to reduce the potential for inter-lek movement that might induce dependence in counts among leks. Variable lek attendance during the survey period (Blomberg et al. 2013) or inter-lek movement of males (Fremgen et al. 2017) that causes some leks to be abandoned or occupied between survey occasions may violate the first assumption. In such cases, estimates of occupancy would be more appropriately interpreted as the probability of leks being used by males and estimates of detection probability as the combined probability of male presence on a lek and detection given presence (Kendall et al. 2013). Inter-lek movement that causes some males to be counted at >1 lek during the survey period may violate the second assumption. If inter-lek movement is independent of whether leks are known and the majority of active leks in a population are known, inter-lek movement will likely cause any analysis method based on high male counts summed across leks to overestimate the number of lekking males in the population. Dual-frame estimates also do not account for observer bias or variation in individual or age-specific male detectability, nor do they address other key assumptions for using counts of leks and males to monitor populations, such as variation in sex ratio. Information on these factors is required to extrapolate counts of lekking males to either female or total population size. However, these limitations are not unique to dual-frame surveys; they apply to all analyses based on counts of males on leks, including annual high male counts and N-mixture models (McCaffery et al. 2016, Burkhalter et al. 2018). For that reason, we echo previous recommendations that managers should use caution when interpreting data from monitoring efforts that rely on male counts to assess population trend when key assumptions have not been addressed (Applegate 2000; Walsh et al. 2004, 2010; McCaffery et al. 2016). Another important consideration for dual-frame surveys is how to allocate sampling effort among sampling frames to balance the cost of surveys with precision of estimates. Shyvers (2017) recommended sampling the same proportion of the sample units in each AF stratum and increasing the number of survey occasions from 3 to 4 to improve dualframe estimator precision. Still, decisions about sampling allocation need to consider other potential trade-offs specific 1698 The Journal of Wildlife Management 82(8)