The following is the established format for referencing this article:Mathews, S. R., P. S. Coates, B. G. Prochazka, S. P. Espinosa, and D. J. Delehanty. 2022. Survival of translocated Columbian Sharp-tailed Grouse: recognizing trends in post-release mortality to improve reintroductions. Avian Conservation and Ecology 17(2):28.
ABSTRACTSurvival of translocated prairie-grouse (Tympanuchus spp.) is often low in the first few weeks or months following release into a novel environment. Understanding the processes and mechanisms that influence short-term, post-release survival, such as Allee effects (i.e., inverse density dependence), or individual characteristics of translocated individuals (e.g., age, sex) is imperative for designing successful translocation programs. Additionally, identifying timeframes during which translocated individuals are particularly vulnerable to mortality is especially useful for population management. We translocated 215 Columbian Sharp-tailed Grouse (T. phasianellus columbianus; CSTG) as a species reintroduction project to historical habitat in northeastern Nevada from 2013 to 2017 and quantified annual post-release survival probabilities during the 150-d breeding season. In a Bayesian framework, we first identified a prospective 40-d threshold in which the daily probability of survival substantially increased from before to after the threshold. Next, while accounting for the threshold effect at 40 d, we identified an age by population size interaction in which survival of yearling and adult CSTG changed across time. Yearlings exhibited substantially higher survival probabilities when the population at the release site was small (≤ 10 males on lek) during early translocation years, and adults exhibited substantially higher survival probabilities when the population at the release site was moderate or large (≥ 8 males on lek) during latter years of the translocation. This might represent inverse density dependent effects on survival wherein adult survival improved concomitantly with increases in population sizes. Short-term, post-release survival is less intuitive for yearlings, but yearlings greatly outperformed adults during the critical first three years of the reintroduction (i.e., the establishment phase) when the population at the release site was very small. In future reintroduction projects, establishing a population with translocated yearling CSTG followed by adults might hasten conservation goals.
Augmentation or reintroduction of prairie-grouse (Tympanuchus spp.) populations via translocation are not always successful in establishing stable populations (Snyder et al. 1999, Hoffman et al. 2015) due in part to high mortality of translocated individuals following release (Parker et al. 2012, Carrlson et al. 2014, Mathews et al. 2016). The potential for population restoration can be negatively affected by physiological chronic stress induced during the translocation process (Dickens et al. 2009a, 2010), social disruption, an unfamiliarity with the release location, and small population sizes (Armstrong and Seddon 2008). For these reasons, a better understanding of the effects of translocation on individual life stages is imperative for designing effective translocation programs (World Pheasant Association IUCN SSC Reintroduction Specialist Group 2009). Unfortunately, long-term studies of translocation efforts with translocated grouse (Tetraonidae) are difficult to obtain because of cost (Seddon et al. 2012, Dunham et al. 2016), potential impacts to source populations (Seddon and Armstrong 2016), and because translocated individuals often perish, disperse, or outlive the battery of their radio-tag (Parker et al. 2012).
Translocated prairie-grouse can exhibit high rates of mortality following release into a novel environment (Carrlson et al. 2014, Mathews et al. 2016). This can be problematic during conservation translocations because low post-release survival can reduce effective population sizes at the release site. During reintroductions and translocations, wherein the population at the release site is very small, reduced survival by translocated individuals can result in a high vulnerability to extirpation from demographic and environmental stochasticity, even during periods of favorable environmental conditions that would otherwise predict long-term persistence (Armstrong and Seddon 2008). High post-release survival is consistent across successful translocation programs whereas low post-release survival is consistent across translation programs that are considered failures (Parker et al. 2012). Indeed, the successful translocation of Black Grouse (Lyrurus tetrix) in England followed predator control methods (Warren and Baines 2018), and a primary cause of population failure of reintroduced Capercaillie (Tetrao urogallus) throughout Europe is the high rate of predation of captively reared individuals (Merta et al. 2016). It follows that the identification of distinct post-release phases in which survival probabilities change (i.e., thresholds in post-release survival; Mathews et al. 2016) can help maximize the effectiveness of management actions by highlighting critical periods of survival that can have long-term impacts on the viability of populations aided by translocation. Additionally, accurate predictions of post-release performance derived from characteristics of translocated individuals can improve the overall efficiency of translocation projects and minimize costs to source populations.
Columbian Sharp-tailed Grouse (Tympanuchus phasianellus columbianus; hereafter CSTG) were historically abundant throughout the Columbian drainage and the northern portions of the Great Basin, USA, but now occupy ≤ 10% of historic range (Miller and Graul 1980, Giesen and Connelly 1993). Columbian Sharp-tailed Grouse have been petitioned for listing under the Endangered Species Act of 1973 twice (USFWS 2000, 2006), and both petitions were denied in part because of the size and stability of the remaining metapopulations (J. Bart, unpublished manuscript). Remaining populations, especially those in eastern Idaho, have provided the source for numerous conservation translocations that date back to 1987 with estimated success rates of about 50% (Hoffman et al. 2015).
Despite numerous translocation efforts, the quantification of temporal and demographic characteristics predicting post-release survival of translocated CSTG has been relatively understudied. Mathews et al. (2016) reported a survival threshold at 50 d post release and a novel age effect in which yearling (≤ one year old) CSTG exhibited significantly higher survival than adults (> one year old) during the first two years of two distinct reintroduction projects. Although their findings were consistent across two independent reintroduction projects, they only included the first two years of post-release monitoring at both sites. With additional years of monitoring, long-term trends and more complex relationships can be tested.
In this study, we translocated CSTG from southeastern Idaho to northeastern Nevada as part of a species reintroduction project led by the Nevada Department of Wildlife (NDOW), representing one of the two sites monitored by Mathews et al. (2016). We sought to reassess those findings and test additional hypotheses that can be better analyzed using a longer-term dataset. Data for this analysis were restricted to the Bull Run Basin study area because the Snake Mountains study site investigated by Mathews et al. (2016) lacked resolution in relocation data beyond the initial two years of translocation, which were necessary given our updated goals. Especially germane to this study was the potential for covariates that predict survival to change as the population at the release site grew. That is, the covariates that predict survival during the first two years of a reintroduction are not necessarily the same as those that predict survival during the first five years of a reintroduction. The latter represents a reintroduction and augmentation of a critically small population, whereas covariates identified from the former represent those that are important for reintroduction and establishment of a population in unoccupied range. Columbian Sharp-tailed Grouse are social, lekking grouse and a prey species (Johnsgard 2008); short-term post-release survival may be impacted by population densities at the release site at the time of release, but this hypothesis can only be tested via long-term post-release monitoring programs.
At two reintroduction sites with two years of data per site, Mathews et al. (2016) estimated that survival of translocated CSTG increased substantially after about 50 d post release, effectively representing a “culling” of translocated individuals during the first seven weeks of their translocation projects. We first sought to reexamine the prospective temporal threshold in survival (50 d) identified by Mathews et al. (2016) with an additional three years of data at one site. We estimated survival of translocated CSTG during the first 150 d post release, representing the reproductive season and the period in which short-term translocation effects on survival are most likely to be expressed. Survival estimation during the first 150 d post release is especially important for managers because it is the breeding season, and newly hatched chicks likely out-perform their translocated parents, demographically (Mathews et al. 2021). Maximizing survival of translocated individuals during the breeding season can potentially increase the volume of reproductive output by the population and therefore provide additional recruitment of new individuals into a critically small population (Mathews et al. 2021). Identification of a survival threshold wherein survival probabilities change over time provides managers with distinct manageable phases in which more active or passive management can be applied to improve survival for higher risk groups of translocated CSTG. Secondly, we sought to examine the predictive covariates of sex, age, and population size (at the time of release) as fixed effects across five years of successive translocations. Mathews et al. (2016) detected an age effect wherein translocated yearling CSTG exhibited significantly higher survival prior and following the 50 d threshold than did translocated adults. We also sought to reexamine that finding in this study. Finally, because the most recent three years of the study (2015-2017) were more akin to an augmentation of a small population wherein leks at the release site were small to moderately sized (7 ≥ 19, 2015-2017) compared to the original study (2013-2014) when leks were nonexistent or small (0 ≥ 3, 2013-2014), we tested whether the detected effect of age was mediated by population size at time of release, representing a potential Allee effect (Allee and Bowen 1932).
