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', 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. In revision). 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.tif' and 'perc_recov.tif' are rasters for predicted time to recovery and percent recovery after 100 years, respectively, for 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.

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.

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.

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. Data formatting necessary for this analysis created two datasets, one with reference pixels identified after applying both local and general masks (data_local.csv) and the other with only general masks (data_general.csv).

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. Data formatting necessary for this analysis created two datasets, one with reference pixels identified after applying both local and general masks (data_local.csv) and the other with only general masks (data_general.csv).

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.