Rates of Ovulation and Reproductive Success Estimated from Hunter-Harvested Greater Sage-Grouse in Colorado

Nesting propensity has been defined as the probability that a reproductively mature female will attempt to breed in a given year (Etterson et al. 2011). It is an important component of population growth in avian species, particularly those that are long-lived (Souchay et al. 2014). Nesting propensity is a challenging reproductive rate to estimate because individual females that skip breeding attempts generally will not be represented in samples if they are absent from breeding areas where nesting is monitored (Sedinger et al. 2008). Studies marking females with tracking devices (e.g., very high frequency [VHF] transmitters) to monitor breeding activities may help reduce such bias but can be problematic for at least two reasons. First, if females with lower probabilities of breeding tend to occur away from breeding centers at higher rates, samples may be biased if they are collected only at these locations and not randomly across the landscape. Second, monitoring rates (i.e., the number of times females are located to assess nesting status per unit of time) should be expected to directly influence the number of documented nesting attempts because nests can be lost between monitoring events (Mayfield 1961, 1975). Moreover, nesting attempts can be particularly difficult to document during the preincubation (egg-laying) stage of nesting because females are generally on nests for short periods of time at this stage (Schroeder et al. 1999; Blomberg et al. 2015). Monitoring that begins late in the nesting season can also produce biased propensity rates if first nesting attempts are missed because not all females in a population will renest.

Modern quantitative methods using multistate models have been used to estimate breeding propensity rates of marked individuals when monitored both during and after the breeding season (e.g., Souchay et al. 2014). However, this type of data and monitoring may be limited for most species, and multistate models assume the state (i.e., nesting vs. not-nesting) is correctly assigned without error, which is usually unrealistic. Extensions of the multistate model can help obtain unbiased estimates of breeding propensity (Kendall et al. 2003) but require additional data that may not be readily collected because of the sampling effort required. The use of biological samples from hunter-harvested females in the absence of a mark–recapture study design can provide information to calculate estimates of breeding propensity for some avian taxa (Dalke et al. 1963). These biologically derived estimates should presumably be less biased if harvested females are randomly sampled from the population, but violations of this assumption may introduce bias into reproductive estimates derived from hunter-harvest data (e.g., Asmyhr et al. 2012). Nonetheless, even if random sampling is violated, these data still contain useful information for trend analyses (Hagen et al. 2018), or as a source for relative comparisons with other hunter-harvested reproductive metrics. They also can provide a useful comparison with bias-corrected estimates obtained from nest monitoring data.

Postovulatory follicles are the ovarian remains of mature follicles that have ovulated (i.e., formed eggs; Hannon 1981). The presence of postovulatory follicles in some avian families may persist for several months following egg-laying events, making it possible to directly determine whether a female attempted to breed. Postovulatory follicles in Galliformes have been used to estimate clutch size of ring-necked pheasants Phasianus colchicus (Meyer et al. 1947; Kabat et al. 1948), California quail Callipepla californica (Lewin 1963), greater sage-grouse Centrocercus urophasianus (Pyrah 1958; Dalke et al. 1963), and sooty grouse Dendragapus fuliginosus (Hannon et al. 1979; Hannon 1981). Hannon (1981) found the technique to be an unreliable measure of clutch size for sooty grouse if ovaries were examined >33 d post–egg-laying. This was due to a subset of postovulatory follicles regressing beyond the point that they could be correctly classified at the time of their examination, leading to false-negative classifications of follicles. However, Hannon (1981) found that reproductive effort (i.e., if a female attempted to lay ≥1 egg in a season) could be reliably estimated. Dalke et al. (1963) indicated that ovulated follicle counts of greater sage-grouse collected during the autumn hunting season in Idaho were inaccurate as a measure of the number of eggs laid but were useful in ascertaining differences in laying effort between yearlings and adults. Therefore, postovulatory follicles are assumed to be a reliable measure of whether a bird attempted to breed for at least two grouse species collected during the autumn hunting season but are unlikely to be a reliable source to estimate number of eggs produced in a season.

