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Variation in the sex ratio of breeding populations strongly influences population dynamics and has direct implications for the management and conservation of wildlife populations (Ellis et al. 2022). However, sex ratio, and the magnitude of annual variation in sex ratio, are often unknown in wildlife species where the sexes vary in detectability or cannot be readily distinguished in the field (Donald 2007, Ancona et al. 2017). In species where reliable count data are only available for one sex, estimates of sex ratio are required to make inference to total population size. For small populations and species of conservation concern, estimates of sex ratio are needed to estimate the number of breeding individuals and effective population size, as well as to conduct population viability analyses to gain insights into persistence likelihood (Lande and Barrowclough 1987, Frankham 1995, Brook et al. 2000, McCaffery and Lukacs 2016).The greater sage‐grouse Centrocercus urophasianus (hereafter ‘sage‐grouse') is one of several species of lek‐breeding grouse of conservation concern in North America that has experienced substantial declines in abundance and distribution over the past century (Connelly and Braun 1997, Connelly et al. 2004, Schroeder et al. 2004, Aldridge et al. 2008). State and provincial wildlife agencies monitor sage‐grouse populations throughout their range using high counts of males attending leks (i.e. lek counts) during the spring breeding season as an index of total population size (Connelly et al. 2004, Garton et al. 2011, WAFWA 2015). However, lek counts are subject to numerous sources of potential bias, so the index may or may not be proportional to true population size (Johnson and Rowland 2007, Monroe et al. 2016, Baumgardt et al. 2017, Shyvers et al. 2018). Moreover, female sage‐grouse are more difficult to detect and do not regularly attend leks, so lek‐count data typically provide little or no information about female abundance (Connelly et al. 2003, Walsh et al. 2004).Reliable estimates of pre‐breeding sex ratio are important for informing current and future monitoring efforts for sage‐grouse (Naugle and Walker 2007, McCaffery and Lukacs 2016). Estimates of sage‐grouse population trends based on the lek‐count index explicitly or implicitly assume that trends in male counts reflect trends in total population size. This is because they either assume that sex ratio is constant over time and space (Walsh et al. 2004, Sedinger 2007, Connelly et al. 2011) or that any variation in sex ratio has a minimal effect on long‐term trend estimates (Connelly et al. 2004, WAFWA 2015). In this species, lower annual survival of males (0.37 for adult and 0.63 for subadult males vs 0.59 for adult and 0.78 for subadult females; Zablan et al. 2003) results in breeding populations with female‐biased sex ratios. However, populations are generally characterized by substantial annual and geographic variation in reproductive success and survival of different age and sex classes that are expected to lead to fluctuations in sex ratio (Moynahan et al. 2006, Connelly et al. 2011, Taylor et al. 2012). For this reason, extrapolating female or total population size from lek‐count data in any year assuming a constant sex ratio is questionable (Walsh et al. 2004, CGSSC 2008, Guttery et al. 2013). Moreover, failure to account for the effect of spatiotemporal variation in sex ratio on annual abundance estimates in trend analyses may lead to overestimation of the precision of trend estimates. Despite these concerns, most analyses that have estimated total population size or long‐term population trends of sage‐grouse from male lek‐count data have either assumed a constant sex ratio (USFWS 2010, Garton et al. 2011, Foster 2016, USFWS 2019) or, more recently, modeled variation in sex ratios estimated from harvest data (Coates et al. 2019).Typically, breeding‐season sex ratio estimates for sage‐grouse are extrapolated from fall harvest data (Braun et al. 2015), fall mark‐recovery analyses (Hagen et al. 2018), raw counts of males and females attending leks (Patterson 1952), or visual assessment of winter flocks (Beck 1977; Table 1, Fig. 1). However, these estimates are problematic due to inherent biases in sampling methodologies. For example, estimates from harvested birds can be biased by differential vulnerability or selective harvest of specific age and sex classes (Zablan et al. 2003, Sedinger et al. 2010, Hagen et al. 2018). Sex ratios in harvest data will also differ from those during the previous or subsequent breeding season if pre‐ or post‐harvest mortality rates, respectively, differ between males and females (Swenson 1986). Additionally, raw