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.

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.

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 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.

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

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

The distance within which populations respond to features in a landscape (scale of effect) can indicate how disturbance and management may affect wildlife. Using annual counts of male Greater Sage-grouse (Centrocercus urophasianus) attending 584 leks in southwest Wyoming (2003-2019) and estimates of sagebrush cover from the Rangeland Condition Monitoring Assessment and Projection (RCMAP), we used a scale selection approach to jointly estimate the scale of effect and the effect of sagebrush cover in the surrounding landscape for sage-grouse population trends. We estimated these parameters using a state-space model fit with a Bayesian approach. Data formatting necessary for this analysis produced data stored in two lists, one for model constants (nimbleconstants_sg_wlci.txt, including number of years, number of sites [leks], number of scales, number of visits, indicators for site and year, and number of detection parameters) and one for model data (nimbledata_sg_wlci.txt, including lek counts/surveys in both long- and array-format, a matrix for detection covariates, an array for sagebrush cover [scaled], and unscaled arrays for sagebrush, ordinal date, and time since sunrise).

The distance within which populations respond to features in a landscape (scale of effect) can indicate how disturbance and management may affect wildlife. Using annual counts of male Greater Sage-grouse (Centrocercus urophasianus) attending 584 leks in southwest Wyoming (2003-2019) and estimates of sagebrush cover from the Rangeland Condition Monitoring Assessment and Projection (RCMAP), we used a scale selection approach to jointly estimate the scale of effect and the effect of sagebrush cover in the surrounding landscape for sage-grouse population trends. We estimated these parameters using a state-space model fit with a Bayesian approach. Data formatting necessary for this analysis produced data stored in two lists, one for model constants (nimbleconstants_sg_wlci.txt, including number of years, number of sites [leks], number of scales, number of visits, indicators for site and year, and number of detection parameters) and one for model data (nimbledata_sg_wlci.txt, including lek counts/surveys in both long- and array-format, a matrix for detection covariates, an array for sagebrush cover [scaled], and unscaled arrays for sagebrush, ordinal date, and time since sunrise).