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).

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 disturbance and environmental contexts varying 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 and thresholds for greater sage-grouse [Centrocercus urophasianus] habitat) across the study area. This approach substantially increased our sample, and therefore inferential base, and illustrated benefits of using dynamic references and quantile regression to evaluate and project recovery of vegetation such as sagebrush. In this data release, we provide datasets used to fit models and projection maps for percent recovery and years to recovery across the study area and in areas identified as greater sage-grouse habitat.

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 disturbance and environmental contexts varying 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 and thresholds for greater sage-grouse [Centrocercus urophasianus] habitat) across the study area. This approach substantially increased our sample, and therefore inferential base, and illustrated benefits of using dynamic references and quantile regression to evaluate and project recovery of vegetation such as sagebrush. In this data release, we provide datasets used to fit models and projection maps for percent recovery and years to recovery across the study area and in areas identified as greater sage-grouse habitat.

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.

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.