The greater sage-grouse (hereafter, sage-grouse) is a species of special conservation concern that occurs throughout the sagebrush Artemisia spp. biome of western North America. Declining populations have led to recent petitions to list sage-grouse as Threatened or Endangered (U.S Fish and Wildlife Service 2015) under the U.S. Endangered Species Act (ESA 1973, as amended). Reproductive rates in the species have been a source of interest to biologists because declining fecundity is thought to be a major contributor to declining populations (Aldridge and Brigham 2001; Holloran et al. 2005; Aldridge and Boyce 2007). Therefore, particular attention should be given to reproductive rates and how they are estimated. Taylor et al. (2012), in a comprehensive meta-analysis, estimated mean nesting propensity rates (termed ‘nest initiation' rates in the paper) of first nests to be 0.96 (95% CI = 0.94–0.97) for adults and 0.89 (95% CI = 0.87–0.91) for yearlings after correcting apparent estimates of nesting propensity to account for detection bias. However, the literature review from this analysis found many studies that reported apparent nesting propensity rates lower than 0.6 (Taylor et al. 2012 [Appendix A]), which suggests females in local populations may be severely constrained in breeding attempts. Alternatively, at least some of these studies may have suffered from a negative bias in the estimates due to undiscovered nesting attempts. Accurate estimates of nesting propensity rates in either case are important for assessments of population health, such as those conducted in a population viability analysis (e.g., Morris and Doak 2002).

We estimated reproductive rates using postovulatory follicles and wings collected from hunter-harvested female sage-grouse in the North Park population, located in north-central Colorado, USA. Our first objective was to estimate annual nesting propensity and nesting rates from postovulatory follicles and primary molt patterns (respectively) for yearling and adult female sage-grouse, and test whether these rates were related to variables such as seasonal weather (precipitation and temperature) and an index of population abundance (lek counts). Our expectation was that cumulative precipitation during the spring and early summer should positively relate to nesting propensity and nest success as a result of improved habitat conditions (e.g., higher abundance of forbs and insects; Crawford et al. 2004). Our second objective was to test relationships between our hunter-derived reproductive metrics, such as nest success and number of juveniles per female, to evaluate whether they contained predictive information. Our third objective was to compare our estimates of nesting propensity rates for the North Park population to apparent nesting propensity rates available from a relatively recent range-wide meta-analysis of sage-grouse vital rates.

North Park is in Jackson County, Colorado, due south of Wyoming (Figure 1). It is a high mountain basin dominated by sagebrush uplands with a native grass–forb understory and grass–sedge meadows converted for native hay production along stream courses, which were frequently associated with willows Salix spp. The area is dissected by numerous streams that flow north from higher mountains to the west, east, and south into the North Platte River, which exits North Park into Wyoming slightly northwest of the town of Walden. The elevation of the area is approximately 2,000 m. The area is cold and dry in winter and dry and warm in summer with a short growing season. Moisture that falls in winter as snow frequently does not melt until April and May. The area has been well-described by Beck (1977) and Emmons and Braun (1984). During the years of our study, the study area had a mean annual temperature of 3.2°C and mean annual cumulative precipitation of 23 cm (PRISM Climate Group 2015).

We contacted sage-grouse hunters in North Park, Jackson County, Colorado, during hunting seasons from 1975 through 1984 as they exited hunting areas, usually during the opening weekend of the sage-grouse hunting season, which typically occurred on the second Saturday in September. We collected a clipped wing from each body of harvested sage-grouse at hunter check stations, followed by marking each wing with a numbered tag. We initially examined gonads (in the case of breeding-age females) in place and categorized as either male or female. Additionally, we excised ovaries from a sample of those birds for which the body cavity had not been thoroughly cleaned. Not all birds retained ovarian material and not all birds could be internally examined because of lack of time. We placed excised ovaries into individual glass vials that contained Bouin's solution; we sealed each vial with a rubber stopper and marked it with the same number as on the tagged wing that we had collected from that bird. We transported all collected materials (wings and filled vials) to the Wildlife Research Center in Fort Collins, where wings were stored cold or frozen until analysis; filled vials were stored on shelves in the laboratory at room temperature.

We classified wings to sex (either male or female) and age-class (either juvenile, yearling, or adult) following methods described in Beck et al. (1975) and Braun and Schroeder (2015). Nearly all female sage-grouse still retained old primaries 10 and 9 and frequently primaries 8 and 7. Thus, they easily could be classified as yearlings or adults based on the appearance of primaries 10 and 9. We estimated nest success from molt of primaries for yearlings and adults following Braun (1984) and Braun and Schroeder (2015). This estimate is based on a delay in primary molt that occurs when a hen is incubating a nest until the early stages of brooding (Dalke et al. 1963). Therefore, we assumed hens that showed delay in molt progression of outer primaries 8–9 have successfully incubated a nest until hatch.

We classified ovaries in the laboratory during the same year they were collected by removing them from vials and washing in 70% ethyl alcohol, followed by viewing under a binocular dissecting scope for evidence of postovulatory follicles using the method of Hannon et al. (1979) and Hannon (1981). Postovulatory follicles appeared to have an indentation or rupture at the top (also referred to as tulip-shaped; Hannon 1981; Lindstrom et al. 2006). We distinguished postovulatory follicles from atretic follicles (regressing unovulated follicles) following Payne (1966) and Erpino (1969). We classified each ovary as having or not having ovulated follicles. We made no attempt to count the number of postovulatory follicles, although we frequently observed multiple follicles on many of the ovaries examined.

We estimated ovulation rates using postovulatory follicles. Nesting propensity rates should be equal to ovulation rates if all eggs produced are deposited in nests. Therefore, we use the term nesting propensity rate interchangeably with ovulation rate; however, we acknowledge that if not all eggs are deposited in nests, the rates may be slightly different. We classified postovulatory follicles as being either present (1) or absent (0) for harvested female sage-grouse and used this binary data as a response variable. We used logistic regression to model the 0s and 1s and treated age as a categorical predictor variable. We fit seasonal weather variables as covariates. We used counts of males displaying at leks as a proxy for sage-grouse abundance at our study site (described in Beck and Braun 1980; Emmons and Braun 1984). We took the maximum count recorded at each lek for each year and summed them to calculate the annual number of male sage-grouse in North Park, which we refer to as the grouse abundance index. We took these estimates to be minimums and only an approximate index of true abundance because lek attendance rates may vary by year (e.g., Blomberg et al. 2013; Wann et al. 2019), although lek counts have been shown to capture population dynamics (Fedy and Aldridge 2011; Monroe et al. 2016). We calculated seasonal climate variables for each year from monthly gridded (4-km² resolution) spatial climate data (PRISM Climate Group 2015). We extracted variables from climate pixels over a fixed 5,760-km² area spanning the study site using the sp (Pebesma and Bivand 2005) and raster (Hijmans 2019) packages and R program (R Core Team 2019). We calculated the average monthly temperature and the cumulative sum of monthly precipitation across all extracted pixels from February to April over this spatial extent for each year. We considered this period most likely to have weather conditions influencing female sage-grouse nesting propensity rates given that hens may be present on leks within this time period (Jenni and Hartzler 1978).

We evaluated covariates using a two-step modeling approach and an information-theoretic model selection criteria (Burnham and Anderson 2002). First, we constructed a model that contained an intercept only (i.e., null model), and compared the AIC_c score for this model with one that included age as a fixed effect. The intercept-only model had a ΔAIC_c value of 23 indicating the inclusion of age substantially improved the model, and we subsequently included age in each of the three covariate models compared using AIC_c and model weights (ω_i). Next, we fit three separate models, each containing one of the three covariates, and, along with an age-only model, compared their AIC_c. We did not test model structures that included interactions of age with the other covariates because the relationships explored were annual, and we had only a modest number of years from which to obtain estimates. Furthermore, we did not test additive or interactive relationships between the covariates because we were interested only in whether or not nesting propensity rates increased or decreased as a function of these covariates.

We used an approach that was identical to the aforementioned methods to model reproductive success, which was a measure of whether or not a female successfully hatched a clutch (i.e., nest success). However, for reproductive success, we defined the seasonal weather period as occurring from April to July because this was the period over which conditions were expected to influence nest success based on average timing of reproductive events (Schroeder et al. 1999). We classified females as either successful (1) or unsuccessful (0) based on primary molt patterns (Braun 1984; Braun and Schroeder 2015), and we tested whether age improved model fit over an intercept-only model. The intercept-only model had a ΔAIC_c value of 77, and subsequently included age in each of the three covariate models compared using AIC_c and model weights (w_i). Thus, our covariate analysis of both nesting propensity and nest success each produced a candidate model set with four models. For both nesting propensity and nest success, we report estimates for overall mean rates as well as age-specific rates produced from intercept-only models and models including age effects, respectively.

Our data set also provided information on the number of harvested chicks per harvested hen (i.e., chicks per hen) because juvenile wings were distinguishable from breeding-age females. Using generalized linear models, we examined four relationships between our annual estimates of reproduction based on hunter-harvested ovary and wing data, which were 1) chicks per female and nest success probability, 2) chicks per successful female and nest success probability, 3) nest success probability and nesting propensity rate, and 4) chicks per female and nesting propensity rate. We tested the first two relationships because we wanted to assess whether our classifications of nest success based on primary molt were likely to be informative. A positive relationship between these proportions would provide partial support for the use of this classification method because a season with poor nest survival would be unlikely to lead to large numbers of juveniles in the autumn population. We tested the second two relationships because of our interest in the influence nesting propensity rates may have on the average female reproductive success. We modeled the number of chicks per female for relationships (1), (2), and (4) by specifying the log of the number of total females, successful females, and total females, respectively, as offsets and specifying a Poisson distribution. These rates were not constrained between 0 and 1 because the number of chicks produced could be greater than the female breeding population (i.e., positive values >1). The number of successful females for relationship (3) was a proportion constrained between 0 and 1, which was modeled with a binomial distribution and logit link function. We calculated a variance based pseudo-R² (Zhang 2017) for each model as a measure of variance explained. We report the significance of the slope coefficients (α = 0.05) for each model. All generalized linear model modeling and statistic calculations were done using base packages in the R program, and the package ‘rsq' (Zhang 2018) was used for calculating pseudo-R². The electronic data file (Text S1) and R analysis script (Text S2) are available as Supplemental Material.

Here, p is the reported breeding propensity rate and n is the reported number of females monitored. We weighted the resulting SD of each study similarly to the mean of the nesting propensity rate, and calculated standard errors for each group of interest (i.e., yearlings, adults). We made no corrections to nesting propensity rates to account for nests that were not found (as done by Taylor et al. 2012), and the estimates from the literature represent apparent nesting propensity.

We examined ovaries from 941 female sage-grouse collected at hunter-check stations. This included 379 from yearlings and 562 from adults (Table 1). The reported estimates that follow are from intercept-only or univariate models. We estimated an average nesting propensity rate of 0.949 (95% CI = 0.933–0.961) across years and age classes, indicating most female sage-grouse produce at least one egg each breeding season (estimates produced from a intercept-only model). Nesting propensity rates were lower for yearlings (0.926, 95% CI = 0.895–0.948) than adults (0.964, 95% CI = 0.945–0.977) based on estimates produced from an age-only model (β_age = −0.773, SE = 0.300). Modeled relationships between nesting propensity rates and weather covariates indicated modest to moderate effects. Temperature was unrelated to nesting propensity rate, with a nearly neutral effect (β_temp = −0.085, SE = 0.130; Figure 2a), but cumulative spring precipitation was positively related to nesting propensity rate (β_precip = 0.007, SE = 0.003; Figure 2b). The grouse abundance index had a strong positive relationship with nesting propensity rates (β_index = 0.002, SE = 0.001; Figure 2c) and received all model support (w_i = 1; Table 2).

