Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.

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.

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.

These data provide current and future projected post-fire invasion risk by non-native short-lived grasses and forbs based on vegetation cover data from 26,729 plots in the western United States that burned prior to being sampled. Projected post-fire invasion risk was calculated using random forests with gridded climate, soil, and topographic predictor variables following Prevéy et al. (2024). Projections cover the western United States west of -100 longitude over the current time period (1981-2010), mid-century (2041–2070) and the end of the century (2071–2100) under medium (SSP245) and high (SSP585) greenhouse gas emission scenarios for non-native C3 short-lived grasses and non-native short-lived forbs. Each raster file represents the projected post-fire invasion risk for each non-native functional group ('sl_grass' = short-lived C3 grass, 'sl_forb' = short-lived forb), then the time period (current = 1981-2010, mid = 2041-2071, and late = 2071-2100), and lastly, the emissions scenario (none for current, '245' for SSP245, and '585' for SSP585). For example, 'sl_grass_mid_245.tif' is a raster file showing projected post-fire invasion risk for non-native short-lived C3 grasses for mid-century (2041-2070) under the SSP245 emissions scenario.

These data provide current and future projected post-fire invasion risk by non-native short-lived grasses and forbs based on vegetation cover data from 26,729 plots in the western United States that burned prior to being sampled. Projected post-fire invasion risk was calculated using random forests with gridded climate, soil, and topographic predictor variables following Prevéy et al. (2024). Projections cover the western United States west of -100 longitude over the current time period (1981-2010), mid-century (2041–2070) and the end of the century (2071–2100) under medium (SSP245) and high (SSP585) greenhouse gas emission scenarios for non-native C3 short-lived grasses and non-native short-lived forbs. Each raster file represents the projected post-fire invasion risk for each non-native functional group ('sl_grass' = short-lived C3 grass, 'sl_forb' = short-lived forb), then the time period (current = 1981-2010, mid = 2041-2071, and late = 2071-2100), and lastly, the emissions scenario (none for current, '245' for SSP245, and '585' for SSP585). For example, 'sl_grass_mid_245.tif' is a raster file showing projected post-fire invasion risk for non-native short-lived C3 grasses for mid-century (2041-2070) under the SSP245 emissions scenario.

This table summarizes areas of burn severity, sagebrush biophysical types, and soil temperature/moisture regimes within large wildfires from 1984 to 2013 occuring within greater sage-grouse population areas. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

This table summarizes areas of burn severity, sagebrush biophysical types, and soil temperature/moisture regimes within large wildfires from 1984 to 2013 occuring within greater sage-grouse population areas. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 – 2020 and output from global climate models (GCMs) from 1980 – 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. For each ecoregion, we produced 1000 replicate simulations of annual area burned (ha).

This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 – 2020 and output from global climate models (GCMs) from 1980 – 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. For each ecoregion, we produced 1000 replicate simulations of annual area burned (ha).

Wildfire events are becoming more frequent and severe on a global scale. Rising temperatures, prolonged drought, and the presence of pyrophytic invasive grasses are contributing to the degradation of native vegetation communities. Within the Great Basin region of the Western United States, increasing wildfire frequency is transforming the ecosystem toward a higher degree of homogeneity, one dominated by invasive annual grasses and declining landscape productivity. Greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) are a species of conservation concern that rely on large tracts of structurally and functionally diverse sagebrush (Artemisia spp.) communities. Using a 12-year (2008-2019) telemetry dataset, we documented immediate impacts of wildfire on demographic rates of a population of sage-grouse that were exposed to two large wildfire events (Virginia Mountains Fire Complex - 2016; Long Valley Fire - 2017) near the border of California and Nevada. Spatiotemporal heterogeneity in demographic rates were accounted for using a Before-After Control-Impact Paired Series (BACIPS) study design.