These NetCDF data were compiled to investigate how two complementary models can contribute to our understanding of contemporary and future big sagebrush regeneration across the historical and potential future sagebrush region. Objective of our study was to apply both models to address three specific objectives: (i) examine the geographic patterns of big sagebrush regeneration probabilities that the two different models project under historical conditions and future climate scenarios; (ii) quantify the robustness of model projections, e.g., the consistency among climate models in projected changes in regeneration for future time periods; and (iii) identify how model predictions for regeneration potential relate to environmental site characteristics like climate, soil moisture, and soils. Big sagebrush regeneration was modeled based on daily meteorological and ecohydrological variables across the historical and potential future geographic range of big sagebrush distribution in the western United States. These data represent the simulated potential of big sagebrush regeneration representing (i) range-wide big sagebrush regeneration responses in natural vegetation (process-based model, Schlaepfer et al. 2014) and (ii) big sagebrush restoration seeding outcomes following fire in the Great Basin and the Snake River Plains (regression-based model, Shriver et al. 2018) as well as soil moisture and climatic variables for recent climate 1980-2010, and for future projected climate represented by all available climate models under two representative concentration pathways (RCP4.5 and RCP8.5) at two time periods during the 21st century (2020-2050 and 2070-2099) at 10-km resolution based on a simulation experiment described in Bradford et al. 2019 using the SOILWAT2 ecosystem water balance model (Schlaepfer et al. 2021). These data were created by a collaborative research project between the U.S. Geological Survey and Yale University.

This data release contains a formatted dataset compiled from multiple databases on restoration treatments and environmental conditions from across the sagebrush (Artemisia spp.) biome. With these data, we modeled the influence of environmental conditions and restoration treatments on trends in sagebrush cover using generalized additive models. We then used these models to create maps of projected sagebrush cover 30 years following wildfire (no treatment, and aerial or drill seeding of sagebrush). We also provide maps for the probability of recovery after 30 years without treatment, with aerial seeding of sagebrush, or with drill seeding of sagebrush. Widespread degradation of ecosystem function and biodiversity loss has led to calls for massive investments in ecological restoration across the globe, but limited resources necessitate targeted application of restoration efforts. In western North America, disturbances such as wildfire, drought, and invasive species are increasingly altering the sagebrush biome, degrading habitat for species of conservation concern such as greater sage-grouse (Centrocercus urophasianus). Effective restoration is needed to address these challenges, but understanding the conditions determining when, where, and at what rate sagebrush recovery will occur is a pressing research need across the vast and heterogeneous sagebrush landscape. Files included in this data release: sage_dat_release.csv – compiled and formatted multiple treatment and environmental datasets spanning broad spatio-temporal extents sagebrush_notreat.tif – projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment sagebrush_notreat_sd.tif – error (summarized across simulations) in projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment perc_change_sage_aerial_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_aerial_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_drill_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. perc_change_sage_drill_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. prob_recovery_notreat.tif – probability of recovery 30 years after wildfire, without treatment prob_recovery_aerial_artemisia.tif – probability of recovery 30 years after wildfire, with aerial seeding Artemisia spp. prob_recovery_drill_artemisia.tif – probability of recovery 30 years after wildfire, with drill seeding Artemisia spp.