Wings from 2,684 female sage-grouse were collected at hunter-check stations. This included 1,143 from yearlings and 1,541 from adults (Table 3). The average reproductive rate across years and age classes produced from an intercept-only model was 0.497 (95% CI = 0.478–0.516). Reproductive rates were lower for yearlings (0.398, 95% CI = 0.370–0.427) than adults (0.571, 95% CI = 0.546–0.596; P < 0.001) based on estimates produced from an age-only model (β_age = −0.700, SE = 0.079, P < 0.001). Modeled relationships with seasonal weather covariates predicted that nest success was negatively related to average temperature over this same period (β_temp = −0.280, SE = 0.041; Figure 2d). In contrast, nest success was positively related to cumulative precipitation from April to July (β_precip = 0.003, SE = 0.001; Figure 2e). Nest success was unrelated to the grouse abundance index (β_index = −0.0001, SE = 0.0001; Figure 2f). Overall, the model containing a temperature effect received all model support (w_i = 1; Table 4).

Significant relationships were found in 3 of 4 of the models fit to the hunter-harvested reproduction data. The number of chicks per female increased as a function of nesting success probability (β_n.success = 1.778, P < 0.001, Figure 3a), and as a function nesting propensity (β_n.propensity = 1.219, P < 0.001, Figure 3b). The number of chicks per successful female declined as a function of nesting success probability (β_n.success = −0.367, P < 0.05, Figure 3c). Nesting success probability was unrelated to nesting propensity (β_n.propensity = −0.278, P = 0.7, Figure 3d).

We used apparent nesting propensity rate estimates from 22 published studies reported in Appendix A of Taylor et al. (2012) to calculate weighted means and variances (Table S1, Supplemental Material). The apparent nesting propensity estimate pooled across age classes was 0.882 (95% CI = 0.873–0.891), which was 7.1% lower than the estimate derived from postovulatory follicles. Yearling nesting propensity estimates were lower (0.798, 95% CI = 0.770–0.825) than adults (0.931, 95% CI = 0.922–0.940). These estimates were 10.9 and 2.5% lower than the postovulatory-follicle-derived estimates for yearling and adults, respectively.

We estimated nesting propensity rates from hunter harvested female sage-grouse. Adult female sage-grouse had higher ovulation rates than yearling females during a 10-y period in North Park, Colorado. Adults also were more consistent and less variable than yearlings in nesting propensity (Table 1). Furthermore, our estimates of nesting propensity were higher than those reported from the sage-grouse literature. We did not attempt to estimate the number of eggs laid, but the annual probability of laying at least one egg in a season was generally >90% during most years of our study. Other studies of ovulation rates of sage-grouse reported high proportions of both yearlings and adults that laid eggs (Pyrah 1958; Dalke et al. 1963). However, Pyrah (1958) did not provide data other than reporting >95% of the hens had evidence of egg laying from ovarian examination. Dalke et al. (1963) provided sample sizes but combined both age classes of hens. They did not provide sufficient data to closely compare yearlings and adults. Dalke et al. (1963) reported that counts of postovulatory follicles were unreliable for estimating the number of eggs laid. We agree with this assertion because postovulatory follicles that have regressed could be missed, and resulting estimates of the number of eggs laid would undoubtedly be negatively biased. We lacked data to test when all postovulatory follicles would, on average, have completely regressed.

Two important caveats exist for our use of postovulatory follicles. First, we used the term nesting propensity throughout the text because eggs produced were assumed to ultimately end up deposited in the nest of the egg-laying hen. However, nest parasitism (individuals depositing eggs in the nests of other females) has been observed in different species of Galliformes (Krakauer and Kimball 2009). We were unable to find records documenting observed parasitic behavior in sage-grouse, although unusually large clutches have been noted in two cases (see Schroeder et al. 1999), and a genetics study found that it occurred at a rate of 2.2% in an endangered population studied in Alberta (Bird et al. 2013). Thus, propensity rates derived from postovulatory follicles would be positively biased in the presence of nest parasitism. Second, if successful females are harvested at higher rates than unsuccessful females, estimates of nesting propensity produced from postovulatory follicles will be positively biased. Currently, we are unaware of studies that have directly estimated bias between harvest rates of successful and unsuccessful female sage-grouse. Reproductively successful female willow grouse Lagopus lagopus in Sweden were found to have increased exposure to harvest risk because broods were encountered at higher rates than single birds (Asmyhr et al. 2012). We cannot rule out this possibility in our population. However, our rates are very similar to recent studies, which used simulations or correction factors to obtain unbiased estimates of nesting propensity for sage-grouse (discussed below). Thus, if any bias does exists, we expect the magnitude to be low.

