Bryan C. Tarbox Nicholas J. Van Lanen Adrian P. Monroe Cameron L. Aldridge 20250303 raster digital data https://doi.org/10.5066/P13CCNRY 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. This data release contains three suites of spatial conservation prioritizations intended to inform strategic management of sagebrush ecosystems in northwest Colorado. 2020 2023 Most input data was derived from remotely sensed landscape conditions between 2020 and 2023, although some input data were informed by or representative of conditions prior to 2020. Complete None planned Three counties in northwest Colorado: Moffat, Rio Blanco, and Routt. -109.0374 -106.6975 41.2205 39.5842 ISO 19115 Topic Category biota None sagebrush sage-grouse songbirds multi-species management sagebrush conservation design wildfire protection sagebrush restoration spatial conservation prioritization optimization USGS Thesaurus terrestrial ecosystems natural resource management wildlife geospatial datasets adaptive management decision support methods spatial analysis USGS Metadata Identifier USGS:67ae2ed7d34e3f09c0e0f172 Common geographic areas United States Colorado None Spizella breweri Artemisiospiza nevadensis Oreoscoptes montanus Centrocercus urophasianus Artemisia tridentata Domain Eukaryota Kingdom Animalia animals Subkingdom Bilateria triploblasts Infrakingdom Deuterostomia Phylum Chordata chordates Subphylum Vertebrata vertebrates Infraphylum Gnathostomata Superclass Tetrapoda Class Aves Birds Order Passeriformes Perching Birds Family Passerellidae New World sparrows American sparrows towhees Genus Spizella Chipping Sparrows Species Spizella breweri Brewer's Sparrow TSN: 179440 Genus Artemisiospiza Species Artemisiospiza nevadensis Sagebrush Sparrow TSN: 997723 Family Mimidae Mockingbirds Thrashers Genus Oreoscoptes Sage Thrashers Species Oreoscoptes montanus Sage Thrasher TSN: 178654 Order Galliformes Fowls Gallinaceous Birds Family Phasianidae Partridges Turkeys Grouse Pheasants Quail Genus Centrocercus Sage Grouse Species Centrocercus urophasianus Greater Sage Grouse Sage Grouse Greater Sage-Grouse TSN: 175855 Kingdom Plantae plants Subkingdom Viridiplantae green plants Infrakingdom Streptophyta land plants Superdivision Embryophyta Division Tracheophyta vascular plants tracheophytes Subdivision Spermatophytina spermatophytes seed plants Class Magnoliopsida Superorder Asteranae Order Asterales Family Asteraceae sunflowers Genus Artemisia sagebrush Species Artemisia tridentata big sagebrush big sagebush TSN: 35498 None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Bryan C. Tarbox U.S. Geological Survey, ROCKY MOUNTAIN REGION Ecologist mailing

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Fort Collins CO 80526 US 970-226-9309 btarbox@usgs.gov This work was supported by the U.S. Geological Survey and Colorado Parks and Wildlife. Analyses were run under Windows Server 2019 (four 2.5 GHz processors, 1.5 TB RAM) using the packages prioritizr (v. 8.0.3), gurobi (v. 9.5-2), and terra (v. 1.7-78) in R (v. 4.4.1). I.R. Ball H.P. Possingham M.E. Watts 20090528 In: Moilanen, A., Wilson, K.A., Possingham, H.P. (Eds), Chapter: Pages 185 - 195 in Spatial Conservation Prioritization: Quantitative Methods and Computational Tools publication Oxford, UK. Oxford University Press https://doi.org/10.1093/oso/9780199547760.003.0014 C.J. Duchardt A.P. Monroe J.A. Heinrichs M.S. O'Donnell D.R. Edmunds C.L. Aldridge 20210617 Biological Conservation publication https://doi.org/10.1016/j.biocon.2021.109212 K. Doherty D.M. Theobald J.B. Bradford L.A. Wiechman G. Bedrosian C.S. Boyd M. Cahill P.S. Coates M.K. Creutzburg M.R. Crist S.P. Finn A.V. Kumar C.E. Littlefield J.D. Maestas K.L. Prentice B.G. Prochazka T.E. Remington W.D. Sparklin J.C. Tull Z. Wurtzebach K.A. Zeller 20220922 publication n/a U.S. Geological Survey Department of the Interior Open-File Report 2022–1081 https://doi.org/10.3133/ofr20221081 Gurobi Optimization and LLC 2021 application/service https://www.gurobi.com/ J.O. Hanson R. Schuster N. Morrell M. Strimas-Mackey B.P.M. Edwards M.E. Watts P. Arcese J. Bennett H.P. Possingham 20230816 application/service https://prioritizr.net/index.html R. Hijmans 2024 remote-sensing image https://rspatial.github.io/terra/ R Core Team 20240615 4.4.1 application/service Vienna, Austria R Foundation for Statistical Computing https://www.R-project.org B.C. Tarbox A.P. Monroe M.I. Jeffries J.L. Welty M.S. O'Donnell R.S. Arkle D.S. Pilliod P.S. Coates J.A. Heinrichs D.J. Manier C.L. Aldridge 20241019 publication Landscape Ecology Springer https://doi.org/10.1007/s10980-024-01968-z G.T. Wann N.D. Van Schmidt J.E. Shyvers B.C. Tarbox M.M. McLachlan M.S. O'Donnell A.J. Titolo P.S. Coates D.R. Edmunds J.A. Heinrichs A.P. Monroe C.L. Aldridge 2023 publication Global Ecology and Conservation Elsevier https://doi.org/10.1016/j.gecco.2022.e02349 N.J. Van Lanen A.P. Monroe C.L. Aldridge 20231114 publication Ecology and Evolution Wiley https://doi.org/10.1002/ece3.10648 N.J. Van Lanen J.E. Shyvers B.C. Tarbox A.P. Monroe P.J. Anderson D.K. Jones K.G. Dahm C.L. Aldridge 20240707 publication Conservation Science and Practice Society for Conservation Biology https://doi.org/10.1111/csp2.13154 Output raster layers were inspected to ensure prioritization tiers (1 to 3) reflected reasonable outcomes relative to input data. Custom planning unit raster layers were inspected to ensure all sites available for optimization (i.e., 1) reflected expectations of potential sagebrush cover or degraded sagebrush. Custom feature raster layers were inspected to ensure their range reflected reasonable estimates of sagebrush cover or connectivity loss relative to their source layers. Output raster layers were inspected to ensure all values ranged from 1 to 3, indicating the prioritization tier of each pixel (or NA, meaning unselected). Custom planning unit raster layers were inspected to ensure all values were either 1 (available for optimization) or NA (not available for optimization). Custom feature raster layers were inspected to ensure their range reflected reasonable estimates of sagebrush cover or connectivity loss relative to their source layers. No evidence was found of duplicates, omissions or topological errors. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. However, raster layers were inspected to ensure spatial location aligned with area of interest. A formal accuracy assessment of the vertical positional information in the data set is not applicable. LANDFIRE 2016 raster digital data n/a U.S. Geological Survey Department of the Interior https://landfire.gov/vegetation/evt Digital and/or Hardcopy 2016 publication date LANDFIRE EVT 2016 Data was used to identify planning units (PReSET_NWCO_PUs_AllSagebrush.tif) for optimization problems 1 (SBConservation) and 2 (SBWildfireProtection). Data were also used to identify sagebrush and sagebrush-associated habitats for calculating another Feature Layer: Sagebrush Core Area Index. Nicholas J Van Lanen Adrian P Monroe Cameron L Aldridge 20230817 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P9MJHTMQ Digital and/or Hardcopy 20200401 20200731 publication date Predicted Songbird Densities Predicted densities of Brewer's sparrow, sage thrasher and sagebrush sparrow were used as Feature Layers to inform sagebrush songbird iterations (Veg_GRSG_Songbirds) of all three optimization problems. G. K. Dillon J. H. Scott M. R. Jaffe J. H. Olszewski K. C. Vogler M. A. Finney K. C. Short K. L. Riley I. C. Grenfell W. M. Jolly S. Brittain 2023 3rd raster digital data n/a U.S. Forest Service Department of Agriculture https://doi.org/10.2737/RDS-2016-0034-3 Digital and/or Hardcopy 2020 ground condition Burn Probability Burn probability data were used as a Feature Layer for optimization problem 2 (SBWildfireProtection) to prioritize sites at higher risk of wildfire. Matthew B. Rigge Brett Bunde Kory Postma Hua Shi 20240117 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P9SJXUI1 Digital