Conservation translocations are a common wildlife management tool that can be difficult to implement and evaluate for effectiveness. Genetic information can provide unique insight regarding local impact of translocations (e.g., presence and retention of introduced genetic variation) and identifying suitable source and recipient populations (e.g., adaptive similarity). We developed two genetic data sets and wrote statistical code to evaluate conservation translocation effectiveness into the isolated northwestern region of the greater sage-grouse (Centrocercus urophasianus) distribution and to retrospectively evaluate adaptive divergence among source and recipient populations. Our first data set was microsatellite-based and derived from biological samples (feathers, tissue, and blood) collected from the translocation source populations and the northwestern recipient populations (in Washington state) before and after translocation. These data were used to evaluate neutral change in genetic variation resulting from translocation efforts. We wrote code for statistical analyses to evaluate two things in our microsatellite-based data. First, we developed a simulation model to predict the genetic effect of conservation translocations and compare the predictions to what was observed. Second, we developed a statistical model to estimate the probability that individuals sampled post-translocation are the offspring of two individuals from the same population or from individuals from two distinct populations. Our second data set was whole-genome sequencing data (derived from tissue and blood samples) for the source and Washington populations prior to translocation efforts. These data were used to characterize genome-wide adaptive divergence patterns that may influence translocation outcomes.

Wildlife managers translocate greater sage-grouse (Centrocercus urophasianus; sage-grouse) to augment small populations, but translocated sage-grouse often fail to reproduce post-release, sometimes hampering conservation objectives. We performed two distinct sage-grouse translocation projects in California and North Dakota from 2017-2020 and employed two translocation methods at both sites: an established method of translocating females prior to the nesting season (i.e., a pre-nesting translocation), and a novel method wherein females were translocated with chicks after successfully hatching a nest in the source population (i.e., a brood translocation). Using an integrated population model (IPM), we estimated recruitment by females translocated with each method. We also estimated the finite rate of change in abundance in recipient and source populations that underwent brood and pre-nesting translocations to evaluate each method using a cost-benefit metric.

Wildlife managers translocate greater sage-grouse (Centrocercus urophasianus; sage-grouse) to augment small populations, but translocated sage-grouse often fail to reproduce post-release, sometimes hampering conservation objectives. We performed two distinct sage-grouse translocation projects in California and North Dakota from 2017-2020 and employed two translocation methods at both sites: an established method of translocating females prior to the nesting season (i.e., a pre-nesting translocation), and a novel method wherein females were translocated with chicks after successfully hatching a nest in the source population (i.e., a brood translocation). Using an integrated population model (IPM), we estimated recruitment by females translocated with each method. We also estimated the finite rate of change in abundance in recipient and source populations that underwent brood and pre-nesting translocations to evaluate each method using a cost-benefit metric.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

Maintenance of genetic diversity is important for conserving species, especially those with fragmented habitats and/or ranges. In the absence of natural dispersal, translocation can be used to achieve this goal. However, the long-term impacts from translocation can be expensive and difficult to evaluate. This dataset is used to evaluate genetic change as a result of translocation and represents samples collected before and after translocations were conducted.

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.

These data are the results of a spatially interpolated integrated population model (SIIPM) fit to count and demographic data collected from populations of Greater Sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) located in Nevada, U.S.A. during 2013-2021. We used a novel framework, using integrated population models (IPMs), to express demographic relatedness among sampled and unsampled populations using geographic principles of spatial autocorrelation (Shepard, 1968; Tobler, 1970). Specifically, the framework pairs relatively inexpensive population count data with spatially interpolated demographic estimates. When conducted within a Bayesian framework, spatially interpolated demographic parameters can be expressed as probability distributions for unobserved populations. Though novel to the IPM framework, the method is remarkably similar to Tobler’s seminal work on the topic of spatial autocorrelation (Tobler, 1970), which used the Markovian process of human population dynamics to map urban growth over a partially sampled plane. Spatially explicit estimates of survival, recruitment, and finite rate of population change (lambda) represent the 50th percentile of the posterior distribution for each parameter. References cited: Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, ACM 1968. (pp. 517-524). Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234-240. https://doi.org/10.2307/143141

Monitoring change in genetic diversity in wildlife populations across multiple scales could facilitate prioritization of conservation efforts. We used microsatellite genotypes from 7,080 previously collected genetic samples from across the greater sage-grouse (Centrocercus urophasianus) range to develop a modelling framework for estimating genetic diversity within a recently developed hierarchically nested monitoring framework (clusters). The majority of these genetic samples (n=6560) were used in previous research (Oyler-McCance et al. 2014; Cross et. al 2018; Row et. al. 2018). Genetic diversity values associated with clusters across multiple scales could facilitate the identification of areas with low genetic diversity and inform the potential management or conservation priority and response. We also report the data used to define genetic diversity thresholds of conservation concern and a full reporting of the genetic diversity estimates associated with the evaluated clusters.

Characterizing genetic structure across a species’ range is relevant for management and conservation as it can be used to define population boundaries and quantify connectivity. Here, we characterized population structure and estimated effective migration in Greater Sage-grouse (Centrocercus urophasianus). Our objectives were to (1) describe large-scale patterns of population genetic structure and gene flow and (2) to characterize genetic subpopulation centers across the range of Greater Sage-grouse. Samples from 2,134 individuals were genotyped at 15 microsatellite loci. Using standard STRUCTURE and spatial principal components analyses, we found evidence for four or six areas of large-scale genetic differentiation and, following our novel method, 12 subpopulation centers of differentiation. The subpopulation centers defined here could be monitored to maintain genetic diversity and connectivity with other subpopulation centers. Many areas outside subpopulation centers are contact zones where different genetic groups converge and could be priorities for maintaining overall connectivity. Our novel method and process of leveraging multiple different analyses to find common genetic patterns provides a path forward to characterizing genetic structure in wide-ranging, continuously distributed species. The files associated with this data release include the raw genetic data (both a full data set and one thinned to create even sampling distribution), the estimated effective migration surface, and boundaries of subpopulation centers at K=6 from a Structure analysis using 25, 50, and 75% kernel density estimates to determine genetically distinct groups.