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.

The authors and the Bureau of Land Management (BLM) have expanded the greater sage-grouse (GRSG [also referenced as sage-grouse]; Centrocercus urophasianus) hierarchical population units/clusters to ensure inclusion of the proposed revisions (2025) of the BLM habitat management areas (HMAs) in future management implementation decisions. The authors used a consolidated dataset of all GRSG HMAs provided by the BLM from individual Records of Decision (ROD) and Approved Resource Management Plan Amendments (ARMPA) for GRSG in Oregon and Colorado. In addition, the proposed HMAs from the Greater Sage-Grouse Rangewide Planning Proposed Resource Management Plan Amendment and Final Environmental Impact Statement (EIS) for California, Idaho, Montana, Nevada, North Dakota, South Dakota, Utah, and Wyoming were incorporated (DOI-BLM-WO-2300-2022-0001-RMP-EIS). The Federal Land Policy and Management Act requires that Resource Management Plans (RMPs) for managing public lands be developed and maintained, and the National Environmental Policy Act requires that an environmental impact statement (EIS) be prepared for Federal actions significantly affecting the quality of the human environment. The EIS for the Greater Sage-Grouse RMPAs identified updated HMAs, areas of highest conservation value for the species, based on new habitat use data. A consolidated version of the HMAs were provided to USGS authors. Information about designated HMAs is described in the "Supplemental" section of the metadata file. The authors developed three new datasets that reflect revised GRSG HMA boundaries produced by BLM. The new data include a revised GRSG boundary, cluster level 2 (neighborhood clusters; NC), and cluster level 13 (climate clusters; CC). These revisions include any designations or proposed designations of HMAs falling outside previously published population unit/cluster versions (O’Donnell et al. 2022; https://doi.org/10.5066/P9D1K0LX). Background information on original population units/clusters of GRSG: We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for GRSG across the western United States. Polygons (clusters) within each cluster level group a population of GRSG 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.

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.

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.

We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

This data, grsg_lcp_ThiessenPoly_mst3, is one of five hierarchical delineations of greater sage-grouse population structure. The data represent Thiessen polygons of graph constructs (least-cost path minimum spanning tree [LCP-MST]) that defined our population structure of sage-grouse breeding sites in the western United States. This data was developed by applying dispersal and genetic rules to decompose the fully connected population structure (graph) into the product presented here. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

This data, grsg_lcp_ThiessenPoly_mst3, is one of five hierarchical delineations of greater sage-grouse population structure. The data represent Thiessen polygons of graph constructs (least-cost path minimum spanning tree [LCP-MST]) that defined our population structure of sage-grouse breeding sites in the western United States. This data was developed by applying dispersal and genetic rules to decompose the fully connected population structure (graph) into the product presented here. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.