This data release contains a formatted dataset compiled from multiple databases on restoration treatments and environmental conditions from across the sagebrush (Artemisia spp.) biome. With these data, we modeled the influence of environmental conditions and restoration treatments on trends in sagebrush cover using generalized additive models. We then used these models to create maps of projected sagebrush cover 30 years following wildfire (no treatment, and aerial or drill seeding of sagebrush). We also provide maps for the probability of recovery after 30 years without treatment, with aerial seeding of sagebrush, or with drill seeding of sagebrush. Widespread degradation of ecosystem function and biodiversity loss has led to calls for massive investments in ecological restoration across the globe, but limited resources necessitate targeted application of restoration efforts. In western North America, disturbances such as wildfire, drought, and invasive species are increasingly altering the sagebrush biome, degrading habitat for species of conservation concern such as greater sage-grouse (Centrocercus urophasianus). Effective restoration is needed to address these challenges, but understanding the conditions determining when, where, and at what rate sagebrush recovery will occur is a pressing research need across the vast and heterogeneous sagebrush landscape. Files included in this data release: sage_dat_release.csv – compiled and formatted multiple treatment and environmental datasets spanning broad spatio-temporal extents sagebrush_notreat.tif – projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment sagebrush_notreat_sd.tif – error (summarized across simulations) in projected sagebrush cover 30 years following wildfire given local environmental conditions, without treatment perc_change_sage_aerial_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_aerial_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with aerial seeding Artemisia spp. perc_change_sage_drill_artemisia.tif – projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. perc_change_sage_drill_artemisia_sd.tif – error (summarized across simulations) in projected change in sagebrush cover (relative to no treatment) 30 years following wildfire given local environmental conditions, with drill seeding Artemisia spp. prob_recovery_notreat.tif – probability of recovery 30 years after wildfire, without treatment prob_recovery_aerial_artemisia.tif – probability of recovery 30 years after wildfire, with aerial seeding Artemisia spp. prob_recovery_drill_artemisia.tif – probability of recovery 30 years after wildfire, with drill seeding Artemisia spp.

In 'Predicted (1989-2015) and forecasted (2015-2114) rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA (ver. 2.0, January 2021)', we provide spatially- and temporally-explicit maps of predictions for the rate of change and time to recovery and percent recovery of sagebrush cover after 100 years (Monroe et al. 2020). The rasters beginning with "sage.rate" depict the predicted annual rate of change in sagebrush cover for each timestamp interval, across the Wyoming Landscape Conservation Initiative area (WLCI) in southwestern Wyoming, USA (1989-2015). The files 'time_to_recov_v2.0.tif' and 'perc_recov_v2.0.tif' are rasters for predicted time to recovery and percent recovery after 100 years, respectively, following energy development across the WLCI area. Literature cited: Monroe, A. P., C. L. Aldridge, M. S. O'DOnnell, D. J. Manier, C. G. Homer, and P. J. Anderson. 2020. Using remote sensing products to predict recovery of vegetation across space and time following energy development. Ecological Indicators 110:105872.

In 'Predicted (1989-2015) and forecasted (2015-2114) rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA (ver. 2.0, January 2021)', we provide spatially- and temporally-explicit maps of predictions for the rate of change and time to recovery and percent recovery of sagebrush cover after 100 years (Monroe et al. 2020). The rasters beginning with "sage.rate" depict the predicted annual rate of change in sagebrush cover for each timestamp interval, across the Wyoming Landscape Conservation Initiative area (WLCI) in southwestern Wyoming, USA (1989-2015). The files 'time_to_recov_v2.0.tif' and 'perc_recov_v2.0.tif' are rasters for predicted time to recovery and percent recovery after 100 years, respectively, following energy development across the WLCI area. Literature cited: Monroe, A. P., C. L. Aldridge, M. S. O'DOnnell, D. J. Manier, C. G. Homer, and P. J. Anderson. 2020. Using remote sensing products to predict recovery of vegetation across space and time following energy development. Ecological Indicators 110:105872.

These data were compiled to provide a quantitative, spatially explicit estimate of ecological resilience and resistance (R&R) under ambient and projected future climate conditions. Objective of our study was to understand where and why climate change will alter the distribution of ecological resilience and resistance in the sagebrush biome throughout the 21st century. To accomplish this, we pursued four specific objectives: we estimated the new R&R indicators under future climate conditions and quantified changes from historical conditions; we developed a continuous R&R index that integrates probability information from the underlying predictive R&R models; we assessed the robustness of projected changes in R&R to uncertainty in future climate conditions. These data represent spatially-explicit estimates of ecological resilience and resistance (R&R; categorical indicators, probabilities, continuous indices) under ambient and downscaled projected historical and future climate conditions (historical, RCP 4.5, and RCP 8.5 CMIP5 scenarios). These data were created in rangelands and open woodlands across the sagebrush biome in 2023. These data were created by a collaboration between Northern Arizona University and the U.S. Geological Survey, Southwest Biological Science Center based on modeling which utilized predictive R&R models utilizing ecological and climate metrics which were based on soil properties (NRCS), ambient climate data (gridMET), and downscaled climate projections (MACAv2-METDATA). These data can be used to assess geographic patterns in resilience and resistance under ambient and projected future climate conditions.