Taylor et al. (2012) completed a comprehensive literature review of reported vital rates for sage-grouse to parameterize an age-structured model used to conduct a life-stage simulation analysis (Wisdom et al. 2000). The vital rates used in the analysis included nesting propensity rates for first, second, and third nesting events. The authors recognized the inherent negative bias in the reported nesting propensity rates and used an ad hoc adjustment based on studies that reported apparent nest success. The ad hoc adjustment used the number of marked females (n), number of successful nests (s), and an estimate of true nest success (ψ) to correct apparent nesting propensity based on the mathematical relationship s/nψ. The resulting corrected nesting propensity estimates for first nest attempts were 0.89 (95% CI = 0.87–0.91) for yearlings, and 0.96 (95% CI = 0.94–0.97) for adults. These estimates and their associated error are within the ranges of our own estimates produced from postovulatory follicles, suggesting the North Park population of sage-grouse during the years of our study had similar nesting propensity rates to the mean of the populations range-wide. Blomberg et al. (2017) also recognized bias in nesting propensity rates calculated from radiomarked hens. The authors quantified this bias by conducting a simulation study based on different monitoring frequencies and nest survival rates (i.e., number of times a female marked sage-grouse was visited weekly and the daily probability of a nest failing). They found that as survival rates and monitoring frequencies increased, bias in estimates of nesting propensity decreased, and the magnitude in the bias was most sensitive to decreasing monitoring frequencies. This was predicted to lead to a 0.05 negative bias in estimated nesting propensity of first nests (pooled across age classes), reported as 0.87 (95% CI = 0.84–0.90), based on their monitoring frequency of 3 d between visits (Blomberg et al. 2017). Therefore, the corrected point estimate in this case would be 0.92, which was the same as our estimate for yearlings (0.92), but slightly less than our estimate for adults (0.96). Overall, our work, taken together with recent studies (Taylor et al. 2012; Blomberg et al. 2017), indicates sage-grouse initiate nests at rates higher than those reported in telemetry studies. We suggest caution for any studies reporting low apparent nesting propensity rates (e.g., <0.7), particularly if they are used for population viability analysis. Almost all studies reporting apparent nesting propensity rates also reported monitoring frequencies at a single value or range (e.g., “Twice weekly,” “1–7 days weekly”). Reporting the monitoring frequency (i.e., mean and SE) will help evaluate the potential bias in the estimate (Blomberg et al. 2017).

The drivers of annual variability in nesting propensity rates of the North Park sage-grouse population could only be examined through a regression approach. Our top-ranked model predicted a strong positive relationship between the number of males counted and the nesting propensity rate. Spring precipitation was also positively related to nesting propensity, while temperature was negatively related. We can only speculate as to any underlying mechanisms that may be responsible for the observed relationships between spring precipitation and temperatures on nesting propensity rates. Wann et al. (2019) found peak male lek attendance rates of sage-grouse studied in Nevada were greatest following high-precipitation winters. They suggested higher precipitation might lead to better environmental conditions for females to initiate nests and, by extension, more mating opportunities for males on leks. We suggest this may be a plausible mechanism. In contrast, Blomberg et al. (2017) found evidence that nesting propensity was density-dependent with lowest propensity rates occurring when lek counts were highest. These results differed from our own findings, suggesting the relationships may vary by site (or region).

Hunter-harvested wings provided useful information as to whether a hen was successful at nesting, and factors related to nest success. Our use of hunter-harvested wings to assign this information (i.e., if a nest survived at least to the late incubation stage) depends on delays in primary feather molt during the incubation period and has been used for other species of gamebirds in the past (e.g., banded and recaptured or reobserved female white-tailed ptarmigan Lagopus leucura;Braun 1969). The probability of female sage-grouse being classified as successful was found to negatively correlate with average monthly temperature and positively correlate with cumulative precipitation during April–July. These results are consistent with other sage-grouse studies that found drought conditions negatively affect nest success of females (Gibson et al. 2017), which may be due to reductions in vegetation that provide both food and cover for chicks (Bates et al. 2006). There was no relationship between the probability of being successful and counts of males on leks, suggesting that density-dependence did not influence this reproductive metric.

