These data consist of six separate tables. Two tables represent observed and expected greater sage-grouse (hereafter; sage-grouse) lek abundances, averaged within neighborhood clusters and the Bi-State Distinct Population segment as a whole. Three tables are the input tables for seasonal habitat selection models. These tables are the result of extracting values from rasters to both 'used' and 'available' locations; 'used' refers to an observation of a sage-grouse nesting or brood rearing, 'available' is a randomly-generated location proximal to a paired 'used' location. For these locations, we extract values from multiple rasters expressing landscape characteristics such as landcover (such as sagebrush, annual grass, or shrubs, expressed as a percentage), height of sagebrush, distance to water features, distance to anthropogenic features, and topographic transformations (such as slope, heat load index, and roughness). Ultimately, the locations were removed from these tables as sage-grouse are considered to be a sensitive species. The final table contains volume of sage-grouse abundance and area measurements of sage-grouse range annually 1995 through 2023, and projected out to 3 future population nadirs 2027, 2036, and 2044.

Rasters representing Greater Sage-grouse (hereafter sage-grouse) habitat selection indices (HSI), habitat selection categories, HSI combined with space-use, and example management categories. Researchers with the U.S. Geological Survey, in close cooperation with multiple state and federal resource agency partners, sought to map sage-grouse distribution and produce example habitat designations in these states. Herein, we report results of our primary study objective, which was to map sage-grouse distribution and create example habitat management and priority designations, based on more than a decade of location and survival data collected from marked sage-grouse across the study region.

Rasters representing Greater Sage-grouse (hereafter sage-grouse) habitat selection indices (HSI), habitat selection categories, HSI combined with space-use, and example management categories. Researchers with the U.S. Geological Survey, in close cooperation with multiple state and federal resource agency partners, sought to map sage-grouse distribution and produce example habitat designations in these states. Herein, we report results of our primary study objective, which was to map sage-grouse distribution and create example habitat management and priority designations, based on more than a decade of location and survival data collected from marked sage-grouse across the study region.

These data serve as inputs for statistical models which aim to assess habitat selection and survival of greater sage-grouse over different life stages and seasons.

Greater sage-grouse (Centrocercus urophasianus; hereinafter sage-grouse) is a sagebrush obligate species and widely considered an indicator species for sagebrush ecosystems and other sagebrush-dependent species (Hanser and Knick, 2011; Prochazka and others, 2023). Sagebrush ecosystems are threatened by a wide range of disturbances and anthropogenic factors, including climate change, severe drought, altered wildfire regimes, expansion of invasive species, and anthropogenic development. Collectively, these threats have led to reduced ecological integrity and sage-grouse habitat quality within the sagebrush biome (Doherty and others, 2022). Steady and long-term declines in sage-grouse populations have led to large-scale efforts to improve population performance and prevent additional loss of habitat for sage-grouse and other sagebrush-dependent species (Coates and others, 2021). Due to their complex space use and habitat selection patterns during different life stages, requirements for large intact tracts of sagebrush, declining population trends, and status as a proposed protected species, sage-grouse have become integral to land management and conservation policy throughout the western United States (Western Association of Fish and Wildlife Agencies, 2015; Doherty and others, 2022). References cited: Coates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2022: U.S. Geological Survey Data Report 1175, 17 p., accessed December 7, 2023, at [Available at https://doi.org/10.3133/dr1175.] Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022-1081, 38 p., accessed December 6, 2023, at https://doi.org/10.3133/ofr20221081. Hanser, S.E., and Knick, S.T., 2011, Greater sage-grouse as an umbrella species for shrubland passerine birds-A multiscale assessment, chap. 19 in Knick, S.T., eds., Greater sage grouse-Ecology and conservation of a landscape species and its habitats: University of California Press, p. 474-487. [Available at https://doi.org/10.1525/california/9780520267114.003.0020.] Prochazka, B.G., Coates, P.S., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., Doherty, K.E., Chenaille, M.P., and Aldridge, C.L., 2023, A targeted annual warning system developed for the conservation of a sagebrush indicator species: Ecological Indicators, v. 148. [Available at https://doi.org/10.1016/j.ecolind.2023.110097.] Western Association of Fish and Wildlife Agencies, 2015, Greater sage-grouse population trends: an analysis of lek count databases 1965-2015: Cheyenne, Wyo., Western Association of Fish and Wildlife Agencies, 55 p., accessed 07 12, 2023, at https://ir.library.oregonstate.edu/concern/technical_reports/ng451p621

A shapefile representing greater sage-grouse (hereafter sage-grouse) space use and lek abundance in the Bi-State Distinct Population Segment (DPS) of California and Nevada. These data were derived by combining a kernel density estimation of sage-grouse lek abundance combined with another raster representing distance to lek. The 85 percent isopleth was then used to define "high space-use."

A shapefile representing greater sage-grouse (hereafter sage-grouse) space use and lek abundance in the Bi-State Distinct Population Segment (DPS) of California and Nevada. These data were derived by combining a kernel density estimation of sage-grouse lek abundance combined with another raster representing distance to lek. The 85 percent isopleth was then used to define "high space-use."

A raster representing Greater Sage-grouse (hereafter sage-grouse) space-use and lek abundance. A higher pixel value corresponds to a greater amount of likelihood that the area is utilized by sage-grouse. Values are the result of combining a kernel density estimation on lek abundances with a raster representing distance to lek. The kernel density was calculated using maximum lek abundances observed between the most recent population nadir for the Great Basin region (2013) and the most recent lek counts available (2021). Polygons representing high-space use areas of Greater Sage-grouse (hereafter sage-grouse) space-use and lek abundance. Areas represent the 85 percent isopleth of the abundance and space-use index (ASUI) as well as a 5-kilometer buffer around remote leks that did not fall within the 85 percent isopleth, so that remote leks were not under-represented.

A raster representing Greater Sage-grouse (hereafter sage-grouse) space-use and lek abundance. A higher pixel value corresponds to a greater amount of likelihood that the area is utilized by sage-grouse. Values are the result of combining a kernel density estimation on lek abundances with a raster representing distance to lek. The kernel density was calculated using maximum lek abundances observed between the most recent population nadir for the Great Basin region (2013) and the most recent lek counts available (2021). Polygons representing high-space use areas of Greater Sage-grouse (hereafter sage-grouse) space-use and lek abundance. Areas represent the 85 percent isopleth of the abundance and space-use index (ASUI) as well as a 5-kilometer buffer around remote leks that did not fall within the 85 percent isopleth, so that remote leks were not under-represented.

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June, and is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into