and/or Hardcopy 19850101 20230927 ground condition RCMAP Time-Series Sagebrush cover estimates were used to identify planning units for all optimization problems (PReSET_NWCO_PUs_AllSagebrush.tif, PReSET_NWCO_PUs_DegSagebrush.tif). Sagebrush cover estimates were also used to generate two Feature Layers: Current Sagebrush Cover (mean cover from 2021-2023, capped at 10%) and Maximum Sagebrush Cover (maximum cover from 2004-2023, capped at 15%). Matthew B. Rigge Kory Postma Brett Bunde Hua Shi 20230925 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P9J490BH Digital and/or Hardcopy 20200101 20850101 ground condition RCMAP Future Projections Estimated sagebrush cover for 2041-2070 (RCP 4.5, capped at 15%) was used as a Feature Layer to prioritize long-term conservation of sagebrush in optimization problem 1 (SBConservation). Estimated tree cover for 2041-2070 (RCP 4.5) was used as a Feature Layer in optimization problem 3 (SBRestoration) to guide restoration away from sites expected to convert to pinyon-juniper cover in the future. Matthew B. Rigge Debra K. Meyer Brett Bunde 20210427 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P9D2597G Digital and/or Hardcopy 1985 2018 ground condition RCMAP Ecological Potential Ecological potential of sagebrush cover was used to identify planning units (PReSET_NWCO_PUs_DegSagebrush.tif) for optimization problem 3 (SBRestoration). Gregory T. Wann Nathan D. Van Schmidt Jessica E. Shyvers Bryan C. Tarbox Megan M. McLachlan Michael S. O'Donnell Anthony J. Titolo Peter S. Coates David R. Edmunds Julie A. Heinrichs Adrian P. Monroe Cameron L. Aldridge 20221102 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P95YAUPH Digital and/or Hardcopy 2019 ground condition Lek Persistence Probability Data were used as a Feature Layer to inform greater sage-grouse (Veg_GRSG), sagebrush songbird (Veg_GRSG_Songbirds), and sagebrush conservation design (Veg_GRSG_SCD) iterations of all three optimization problems. Erin K. Buchholtz Michael S. O'Donnell Julie A. Heinrichs Cameron L. Aldridge 20230605 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P9ED3OHH Digital and/or Hardcopy 19850101 20201201 ground condition Sagebrush Connectivity Data from 2020 (sageconn_2020_ccdorig_270m.tif) were used as a Feature Layer in optimization problems 1 (SBConservation) and 2 (SBWildfireProtection) to prioritize sites with high sagebrush connectivity. Data from 1985 (sageconn_1985_ccdorig_270m.tif) and 2020 (sageconn_2020_ccdorig_270m.tif) were used to derive another Feature Layer: Sagebrush Connectivity Loss, which is included in this data release (PReSET_NWCO_Feature_ConnectivityLoss19852020.tif). Adrian P. Monroe Bryan C. Tarbox Michelle I. Jeffries Justin L. Welty Michael S. O'Donnell Robert Arkle David Pilliod Peter S. Coates Julie A. Heinrichs Daniel Manier Cameron L. Aldridge 20240905 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P13HMGUZ Digital and/or Hardcopy 1986 2021 ground condition Predicted Sagebrush Recovery (No Treatment) Predicted sagebrush cover with no restoration treatments 30 years after simulated wildfire (sagebrush_notreat.tif) were used as a Feature Layer in optimization problem 2 (SBWildfireProtection) to prioritize sites unlikely to recover from wildfire for protection. Models used to generate these data were also modified to derive another Feature Layer: Predicted Sagebrush Increase (Drill Seeding; PReSET_NWCO_Feature_SBIncrease_Drill_NoFire.tif) and an intermediate dataset (PReSET_NWCO_Feature_SBCover_Drill_NoFire.tif). Kevin Doherty David M. Theobald Martin C. Holdredge Lief A. Wiechman John B. Bradford 20220826 raster digital data n/a U.S. Geological Survey Department of the Interior https://doi.org/10.5066/P94Y5CDV Digital and/or Hardcopy 2021 2022 ground condition SCD Data (SEI_2017_2020_30_Current.tif) were used to identify planning units for Sagebrush Conservation Design (Veg_GRSG_SCD) iterations of each optimization problem. Step 1: We processed or developed feature and planning unit