counts on leks can be biased by different timing and rates of lek attendance or different detection probabilities of males and females (Connelly et al. 2003, Walsh et al. 2004, Johnson and Rowland 2007). Finally, estimates of sex ratio from winter flock observations (Beck 1977) can be biased by the difficulty of correctly determining the sex of birds in the field (especially distinguishing females from juvenile/yearling males) or by differences in the probability of encountering male‐ and female‐biased flocks.1FigureEmpirical tertiary sex ratio point estimates (F:M) for greater sage‐grouse and Gunnison sage‐grouse by season and data type from 21 published studies in relation to a fixed 2:1 sex ratio (horizontal dashed line). Sex ratio is shown as the estimated value in each year of the study or as the mean across locations, years, or both. Estimates are color‐coded as: brown squares = early fall (all ages), light brown squares = early fall (breeding‐age), blue diamonds = winter (all ages), pink triangles = spring (breeding‐age), and black circles (winter, all ages, this study). Study no. corresponds to those in Table 1.1 TableEmpirical tertiary sex ratio estimates (F:M) for greater sage‐grouse and Gunnison sage‐grouse by season and data type from 21 published studies. Sex ratio is shown as the estimated value in each year of the study or as mean ± SD (range) across locations, years, or both, unless specified otherwise. Study no. corresponds to those in Figure 1Study no.StudySpeciesStateSeasonData typeSex ratio1Patterson 1952GRSGWYEarly fallHarvest (all ages)1.51, 1.682Rogers 1964BothCOEarly fallHarvest (all ages)1.41 ± 0.38 (0.87–1.92)3Autenrieth 1981GRSGIDEarly fallHarvest (all ages)1.50 ± 0.51 (0.56–4.76)4Guttery et al. 2013GRSGUTEarly fallHarvest (all ages)1.46 (95% CI: 1.31–1.62)5Broms et al. 2010GRSGOREarly fallHarvest (all ages)1.41 ± 0.25 (1.13–1.92)6Braun et al. 2015GRSGCOEarly fallHarvest (all ages)1.57 ± 0.17 (1.29–1.87)6Braun et al. 2015GRSGOREarly fallHarvest (all ages)1.41 ± 0.23 (1.13–1.92)6Braun et al. 2015GUSGCOEarly fallHarvest (all ages)1.30 ± 0.27 (0.77–1.86)7Hagen et al. 2018GRSGCOEarly fallMark‐recovery (all ages)1.56 ± 0.25 (1.27–1.88)8Autenrieth 19811GRSGIDEarly fallHarvest (‘adults')2.69 ± 1.87 (0.42–14.29)9Broms et al. 20101GRSGOREarly fallHarvest (‘adults')1.73 ± 0.43 (1.11–2.48)10Braun et al. 20152GRSGCOEarly fallHarvest (breeding‐age)2.28 ± 0.40 (1.53–3.03)10Braun et al. 20152GRSGOREarly fallHarvest (breeding‐age)1.74 ± 0.45 (1.11–2.61)10Braun et al. 20152GUSGCOEarly fallHarvest (breeding‐age)1.65 ± 0.51 (0.63–3.31)11Guttery et al. 20132GRSGUTEarly fallHarvest (breeding‐age)1.6512Patterson 19521GRSGWYEarly fallHarvest (breeding‐age)2.18, 2.6813Rogers 1964BothCOEarly fallHarvest (breeding‐age)1.33 ± 0.38 (0.82–1.96)14Hagen et al. 20182GRSGCOEarly fallMark‐recovery (breeding‐age)2.06 ± 0.55 (1.35–2.61)15Beck 1977GRSGCOWinterVisual counts of flocks (all ages)1.56, 1.6316Patterson 19523GRSGWYSpringLek and nest censuses (breeding‐age)3.017Walsh 20024GRSGCOSpringMark‐resight (breeding‐age)2.30, 3.2418Walsh et al. 20105GRSGCOSpringMark‐resight (breeding‐age)2.4419Stiver 20076GUSGCOSpringMark‐resight (breeding‐age)2.10, 2.1620Walsh et al. 20107GUSGCOSpringMark‐resight (breeding‐age)2.17, 2.1620Walsh et al. 20108GUSGCOSpringMark‐resight (breeding‐age)2.66, 3.3521This studyGRSGCOWinterGenetic mark–recapture (all ages)3.29, 1.541Based on data from ‘adults' (i.e. excluding data from juveniles).2Based on data from adults and yearlings (i.e. adults and yearlings, excluding data from juveniles).3Based on ‘three‐quarters' of the spring breeding population being female (Patterson 1952, p. 140).4Estimates for one year using Bowden's and joint hypergeometric mark‐resight abundance estimators, respectively.5Estimate for one year from reanalysis of data in Walsh (2002) using a mixed logit‐normal mark‐resight abundance estimator.6Estimates for two years using Bowden's abundance estimator.7Estimates for two years from reanalysis of data in Stiver (2007) using Bowden's abundance estimator.8Estimates for two years from reanalysis of data in Stiver (2007) using a mixed logit‐normal mark‐resight abundance estimator.Rigorous estimates of tertiary sex ratio (i.e. those based on methodologies that account for and reduce potential bias) for sage‐grouse during the breeding season are limited to three studies based on mark‐resight or mark‐recovery estimates of female and male abundance. Walsh (2002) reported spring sex ratios of 2.30 and 3.24 females per male for sage‐grouse in one year in Middle Park, Colorado using two different estimators. Stiver (2007) reported sex ratios of 2.10 and 2.16 during two consecutive breeding seasons in the San Miguel population of Gunnison sage‐grouse C. minimus. Walsh et al. (2010) later reanalyzed data from those two studies, which resulted in a revised estimate of 2.44 females per male for sage‐grouse in Middle Park and revised estimates of 2.66 and 2.17 for the first year and 3.35 and 2.16 for the second year using two different abundance estimators for Gunnison sage‐grouse in San Miguel. Additional rigorous, empirical estimates of sex ratio are needed to improve our understanding of the natural range of variation in sex ratio among years and populations and to help biologists assess the reliability of population size and trend estimates based on male lek‐count data (Naugle and Walker 2007, McCaffery and Lukacs 2016).Genetic sampling using non‐invasively collected sources of DNA (e.g. fecal pellets or shed feathers) is a promising tool for conservation and management of wildlife species and can provide valuable information about sex ratio that cannot be obtained using traditional monitoring approaches (Schwartz et al. 2006). Genetic analysis of fecal samples collected non‐invasively in winter are more likely to provide more reliable estimates of pre‐breeding sex ratio for sage‐grouse than previous methods (Garton et al. 2011, Baumgardt et al. 2013). These analyses allow for reliable identification of the sex of sampled individuals (Taberlet et al. 1993), and genetic mark–recapture models using the resulting data can account for different detection probabilities of males and females. Estimates of population size and sex ratio in winter should also be closer to those at the start of the spring breeding season than estimates based on harvest because overwinter survival of sage‐grouse of both sexes is typically high (Beck et al. 2006, Batazzo 2007, Connelly et al. 2011). Additionally, although point estimates of the proportion of male and female sage‐grouse can be derived from abundance estimators provided by closed mark‐recapture models, those estimates typically fail to account for non‐independence of male and female proportions in the population and therefore lack measures of uncertainty, an important consideration in management applications.The primary objective of our study was to assess the validity of using a constant sex ratio assumption to extrapolate female or total sage‐grouse population size from male lek‐count data. We used genetic mark–recapture data from a recent two‐year study (Shyvers et al. 2020) in a peripheral, geographically isolated sage‐grouse population in northwestern Colorado to derive estimates of winter (i.e. pre‐breeding) sex ratio. We used mark‐recapture models that account for non‐independence of male and female proportions in the population, thereby allowing us to generate associated measures of uncertainty and evaluate the potential for annual variation in the sex ratio in the population.Study areaThe study area encompassed Colorado Parks and Wildlife's current occupied range boundary for sage‐grouse in the Parachute‐Piceance‐Roan (PPR) population in northwestern Colorado (Fig. 2), excluding two small, inaccessible areas of occupied range in the southwestern part of the population (Kimball Mountain and 4A Ridge). Birds in this population winter and breed in lower‐elevation portions of occupied range, then move upslope during summer and fall (Walker et al. 2016). The PPR population is small, representing only ~ 4% of Colorado's estimated total population, with a maximum high count across all leks of only 250 males from 2005 to 2022 (Colorado Parks and Wildlife unpubl.). The PPR is on the southern periphery of the species' range in Colorado and is geographically isolated from neighboring populations to the north by extensive pinyon‐juniper forests and the White River valley (CGSSC 2008). While the lekking male population is currently monitored annually using lek counts conducted by helicopter (Shyvers et al. 2018), there are currently no empirical estimates of sex ratio available with which to calculate female population size, total population size, or effective population size to inform monitoring, conservation, or management.2FigureStudy area boundary in the Parachute‐Piceance‐Roan greater sage‐grouse population in northwestern Colorado in winter 2012–2013 and winter 2013–2014 with underlying terrain and the north‐south region boundary used in analysis. Inset shows the location of the study area in relation to the species' current distribution (gray) in western North America (from Schroeder et al. 2004).Material and methodsWe used microsatellite analysis data from a genetic mark–recapture study on sage‐grouse (Shyvers et al. 2020). These data were based on 2357 genetic samples derived from non‐invasive stratified‐random and incidental sampling of fecal pellets (n = 2201) and shed feathers (n = 63) collected during two consecutive winters (2012–2013 and 2013–2014), as well as feathers from marked birds collected during captures (n = 91; Shyvers et al. 2020). Samples were collected across seven sampling occasions and 120 flock‐use sites in winter 2012–2013 and across eight occasions and 146 flock‐use sites in winter 2013–2014. The data represented 543 unique individual sage‐grouse, with 62 males and 173 females identified in winter 2012–2013, 154 males and 236 females identified in winter 2013–2014, and 82 individuals (21 males and 61 females) identified in both winters (Shyvers et al. 2020). Full details on field sampling and genetic analysis are presented in Shyvers et al. (2020) but briefly summarized here for clarity.Sample collectionField crews trapped sage‐grouse in spring‐fall of 2012 and 2013 in proportion to the amount of predicted winter/breeding habitat in each part of the study area with the aim of obtaining samples proportional to the number of breeding birds in each area. Adult and yearling males were marked with global positioning system (GPS) satellite transmitters and adult and yearling females with VHF necklace‐style transmitters equipped with mortality sensors. Marked birds were monitored daily (GPS) or every other week (VHF) to detect mortalities and emigration events. Feather samples were collected from all captured birds and used as the first mark‐recapture occasion each year.The study area was divided into 200 × 200 m plots with a spatially balanced random sample of 1000 candidate survey plots selected for genetic surveys using a reversed randomized quadrant‐recursive raster (RRQRR) algorithm (Theobald et al. 2007), with plots stratified by predicted relative intensity of winter/breeding use (Walker 2010). Sets of plots were surveyed using a rotating panel approach (McDonald 2003) to ensure surveys were spatially representative of the population and to maximize detection probability. Crews surveyed plots for evidence of use by sage‐grouse (e.g. tracks, pellets, feathers, or birds) from early November through mid‐March each year. Crews also surveyed additional sage‐grouse habitat on foot and with binoculars while traveling to and from random plots and assigned flock‐use sites sampled incidentally to the sampling occasion of the associated survey plot. When a flock‐use site was detected, crews searched the immediate area and collected genetic samples. Shyvers et al. (2020) extracted genetic data from samples and analyzed microsatellites to identify individual birds and generate capture histories for each individual. Sex of each bird was determined by amplifying a region of the CHD gene using primers 1237L and 1272H (Kahn et al. 1998) and confirmed by at least two amplifications of each sample. DNA extraction and amplification rates were generally high, with 100% of capture feathers successfully analyzed (having six or more loci with confirmed genotype scores), followed by fecal pellets (83%), and shed feathers (46%; Shyvers et al. 2020). See Shyvers et al. (2020) for detailed methods on genetic sample collection, extraction success, and microsatellite analysis.AnalysisTo account for covariances in detection probabilities between males and females, we fit ‘Closed Robust Design Multi‐State' models to mark‐recapture data from Shyvers et al. (2020) using the same time (t; sampling occasion) and group (g; sex)‐varying effects in model structures and the ‘Huggins' p and c' with state probabilities model in program MARK (White and Burnham 1999) (Table 2). We assigned sex as the ‘state'. We set p (initial detection probability) equal to c (subsequent capture probability) and refer to this variable as detection probability (p). We included a region effect to model heterogeneity in detection probabilities resulting from differences in winter access to the northern vs southern portions of the study area (Fig. 2; Shyvers et al. 2020). Mark‐recapture data used in the analysis are available in Shyvers (2023). These models allowed us to estimate Ω (the probability of being ‘male'), 1‐Ω (the probability of being ‘female'), and associated confidence intervals. To apply the robust design model based on data collected separately for each winter, we included a dummy primary occasion consisting of all zeros to represent second sampling seasons (Kendall et al. 2012) and fixed parameters for survival and Ω for the dummy season to zero. We used model averaging based on Akaike information criterion adjusted for small sample size (AICc; Burnham and Anderson 2002) values to obtain parameter estimates for Ω. We assumed no uncertainty in sex assignment of individuals given the methodology used to confirm sex of genetic samples (Shyvers et al. 2020). Detection probabilities across sampling