Janet S. Prevéy Catherine S. Jarnevich Ian S. Pearse Seth M. Munson Jens T. Stevens Jonathan D. Coop Paula J. Fornwalt Camille S. Stevens-Rumann Meg A. Krawchuk Judith D. Springer Micheal T. Stoddard Becky K. Kerns Kevin J. Barrett Dave Firmage Claire M. Tortorelli James D. Johnston Michelle A. Day Ty C. Nietupski Becky A. Miller Katharine M. Haynes Jacquilyn Roque 20240904 raster digital data https://doi.org/10.5066/P14X9ZQW Janet S. Prevéy Catherine S. Jarnevich Ian S. Pearse Seth M. Munson Jens T. Stevens Kevin J. Barrett Jonathan D. Coop Michelle A. Day David Firmage Paula J. Fornwalt Katharine M. Haynes James D. Johnston Becky K. Kerns Meg A. Krawchuk Becky A. Miller Ty C. Nietupski Jacquilyn Roque Judith D. Springer Camille S. Stevens-Rumann Michael T. Stoddard Claire M. Tortorelli 20240202 publication Biological Invasions Springer Science and Business Media LLC https://doi.org/10.1007/s10530-023-03235-9 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. The data synthesis informing these projections was conducted to examine how the abundance of non-native plants after fire varies in relation to environmental conditions such as soils, topography, and climate, and identify regions where post-fire plant invasions are most common following fires. The resulting spatial projections can help identify hotspots where invasions are the most common following fire now and in the future and allow for more targeted mitigation, monitoring, and restoration. 1981 2100 ground condition Complete None planned western United States -124.9400 -97.9980 49.0000 27.2156 ISO 19115 Topic Category biota USGS Thesaurus fires invasive species ecosystem management vegetation terrestrial ecosystems wildfire risk analysis Marine Realms Information Bank (MRIB) keywords climate change USGS Metadata Identifier USGS:667f2bb8d34e84b99fe5c149 None western USA USA 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. Janet S Prevey U.S. Geological Survey, ROCKY MOUNTAIN REGION Research Ecologist mailing address

