We expanded on previously developed methodology to incorporate information on habitat selection and survival during reproductive life stages and specific seasons with updated sage-grouse location and known fate datasets, while also including brood-rearing areas that are understood to be threatened and important for population persistence. We combined predictive habitat map surfaces for each life stage and season with updated information on current occupancy patterns to classify habitat based on its suitability and probability of occupancy. We carried out additional steps to delineate specific example habitat management areas, specifically (1) incorporated corridors connecting key nesting and brood-rearing habitat, (2) corrected outputs for pre-wildfire habitat conditions within areas burned in the last 16 years, and (3) masked out areas of anthropogenic development. Our methodological example of deriving habitat management areas was intended to help inform decisions by BLM and other land managers regarding conservation and management of sage-grouse. Associated data products in the form of habitat maps provide updated, detailed, and comprehensive information about the status of habitats and can be useful to partner agencies in their efforts to designate and rank habitats for this species of high conservation concern in Nevada and California, with full recognition that on-the-ground field data and local sources of information and expertise should be used in conjunction with inferences from these models.

This raster dataset depicts phase 1 pinyon-juniper expansion , where shrubs and herbs are the dominant vegetation and conifers occupy greater than zero percent to ten percent, intersecting documented sage-grouse habitat management categories (Coates et al., 2016a, Coates et al., 2016b). These data support the following publication: K. Benjamin Gustafson, Peter S. Coates, Cali L. Roth, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, Michael L. Casazza, Using object-based image analysis to conduct high- resolution conifer extraction at regional spatial scales, International Journal of Applied Earth Observation and Geoinformation, Volume 73, December 2018, Pages 148-155, ISSN 0303-2434, https://doi.org/10.1016/j.jag.2018.06.002. Cali L. Roth, Peter S. Coates, K. Benjamin Gustafson, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, and Michael L. Casazza, 2018. A customized framework for regional classification of conifers using automated feature extraction. Journal of Agricultural and Biological Science, in review. References: Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., Delehanty, D.J. 2016a, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey Open-File Report 2016-1080, 160 p., http://doi.org/10.3133/ofr20161080. ISSN: 2331-1258 (online) Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., and Delehanty, D.J., 2016b, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey data release, http://doi.org/10.5066/F7CC0XRV.

This raster dataset depicts phase 1 pinyon-juniper expansion , where shrubs and herbs are the dominant vegetation and conifers occupy greater than zero percent to ten percent, intersecting documented sage-grouse habitat management categories (Coates et al., 2016a, Coates et al., 2016b). These data support the following publication: K. Benjamin Gustafson, Peter S. Coates, Cali L. Roth, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, Michael L. Casazza, Using object-based image analysis to conduct high- resolution conifer extraction at regional spatial scales, International Journal of Applied Earth Observation and Geoinformation, Volume 73, December 2018, Pages 148-155, ISSN 0303-2434, https://doi.org/10.1016/j.jag.2018.06.002. Cali L. Roth, Peter S. Coates, K. Benjamin Gustafson, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, and Michael L. Casazza, 2018. A customized framework for regional classification of conifers using automated feature extraction. Journal of Agricultural and Biological Science, in review. References: Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., Delehanty, D.J. 2016a, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey Open-File Report 2016-1080, 160 p., http://doi.org/10.3133/ofr20161080. ISSN: 2331-1258 (online) Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., and Delehanty, D.J., 2016b, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—an updated decision-support tool for management: U.S. Geological Survey data release, http://doi.org/10.5066/F7CC0XRV.

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.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Raster layers (360x360m cell size) associated with figure 4 in Duchardt et al. 2021 include a comparison of selected cells with no connectivity requirements (PReSETnoconn_nolock.tif), with a penalty for unconnected features (PReSEThighconn_nolock.tif), and with a penalty for unconnected features and including “locked-in” protected lands (PReSEThighconn_lock.tif). PReSET tool is currently housed at USGS FORT.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Raster layers (360x360m cell size) associated with figure 4 in Duchardt et al. 2021 include a comparison of selected cells with no connectivity requirements (PReSETnoconn_nolock.tif), with a penalty for unconnected features (PReSEThighconn_nolock.tif), and with a penalty for unconnected features and including “locked-in” protected lands (PReSEThighconn_lock.tif). PReSET tool is currently housed at USGS FORT.

This raster represents a continuous surface of sage-grouse habitat probability values for Nevada and California. These values are derived from modeling the resource selection function (RSF) for the region (see supplemental information for more details). Higher values indicate a higher probability of quality sage-grouse habitat.NOTE: This file does not include habitat for the Bi-State management area.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)

This raster represents a continuous surface of sage-grouse habitat probability values for Nevada and California. These values are derived from modeling the resource selection function (RSF) for the region (see supplemental information for more details). Higher values indicate a higher probability of quality sage-grouse habitat.NOTE: This file does not include habitat for the Bi-State management area.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada 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). Summer included telemetry locations (n = 14,058) from mid-March to June. 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 and with broods). 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 using R Software (v 3.13) 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. 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. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton,