These data are the result of an intersection between a surface representing the delta-finite rate of population change and another surface representing Greater Sage-grouse abundance and space-use. It was used to rank candidate sites according to greatest potential impact to Greater Sage-grouse populations resulting from the presence of geothermal energy activity. In 2022, candidate geothermal sites were identified in Nevada and eastern California, then buffered by 10 kilometers. While the extent of the overall raster layer encompasses a very large swath of the western US, data values are limited to fall within these 10 kilometer buffers.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for low oil and gas development, high population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically , the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for medium oil and gas development, high population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically , the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for medium oil and gas development, high population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for low oil and gas development, low population size, and with effects of climate change under an RCP 8.5 scenario (2050). The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and with effects of climate change under an RCP 8.5 scenario (2050) . The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

We quantified the relative abundance of greater sage-grouse (Centrocercus urophasianus) populations within Bureau of Land Management (BLM) Sagebrush Focal Areas (SFAs).

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada. HSIs were calculated for spring, summer, and winter sage-grouse seasons, and then multiplied together to create this composite dataset. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) 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, SageStitch, LANDFIRE, and the CA Fire and Resource Assessment Program. 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) and conifer (P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. 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 for each subregion using R software (v 3.13) 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 for each 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. The three seasonal HSIs were then multiplied to create a composite annual HSI. REFERENCES: California Forest and Resource Assessment Program. 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 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) Comer, P.,