Despite one continuous conservation project, survival estimates of translocated CSTG may vary across distinct phases of the translocation (i.e., reintroduction or augmentation). Potential changes in survival rates across distinct phases of a reintroduction can only be elucidated with rigorous post-release monitoring and with datasets that span several years. Our study builds upon previous findings with three additional years of monitoring and was designed to better inform future CSTG and prairie-grouse translocation projects.
Idaho State University Institutional Animal Care and Use Committee approved use of CSTG in this study, including the capture, handling, and monitoring of individuals, under protocol 717.
Capture, translocation, and post-release monitoring
We translocated 215 CSTG from 2013-2017. The reintroduction site was in the Bull Run Basin of the northern Independence Mountains of Elko County, Nevada, USA (elevation 1800-2500 m; Fig. 1). Several release sites were identified in this area; however, the Bull Run Basin release site was selected based upon its physiographic similarity as a grass/shrub steppe mixture to CSTG nesting habitat in southeastern Idaho (Coates et al. 2011). Columbian Sharp-tailed Grouse were captured on leks in April of each year using walk-in funnel traps (Schroeder and Braun 1991) and nighttime spotlighting techniques (Wakkinen et al. 1992). Sex and age at capture were recorded for each individual; age was assessed based on the wear patterns of primary remiges 9 and 10 (Ammann 1944), and sex was classified based on presence (male) or absence (female) of a well-developed supraorbital comb, presence (male) or absence (female) of a non-intromittent phallus, and by evaluation of feather pigmentation patterns of the crown and ventral aspect of the rectrices (Johnsgard 2008). Each CSTG was fitted with an individual size-12 aluminum leg band, and most individuals were outfitted with a necklace-style VHF radio-transmitter (16g, < 3% body mass; Advanced Telemetry Systems Inc., Isanti, MN, USA), which produced a mortality signal upon ≥ 8 hours of inactivity. After processing, individual CSTG were placed into wooden release boxes (modified from Musil 1989) and were transported to the release site where they were released the morning following capture. In the first year, CSTG were released via a modified hard-release method at an artificial lek (Rodgers 1992), but in all subsequent years, individuals were released at active nascent leks that naturally formed at the release site.
We tracked translocated CSTG using handheld (Advanced Telemetry Systems Inc., Isanti, MN, USA) and aerial telemetry every 3-10 d. On the ground, we circled individuals at a radius of 50-150 m and then estimated their location using a handheld GPS and compass. To avoid excessive disturbance of newly released CSTG, we did not seek to relocate individuals daily, but did monitor live or dead signals of all CSTG from a distance of ≥ 1.5 km on a daily basis from atop several high elevation locations throughout the study site. Although this allowed for us to record the live or dead status of every individual, it precluded accurate location data that could be used in behavior or habitat analyses. We attempted to recover all mortalities within 48 hours of detecting a mortality signal to confirm that death had occurred. In all years, we intensively monitored individuals for 150 d following their release, representing the nesting and brood rearing seasons from early April to early September. Grouse that survived beyond the 150-d window were not included in the analysis beyond their initial 150 d post release, or when the immediate effects of translocation on survival would be most apparent.
Lek counts were performed immediately prior to translocations each year and were conducted following established lek count protocols (Connelly et al. 2003). Because translocation dates were predicated on source population dynamics and source population lek attendance varied across years, the date of annual lek counts varied between 30 March and 8 April 2013-2017. Lek observations are imperfect and can be confounded by observer sightability (Coates et al. 2019) and attendance rates of males (Wann et al. 2019). Therefore, we counted leks multiple times each year and recognized the largest value as a population size index rather than a true abundance. We used the highest lek count of males prior to new translocations each year because we could not reliably discern all band-IDs and could not differentiate newly translocated males from residents during counts. Additionally, we performed extensive searches for new leks on an annual basis during the lekking period for confidence in counts as a population index.
Individual CSTG were scored as alive, dead, or missing during each sampling interval and were right censored when we did not observe the date of death. Censored individuals contribute to the analysis in the intervals in which their status was known but do not contribute when their status was unknown. We considered censoring to be a random process because it only occurred when: (1) the individual survived the study period, (2) the transmitter failed before we could observe death, or (3) individuals went missing and could not be relocated. We assumed that a missing (i.e., censored) individual had an equal probability of being alive or dead, and therefore censoring did not bias estimated daily survival rate (DSR) directionally. Thus, all individuals in the study either died or were right censored.
Columbian Sharp-tailed Grouse were released successively across 21 separate calendar dates within the month of April across the 5-year project. Therefore, calendar dates encompassing the 150-d post-release survival period varied among individuals. We used shared frailty models (Halstead et al. 2012) to estimate the daily hazard rate encountered by translocated CSTG, which we then used to calculate the DSR. All individuals were assigned either two or three dates when developing their encounter histories depending on their survival status, and all dates were assigned relative to the individual’s release date. For all grouse, the dates included the date that an individual was released at the reintroduction site and the last date when it was known to be alive. For mortalities we also recorded the first date that a mortality signal was observed. Within the shared frailty model framework, survival was assigned to all days within the window when translocated CSTG were known to be alive, and an unknown status was assigned to all days of unknown survival. Columbian Sharp-tailed Grouse of unknown survival status that were confirmed to be dead in a later interval were not excluded from the analysis unless their mortality date was observed > 150-d post release, in which case they were censored for all periods of unknown survival. Although inclusion of individuals that were missing for extended periods of time before being found dead increases uncertainty in the DSR, all individuals had to be found dead within our 150-d breeding season to be retained in the model. We believed this to be acceptable given the common behavior of translocated galliform birds to regularly disappear following release into a novel environment (Snyder et al. 1999, Dickens et al. 2009b, Vogel et al. 2015).
Our null model was a 150-d intercept only model in which no time or descriptive covariates were used, representing a biological hypothesis that there is no change in DSR of translocated CSTG through the first 150-d post release. We used posterior predictive p-values (PPP; Link and Barker 2010, Gelman 2013) to evaluate the fit of our null model. Posterior predictive p-values assess how well simulated data resemble observed data given the model considered (Conn et al. 2018) and are interpreted as the probability that a hypothetical replicate posterior distribution will be larger than the currently estimated posterior distribution if the model were analyzed again (Conn et al. 2018). In this exploration, a PPP-value near 0.5 would indicate that the 150-d null model is adequate for survival estimation (Gelman 2013).
After determining that the null model produced reasonable survival estimates and fit the data well using a PPP-value, we used a two-step approach to improve the model and evaluate temporal and individual factors that influence survival. The first step identified temporal effects on survival irrespective of individual covariates. Then, while accounting for temporal effects identified in step one, we sought to assess the additive effects of grouse age, sex, and population size at time of release (step two). Modeling was completed via Bayesian Markov Chain Monte Carlo (MCMC) simulations in program JAGS (Plummer 2017) implemented through program R (R Core Team 2017) and the rjags package (Plummer 2016). We required that all chains converge with a Gelman statistic ≤ 1.1 (Gelman et al. 2003) for results to be retained, and we visually inspected autocorrelation plots of the model chains to check for autocorrelation between model iterations. All models were evaluated using three Markov Chains of 50,000 iterations following a burn in of 50,000, with a thinning rate of 10.