occasions ranged from 0.100 to 0.326 (mean 0.201) in winter 2012–2013 and from 0.000 to 0.168 (mean 0.087) in winter 2013–2014 for males, and from 0.085 to 0.225 (mean 0.147) in winter 2012–2013 and from 0.000 to 0.167 (mean 0.086) in winter 2013–2014 for females (Shyvers et al. 2020).2 TableModel selection and results summary for Closed Robust Design Multi‐state Huggins' p and c with state probabilities mark‐recapture models used to estimate the proportion of male greater sage‐grouse (Ω) in the Parachute‐Piceance‐Roan population in northwestern in Colorado in winter 2012–2013 and winter 2013–2014. Model parameters varied by sex (group; g) and sampling occasion (time, t). All models set initial detection probability (p) equal to subsequent capture probability (c) and included an individual covariate for region of the study area (north or south) where birds were first detected. AICc = Akaike Information Criterion adjusted for small sample size, w = model weight, L = model likelihood, K = number of model parameters, Dev = deviance, and 95% CI = 95% confidence intervalModelAICcΔAICcwLKDevΩSE95% CIWinter 2012–2013p(g × t) + Region1892.790.000.661.00151861.450.2310.0290.178–0.293p(g + t) + Region1894.621.820.270.4091876.120.2320.0290.179–0.294p(t) + Region1897.304.510.070.1181880.900.2640.0290.211–0.324p(g) + Region1918.8426.050.000.0031912.770.2310.0300.178–0.294p(.) + Region1921.5428.750.000.0021917.510.2640.0290.211–0.183Winter 2013–2014p(t) + Region2602.810.000.731.0092584.450.3950.0250.348–0.444p(g + t) + Region2604.852.040.260.36102584.410.3900.0370.320–0.464p(g × t) + Region2615.4112.60<0.01<0.01172580.170.3890.0370.320–0.463p(.) + Region2996.78393.970.000.0022992.750.3950.0250.348–0.444p(g) + Region2998.76395.960.000.0032992.720.3890.0380.318–0.466Model assumptionsModel assumptions described in Shyvers et al. (2020) also apply to the closed‐population mark‐recapture models used in this study. These assumptions include: 1) demographic and geographic closure of the population during winter survey periods, 2) no unexplained (i.e. unmodeled) heterogeneity in overall detection probabilities of individual birds, 3) genotypes were correctly determined, and 4) differences in success rates for extracting genetic data from samples were the result of random differences in environmental conditions.Genetic mark–recapture estimates vs lek‐count indicesWe also quantified discrepancies between population size estimates obtained from genetic mark–recapture analysis vs abundance indices extrapolated from lek counts. To do this, we divided genetic mark–recapture estimates of abundance for males, females, and total population size in each year by male, female, and total abundance index values extrapolated from high male counts the following spring assuming a static 2:1 F:M sex ratio. We also compared index values against confidence intervals for genetic mark–recapture estimates.ResultsWe found the greatest support for a group × time interaction + region covariate model (p[g × t] + Region) in winter 2012–2013 (Table 2). That model received more than two times the support as the second‐ranked model and indicated that detection probability varied with sex and sampling occasion. In contrast, we found the greatest support for a time‐varying model (p[t] + Region) in winter 2013–2014 (Table 2). That model received nearly three times the support of the second‐ranked model and indicated that detection probability varied by sampling occasion but not by sex. In both years, the additive effects models (group + time, p[g + t] + Region) were the next best supported models, whereas other models received little or no support.Model‐averaged estimates of Ω, the proportion of males in the population, were lower in winter 2012–2013 (0.233, 95% confidence interval [CI] 0.179–0.298) than in winter 2013–2014 (0.393, 95% CI 0.339–0.451) (Table 3). Model‐averaged estimates of F:M sex ratio in winter 2012–2013 (3.29, 95% CI 2.36–4.59) were more than twice that in winter 2013–2014 (1.54, 95% CI 1.22–1.95), with an average of 2.42 for both winters combined.3 TableModel‐averaged estimates of pre‐breeding abundance (N^) for male, female, and total greater sage‐grouse (from Shyvers et al. 2020), proportion of males (Ω), proportion of females (1‐Ω), and sex ratio (F:M) for the Parachute‐Piceance‐Roan population in northwestern Colorado in winter 2012–2013 and winter 2013–2014. 