These data were compiled to provide a quantitative, spatially explicit estimate of ecological resilience and resistance (R&R) under ambient and projected future climate conditions. Objective of our study was to understand where and why climate change will alter the distribution of ecological resilience and resistance in the sagebrush biome throughout the 21st century. To accomplish this, we pursued four specific objectives: we estimated the new R&R indicators under future climate conditions and quantified changes from historical conditions; we developed a continuous R&R index that integrates probability information from the underlying predictive R&R models; we assessed the robustness of projected changes in R&R to uncertainty in future climate conditions. These data represent spatially-explicit estimates of ecological resilience and resistance (R&R; categorical indicators, probabilities, continuous indices) under ambient and downscaled projected historical and future climate conditions (historical, RCP 4.5, and RCP 8.5 CMIP5 scenarios). These data were created in rangelands and open woodlands across the sagebrush biome in 2023. These data were created by a collaboration between Northern Arizona University and the U.S. Geological Survey, Southwest Biological Science Center based on modeling which utilized predictive R&R models utilizing ecological and climate metrics which were based on soil properties (NRCS), ambient climate data (gridMET), and downscaled climate projections (MACAv2-METDATA). These data can be used to assess geographic patterns in resilience and resistance under ambient and projected future climate conditions.

Identifying ecologically relevant reference sites is important for evaluating ecosystem recovery, but the relevance of references that are temporally static is unclear in the context of vast landscapes with varying disturbance and environmental contexts over space and time. This question is pertinent for landscapes dominated by sagebrush (Artemisia spp.) which face a suite of threats from disturbance and development but also have lengthy recovery times. Here, we applied a dynamic reference approach to studying and projecting recovery of sagebrush on former oil and gas well pads in southwestern Wyoming, USA, using over 3 decades of remote sensing data (1985–2018). We also used quantile regression to evaluate factors that may affect recovery including soils, weather, elevation, and well pad characteristics. We then created projections for percent recovery and years to recovery (relative to references) across the study area, as well as comparisons among weather covariates (root mean square error), resulting in 8 rasters, each with 5 bands representing 5 quantiles.

Identifying ecologically relevant reference sites is important for evaluating ecosystem recovery, but the relevance of references that are temporally static is unclear in the context of vast landscapes with varying disturbance and environmental contexts over space and time. This question is pertinent for landscapes dominated by sagebrush (Artemisia spp.) which face a suite of threats from disturbance and development but also have lengthy recovery times. Here, we applied a dynamic reference approach to studying and projecting recovery of sagebrush on former oil and gas well pads in southwestern Wyoming, USA, using over 3 decades of remote sensing data (1985–2018). We also used quantile regression to evaluate factors that may affect recovery including soils, weather, elevation, and well pad characteristics. We then created projections for percent recovery and years to recovery (relative to references) across the study area, as well as comparisons among weather covariates (root mean square error), resulting in 8 rasters, each with 5 bands representing 5 quantiles.

Identifying ecologically relevant reference sites is important for evaluating ecosystem recovery, but the relevance of references that are temporally static is unclear in the context of vast landscapes with varying disturbance and environmental contexts over space and time. This question is pertinent for landscapes dominated by sagebrush (Artemisia spp.) which face a suite of threats from disturbance and development but also have lengthy recovery times. Here, we applied a dynamic reference approach to studying and projecting recovery of sagebrush on former oil and gas well pads in southwestern Wyoming, USA using over 3 decades of remote sensing data (1985–2018). We also used quantile regression to evaluate factors that may affect recovery including soils, weather, elevation, and well pad characteristics. We then created projections for percent recovery and years to recovery (relative to thresholds for greater sage-grouse [Centrocercus urophasianus] habitat) in areas identified as either nesting or summer (brood-rearing) habitat, resulting in 4 rasters, each with 5 bands representing 5 quantiles.