All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration.Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). This data release consists of 9 rasters and 1 polygon shapefile organized into three bundles associated with distinct problem sets addressing issues in sagebrush restoration. 1) Landscape weighting scenarios (Fig 3), 2) Protected lands connectivity (Fig. 4), and 3) Local-scale wellpad restoration (Fig. 5). Each of these bundles is described further in subsequent metadata files. Spatial data associated with these data sets include selected pixels or polygons under various scenarios prioritizing sagebrush restoration. Raster layers (90x90m cell size) associated with Figure 3 in the manuscript include selected pixels based on time-to recovery of sagebrush and habitat potential for target species, for all species and a stacked raster layer representing the number of times each pixel occurred in one or more scenarios. Raster layers (360x360m cell size) associated with figure 4 in the manuscript include a comparison of selected cells with no connectivity requirements, with a penalty for unconnected features, and with a penalty for unconnected features and including “locked-in” protected lands. Spatial polygons associated with Figure 5 in the manuscript include selection wellpads under projected oil and gas buildout, requiring protection of 10% of habitat for all species under no, low, moderate, and high connectivity requirements. PReSET tool is currently housed at USGS FORT.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Raster layers (360x360m cell size) associated with figure 4 in Duchardt et al. 2021 include a comparison of selected cells with no connectivity requirements (PReSETnoconn_nolock.tif), with a penalty for unconnected features (PReSEThighconn_nolock.tif), and with a penalty for unconnected features and including “locked-in” protected lands (PReSEThighconn_lock.tif). PReSET tool is currently housed at USGS FORT.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Raster layers (360x360m cell size) associated with figure 4 in Duchardt et al. 2021 include a comparison of selected cells with no connectivity requirements (PReSETnoconn_nolock.tif), with a penalty for unconnected features (PReSEThighconn_nolock.tif), and with a penalty for unconnected features and including “locked-in” protected lands (PReSEThighconn_lock.tif). PReSET tool is currently housed at USGS FORT.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Spatial polygons associated with Figure 5 in Duchardt et al. 2021 include selection wellpads under projected oil and gas buildout, requiring protection of 10% of habitat for all species under no, low, moderate , and high connectivity requirements. PReSET tool is currently housed at USGS FORT.

All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and meaningful sagebrush restoration. Inputs into the tool included occupancy data layers for six focal species (Brewer’s sparrow (Spizella breweri), sagebrush sparrow (Artemisiospiza nevadensis), sage thrasher (Oreoscoptes montanus), greater sage-grouse (Centrocercus urophasianus), pronghorn (Antilocapra americana) and greater short-horned lizard (Phrynosoma hernandesi) generated within the Sagebrush Ecosystem Conservation and Management: Ecoregional Assessment Tools and Models for the Wyoming Basins (Hanser et al. 2011). The layer to assess restoration suitability was predicted time to sagebrush recovery (Monroe et al. 2021; https://doi.org/10.5066/P9XV8GH7). Spatial polygons associated with Figure 5 in Duchardt et al. 2021 include selection wellpads under projected oil and gas buildout, requiring protection of 10% of habitat for all species under no, low, moderate , and high connectivity requirements. PReSET tool is currently housed at USGS FORT.

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.

This data release includes the results (and some input data) of a spatial conservation prioritization intended to guide management of sagebrush ecosystems in northwest Colorado. Stemming the loss and degradation of sagebrush ecosystems requires science-based tools to balance diverse habitat requirements of species and guide management actions to where they are most likely to successfully achieve desired outcomes. Through a series of end-user engagement workshops, we identified northwest Colorado as an ideal location for co-developing a decision support tool that can guide strategic conservation delivery by identifying optimal areas for specific sagebrush management actions. We partnered with Colorado Parks and Wildlife (CPW) staff to adapt a local application of the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) to address three primary sagebrush management actions implemented by CPW: 1) long-term conservation of important sagebrush habitat (SBConservation), 2) protection of fire vulnerable sagebrush habitat (SBWildfireProtection), and 3) restoration of sagebrush habitat where success was most likely (SBRestoration). To meet CPW objectives, we ran four iterations related to each conservation action using varied combinations of input data representing: a) vegetation only (VegOnly), b) vegetation and greater sage-grouse (Centrocercus urophasianus; Veg_GRSG), c) vegetation, greater sage-grouse, and sagebrush songbirds (Veg_GRSG_Songbirds), and d) vegetation and greater sage-grouse, constrained by the Sagebrush Conservation Design (Veg_GRSG_SCD). This structure culminated in twelve unique results outputs, where the base file name is composed of the problem number (e.g., Problem 1a), followed by the management action (e.g., _SBConservation) and the iteration (e.g., _VegOnly.tif). Each results layer depicts three tiers of prioritization: Tier 1) the highest priority sites totaling 50,000 acres, Tier 2) high priority sites totaling 100,000 acres, and Tier 3) medium priority sites totaling 150,000 acres. In total, each output file prioritizes 300,000 acres for management. Finally, to facilitate interpretation and reproducibility of our results, this data release also includes two planning unit layers (PlanningUnits_Problems1and2_PotentialSB.tif, PlanningUnits_Problem3_DegradedSB.tif) and three feature layers (SageConn_CCDConn_Loss1985_2020.tif, SBRecovery_SBCover_Drill_Artemisia_NoFire.tif, SBRecovery_SBCoverIncrease_Drill_Artemisia_NoFire.tif) we derived from published datasets specifically for this effort.