95% CI = 95% confidence intervalN^ (95% CI)Ω (95% CI)1‐Ω (95% CI)Sex Ratio (F:M)Winter 2012–2013 Male78 (65–90) Female257 (222–292) Total335 (287–382)0.233 (0.179–0.298)0.767 (0.702–0.821)3.29 (2.36–4.59)Winter 2013–2014 Male293 (243–344) Female452 (384–520) Total745 (627–864)0.393 (0.339–0.451)0.607 (0.549–0.661)1.54 (1.22–1.95)There were significant discrepancies between genetic mark–recapture estimates from winter 2012–2013 and extrapolated abundance indices for spring 2013 (Fig. 3). Genetic mark–recapture estimates for males in winter 2012–2013 and winter 2013–2014 were 60% and 117% of high male counts in spring 2013 (129 males) and 2014 (250 males), respectively (Fig. 3, figure 5 in Shyvers et al. 2020). The high male count in spring 2013 fell outside the 95% CI for the genetic mark–recapture estimate for males in winter 2012–2013 (65–90 males). Genetic mark–recapture abundance estimates for females were 100% and 90% of extrapolated abundance indices for females in spring 2013 (258 females) and 2014 (500 females), respectively. Genetic mark–recapture estimates for total population size were 87% and 99% of total abundance indices in spring 2013 (387 birds) and 2014 (750 birds), respectively. The total abundance index in spring 2013 also fell outside the 95% CI for the genetic mark–recapture estimate for winter 2012–2013 (287–382 birds).3FigureComparison of estimates of male (green), female (orange), and total abundance (purple) from genetic mark–recapture (GMR) analysis (circles; with 95% CIs) for winter 2012–2013 and winter 2013–2014 vs abundance indices (squares) extrapolated from the high male count of males across leks in spring 2013 and 2014 assuming a 2:1 F:M sex ratio for greater sage‐grouse in the Parachute‐Piceance‐Roan population in northwestern Colorado. Index values for male and total abundance in spring 2013 were higher than 95% confidence intervals for GMR estimates for male and total population size, respectively, in winter 2012–2013.DiscussionOur study provides the first estimate of winter (i.e. pre‐breeding) sex ratio for any sage‐grouse population derived from population‐wide, largely non‐invasive genetic sampling and that also includes measures of uncertainty for those estimates. Our results demonstrate the potential for substantial annual variation in sex ratio in sage‐grouse populations, an important consideration when assessing long‐term population trends based on lek count data. Sex ratio variation is unlikely to be specific to small, isolated, or peripheral populations, indeed, substantial annual variation in sex ratio has been documented in large, core populations in fall as well (Hagen et al. 2018). Given our success using genetic mark–recapture methods in the PPR in winter, this approach may be feasible for non‐invasively estimating sex ratio in small, isolated populations of sage‐grouse across the species' range as well as in larger, core populations if logistics and funding allow.The pronounced difference we observed in sex ratio estimates between years illustrates the potential pitfalls of using a constant sex ratio assumption to make inferences about female or total population size in any given year from high male counts. Our findings also reinforce previous conclusions that assuming a constant sex ratio (e.g. 2.0, or any other fixed value) can be problematic for generating unbiased point estimates of population size from male lek‐count data (Walsh et al. 2004, CGSSC 2008). We found significant discrepancies between high male counts and genetic mark–recapture estimates in one of two years for males and in one of two years for total population size, but no discrepancy for females in either year. Although this may be by chance, it also raises the possibility that lek counts might be biased higher in years with higher sex ratios (e.g. if male attendance or inter‐lek movement is higher in those years), and vice versa, which conceivably could offset some of the bias introduced by variation in sex ratio when extrapolating female or total population size from high male counts. Nonetheless, using biased indices of abundance may be harmful from a conservation perspective, because the potential for population growth in polygynous species like sage‐grouse is largely a function of female, rather than male, abundance (Caswell 2001, Skalski et al. 2005). Therefore, estimates of female population size derived from lek‐count data using a constant sex ratio assumption may not accurately reflect a population's actual reproductive potential in any given year. Annual variation in sex ratio may also introduce an unknown level of bias in population trend estimates derived from lek‐count data (Garton et al. 2011, WAFWA 2015). Future analyses should model the effects of annual variation in breeding sex ratio as an additional source of uncertainty influencing the precision of trend estimates (Coates et al. 2019).As in previous studies on sage‐grouse, we observed a female‐biased sex ratio in both winters in our study. While this was expected, we also observed a substantial decrease in the sex ratio from one winter to the next