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.

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.

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).

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.