Boundary of the conservation assessment of Greater Sage-grouse and sagebrush habitat conducted by the Western Association of Fish and Wildlife Agencies. The boundary is derived from the pre-settlement distribution of the Sage-grouse.

Agricultural land cover in the study area for the conservation assessment of Greater Sage-grouse conducted by the Western Association of Fish and Wildlife Agencies. This dataset was developed from Sagestitch, an Eastern Washington Shrubsteppe Mapping Project, several state-level Gap Analysis Program (GAP) land cover products (AZ, CA, NM, OR, and WA), National Land Cover Data (NLCD) (ND,SD,NE), and the Prairie Farm Rehabilitation Administration (PFRA) Generalized Landcover (Alberta, Saskatchewan).

Agricultural land cover in the study area for the conservation assessment of Greater Sage-grouse conducted by the Western Association of Fish and Wildlife Agencies. This dataset was developed from Sagestitch, an Eastern Washington Shrubsteppe Mapping Project, several state-level Gap Analysis Program (GAP) land cover products (AZ, CA, NM, OR, and WA), National Land Cover Data (NLCD) (ND,SD,NE), and the Prairie Farm Rehabilitation Administration (PFRA) Generalized Landcover (Alberta, Saskatchewan).

This geospatial layer is a spatial index for the CED (Conservation Efforts Database https://conservationefforts.org/), serving as a spatial framework for summary reports by area (a.k.a. polygon). In addition, this SRU (Sagebrush Reporting Unit) data is an option for data providers to provide spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts efforts and to protect Personally Identifiable Information. This option allows CED data providers to pick a predetermined SRU instead of submitting the explicit effort boundary. These SRUs are large enough to provide spatial ambiguity and obscure private landowner locations. This SRU data is in the format of a GIS polygon layer and is an aggregate of USGS partner’s lek cluster layer, BLM HAF data modified by Oregon, Idaho layers, and CED development team modification for CED purposes.

This geospatial layer is a spatial index for the CED (Conservation Efforts Database https://conservationefforts.org/), serving as a spatial framework for summary reports by area (a.k.a. polygon). In addition, this SRU (Sagebrush Reporting Unit) data is an option for data providers to provide spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts efforts and to protect Personally Identifiable Information. This option allows CED data providers to pick a predetermined SRU instead of submitting the explicit effort boundary. These SRUs are large enough to provide spatial ambiguity and obscure private landowner locations. This SRU data is in the format of a GIS polygon layer and is an aggregate of USGS partner’s lek cluster layer, BLM HAF data modified by Oregon, Idaho layers, and CED development team modification for CED purposes.

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.

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.

Greater sage-grouse (Centrocercus urophasianus) has been identified as a potential umbrella species with the assumption that conservation of their habitats in sagebrush ecosystems may benefit multiple other wildlife species, but co-occurrence with an umbrella species does not necessarily guarantee species will respond positively to management for sage-grouse. This may be particularly true for ecotones, such as at the eastern edge of the sage-grouse range where sagebrush steppe and Great Plains grasslands intersect. We used a novel application of neutral landscape models (Etherington et al., 2015) to evaluate observed and expected overlap between greater sage-grouse habitat (1994-2010; Fedy et al., 2014) and distribution of eight grassland songbird species (2008-2018; Monroe et al., 2021) in northeast Wyoming. Data formatting necessary for this analysis produced 17 rasters including sage-grouse habitat (nestGRSG300.tif and summerGRSG300.tif), songbird species distribution (grspFP300.tif, holaFP300.tif, larbFP300.tif, laspFP300.tif, loshFP300.tif, vespFP300.tif, wekiFP300.tif, wemeFP300.tif), songbird community metrics (total density and richness) thresholded at the 0.5 (abund_50_300.tif and rich_50_300.tif), 0.75 (abund_75_300.tif and rich_75_300.tif), and 0.9 quantiles (abund_90_300.tif and rich_90_300.tif), and a general mask to constrain simulations to pixels with songbird predictions (mask300.tif). We also include rasters for grasshopper sparrow (Ammodramus savannarum), sage-grouse nesting habitat, and associated masks but with pixels aggregated to 90 meters, 150 m, 450 m, and 600 m to evaluate sensitivity of results to raster resolution. Finally, in code.zip we provide example Python code to simulate neutral landscape models (nlm_umbrella_larb_habitat.py) and R code to determine the spatial autocorrelation parameter (h) for sage-grouse nesting and summer (brood-rearing) habitat using semivariograms (h_parameter_code.R).

Greater sage-grouse (Centrocercus urophasianus) has been identified as a potential umbrella species with the assumption that conservation of their habitats in sagebrush ecosystems may benefit multiple other wildlife species, but co-occurrence with an umbrella species does not necessarily guarantee species will respond positively to management for sage-grouse. This may be particularly true for ecotones, such as at the eastern edge of the sage-grouse range where sagebrush steppe and Great Plains grasslands intersect. We used a novel application of neutral landscape models (Etherington et al., 2015) to evaluate observed and expected overlap between greater sage-grouse habitat (1994-2010; Fedy et al., 2014) and distribution of eight grassland songbird species (2008-2018; Monroe et al., 2021) in northeast Wyoming. Data formatting necessary for this analysis produced 17 rasters including sage-grouse habitat (nestGRSG300.tif and summerGRSG300.tif), songbird species distribution (grspFP300.tif, holaFP300.tif, larbFP300.tif, laspFP300.tif, loshFP300.tif, vespFP300.tif, wekiFP300.tif, wemeFP300.tif), songbird community metrics (total density and richness) thresholded at the 0.5 (abund_50_300.tif and rich_50_300.tif), 0.75 (abund_75_300.tif and rich_75_300.tif), and 0.9 quantiles (abund_90_300.tif and rich_90_300.tif), and a general mask to constrain simulations to pixels with songbird predictions (mask300.tif). We also include rasters for grasshopper sparrow (Ammodramus savannarum), sage-grouse nesting habitat, and associated masks but with pixels aggregated to 90 meters, 150 m, 450 m, and 600 m to evaluate sensitivity of results to raster resolution. Finally, in code.zip we provide example Python code to simulate neutral landscape models (nlm_umbrella_larb_habitat.py) and R code to determine the spatial autocorrelation parameter (h) for sage-grouse nesting and summer (brood-rearing) habitat using semivariograms (h_parameter_code.R).

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.