Step one, temporal effects
In step one, we assessed evidence of a temporal effect in survival probabilities during the first 150 d following release into a novel environment. Biologically, we hypothesized that the daily hazard would diminish progressively following release, but the change in hazard rates might not be constant across time. That is, we hypothesized that factors influencing high post-release mortality (e.g., individual responses to physiological chronic stress, reconnaissance of a novel site, habitat changes through time, etc.) may change some time after release. Our approach led to the development of 10 temporal hypotheses, similar to Troy et al. (2013) and Mathews et al. (2016), designed specifically to inform future translocation programs. Seven threshold models consisted of two estimated daily hazard rates; one daily hazard rate prior and one after a prospective threshold date, with a different threshold date per model. We evaluated evidence for these prospective temporal thresholds by examining the posterior distribution of the DSR prior- and post-threshold within each model, and we defined the best supported threshold as the model with the lowest widely applicable information criterion (WAIC; Watanabe 2010). For generalized linear mixed models (GLMM), WAIC is asymptotically equivalent to Akaike information criterion (AIC; Akaike 1973, Watanabe 2013) and represents a fully Bayesian information criterion (Watanabe 2010). Essentially, WAIC can be interpreted similarly to AIC, and this value represents a measure of the predictive loss per model within a relative model set (Watanabe 2010). Our first model evaluated a prospective threshold in DSR at 10 d post release. The model consisted of 2 survival estimates: 1 estimate constrained across the first 10 d time interval (days 1-10) and the second estimate constrained across the remaining 140 d (days 11-150). Creating additional thresholds, we then evaluated 6 additional models that systematically evaluated prospective temporal thresholds occurring at 20, 30, 40, 50, 60, and 90 d following release (Table 1). Additionally, we included three models that represented seasonal and time-dependent hypotheses. The seasonal model represented the hypothesis that vegetation changes across the study period can explain post-release survival patterns and that those vegetation patterns can be indirectly indexed by season. The seasonal model estimated 3 DSR’s constrained across 3 equal 50-d seasons representing spring (days 1-50 post release; early April through May), early summer (days 51-100 post release; June through late July), and late summer (days 101-150; late July through early September). We included a linear model that, rather than constrain survival probabilities across blocked days post release, we multiplied a continuous fixed-effect variable with the maximum number of days an individual was known to be alive post release. That is, for every individual, we calculated the number of known days alive as the number of days between their release and their last known alive signal, then multiplied that number with a fixed effect to represent the hypothesis that post-release survival improves in a linear fashion following translocation release. Finally, we included a weekly-time-dependent model in which we estimated an independent DSR for each week of the 150-d post-release window. This final model represented the hypothesis that researcher efforts or extreme environmental events could explain observed post-release survival patterns that would be evidenced as specific weeks with extremely high or low survival. We compared our 10 candidate models against the null (intercept only) model and retained the single candidate model with the lowest WAIC score. In step two, we included the top performing model identified in step one as the null and therefore made additional inference relative to the top performing threshold model identified in step one.
In all models, subscripts indicate indices and brackets indicate vectors of data. The daily (unit) hazard (UH; Halstead et al. 2012) for each prospective model was specified as:
where the UH of individual i on day j, was a function of the baseline, constant log hazard (α), and the log hazard ratio (βthresh), which described a temporal threshold effect. The temporal threshold parameter was multiplied by a time-varying, dummy variable (x[ij]) which took on a value of 0 or 1 (threshold models), 1, 2, or 3 (seasonal model), or 1-22 (weekly model; number of weeks in 150-d post-release period). In the linear model, (x[ij]) represented the known number of days that each individual lived within our study, 1-150. The final component, γyear[j] was the addition of a random effect of year. Prior distributions for the intercept (α) and threshold effect (βthresh) followed a “uniform” distribution between -100 and 0, and a “normal” distribution with a mean of 0 and standard deviation of 30, respectively. The random effect (γyear[j]) is an intercept offset by year, and it followed a normal distribution with mean 0 and standard deviation σ, which was “normally” distributed with a mean of 0 and standard deviation of 10, respectively.
The cumulative hazard per individual (CHi) is the sum of the UHi over a period (1:j), and was estimated as:
where j was any number of days, 1-150. The CHi was used to calculate the survival function (Si), which took the form:
The DSR was estimated when j = 1, and the 150-d cumulative survival rate was derived from the multiplication of the cumulative survival rates of the pre- and post-threshold periods. The null (intercept only) model represented a daily index in which all days were coded as 1, and the cumulative survival rate was derived when j = 150.
Step two, covariate effects
In step two, we evaluated the extent to which demographic characteristics (i.e., age, sex) or population size index (i.e., lek count at time of release) explained DSRs (Table 2). Age and sex both represented categorical dummy variables in which yearlings and males were assigned values of 0, and adults and females were assigned values of 1. The index of population size was an integer representing the maximum lek count of the year prior to the start of translocations on an annual basis. Because CSTG were released across several successive days and because we did not perform daily lek counts during translocation releases, we used the same index value of population size for every individual released per year, i.e., the maximum lek count that year prior to translocations. This resulted in variation stemming from the fixed effects of population size and year to be difficult to distinguish. Mathews et al. (2016) found no evidence for a fixed year effect, and we therefore instead evaluated the a priori hypothesis that an age effect was potentially modified by density; we did not test models with a fixed effect covariate of year. Applying the most supported model from step one to all models in step two, we systematically tested multivariate additive models including one or more descriptive covariates. The largest and most complex model (i.e., the age by population size interaction) was written as:
where βthresh × x[ij] is the threshold identified in step one; βadult × xadult[ij] is the effect of an adult on the intercept (e.g., rather than yearling); βadult.count × (xadult[ij] × xcount[j]) represents the effect of population size on survival of adults and yearlings; and γyear[j] represents the random effect of year. Priors for the intercept, all fixed effects, and the random effect were identical to those in Equation 1. All other models tested in step two were derivations of Equation 4, with fewer terms, and all models in step two included the threshold effect identified in step one. Because there were no univariate models in step two, the null model was recognized as the prospective temporal threshold model identified in step one (Eqn. 1) with no additional covariates.
All models in both steps were evaluated via WAIC, and the model with the lowest score was considered to be the most explanatory (Watanabe 2010). Additionally, we calculated the 95% credible interval (CRI) of the posterior distribution of modeled parameters to quantify evidence for covariate effects. Specifically, effects with posterior distributions for which the 95% CRI did not include 0 were considered substantial, and effects for which the 85% CRI did not include 0 were considered supported.
We translocated 215 CSTG from 2013 to 2017 but only radio-tagged and tracked 175 (n = 134 F; n = 41 M); we observed 80 mortalities. The median DSR of the null model in step one across all years was 0.995 (95 CRI = 0.994-0.996; Table 1). The 150-d, breeding season survival estimate under the null model was 0.500 (95% CRI = 0.420-0.579), and the PPP of the 150-d null model was 0.67, indicating adequate fit of the null model to the data.
Of the 10 temporal models evaluated in step one, the 40-d threshold model displayed the lowest WAIC score, and all but one temporal delineation performed better than the null model (Table 1). Under the 40-d threshold model, the median daily survival probability during the first 40 d was 0.992 (95% CRI = 0.989-0.995; Table 1), which increased to 0.997 (95% CRI = 0.995-0.998; Table 1) during the post-threshold period (d 41-150; Fig. 2), representing 0.5% increase in the daily survival probability. If translocated CSTG exhibited the pre-threshold daily estimate of survival (0.992) for the entire 150-d breeding season, their cumulative survival probability would be 0.30 (95% CRI = 0.19-0.47). Conversely, if translocated CSTG exhibited the daily survival estimate during the post-threshold period (0.997) for the entire 150-d breeding season, their cumulative survival probability would be 0.64 (95% CRI = 0.47-0.74) or 113% higher. Combined, the total survival probability across the 150-d breeding season averaged across years was 0.51 (95% CRI = 0.37-0.65), which was slightly higher than the 150-d null model.
One lek formed in the release site at the exact release location during the reintroduction effort. The population size increased on an annual basis, ranging from 0 male CSTG observed on lek in 2013, to 19 male CSTG observed on lek in 2017. Counts from 2013-2017 were: 0, 3, 7, 11, 19, respectively. In step two, an age-population size interaction exhibited the lowest WAIC score of any model (Table 2; Fig. 3). In that model, the 95% CRI of the βadult for individual age covariate did not overlap 0 (median estimate 1.60, 95% CRI = 0.81-2.47), while the 90% CRI for βadult.count × (xadult × xcount) of yearlings did not overlap 0 with a median of 0.12 (90% CRI = 0.01-0.24; Table 2). The βadult.count × (xadult × xcount) of the adult age class indicated slight to no evidence of an effect with a median of -0.05 and an 85% CRI that included 0 (85% CRI = -0.14-0.04; Table 2). This model indicated that yearling CSTG survival decreased with an increasing index of population size. Predictions from this model indicate that adult CSTG survival of ≥ 0.5 was achieved at population sizes of ≥ 8 males on lek, but yearling CSTG survival dropped below 0.5 when lek sizes increased to ≥ 10 (Fig. 4). The model with the second lowest WAIC score was the single variable additive model of individual age (Table 2). None of the models that included a term for sex performed better than the null (Table 2).
Galliform birds often exhibit a period of elevated mortality following translocation and release into a novel environment (Scott et al. 2012, Troy et al. 2013, Kallioniemi et al. 2015), which is apparent in the short-term survival estimates of translocated lekking Greater Sage-Grouse (Centrocercus urophasianus; Musil et al. 1993, Baxter et al. 2008, Ebenhoch et al. 2019) and prairie-grouse (Carrlson et al. 2014). Many conservation projects recognize this pattern, but few have quantified its duration into distinct manageable phases (Mathews et al. 2016). Using 175 radio-marked CSTG and 80 confirmed mortalities across 5 years, we observed a temporal threshold in estimated survival rates occurring at approximately 40 d following release. Columbian Sharp-tailed Grouse that survived the first 40 d following release subsequently survived at a higher rate during the remaining 110 d of the breeding season. Future translocations of CSTG might be improved by adjusting to these translocation dynamics by adjusting translocation quotas to account for elevated mortality during the first 40 d post release.
Sharp-tailed grouse are philopatric to breeding areas (Meints et al. 1991, Drummer et al. 2011), which they potentially search for following release in a novel environment, sometimes perishing in the process (Dickens et al. 2009b). To gain familiarity with a novel environment, translocated individuals likely engage in risky exploratory behavior (Coates et al. 2006, Dickens et al. 2009b, Ebenhoch et al. 2019). Moreover, translocation induces an unavoidable physiological state of chronic stress upon translocated galliform birds that may take weeks or months to recover from (Dickens et al. 2009a, 2010). For these reasons, mortality rates of translocated individuals might be expected to be atypically high following translocation. Despite this expectation, an assumption of most translocation projects is that high probabilities of mortality among translocated individuals will diminish progressively through time. Assuming that individuals vary in their inherent capacity to manage the challenges of translocation, the most vulnerable individuals will tend to perish rapidly upon release, resulting in a temporal threshold in survival (Mathews et al. 2016). In our study, the 40-d threshold garnered the most support, but was not the clear top-performing model. That is, the 20-d and 30-d model were both competitive (∆WAIC < 2) but exhibited numerically larger WAIC scores. The 40-d threshold identified here may be specific to our system or our study site and is not a rigid demarcation. Regardless, between 20 and 40 d post release, our results demonstrated a period of elevated mortality after translocation and release followed by improved survival. Our data support this threshold at about 40 d, or 6 weeks post release, which can be used as a conservative estimate in future CSTG translocation projects. All grouse were released in the month of April under springtime conditions and post-release survival times were relativized per translocated grouse, not per calendar date, so the 40-d threshold identified here holds true across several calendar weeks during which releases actually occurred. Recognition of this mortality threshold can improve management of reintroduced and translocated populations by providing a timetable for active management that ameliorates mortality factors such as predator management or temporarily excluding people and other animals from the release area (among other potential options). This process is likely generalizable for other CSTG translocation projects and potentially for other communal lekking species in North America, although the 40-d mark may vary across species or study.
Mathews et al. (2016) performed a similar study to ours but only with the first two years of survival data from this population whereas the current study utilized an additional three years of post-release monitoring. We extended on that study with novel data in an effort to test more complex relationships reflecting short-term survival probabilities during the reintroduction, formation, and augmentation of a new population of CSTG in Nevada. In this regard, we measured survival probabilities during a true reintroduction project and during an augmentation of a small population, potentially representing different responses by translocated CSTG. Understanding the effects of small population sizes on behavior and demographic rates of any population has long been a concern for wildlife managers (e.g., Caughley 1994), and well-designed translocation projects like ours (led by Nevada Department of Wildlife) can be valuable opportunities to study relationships that might otherwise be confounded in small populations.
In this study, we observed a substantial age by population size interactive effect wherein yearling CSTG survived at a higher rate than adults, but only at small population sizes (≤ 10 observed males on lek). Although our lek counts were not absolute, we did perform intensive searches each year for new leks prior to annual translocations. By this measure, our counts are likely a reliable index for the relative population size. When populations were larger at the time of release during the final two years (2016-2017), the age effect appeared to reverse, and adults exhibited higher survival than yearlings. Interestingly, survival of adult CSTG increased on a near annual basis, a trend that could only be elucidated through a long-term monitoring program. We hypothesize that survival of translocated adult CSTG is affected by density dependence. This is biologically intuitive because adult CSTG are philopatric to leks (whereas yearlings have not attended lek before and presumably have not yet developed philopatry to lek sites) and might seek out larger leks compared with smaller ones when displaced from their native landscape. If true, managers can help prevent population extirpation by addressing population declines prior to population sizes becoming small enough to impact adult survival. In our study, adult survival improved to ≥ 0.5 when lek sizes reached a minimum of 8 individuals prior to release. Generally, social prey species are more vulnerable to Allee effects associated with predation when at low densities (Gascoigne and Lipcius 2004, Martin et al. 2017). In our study, low-density populations at the release site potentially prevented adults from finding, forming, or remaining in large enough flocks to protect against predation. If true, this effect would be expected to become benign as the population size at the release site increased. Indeed, this prediction matched our estimates in which adults exhibited higher probabilities on a near-annual basis, concomitantly with an increase in lek counts. Additional support for this hypothesis might be found in future studies if age effects are absent in translocation augmentations in which the population at the release site is relatively large prior to release. Furthermore, the timing of natal dispersal by sharp-tailed grouse is unknown (Robb and Schroeder 2012, McNew et al. 2017), but closely related Lesser Prairie-chickens (Tympanuchus pallidicinctus) display bimodal dispersal times occurring in both autumn and spring seasons with much larger spring dispersal distances (Pitman et al. 2006). If yearling sharp-tailed grouse display similar dispersal strategies, they may be relatively unaffected by density effects on survival and translocation might serve as the emigration that yearling CSTG would otherwise normally exhibit, resulting in an age-mediated Allee effect for adults. Unfortunately, behavior analyses per age class and year to test an age-mediated behavior hypothesis was beyond the scope of our data, but this topic warrants investigation in future studies.
Our results indicated a density effect on the survival of translocated yearling CSTG, wherein yearlings exhibited substantially worse survival when population sizes were larger, but we did not recognize biological reality in this result. Yearling CSTG survival declined by ~ 40% from 2015 to 2016 when the population size index increased by only 4 males (i.e., 7 to 11 males on lek). When the population was critically small and presumably not near carrying capacity, we do not believe that an increase of the population index of only four males is a predictable cause of yearling CSTG mortality. Instead, other hypotheses warrant further investigation as to the trends observed here. For example, in the years with highest yearling CSTG survival, we translocated a smaller adult-to-yearling ratio (1.2:1 translocated adults per translocated yearlings, years 2013-2015), than in years with poor yearling CSTG survival (2.5:1 translocated adults per translocated yearling, years 2016-2017). This potentially indicated that there were fewer yearlings available for capture at the source population in later years, or that capture efforts were misaligned with lek visitation by females in the source population (e.g., Coates and Delehanty 2006). Among other possible explanations, fewer yearling CSTG available for capture and translocation might indicate that the few captured yearlings that were translocated in later years were in worse condition than in previous years when they were abundant. Blomberg et al. (2014) documented that survival of juvenile and yearling Greater Sage-Grouse was affected by drought conditions experienced during the post-fledging period. It follows that juvenile grouse of several species are likely more vulnerable to harsh environmental conditions than are adults, and investigation of various drivers of survival probabilities across translocated age groups might warrant future effort. Testing these hypotheses were beyond the scope of this study, but potentially warrant investigation in the future.
We observed survival probabilities of translocated CSTG age classes that appear to behave in opposite fashion and follow a near-annual linear trend. Because of this, correlation can be found from almost any predictor variable that also follows a linear trend (e.g., an annual increase in lek counts). Additionally, the potential variable of “year” and the variable of population size were completely confounded in that the population size only changed on an annual basis, and therefore we cannot fully state that our results differ from simple annual fluctuations. However, we do not believe that an annual year effect is explanatory given the linear trend observed in probabilities. Additionally, we included year as a random effect in our second modeling step to help account for unexplained variation across time. In future translocations, disentangling the effects of translocation, annual demographic stochasticity, and predictor variables will be difficult without a reference group (i.e., non-translocated CSTG) for comparison. Without a reference group of non-translocated individuals, we cannot fully state that our results are different from non-translocated populations. Regardless, we feel that it is unlikely that translocation effects did not influence survival probabilities because our results are similar to previous studies (e.g., Carlson et al. 2014, Mathews et al. 2016, Ebenhoch et al. 2019), and because translocated CSTG in our study exhibited extremely low survival probabilities compared to non-translocated populations reported in the literature (e.g., Milligan et al. 2020) despite high-quality habitat at the release site (Coates et al. 2011).
Our results potentially indicate inverse density-dependent effects on a specific vital rate (i.e., survival) of a translocated population of grouse. Although we were unable to fully test this hypothesis without a reference group, our studied population was small and exhibited substantially lower survival probabilities than non-translocated STG during the breeding season (e.g., Milligan et al. 2020). Because our primary goal was one of conservation, we were cautious when relocating CSTG post release and therefore lacked sufficient spatial relocation resolution to incorporate behavior or habitat variables during the first 40-d post release into our analysis. With the advent of newer, smaller GPS devices, a rigorous analysis of post-release movement, behavior differences by age class and population size at time of release, and habitat selection (e.g., Picardi et al. 2021) could be interesting investigations in future translocation projects. These findings highlight the importance of long-term monitoring programs when evaluating translocated and reintroduced populations. We identified important relationships in post-release survival only after five years, building upon simpler analyses already finished but lacked long-term data (i.e., Mathews et al. 2016). Future translocation studies will likely benefit from equally long or longer monitoring programs with rigorous post-release data collection protocols designed specifically to test complex hypotheses like these.
Our results indicate that translocated CSTG are especially vulnerable to mortality during the first 40 d following release, probably for several ecological and social reasons as well as from physiological impairment resulting from chronic stress following capture and translocation. A better understanding of avian recovery time from chronic stress resulting from the translocation process would be especially valuable in understanding the post-release threshold in survival. It follows that minimizing translocation stressors and releasing individuals into high quality reproductive habitat with conspecifics (Coates et al. 2006) are reasonable and intuitive measures. Future studies that consider deploying GPS tracking devices on a subset of individuals should also consider that rump-mounted GPS devices increased the mortality risk of Sage-Grouse (Severson et al. 2019), and trade-offs between fine-scale movement patterns and survival should be carefully considered before deploying GPS units on translocated CSTG.
Translocation projects that are conducted across many years and can transition from a reintroduction to an augmentation could potentially benefit from a lesser degree of exploratory behavior by translocated individuals in later years, likely resulting in higher survival of translocated individuals, a larger effective population size, and a better “return on investment” for wildlife managers. For these reasons, translocations conducted across many years are more likely to yield successful outcomes. Identification of declining populations and augmentation while populations are small, rather than after extirpation, could shorten the number of years needed for translocation to achieve population restoration.
Overall, these results indicate a period of elevated vulnerability during the first 40 d following translocation in all years, but that age effects are correlated with population size at the time of the release, as indexed by lek counts. The identification of age or sex classes that are poor candidates for translocation would help inform managers in future translocation projects. Unfortunately, our methods do not allow for the distinction between age-mediated Allee effects or behavior hypotheses because our telemetry relocation data were too coarse for fine-scale, post-release movement analyses during the initial post-release period. Regardless, our data suggest that yearlings are superior candidates for the initial years of reintroduction, agreeing with results by Mathews et al. (2016). We base this on higher yearling survival probabilities before and after the critical 40-d post-release threshold relative to adults in the first three years of the study. Targeting yearlings during the initial years of a reintroduction carries an additional benefit of leaving high quality adult female breeders within the source population where they will likely perform better, demographically, than if translocated. In our analysis, a probability of survival of 0.5 for adults during the 150-d reproductive season was achieved at a minimum lek size of 8 individuals displaying on lek at the time of translocation release (Fig. 4). Below this level, managers might consider focusing on yearling female CSTG, which were not affected by small population sizes at the release site. Additionally, this value might represent a management cutoff wherein managers attempt to preemptively restore populations, potentially including translocation as a tool, to prevent population sizes from dropping below 8 individuals on lek.
This study may help to refine management actions that reflect breeding behavior of translocated CSTG and the potential capacity of young individuals to adjust to the challenges of being captured and then released into a new location. There was a clear threshold in survival at 40 d post release, after which all age and sex classes experienced higher survival. We found that adult survival was ≤ 0.5 when our index of population size was < 8 individuals on lek. A rigorous statistical design in future translocation projects can help elucidate some of the questions that arose from our results, such as an apparent density-dependent effect on the survival of translocated yearling CSTG. Furthermore, our study revealed patterns in survival that were only discernable through a long-term monitoring program. Future translocation projects might improve upon our findings if the resources are committed for long term (≥ five years) post-release studies. The continued collection of rigorous field data during translocation projects may help identify relatively “good” or “bad” individuals, age classes, or years in future translocation projects.
This population has grown following the conclusion of this study, with an excess of 40 male CSTG observed on multiple leks in 2022. Lek counts in our study represented only one known lek, but new leks naturally formed at the release site in the years following the conclusion of translocations. Although the population appears to be currently sustainable as indexed by annual lek counts, it is small and vulnerable to environmental or demographic stochasticity. For example, approximately half of the release site burned as part of the South Sugarloaf wildfire in 2018 and the long-term impacts of this disturbance are not yet known. Further translocation might be warranted to act as immigration and to provide genetic diversity to this small and isolated population.
RESPONSES TO THIS ARTICLEResponses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.
All authors contributed to this manuscript equally.
Funding for this project was provided by Nevada Department of Wildlife Upland Game Stamp Fund and grants from the Nevada Game Management Grant (W-84), Nevada Chukar Foundation, Carson Valley Chukar Club, and Nevada Bighorns Unlimited. Funders required no input in research or preparation of this manuscript. We thank M. Jeffress and K. Gray of NDOW, and Z. Lockyer, M. Wackenhut, and J. Knetter of IDFG for field logistical support in Idaho and Nevada, respectfully. We thank Newmont Mining Corporation, IL Ranch, Spanish Ranch, and the ORMAT Tuscarora Geothermal facility for access to private land throughout the field site. We thank field technicians S. Baker, C. Engstrom, C. Bowman, C. Tulimiero, A. Cain, G. Thompson, R. Gardner, N. St. John, B. Hodinka, and A. Bessenaire for their diligence in field efforts. We thank M. Ricca and other U.S. Geological Survey personnel for assistance in manuscript preparation.
Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. Pages 27-281 in B. N. Petrov and S. Caski, editors. Proceedings of the second International Symposium on Information Theory. Akademiai Kaido, Budapest, Hungary.
Allee, W. C., and E. S. Bowen. 1932. Studies in animal aggregations: mass protection against colloidal silver among goldfishes. Journal of Experimental Zoology 61:185-207. https://doi.org/10.1002/jez.1400610202
Ammann, G. A. 1944. Determining the age of pinnated and sharp-tailed grouses. Journal of Wildlife Management 8:170-171. https://doi.org/10.2307/3796451
Armstrong, D. P., and P. J. Seddon. 2008. Directions in reintroduction biology. Trends in Ecology and Evolution 23:20-25. https://doi.org/10.1016/j.tree.2007.10.003
Baxter, R. J., J. T. Flinders, and D. L. Mitchell. 2008. Survival, movements, and reproduction of translocated Greater Sage-Grouse in Strawberry Valley, Utah. Journal of Wildlife Management 72:179-186. https://doi.org/10.2193/2006-402
Blomberg, E. J., J. S. Sedinger, D. Gibson, P. S. Coates, and M. L. Casazza. 2014. Carryover effects and climatic conditions influence the postfledging survival of Greater Sage-Grouse. Ecology and Evolution 4(23):4488-4499. https://doi.org/10.1002/ece3.1139
Carrlson, K., D. C. Kessler, and T. R. Thompson. 2014. Survival and habitat use in translocated and resident Greater Prairie-Chickens. Journal for Nature Conservation 22:405-412. https://doi.org/10.1016/j.jnc.2014.03.008
Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63:215-244. https://doi.org/10.2307/5542
Coates, P. S., and D. J. Delehanty. 2006. Effect of capture date on nest-attempt rate of translocated Sharp-tailed Grouse Tympanuchus phasianellus. Wildlife Biology 12:277-283. https://doi.org/10.2981/0909-6396(2006)12[277:EOCDON]2.0.CO;2
Coates, P. S., J. E. Dudko, D. J. Delehanty, and M. L. Casazza. 2011. Data summary of a Columbian Sharp-tailed Grouse habitat suitability examination between Idaho and Nevada. Report by United States Geological Survey to Nevada Department of Wildlife, Reno, Nevada, USA.
Coates, P. S., S. J. Stiver, and D. J. Delehanty. 2006. Using Sharp-tailed Grouse movement patterns to guide release site selection. Wildlife Society Bulletin 34:1376-1382. https://doi.org/10.2193/0091-7648(2006)34[1376:USGMPT]2.0.CO;2
Coates, P. S., G. T. Wann, G. L. Gillette, M. A. Ricca, B. G. Prochazka, J. P. Severson, K. M. Andrle, S. P. Espinosa, M. L. Casazza, and D. J. Delehanty. 2019. Estimating sightability of Greater Sage-Grouse at leks using an aerial infrared system and N-mixture models. Wildlife Biology 1:1-11. https://doi.org/10.2981/wlb.00552
Conn, P. B., D. S. Johnson, P. J. Williams, S. R. Melin, and M. B. Hooten. 2018. A guide to Bayesian model checking for ecologists. Ecological Monographs 88:526-542. https://doi.org/10.1002/ecm.1314
Connelly, J. W., K. P. Reese, and M. A. Schroder. 2003. Monitoring of Greater Sage-Grouse habitats and populations. University of Idaho College of Natural Resources Experimental Station Bulletin 80:1-47. https://doi.org/10.5962/bhl.title.153828
Dickens, M. J., D. J. Delehanty, J. M. Reed, and L. M. Romero. 2009b. What happens to translocated game birds that ‘disappear’? Animal Conservation 12:418-425. https://doi.org/10.1111/j.1469-1795.2009.00265.x
Dickens, M. J., D. J. Delehanty, and L. M. Romero. 2009a. Stress and translocations: alterations of the stress physiology of translocated birds. Proceedings of the Royal Society Biological Sciences 276:2051-2056. https://doi.org/10.1098/rspb.2008.1778
Dickens, M. J., D. J. Delehanty, and L. M. Romero. 2010. Stress: an inevitable component of animal translocation. Biological Conservation 143:1329-1341. https://doi.org/10.1016/j.biocon.2010.02.032
Drummer, T. D., R. G. Corace III, and S. J. Sjogren. 2011. Sharp-tailed Grouse lek attendance and fidelity in upper Michigan. Journal of Wildlife Management 75:311-318. https://doi.org/10.1002/jwmg.42
Dunham, J. B., R. White, C. S. Allen, B. G. Marcot, and D. Shively. 2016. The reintroduction landscape: finding success at the intersection of ecological, social, and institutional dimensions. Pages 79-104 in D. S. Jachowski, J. J. Millspaugh, P. L. Angermeier, and R. Slotow, editors. Reintroduction of fish and wildlife populations. University of California Press, Berkeley, California, USA. https://doi.org/10.1525/9780520960381-007
Ebenhoch, K., D. Thornton, L. Shipley, J. A. Manning, and K. White. 2019. Effects of post-release movements on survival of translocated Sage-Grouse. Journal of Wildlife Management 83:1314-1325. https://doi.org/10.1002/jwmg.21720
Gascoigne, J. C., and R. N. Lipcius. 2004. Allee effects driven by predation. Journal of Applied Ecology 41:801-810. https://doi.org/10.1111/j.0021-8901.2004.00944.x
Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 2003. Bayesian data analysis. Second edition. Chapman and Hall/CRC Press, New York, New York, USA. https://doi.org/10.1201/9780429258480
Gelman, A. 2013. Two simple examples for understanding posterior p-values whose distributions are far from uniform. Electronic Journal of Statistics 7:2595-2602. https://doi.org/10.1214/13-EJS854
Giesen, K. M., and J. W. Connelly. 1993. Guidelines for management of Columbian Sharp-tailed Grouse habitats. Wildlife Society Bulletin 21:325-333.
Griffith, B., J. M. Scott, J. W. Carpenter, and C. Reed. 1989. Translocation as a species conservation tool: status and strategy. Science 245:477-480. https://doi.org/10.1126/science.245.4917.477
Hoffman, R. W., K. A. Griffin, J. M. Knetter, M. A. Schroeder, A. D. Apa, J. D. Robinson, S. P. Espinosa, T. J. Christiansen, R. D. Northrup, D. A. Budeau, and M. J. Chutter. 2015. Guidelines for the management of Columbian Sharp-tailed Grouse populations and their habitats. Sage and Columbian Sharp-tailed Grouse Technical Committee, Western Association of Fish and Wildlife Agencies, Cheyenne, Wyoming, USA. https://wafwa.org/wp-content/uploads/2020/09/Guidelines_Mgmt_Columbian_Sharp-tailed_Grouse_WAFWA.pdf
Johnsgard, P. A. 2008. Grouse and quails of North America. University of Nebraska-Lincoln, Lincoln, Nebraska, USA. https://digitalcommons.unl.edu/bioscigrouse/
Kallioniemi, H., V.-M. Väänänen, P. Nummi, and J. Virtanen. 2015. Bird quality, origin and predation level affect survival and reproduction of translocated Common Pheasants Phasianus colchicus. Wildlife Biology 21:269-276. https://doi.org/10.2981/wlb.00052
Link, W. A., and R. J. Barker. 2010. Bayesian inference with ecological examples. Academic Press, London, UK. https://doi.org/10.1016/C2009-0-01674-2
Martin, J. A., R. D. Applegate, T. V. Dailey, M. Downey, B. Emmerich, F. Hernández, M. M. MCConnell, K. S. Reyna, D. Rollins, R. E. Ruzicka, and T. M. Terhune, II. 2017. Translocation as a population restoration technique for Northern Bobwhites: a review and synthesis. National Quail Symposium Proceedings 8:1-16. http://trace.tennessee.edu/nqsp/vol8/iss1/11
Mathews, S. R., P. S. Coates, and D. J. Delehanty. 2016. Survival of translocated Sharp-tailed Grouse: temporal threshold and age effects. Wildlife Research 43:220-227. https://doi.org/10.1071/WR15158
Mathews, S. R., P. S. Coates, B. G. Prochazka, S. P. Espinosa, and D. J. Delehanty. 2021. Offspring of translocated individuals drive the successful reintroduction of Columbian Sharp-tailed Grouse in Nevada, USA. Ornithological Applications 123(4):duab044. https://doi.org/10.1093/ornithapp/duab044
McNew, L. B., B. Cascaddan, A. Hicks-Lynch, M. Milligan, A. Netter, S. Otto, J. M. Payne, S. T. Vold, S. L. Wells, and S. A. Wyffels. 2017. Restoration plan for Sharp-tailed Grouse recovery in Western Montana. Developed for the Montana Department of Fish, Wildlife, and Parks, Helena, Montana, USA.
Meints, D. R. 1991. Seasonal movements, habitat use and productivity of Columbian Sharp-tailed Grouse in southeastern Idaho. Thesis. University of Idaho, Moscow, Idaho, USA.
Merta, D., J. Kobielski, J. Theuerkauf, and R. Gula. 2016. Towards a successful reintroduction of Capercaillies - activity, movements and diet of young released to the Lower Silesia Forest, Poland. Wildlife Biology 22 (3):130-135. https://doi.org/10.2981/wlb.00208
Miller, G. C., and W. D. Graul. 1980. Status of Sharp-tailed Grouse in North America. In P. A. Vohs and F. L. Knopf, editors. Proceedings of the Prairie Grouse symposium. Oklahoma State University, Stillwater, Oklahoma, USA.
Milligan, M. C., L. I. Berkeley, and L. B. McNew. 2020. Survival of Sharp-tailed Grouse under variable livestock grazing management. Journal of Wildlife Management 84(7):1296-1305. https://doi.org/10.1002/jwmg.21909
Musil, D. D. 1989. Movements, survival, and habitat use of Sage Grouse translocated into the Sawtooth Valley, Idaho. Thesis. University of Idaho, Moscow, Idaho, USA.
Musil, D. D., J. W. Connelly, and K. P. Reese. 1993. Movements, survival, and reproduction of Sage Grouse translocated into central Idaho. Journal of Wildlife Management 57:85-91. https://doi.org/10.2307/3809004
Odum, E. P. 1953. Fundamentals of ecology. WB Saunders, Philadelphia, Pennsylvania, USA.
Parker, K. A., M. J. Dickens, R. H. Clarke, and T. G. Lovegrove. 2012. The theory and practice of holding, moving, and releasing animals. Pages 105-137 in J. G. Ewen, D. P. Armstrong, K. A. Parker, and P. J. Seddon, editors. Reintroduction biology; integrating science and management. Blackwell, Hoboken, New Jersey, USA. https://doi.org/10.1002/9781444355833.ch4
Plummer, M. 2016. rjags: Bayesian graphical models using MCMC. R Package version 4-6. R Foundation for Statistical Computing, Vienna, Austria. https://CRAN.R-project.org/package=rjags
Plummer, M. 2017. JAGS: a program for analysis graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing. Vienna, Austria. http://mcmc-jags.sourceforge.net/
Picardi, S., N. Ranc, B. J. Smith, P. S. Coates, S. R. Mathews, and D. K. Dahlgren. 2021. Individual variation in temporal dynamics of post-release habitat selection. Frontiers in Conservation Science 2:1-8. https://doi.org/10.3389/fcosc.2021.703906
Pitman, J. C., B. E. Jamison, C. A. Hagen, R. J. Robel, and R. D. Applegate. 2006. Brood break-up and juvenile dispersal of Lesser Prairie-chicken in Kansas. Prairie Naturalist 38:85-99.
R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/
Robb, L., and M. A. Schroeder. 2012. Habitat connectivity for Sharp-tailed Grouse (Tympanuchus phasianellus) in the Columbia Plateau Ecoregion. Washington Wildlife Habitat Connectivity Working Group (WHCWG). Washington connected landscapes project: analysis of the Columbia Plateau Ecoregion. Washington’s Department of Fish and Wildlife and Department of Transportation, Olympia, Washington, USA. https://waconnected.org/wp-content/themes/whcwg/docs/A1_Sharp-tailedGrouse_ColumbiaPlateau_2012.pdf
Rodgers, R. D. 1992. A technique for establishing Sharp-tailed Grouse in unoccupied range. Wildlife Society Bulletin 20:101-106.
Schroeder, M. A., and C. E. Braun. 1991. Walk-in traps for capturing Greater Prairie-chickens on leks. Journal of Field Ornithology 62:378-385. https://sora.unm.edu/sites/default/files/journals/jfo/v062n03/p0378-p0385.pdf
Scott, J. L., F. Hernández, L. A. Brennan, B. M. Ballard, M. Janis, and N. D. Forrester. 2012. Population demographics of translocated Northern Bobwhite on fragmented habitat. Wildlife Society Bulletin 37:168-176. https://doi.org/10.1002/wsb.239
Seddon, P. J., and D. P. Armstrong. 2016. Reintroduction and other conservation translocations: history and future developments, Pages 7-28 in D. S. Jachowski, J. J. Millspaugh, P. L. Angermeier, and R. Slotow, editors. Reintroduction of fish and wildlife populations. University of California Press, Oakland, California, USA. https://doi.org/10.1525/9780520960381-004
Seddon, P. J., W. M. Strauss, and J. Innes. 2012. Animal translocations: what are they and why do we do them? Pages 1-32 in J. G. Ewen, D. P. Armstrong, K. A. Parker, and P. J. Seddon, editors. Reintroduction biology: integrating science and management. Blackwell, Hoboken, New Jersey, USA. https://doi.org/10.1002/9781444355833.ch1
Severson, J. P., P. S. Coates, B. G. Prochazka, M. A. Ricca, M. L. Casazza, and D. J. Delehanty. 2019. Global positioning system tracking devices can decrease Greater Sage-Grouse survival. Condor 121 (3):duz032. https://doi.org/10.1093/condor/duz032
Snyder, J. W., E. C. Pelren, and J. A. Crawford. 1999. Translocation histories of prairie grouse in the United States. Wildlife Society Bulletin 27:428-432.