layers for inclusion in our prioritization efforts. Planning Unit Layers We developed two planning unit layers for this project: 1) Potential Conservation/Protection Sites and 2) Potential Restoration Sites. Additionally, we used the Sagebrush Conservation Design (source: SCD) to identify planning units for the Sagebrush Conservation Design iteration (Veg_GRSG_SCD) of each optimization problem, where the planning unit layer included only sites identified by the SCD as core sagebrush areas or growth opportunity areas. Potential Conservation/Protection Sites: This planning unit layer was used for the first two optimization problems (SBConservation, SBWildfireProtection) and was intended to represent all potential sagebrush habitat. Each included pixel met one or more of the following three requirements: 1) Maximum sagebrush cover (1985-2023) >=4% (source: RCMAP Time-Series); 2) Future sagebrush cover >= 4% (source: RCMAP Future Projections); or 3) Landfire Existing Vegetation Type (source: LANDFIRE EVT 2016) was classified as sagebrush habitat: Colorado Plateau Mixed Low Sagebrush Shrubland (EVT 7064), Wyoming Basins Dwarf Sagebrush Shrubland and Steppe (EVT 7072), Inter-Mountain Basins Big Sagebrush Shrubland (EVT 7080), Inter-Mountain Basins Big Sagebrush Steppe (EVT 7125), or Inter-Mountain Basins Montane Sagebrush Steppe (EVT 7126). This layer is included in this data release (PlanningUnits_Problems1and2_PotentialSB.tif). Potential Restoration Sites: This planning unit layer was used for the third optimization problem (SBRestoration) and was intended to represent potentially degraded sagebrush habitat. Sagebrush habitat was considered potentially degraded at sites where sagebrush loss was at least two standard deviations above mean change from historic cover (1985-1986; source: RCMAP Time-Series) or the ecological potential cover (i.e., expected cover under undisturbed conditions; source: RCMAP Ecological Potential). This layer is included in this data release (PlanningUnits_Problem3_DegradedSB.tif). Feature Layers We used a total of 14 feature layers to identify optimal sites for implementing CPW's three sagebrush management actions (i.e., optimization problems): Vegetation feature layers (used in all iterations): Burn Probability (source: Burn Probability), Current Sagebrush Cover and Maximum Sagebrush Cover (source: RCMAP Time-Series), Future Sagebrush Cover and Future Tree Cover (source: RCMAP Future Projections), Sagebrush Connectivity and Sagebrush Connectivity Loss (source: Sagebrush Connectivity), Sagebrush Core Area Index (source: LANDFIRE EVT 2016), Predicted Sagebrush Cover (No Treatment) and Predicted Sagebrush Increase (Drill Seeding) (source: Predicted Sagebrush Recovery (No Treatment)). Greater sage-grouse feature layer (used in Veg_GRSG, Veg_GRSG_Songbirds and Veg_GRSG_SCD iterations): Lek Persistence Probability (source: Lek Persistence Probability). Sagebrush songbird feature layers (used in Veg_GRSG_Songbirds iterations): Brewer's Sparrow Density, Sage Thrasher Density and Sagebrush Sparrow Density (source: Predicted Songbird Densities). For most feature layers, we used data as published (see Source Inputs). However, some feature layers were minimally processed. Current Sagebrush Cover was calculated as the mean sagebrush cover from 2021 to 2023, while Maximum Sagebrush Cover was calculated as the maximum value of sagebrush cover from 2004 to 2023 (source: RCMAP Time-Series). To reflect CPW objectives, we capped Maximum Sagebrush Cover and Future Sagebrush Cover at a maximum value of 15% cover for SBConservation, and Current Sagebrush Cover at a maximum value of 10% cover for SBWildfireProtection. Sagebrush Core Area Index was calculated using the R package landscapemetrics (v. 1.5.0; Hesselbarth et al. 2019) and a binary layer provided by the Bureau of Land Management that reclassified LANDFIRE EVT categories to identify sagebrush (7064, 7072, 7079, 7080, 7124, 7125, 7126) and sagebrush-associated (7062, 7065, 7081, 