A raster identifying previously burned areas as being 1) recovered (to sagebrush-dominant ecosystem), 2) recovering, or 3) transitioned to annual grass-dominated.

Land management treatments in sagebrush steppe are an important opportunity to break the annual-grass fire cycle, provided they offer long-lasting resistance to annual-grass invasion and do not burn. However, for BLM areas seeded as part of the Emergency Stabilization and Rehabilitation (ESR) program, one of the largest programs for land management treatments, about 1/4 have at least partially reburned over the last 30 years, according to a recent study. Reburning of treatments can cause a loss of investment if fire-intolerant perennials do not recover and/or significant invasions occur, in which case the risks of wildfire are compounded by increased potential for ecological degradation. Alternatively, recovery of fire-tolerant perennials occurs naturally or due to treatments would represent a significant return on prior investment and the occurrence of fire would thus pose reduced ecological hazard risks. Fire risks are highly variable across sagebrush landscapes, owing to variability in fuel loading, ignition potential, and fire transmission. Information is needed on predicting future risks related to reburning - including post-fire hazards related to ecological degradation - for past land management investments to a) identify protection measures that could be applied now, and b) help design and positioning of future treatment investments to minimize their risk of reburning in ways that cause ecological degradation. This dataset was compiled in order to predict reburn risk to areas that had previously burned and were retreated.

Wildfires are increasingly modifying wildlife habitat in the western United States and managers need ways to scope the pace and degree to which post-fire restoration actions can re-create habitat in dynamic landscapes. We simulated post-fire revegetation and greater sage-grouse (Centrocercus urophasianus) habitat restoration using a spatially explicit state-transition simulation model (STSM) developed for sagebrush ecosystems. The STSM represented the vegetation dynamics of the sagebrush ecosystem and included annual fires, annual grass invasion, conifer encroachment, and sagebrush revegetation restoration. We compared simulated vegetation output with sage-grouse perennial grass and sagebrush cover habitat needs and evaluated trajectories of potential habitat for three sage-grouse Priority Area for Conservation (PACs) populations located along the northwestern, central, and eastern edge of the Great Basin. This data release is organized into two general datasets: the ST-Sim library and the associated projections of potential sage-grouse habitat (organized by population). Habitat layers illustrate a time series of potential habitat and 50-year potential change in habitat classification for sage-grouse across space and time. The structure of these data follow: A) STSM Model – contains the ST-Sim library, input, and output files; SagebrushSteppeRestoration.ssim, B) KLAM Habitat Data – contains habitat data for the Klamath Oregon/California PAC (located in the northwestern region of the Great Basin), C) NWINV Habitat Data – contains habitat data for the NW Interior Nevada PAC (located in the central region of the Great Basin), and D) STRAW Habitat Data – contains habitat data for the Strawberry Utah PAC (located along the eastern edge of the Great Basin). The STSM was built using the Syncrosim ST-Sim platform with the software's integrated stock-flow submodel to simulate and track continuous vegetation component cover changes caused by annual growth, natural regeneration, and post-fire sagebrush seeding and planting restoration. Thirteen restoration scenarios representing a combination of three revegetation alternatives (no restoration, seeding, planting) under three effort levels (average, double, maximum), and two durations (single-year, multi-year) were simulated for each PAC landscape. Seeding and planting effort levels were based on historic treatment area polygon data (median size) for sagebrush seeding (6 km2) and planting (4 km2). Area was used as a measure of effort that represented an annual fire response equivalent to average effort, double effort (2x area median), and maximum effort (45 km2). The ‘maximum effort’ scenario represented a hypothetical management response 7-11 times larger than average post-fire revegetation treatment area sizes. Planting scenarios represented the sagebrush cover gains of planting 4 plants/m2 (low-density; LD planting) and 8 plants/m2 (high-density; HD planting). A combination seeding-planting scenario representing single-year gains from seeding and multi-year gains from HD planting (two additional years of sagebrush cover gains) and a passive no restoration scenario equivalent to ‘no effort’ were simulated to compare with single- and multi-year seeding or planting scenarios. Habitat layers were generated at 10-year intervals using the simulated vegetation outputs from the five best restoration scenarios of each type (no restoration, seeding, LD planting, HD planting, multi-year) for each PAC landscape. Sagebrush and perennial grass cover from projected continuous component cover values tracked in the STSM stock-flow (SF) submodel were used to characterize potential habitat based on sage-grouse seasonal life stage cover requirements. Habitat distinctions were based on a given pixel meeting minimum cover amounts and classified pixels as suitable, marginally suitable, and unsuitable relative to seasonal spring (i.e., breeding period), summer (i.e., brood-rearing period), and winter sagebrush

We developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit Greater Sage-Grouse (Centrocercus urophasianus; hereafter sage-grouse) populations through time. Specifically, we estimated sagebrush (Artemisia spp.) recovery following wildfire and risk of non-native annual grass invasion under three scenarios: passive recovery, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

We developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit Greater Sage-Grouse (Centrocercus urophasianus; hereafter sage-grouse) populations through time. Specifically, we estimated sagebrush (Artemisia spp.) recovery following wildfire and risk of non-native annual grass invasion under three scenarios: passive recovery, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

We evaluated the expected success of habitat recovery in priority areas under 3 different restoration scenarios: passive, planting, and seeding. Passive means no human intervention following a fire disturbance. Under a planting scenario, field technicians methodically plant young sagebrush saplings at the burned site. The seeding scenario involves distributing large amounts of sagebrush seeds throughout the affected area.

We evaluated the expected success of habitat recovery in priority areas under 3 different restoration scenarios: passive, planting, and seeding. Passive means no human intervention following a fire disturbance. Under a planting scenario, field technicians methodically plant young sagebrush saplings at the burned site. The seeding scenario involves distributing large amounts of sagebrush seeds throughout the affected area.

Conservation planning efforts for sagebrush ecosystems of western North America increasingly focus on enhancing operational resilience though decision-support tools that link spatially explicit variation in soil and plant processes to outcomes of biotic and abiotic disturbances spanning large spatial extents. However, failure to consider higher trophic-level fauna (e.g. wildlife) in these tools can hinder efforts to operationalize resilience owing to spatiotemporal lags between slower reorganization of plant and soil processes following disturbance, and faster behavioral and demographic responses of fauna to disturbance. These spatial products provide additional examples for managers of sagebrush ecosystems and greater sage-grouse (Centrocercus urophasianus) populations in the Great Basin to aid with decisions regarding: 1) wildfire prevention, suppression, and management; and 2) removal of encroaching conifers. These products integrate models of ecological resilience mapped to variation in soil moisture and temperature regimes, wildlife risk and recovery processes, and potential ecological traps with measures of sage-grouse habitat selection and abundance. Please refer to Ricca and Coates (2019) and examples within for further details on methodology.

Conservation planning efforts for sagebrush ecosystems of western North America increasingly focus on enhancing operational resilience though decision-support tools that link spatially explicit variation in soil and plant processes to outcomes of biotic and abiotic disturbances spanning large spatial extents. However, failure to consider higher trophic-level fauna (e.g. wildlife) in these tools can hinder efforts to operationalize resilience owing to spatiotemporal lags between slower reorganization of plant and soil processes following disturbance, and faster behavioral and demographic responses of fauna to disturbance. These spatial products provide additional examples for managers of sagebrush ecosystems and greater sage-grouse (Centrocercus urophasianus) populations in the Great Basin to aid with decisions regarding: 1) wildfire prevention, suppression, and management; and 2) removal of encroaching conifers. These products integrate models of ecological resilience mapped to variation in soil moisture and temperature regimes, wildlife risk and recovery processes, and potential ecological traps with measures of sage-grouse habitat selection and abundance. Please refer to Ricca and Coates (2019) and examples within for further details on methodology.

These data were compiled so that annual wildfire could be modelled across the sagebrush region in the western United States. Our goal was to understand how wildfire probability relates to climate and fuel conditions across the entire sagebrush region. To do this we developed a statistical model that represents the relationship between annual wildfire probability and a small number of climate and fuel variables. Specifically, created predictions of wildfire probability using a biologically plausible logistic regression model that related wildfire probability to mean temperature, annual precipitation, the proportion summer precipitation (PSP), and aboveground biomass of annual herbaceous plants and perennial herbaceous plants. The biomass variables were used as proxies for fine fuel availability. These data represent annual fire occurrence in 1 km pixels (i.e. did a given pixel burn that year), predicted wildfire probability, as well as the three year running average (i.e. average across the current and previous two years) of climate and vegetation variables. These data were collected across the sagebrush region (the extent of the study area is provided by the cell_number_ids.tif file). The climate and vegetation data were compiled using a existing gridded dataset (Daymet) of daily precipitation and temperature, and vegetation data were summaries of annual estimates of aboveground biomass of annual and perennial herbaceous plants from the Rangeland Analysis Platform (https://rangelands.app/). These data can be used to understand spatial and temporal variability in wildfire occurrence and modelled wildfire probability between 1988 and 2019 and how that variability relates to spatial and temporal variability in climate and vegetation.