Troy, R. J., P. S. Coates, J. W. Connelly, G. Gillette, and D. J. Delehanty. 2013. Survival of Mountain Quail translocated from two distinct source populations. Journal of Wildlife Management 77:1031-1037. https://doi.org/10.1002/jwmg.549
United States Fish and Wildlife Service (USFWS). 2000. Endangered and threatened wildlife and plants; 12-month finding for a petition to list the Columbian Sharp-tailed Grouse as threatened. FR 65, 60391-60396. United States Fish and Wildlife Service, Washington, D.C., USA. https://www.fws.gov/species-publication-action/endangered-and-threatened-wildlife-and-plants-12-month-finding-12
United States Fish and Wildlife Service (USFWS). 2006. Endangered and threatened wildlife and plants: 90-day finding on a petition to list the Columbian Sharp-tailed Grouse as threatened. FR 71, 67318-67325. United States Fish and Wildlife Service, Washington, D.C., USA.https://www.fws.gov/species-publication-action/90-day-finding-petition-list-columbian-sharp-tailed-grouse-threatened-or
Vogel, J. A., S. E. Shepherd, and D. M. Debinski. 2015. An unexpected journey: Greater Prairie-chicken travels nearly 4000 km after translocation to Iowa. American Midland Naturalist 174(2):343-349.
Wakkinen, W. L., K. P. Reese, J. W. Connelly, and R. A. Fischer. 1992. An improved spotlighting technique for capturing Sage-Grouse. Wildlife Society Bulletin 20:425-426.
Wann, G. T., P. S. Coates, B. G. Prochazka, J. P. Severson, A. P. Monroe, and C. L. Aldridge. 2019. Assessing lek attendance of male Greater Sage-Grouse using fine-resolution GPS data: implications for population monitoring of lek mating grouse. Population Ecology 61:183-197. https://doi.org/10.1002/1438-390X.1019
Warren, P., and D. Baines. 2018. Expanding the range of Black Grouse Lyrurus tetrix in northern England - can wild females be successfully translocated? Wildlife Biology 2018:1-7. https://doi.org/10.2981/wlb.00435
Watanabe, S. 2010. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11:3571-3594. https://www.jmlr.org/papers/volume11/watanabe10a/watanabe10a.pdf
Watanabe, S. 2013. WAIC and WBIC are information criteria for singular statistical model evaluation. Proceedings of the Sixth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE) 2013:90-94.
World Pheasant Association and IUCN/SSC Re-introduction Specialist Group. 2009. Guidelines for the re-introduction of Galliformes for conservation purposes. IUCN, Gland, Switzerland and World Pheasant Association, Newcastle-upon-Tyne, UK. https://doi.org/10.2305/IUCN.CH.2009.SSC-OP.41.en
Table 1. Model results evaluating temporal thresholds in survival during the first 150 d following release of 175 translocated Columbian Sharp-tailed Grouse (Tympanuchus phasianellus columbianus). Data were collected from northcentral Nevada during a five-year reintroduction effort from 2013-2017. Daily survival rates (DSR) were constrained across two prospective temporal periods delineated by the prospective threshold date (i.e., the date that survival changed from one period to the next), and all dates were relative to each individual grouse. Threshold β is the median of the posterior distribution of the threshold-date effect on an intercept-only model (e.g., the magnitude of the change in daily hazard from one threshold period to the next). Pre- or post-DSRs indicate the median of the posterior distribution of the DSR either pre- or post-threshold, respectively. 95% credible intervals (CRI) are the central 95% of the posterior distributions, and WAIC = widely applicable information criterion (Watanabe, 2010). Because the 150-d model represented a null where the survival probability was constrained across 1 interval representing the entire 150-d period, estimates are listed under the “Pre” columns. The seasonal and weekly models have multiple periods (> two) and are not threshold models; they are included in this table to display their WAIC values and relative ranking.
|Perspective Threshold Date||WAIC||∆WAIC||Threshold β||CRI||Pre-DSR||CRI||Post-DSR||CRI|
|40-d||575.31||0||-0.94||(-1.44 - -0.46)||0.992||(0.989 - 0.995)||0.997||(0.995 - 0.998)|
|20-d||576.18||0.88||-1.02||(-1.50 -- -0.51)||0.99||(0.983 - 0.995)||0.996||(0.994 - 0.998)|
|30-d||577.01||1.70||-0.90||(-1.37 -- -0.44)||0.992||(0.986 - 0.995)||0.997||(0.994 - 0.998)|
|50-d||578.49||3.18||-0.85||(-1.37 - -0.39)||0.993||(0.989 - 0.996)||0.997||(0.995 - 0.998)|
|Linear||578.87||3.56||-0.01||(-0.02 - -0.00)||0.991||(0.986 - 0.995)||NA||NA|
|10-d||580.76||5.46||-1.04||(-1.60 - -0.42)||0.989||(0.979 - 0.995)||0.996||(0.993 - 0.998)|
|60-d||581.33||6.03||-0.81||(-1.31 - -0.33)||0.993||(0.989 - 0.996)||0.997||(0.995 - 0.998)|
|90-d||586.73||11.42||-0.64||(-1.26 - -0.06)||0.994||(0.991 - 0.997)||0.997||(0.994 - 0.999)|
|150-d||589.00||13.69||NA||NA||0.995||(0.994 - 0.996)||NA||NA|
Table 2. Model results regarding survival of 175 translocated Columbian Sharp-tailed Grouse (Tympanuchus phasianellus columbianus; CSTG) during the first 150 d post-release in a novel environment. Data were collected from northcentral Nevada during a five-year reintroduction effort (2013-2017). Included in all models was an intercept and a covariate representing the 40-d threshold identified in step one (Table 1) and an additive random effect for year. The β column identifies all estimated parameters of the model, and their effects were measured by their median and lower and upper credible intervals (CRI) of their central 95% posterior distribution, respectively. Median estimates represent hazard ratios (Halstead et al. 2012), and therefore an increase in hazard (i.e., positive values) relates to a decrease in survival, and vice versa. WAIC = widely applicable information criterion (Watanabe, 2010). Posterior distributions for random effects of years 1-5 varied only slightly between models and are not provided.
|Age × Population Size Interaction||564.27||0.00||Intercept||-6.08||-7.86||-4.59|
|Yearling × Population Size||0.12||-0.02||0.29|
|Adult × Population Size||-0.05||-0.20||0.12|
|Null (40-d Threshold)||575.31||11.04||Intercept||-4.90||-5.69||-4.22|
|Age + Population Size||576.06||11.80||Intercept||-5.30||-7.25||-3.43|
|Age + Sex||576.78||12.51||Intercept||-5.24||-6.02||-4.51|
|Sex + Population Size||578.30||14.03||Intercept||-5.16||-6.99||-3.67|