The lack of correlation between the probabilities of nest success and nesting propensity suggests nest success is unlikely to be influenced by density-dependence mechanisms in North Park. Alternatively, our samples may have been inadequate to detect the presence of density-dependence between these reproductive rates. We did not evaluate the sampling effort required to detect a theoretical relationship between the two rates. The presence of a significant relationship between the number of chicks per female and nesting success indicates that classifying the reproductive outcome based on primary molt has merit and is likely to contain information on the reproductive performance of a population that has been sampled. We acknowledge biases may exist with age ratios as an index to productivity in gamebird populations. Therefore, a comprehensive study pairing reproductive performance of sage-grouse through intensive monitoring (i.e., VHF- or Global Positioning System–marked females providing information on nest and brood survival) and hunter-harvest data (i.e., wing collection) would greatly enhance our understanding of bias in reproductive metrics derived from wings. Furthermore, if biases do exist, this is critical information for wildlife managers to understand, because killing the segment of the population contributing most to the production of young is undoubtedly undesirable.

The accurate estimation of nesting propensity rates in avian populations is important because it ultimately affects the proportion of females that can contribute to the following season's recruitment class of breeders and, as a result, is of considerable interest to population biologists (Reed et al. 2004). Reliance on apparent nesting propensity rates is likely to be negatively biased and may have important consequences if they are used in population viability analyses or any assessment of vital rate sensitivity. Wildlife managers working with hunted populations of sage-grouse may benefit from collecting ovaries from harvested females at check stations in addition to wings, if nesting propensity is of interest. Hunting restrictions may also become more common for sage-grouse populations given their declines and concerns about additive mortality (Gibson et al. 2011), potentially leading to fewer opportunities to use biological samples to estimate nesting propensity rates. Nonetheless, the collection of ovaries and wings from exploited sage-grouse populations can provide valuable information with respect to reproductive effort and productivity, as demonstrated through the positive relationships developed using reproductive metric data from the North Park population.

Please note: The Journal of Fish and Wildlife Management is not responsible for the content or functionality of any supplemental material. Queries should be directed to the corresponding author for the article.

Table S1. Excel Workbook containing the literature reports of apparent nesting propensity rates presented in Taylor et al. (2012) for greater sage-grouse Centrocercus urophasianus. Metadata are presented in the first worksheet, the original full listing of nesting propensity rates are presented in the second sheet, and the nesting propensity rates with removed records are presented in the third worksheet.

Found at DOI: https://doi.org/10.3996/072019-JFWM-063.S1 (52 KB XLSX).

Text S1. R data file used in the analysis of hunter-harvest data collected for greater sage-grouse Centrocercus urophasianus in North Park, Jackson County, Colorado. Data were obtained from hunter-killed grouse collected at check stations from 1975 to 1984.

Found at DOI: https://doi.org/10.3996/072019-JFWM-063.S2 (4 KB R).

Text S2. R script file containing code used to conduct the analysis of hunter-harvest data collected for greater sage-grouse Centrocercus urophasianus in North Park, Jackson County, Colorado. Data were obtained from hunter-killed grouse collected at check stations from 1975 to 1984. The script loads File S1, fits generalized linear models, and conducts model selection for best models explaining nesting propensity and nest success rates.

Found at DOI: https://doi.org/10.3996/072019-JFWM-063.S3 (20 KB R).

We thank the hunters who allowed us to examine and take biological specimens (a wing and gonads) from greater sage-grouse they harvested. We also appreciate the advice and review of our work by the late I. O. Buss during the initial stages (1976) analyzing ovaries collected at hunter check-stations. We thank the Colorado Division of Wildlife for funding through Pittman–Robertson Federal Aid to Wildlife Restoration Project W-37-R. John Severson provided helpful feedback on an earlier draft of this manuscript. We thank the Associate Editor and three anonymous reviewers for their help improving the quality and clarity of this work.

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