7085, 7086, 7107, 7123, 7127, 7141, 7148, 7250) habitats (source: LANDFIRE EVT 2016). Two additional feature layers were more substantially modified and are thus included in this data release: We derived Sagebrush Connectivity Loss (SageConn_CCDConn_Loss1985_2020.tif) by subtracting 2020 estimates of Sagebrush Connectivity from 1985 estimates of Sagebrush Connectivity (source: Sagebrush Connectivity) and setting negative values to 0. We derived Predicted Sagebrush Increase (Drill Seeding) by modifying generalized additive models described by Tarbox et al. (2024) to predict sagebrush recovery based on current (i.e., 2021) sagebrush cover instead of 1% sagebrush cover (i.e., simulated wildfire). To do this, we assumed existing presence of sagebrush cover, skipping the zero-inflated probability of presence component of the published models. Additionally, we did not add 1 to the cumulative burns covariate of the sagebrush abundance model used to predict recovery. This generated a layer predicting sagebrush cover 30 years from 2021 (i.e., 2051) without a simulated wildfire that could guide restoration of already degraded sagebrush habitats based on current conditions (SBRecovery_SBCover_Drill_Artemisia_NoFire.tif). To identify the additive benefit of restoration relative to current conditions, we subtracted sagebrush cover in 2023 (source: RCMAP Time-Series) from this layer to derive the feature layer used in optimization problem 3 (SBRestoration): SBRecovery_SBCoverIncrease_Drill_Artemisia_NoFire.tif. We projected all input data to Albers Conical Equal Area, NAD 1983, EPSG 9001 and cropped and masked each input layer to northwest Colorado (Moffat, Rio Blanco, and Routt counties) using the terra package (v. 1.7-78; Hijmans 2024) in R (v. 4.4.1; R Core Team 2024). We also normalized feature layer data (i.e., rescaled from 0 to 1) by subtracting the minimum value and dividing by the maximum minus the minimum. References Buchholtz, E.K., O'Donnell, M.S., Heinrichs, J.A., and Aldridge, C.L. 2023. Sagebrush structural connectivity yearly and temporal trends based on RCMAP sagebrush products, biome-wide from 1985 to 2020: U.S. Geological data release, https://doi.org/10.5066/P9ED3OHH Dillon, G.K., Scott, J.H., Jaffe, M.R., Olszewski, J.H., Vogler, K.C., Finney, M.A., Short, K.C., Riley, K.L., Grenfell, I.C., Jolly, W.M., and Brittain, S. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3 Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A. 2022. A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081 Hesselbarth, M., Sciaini, M., Wiegand, K.A., and Nowosad, J. 2019. Landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42: 1648-1657. Hijmans, R. 2024. terra: Spatial Data Analysis. R package version 1.7-78, https://rspatial.github.io/terra/, https://rspatial.org/ LANDFIRE. 2016. Existing Vegetation Type Layer, LANDFIRE 2.0.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture. https://landfire.gov/vegetation/evt Monroe, A.P., Tarbox, B.C., Jeffries, M.I., Welty, J.L., O'Donnell, M.S., Arkle, R.S., Pilliod, D.S., Coates, P.S., Heinrichs, J.A., Manier, D.J., and Aldridge, C.L. 2024. Sagebrush recovery projections across the biome, 30 years after two seeding treatment applications, and associated model data (1986-2021): U.S. Geological Survey data release, https://doi.org/10.5066/P13HMGUZ R Core Team. 2024. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org Rigge, M.B., Bunde, B., Postma, K., and Shi, H. 2024. Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2023: U.S. Geological Survey data release, https://doi.org/10.5066/P9SJXUI1 Rigge, M., Meyer, D.K., and Bunde, B. 2021. Ecological Potential Fractional Component Cover Based on Long-Term Satellite Observations Across the Western United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9D2597G Rigge, M., Postma, K., Bunde, B., and Shi, H. 2023. Projections of Rangeland Fractional Component Cover Across Western US Rangelands for Representative Concentration Pathways (RCP) 4.5 and 8.5 Scenarios for the 2020s, 2050s, and 2080s Time-Periods: U.S. Geological Survey data release, https://doi.org/10.5066/P9J490BH Tarbox, B.C., A.P. Monroe, M.I. Jeffries, J.L. Welty, M.S. O’Donnell, R.S. Arkle, D.S. Pilliod, P.S. Coates, J.A. Heinrichs, D.J. Manier, and C.L. Aldridge. 2024. Leveraging extensive soil, vegetation, fire, and land treatment data to inform restoration across the sagebrush biome. Landscape Ecology 39, 184(2024). https://doi.org/10.1007/s10980-024-01968-z Van Lanen, N.J., Monroe, A.P., and Aldridge, C.L. 2023. Predicted 2020 densities for 11 songbird species across the western United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9MJHTMQ Wann, G.T., Van Schmidt, N.D., Shyvers, J.E., Tarbox, B.C., McLachlan, M.M., O'Donnell, M.S., Titolo, A.J., Coates, P.S., Edmunds, D.R., Heinrichs, J.A., Monroe, A.P., and Aldridge, C.L. 2022. U.S. range-wide spatial prediction layers of lek persistence probabilities for greater sage-grouse: U.S. Geological Survey data release, https://doi.org/10.5066/P95YAUPH SCD RCMAP Time-Series RCMAP Future Projections LANDFIRE EVT 2016 RCMAP Ecological Potential Burn Probability Sagebrush Connectivity Predicted Sagebrush Recovery (No Treatment) Lek Persistence Probability Predicted Songbird Densities 20240822 Bryan Tarbox U.S. Geological Survey, ROCKY MOUNTAIN REGION Ecologist mailing

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Fort Collins CO 80526 US 970-226-9309 btarbox@usgs.gov Step 2: We designed three optimization problems to prioritize sagebrush management actions: 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). The first two problems (SBConservation and SBWildfireProtection) used the same planning units layer (source: Potential Conservation/Protection Sites; published in this data release as ‘PlanningUnits_Problems1and2_PotentialSB.tif’), while problem 3 (SBRestoration) used a different planning units layer (source: Potential Restoration Sites; published in this data release as ‘PlanningUnits_Problem3_DegradedSB.tif’). For all three problems, we used a cost layer where each pixel (i.e., 30 x 30 m) was equal to 0.22239484 acres. This allowed us to set acreage budgets to meet priority tiers for each problem (i.e., 50,000 to 150,000 acres). For each optimization problem, we ran four iterations to meet CPW objectives: a) vegetation only (VegOnly), b) vegetation and greater sage-grouse (Veg_GRSG), c) vegetation, greater sage-grouse, and sagebrush songbirds (Veg_GRSG_Songbirds), and d) vegetation and greater sage-grouse, constrained by Core Sagebrush and Growth Opportunity Areas identified by the Sagebrush Conservation Design (Veg_GRSG_SCD). This approach resulted in 12 total solutions (e.g., Problem 1a to Problem 3d), each including three priority tiers totaling 300,000 acres (Tier 1: 50,000 acres, Tier 2: 100,000 acres, and Tier 3: 150,000 acres). Below we describe the specific design of each optimization problem. Problem 1: Long-term conservation of important sagebrush habitat (SBConservation) Problem 1a (VegOnly) included four feature layers (source: RCMAP Future Projections, RCMAP Time-Series, Sagebrush Connectivity, and LANDFIRE EVT 2016). Future Sagebrush Cover and Sagebrush Connectivity were multiplied by 0.5 to reduce their influence on prioritized sites. Problem 1b (Veg_GRSG) included one additional feature layer (Lek Persistence Probability). Problem 1c (Veg_GRSG_Songbirds) used the same feature layers as problem 1b (Veg_GRSG), plus three additional feature layers representing Brewer's sparrow, sage thrasher and sagebrush sparrow densities (source: Predicted Songbird Densities). The additional songbird feature layers used in problem 1c (Veg_GRSG_Songbirds) were multiplied by 0.25 to reduce their influence on prioritized sites. Problem 1d (Veg_GRSG_SCD) used the same feature layers as problem 1b (Veg_GRSG), but substituted in a different planning unit layer (source: SCD). Problem 2: Protection of fire vulnerable sagebrush habitat (SBWildfireProtection) Problem 