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.

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.

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.

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.

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.

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.

Habitat fragmentation and degradation impacts an organism's ability to navigate the landscape, ultimately resulting in decreased gene flow and increased extinction risk. Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.

Habitat fragmentation and degradation impacts an organism's ability to navigate the landscape, ultimately resulting in decreased gene flow and increased extinction risk. Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.