2150 Centre Avenue Bldg C

Fort Collins CO 80526 US 360-701-6466 970-226-9230 jprevey@usgs.gov The gridded values presented here are projected post-fire invasion risk values based on the percent cover of two non-native functional groups from vegetation plots measured after fires. Accuracy of projections for the current time period were assessed using R squared and root-mean-squared-error (RMSE) values for predictions compared with actual values. Detailed methods describing random forest models and projections can be found in Prevéy et al. (2024). We checked to ensure that the data match the details reported in the metadata and that all values fell within expected ranges. Data represent projected post-fire invasion risk by non-native short-lived C3 grasses and short-lived forbs over the western USA (spatial extent: 31°- 49° N, 124°-100° W). T. Wang A. Hamann C. Carroll 2022 raster digital data AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org https://adaptwest.databasin.org/pages/adaptwest-climatena/ Digital and/or Hardcopy 1981 2100 ground condition ClimateNA Environmental predictors Laís Petri Evelyn M. Beaury Jeffrey Corbin Kristen Peach Helen Sofaer Ian S. Pearse Regan Early David T. Barnett Inés Ibáñez Robert K. Peet Michael Schafale Thomas R. Wentworth James P. Vanderhorst David N. Zaya Greg Spyreas Bethany A. Bradley 20230112 spreadsheet Ecology vol. 104, issue 3 n/a Wiley https://doi.org/10.1002/ecy.3947 Digital and/or Hardcopy 19630901 20200801 ground condition SPCIS plot-level vegetation data Amanda Ramcharan Tomislav Hengl Travis Nauman Colby Brungard Sharon Waltman Skye Wills James Thompson 20180112 raster digital data Soil Science Society of America Journal vol. 82, issue 1 n/a Wiley ppg. 186-201 https://doi.org/10.2136/sssaj2017.04.0122 Digital and/or Hardcopy 2018 ground condition Soils variables Environmental predictors LANDFIRE 2016 tabular digital data LANDFIRE (LF) 2016 Remap (LF 2.0.0) reference database (LFRDB). https://landfire.gov/lfrdb.php https://landfire.gov/reference/lfrdb Digital and/or Hardcopy 1960 2016 ground condition LANDFIRE reference database plot-level vegetation data LANDFIRE 2016 tabular digital data https://landfire.gov/bps.php Digital and/or Hardcopy 2016 ground condition LANDFIRE biophysical settings Environmental predictors United States Census 20230101 vector digital data https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html Digital and/or Hardcopy 2007 2023 2007-2023 Transportation data Used to create a spatial layer denoting distances from roads and trails. Raster values were calculated using projections from random forests in the statistical program R. The random forests used plot-level cover of non-native functional types in burned plots as response variables, and bioclimatic variables derived from ClimateNA normals from 1981 to 2010, transportation data calculating distance from roads and trails, Landfire biophysical layers, soil variables (% sand, % clay), and geography (continuous heat-insolation load index) as predictor variables. To project potential climate-driven changes in post-fire invasion risk of functional types that benefited from fire, we used future projections of climate variables across the western United States to map predicted changes in post-fire risk for mid (2041–2070) and end (2071–2100) of century under medium (SSP245) and high (SSP585) greenhouse gas emission scenarios. We fit random forests using the 'randomForest' package in R, and assessed accuracy using Pearson correlations between training and out of bag samples. We assessed spatial autocorrelation of model results by visual inspection of residuals across plot locations in the western United States to ensure there were no spatial patterns in areas of over or under prediction. ClimateNA SPCIS Soils variables LANDFIRE reference database Transportation data LANDFIRE biophysical settings 2023 Raster Grid Cell 28727 22717 1 Albers Conical Equal Area 20.0 60.0 -96.0 40.0 0.0 0.0 row and column 98.46933389233666 98.46933389233682 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101004 sl_grass_current.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived C3 grasses for the current time period (1981-2010). Raster values were capped at 50%. However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0 50 Percent sl_forb_late_245.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived forbs for the end of the century (2071-2100) under a low emissions scenario (SSP-245). However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0 30 Percent sl_grass_mid_245.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived C3 grasses for mid-century (2041-2070) under a low emissions scenario (SSP245). Raster values were capped at 50%. However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 50.0 Percent sl_forb_current.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived forbs for the current time period (1981-2010). However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 31.0 Percent sl_forb_mid_245.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived forbs for mid-century (2041-2070) under a low emissions scenario (SSP245). However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 31.0 Percent sl_forb_mid_585.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived forbs for mid-century (2041-2070) under a high emissions scenario (SSP585). However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 32.0 Percent sl_forb_late_585.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived forbs by the end of the century (2071-2100) under a high emissions scenario (SSP585). However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 30.0 Percent sl_grass_mid_585.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived C3 grasses for mid-century (2041-2070) under a high emissions scenario (SSP585). Raster values were capped at 50%. However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 50.0 Percent sl_grass_late_585.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived C3 grasses for the end of the century (2071-2100) under a high emissions scenario (SSP585). Raster values were capped at 50%. However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 50.0 Percent sl_grass_late_245.TIF Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the projected post-fire cover of non-native short-lived C3 grasses for the end of the century (2071-2100) under a low emissions scenario (SSP245). Raster values were capped at 50%. However, values should be interpreted as relative risk, with higher values indicating more risk, and lower values indicating lower risk. Producer Defined 0.0 50.0 Percent GS ScienceBase U.S. Geological Survey mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 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. Digital Data https://doi.org/10.5066/P14X9ZQW None 20240904 FORT Data Management U.S. Geological Survey, Fort Collins Science Center FORT Data Management mailing address

2150 Centre Avenue Bldg C

Fort Collins CO 80526 United States 9702269100 fortdatamanagement@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999