2a (VegOnly) included five feature layers (source: Burn Probability, RCMAP Time-Series, Sagebrush Connectivity, LANDFIRE EVT 2016, and Predicted Sagebrush Recovery (No Treatment)). Burn Probability and Sagebrush Connectivity were multiplied by 0.5 to reduce their influence on prioritized sites. Additionally, Predicted Sagebrush Recovery (No Treatment) was transposed using eq.1 so that sites with lower probability of recovery would be prioritized for protection: eq.1: ((raster value - maximum raster value) × -1) + (minimum raster value) Problem 2b (Veg_GRSG) included one additional feature layer (Lek Persistence Probability), which was multiplied by 0.5. Problem 2c (Veg_GRSG_Songbirds) used the same feature layers as problem 2b (Veg_GRSG), plus three additional feature layers representing Brewer's sparrow, sage thrasher and sagebrush sparrow densities (source: Predicted Songbird Densities). The additional songbird feature layers used in problem 2c (Veg_GRSG_Songbirds) were multiplied by 0.25 to reduce their influence on prioritized sites. Problem 2d (Veg_GRSG_SCD) used the same feature layers as problem 2d (Veg_GRSG), but substituted in a different planning unit layer (source: SCD). Problem 3: Restoration of sagebrush habitat where success is most likely (SBRestoration) Problem 3a (VegOnly) included three feature layers (source: RCMAP Future Projections; Sagebrush Connectivity Loss, published in this data release as ‘SageConn_CCDConn_Loss1985_2020.tif’; and Predicted Sagebrush Increase (Drill Seeding), published in this data release as ‘SBRecovery_SBCoverIncrease_Drill_Artemisia_NoFire’). Future Tree Cover and Sagebrush Connectivity Loss were multiplied by 0.5 to reduce their influence on prioritized sites. Additionally, Future Tree Cover was transposed using eq.1 so that sites with lower projections of future tree cover would be prioritized for restoration. Problem 3b (Veg_GRSG) included one additional feature layer (Lek Persistence Probability). Problem 3c (Veg_GRSG_Songbirds) used the same feature layers as problem 3b (Veg_GRSG), plus three additional feature layers representing Brewer's sparrow, sage thrasher and sagebrush sparrow densities (source: Predicted Songbird Densities). The additional songbird feature layers used in problem 3c (Veg_GRSG_Songbirds) were multiplied by 0.25 to reduce their influence on prioritized sites. Problem 3d (Veg_GRSG_SCD) used the same feature layers as problem 3d (Veg_GRSG), but substituted in a different planning unit layer (source: SCD). RCMAP Time-Series RCMAP Future Projections Sagebrush Connectivity Lek Persistence Probability Predicted Songbird Densities SCD Burn Probability Predicted Sagebrush Recovery (No Treatment) LANDFIRE EVT 2016 20240822 Bryan Tarbox U.S. Geological Survey, ROCKY MOUNTAIN REGION Ecologist mailing

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Fort Collins CO 80526 US 970-226-9309 btarbox@usgs.gov Step 3: We used the prioritizr package (v. 8.0.3; Hanson et al. 2023) in R (v. 4.4.1; R Core Team 2024) to solve the optimization problems described in Step 2. We employed the Gurobi solver package (v. 9.5-2; Gurobi Optimization and LLC 2021) to facilitate faster analysis, given the large number of planning units (Schuster et al. 2020, Duchardt et al. 2021). To meet our objectives, we used prioritizr's "maximum utility objective" function which sums the normalized feature layer inputs and selects pixels with the highest total values while meeting a specified budget. For each priority tier, we set a budget reflecting CPW’s acreage targets (e.g., 50,000 for Tier 1) and tracked acreage targets by setting the value of planning unit pixels (i.e., 30-m x 30-m squares totaling 900 square meters) to 0.22239484 acres (equivalent to 900 square meters). Additionally, we used binary decisions (i.e., a pixel is either selected or not) and a gap allowance of 0.01. The resulting solutions from these analyses are published in this data release as individual raster files (.tif), which represent the optimal management locations for each problem. Raster values range from 1-3 representing priority tiers where Tier 1 indicates very high priority (value = 3), Tier 2 indicates high priority (value = 2), and Tier 3 indicates medium priority (value =1). References Duchardt, C. J., A. P. Monroe, J. A. Heinrichs, M. S. O'Donnell, D. R. Edmunds, and C. L. Aldridge. 2021. Prioritizing restoration areas to conserve multiple sagebrush-associated wildlife species. Biological Conservation 260. doi.org/10.1016/j.biocon.2021.109212 Gurobi Optimization and LLC. 2021. gurobi: Gurobi Optimizer 9.5 interface. https://www.gurobi.com/ Hanson, J.O., Schuster, R., Morrell, N., Strimas-Mackey, M., Edwards, B.P.M., Watts, M.E., Arcese, P., Bennett, J., and Possingham, H.P. 2023. prioritizr: Systematic Conservation Prioritization in R. https://prioritizr.net/index.html R Core Team. 2024. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org Schuster, R., J. O. Hanson, M. Strimas-Mackey, and J. R. Bennett. 2020. Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ 8:e9258. 20240918 Bryan Tarbox Ecologist mailing

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Fort Collins Colorado 805626 United States 970-226-9309 btarbox@usgs.gov Raster Grid Cell 5244 7273 1 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 30.000000000077872 30.00000000007792 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101004 Problem1a_SBConservation_VegOnly.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem1b_SBConservation_Veg_GRSG.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem1c_SBConservation_Veg_GRSG_Songbirds.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem1d_SBConservation_Veg_GRSG_SCD.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem2a_SBWildfireProtection_VegOnly.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem2b_SBWildfireProtection_Veg_GRSG.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem2c_SBWildfireProtection_Veg_GRSG_Songbirds.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem2d_SBWildfireProtection_Veg_GRSG_SCD.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem3a_SBRestoration_VegOnly.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem3b_SBRestoration_Veg_GRSG.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem3c_SBRestoration_Veg_GRSG_Songbirds.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 Problem3d_SBRestoration_Veg_GRSG_SCD.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating the priority tier of each pixel selected by the optimization problem. 3 = Tier 1, 2 = Tier 2, 1 = Tier 3. Producer Defined 0.0 3.0 PlanningUnits_Problems1and2_PotentialSB.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating whether a pixel can be considered for prioritization (1 = yes). Producer Defined 1.0 1.0 PlanningUnits_Problem3_DegradedSB.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating whether a pixel can be considered for prioritization (1 = yes, 0 = no). Producer Defined 0.0 1.0 SageConn_CCDConn_Loss1985_2020.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating cumulative current density. Producer Defined 0.0 2187.1274414063 SBRecovery_SBCover_Drill_Artemisia_NoFire.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating percent cover of sagebrush 30 years after drill seeding based on current conditions. Producer Defined 1.0 39.374500274658 SBRecovery_SBCoverIncrease_Drill_Artemisia_NoFire.tif Raster geospatial data file. Producer Defined Value Unique numeric values indicating absolute increase in percent cover of sagebrush compared to 2023 sagebrush cover, 30 years after drill seeding. Producer Defined 0.0 34.27233505249 U.S. Geological Survey - ScienceBase mailing address

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Denver CO 80225 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data https://doi.org/10.5066/P13CCNRY None 20250303 FORT Data Management U.S. Geological Survey FORT Data Management mailing

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