and much greater variation between two consecutive winters than anticipated. It is possible that individual heterogeneity in detection probability could bias abundance estimates (Shyvers et al. 2020), and therefore also our estimates of sex ratio. However, such bias is unlikely to have caused the large decrease in sex ratio we observed. For example, the genetic mark–recapture abundance estimate for males in winter 2012–2013 (78, 95% CI: 65–90) was lower than the high male count across leks in 2013 (129), which indicates that genetic mark–recapture analysis may have underestimated male population size and subsequently, overestimated sex ratio that year. However, we suspect that lek counts in 2013 were inflated. In that year, almost all active leks were known and counted (Shyvers et al. 2018), the male population likely consisted of a high proportion of adults (which attend leks more regularly and are much more likely to be counted than yearlings; Fremgen et al. 2018), and males made extensive inter‐lek movements during the breeding season (Shyvers et al. 2020). Our sex ratio estimates were substantially different than breeding‐season estimates of 2.10 and 2.16 for Gunnison sage‐grouse over two consecutive years (Stiver 2007), but reanalysis of those data by Walsh et al. (2010) produced values (2.66 and 3.35) much closer to our estimate in winter 2012–2013 (3.29). Estimates of breeding‐season sex ratio for greater sage‐grouse using two different abundance estimators were 2.30 and 3.24 (Walsh 2002), with a third estimate of 2.44 following reanalysis (Walsh et al. 2010). Hagen et al. (2018) reported a range of annual variation in adult sex ratio over six years in northwestern Colorado (1.35–2.61) that was similar to the range we observed in the PPR over just two years. Collectively, our results and those of previous studies reinforce that winter (i.e. pre‐breeding) and breeding‐season sex ratios in sage‐grouse species can vary substantially among years and populations.The large decrease in sex ratio we observed in our study was accompanied by a large increase in population size (Shyvers et al. 2018, 2020). Female‐biased ratios in sage‐grouse are attributed to lower annual survival rates of males (Connelly et al. 2011), so populations with older age structure have higher sex ratios (i.e. more females per male; CGSSC 2008). We hypothesize that the large decrease in sex ratio we observed between years was due to a small population size with older age structure (i.e. mostly adults) in winter 2012–2013 combined with high reproductive success and juvenile survival in spring‐fall 2013. These factors combined produced a much larger population with substantially younger age structure (i.e. a much larger proportion of juveniles) in winter 2013–2014. While we lack demographic or habitat condition data needed to test this hypothesis, the large decrease in sex ratio coincided with a large increase in the estimated number of lekking males based on dual‐frame sampling (from 81 to 260 males) from 2013 to 2014 (Shyvers et al. 2018). Sage‐grouse have a ~ 1.0 sex ratio at hatch (Atamian and Sedinger 2010, Thompson 2012, Guttery et al. 2013). Juvenile males are expected to have lower summer‐fall survival in years with poor conditions for brood‐rearing (Wegge 1980, Swenson 1986, Hannon and Martin 2006, Apa et al. 2017), leading to a female‐skewed juvenile sex ratio in winter. However, in years with good conditions for nest success and brood‐rearing, juvenile survival of males and females should be similar (Swenson 1986), leading to a more equal sex ratio among young birds. Therefore, a small winter population with older age structure (and therefore, a higher than average sex ratio) that subsequently experiences high reproductive success and juvenile survival would see a large influx of approximately equal numbers of male and female recruits, and consequently, a much greater proportional increase in males and a more balanced sex ratio (i.e. closer to 1) the following winter. Conversely, years with poor conditions for nest and brood survival (e.g. drought) should cause higher mortality among juvenile males (Swenson 1986) and result in a greater proportional increase in females, and therefore an even more strongly female‐biased sex ratio, the following year. Results in Hagen et al. (2018) are consistent with this explanation, with the only large decrease in adult sex ratio (from 2.52 to 1.35) occurring after a year with high productivity (a juvenile/adult female ratio of 1.54) and the adult sex ratio slowly increasing (from 1.35 to 2.61) over three years of lower productivity (i.e. juvenile/adult female ratios of 0.90–1.10). Biologists should therefore consider the potential for substantial variation in winter (pre‐breeding) and breeding sex ratio depending on relative population size, age structure, reproductive success, and juvenile survival the previous year, and if possible, account for it in trend analyses.Our findings also call into question the practice of applying sex ratios derived from early fall harvest data to spring breeding populations. Mean estimates of sage‐grouse sex ratio ranged from 1.30 to 1.57 for all ages combined and from 1.33 to 2.69 for breeding‐age birds (i.e. adults and yearlings) in fall harvest data (Fig. 1, Table 1). Estimates from fall mark‐recovery analysis ranged from 1.27 to 1.88 for all ages combined and 1.35–2.61 for breeding‐age birds (Hagen et al. 2018). Although the range of values from harvest data were similar to our estimate of 1.54 in winter 2013–2014, our sex ratio estimate of 3.29 in winter 2012–2013 was well above any reported fall value (Fig. 1, Table 1). This discrepancy may arise because of post‐harvest mortality of males in October‐November, a period of relatively higher mortality that coincides with migration from fall to winter habitat (Wik 2002, Connelly et al. 2011). Some previous authors have reported sex ratio estimates based on harvest data only for adults or only for breeding‐age birds (Connelly et al. 2011, Braun et al. 2015) as an estimate of sex ratio during the previous breeding season. Limiting harvest data to breeding‐age birds (i.e. excluding data from juveniles) typically resulted in higher estimates of sex ratio (Fig. 1, Table 1), but it remains unclear whether such estimates are representative of those in spring due to the potential for sex‐specific differences in pre‐harvest (i.e. summer) mortality. For these reasons, we recommend that biologists use caution when applying sex ratios calculated from fall harvest data to extrapolate female or total abundance from male lek‐count data in spring.Acknowledgements – We thank C. L. Aldridge, K. R. Crooks, and J. H. Gammonley for advice, project support, and expertise and W. L. Kendall for assistance with mark‐recapture analysis. R. Y. Conrey, J. S. Ivan, D. Worthington, M. Patten, and three anonymous reviewers provided valuable comments that improved the manuscript. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Funding – Funding for this project came from ExxonMobil Corporation/XTO Energy, Colorado Parks and Wildlife, and Colorado State Univ. with additional project support from the U.S. Geological Survey.Permits – Use of animals in Shyvers et al. (2020) (from which mark‐recapture data were obtained for this study) was in accordance with approved animal care and use protocols (CPW Animal Care and Use Committee [ACUC] approval no. 07‐2011 and no. 08‐2012, USDA Registration no. 84‐R‐0045; with interagency approval from Colorado State Univ. Institutional ACUC) and in compliance with the Guidelines to the Use of Wild Birds in Research (Fair et al. 2010).Author contributionsJessica E. Shyvers: Conceptualization (equal); Data curation (lead); Formal analysis (equal); Funding acquisition (equal); Investigation (lead); Methodology (lead); Project administration (equal); Writing – original draft (lead); Writing – review and editing (equal). Brett L. Walker: Conceptualization (equal); Resources (equal); Supervision (supporting); Writing – original draft (supporting); Writing – review and editing (equal). Sara J. Oyler‐McCance: Methodology (supporting); Resources (equal); Writing – review and editing (supporting). Jennifer A. Fike: Methodology (supporting); Resources (equal); Writing – review and editing (supporting). Barry R. Noon: Conceptualization (equal); Funding acquisition (equal); Methodology (equal); Project administration (equal); Resources (equal); Supervision (lead); Writing – review and editing (equal).Transparent peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1002/wlb3.01085.Data availability statementData are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.nk98sf7xc (Shyvers et al. 2020).ReferencesAldridge, C. L., Nielsen, S. E., Beyer, H. L., Boyce, M. S., Connelly, J. W., Knick, S. T. and Schroeder, M. A. 2008. Range‐wide patterns of greater sage‐grouse persistence. – Divers. Distrib. 14: 983–994.Ancona, S., Denes, F. V., Kruger, O., Szekely, T. and Bessinger, S. R. 2017. Estimating adult sex ratios in nature. – Philos. Trans. R. Soc. B 372: 1–15.Apa, A. D., Thompson, T. R. and Reese, K. P. 2017. Juvenile greater sage‐grouse survival, movements, and recruitment in Colorado. – J. Wildl. 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Wildlife Biology – Wiley
Published: Jul 1, 2023
Keywords: Centrocercus urophasianus; genetic mark–recapture; lek count; microsatellite; noninvasive sampling; sex ratio
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