Janet S. Prevey Cameron J. Reimer Peder S. Engelstad Pairsa N. Belamaric Terri Hogan Jillian M. LaRoe Colter J. Mumford Jennifer L. Sieracki Catherine S. Jarnevich 20250609 raster digital data https://doi.org/10.5066/P13MG455 Prevey, J. Reimer, C. Engelstad, P. Belamaric, P. Hogan, T. LaRoe, J. Mumford, C. Sieracki, J. Jarnevich, C. 2025 Publication https://doi.org/10.2139/ssrn.5342105 These spatial layers were created to provide estimates of non-native species invasion risk across the contiguous United States based on proximity to human population centers and transportation corridors, and proximity to known locations of non-native species. To calculate the human transport risk layer we estimated the proximity to human population centers, transportation corridors, and speed of movement across the landscape. To calculate invasion risk based on known locations of non-native species, we gathered over 30 million records of non-native species occurrences across the contiguous United States from online databases to create nationwide maps of non-native species richness by species type: amphibians, fish, invertebrates, mammals, mollusks, plants, and reptiles. These spatial layers were created to show different invasion risk factors of concern to managers, and be used in conjunction with other spatial data on invasion risk to help managers prioritize areas for management and monitoring. These data were created to be used in spatial assessments of invasion risk across the contiguous United States. 1981 2023 Publicly available data from 1981-2023 were used to calculate layer values. Complete None planned contiguous United States -128.3776 -65.1181 51.2276 23.4336 ISO 19115 Topic Category biota USGS Thesaurus invasive species risk assessment amphibians fish invertebrates mammals mollusks plants (organisms) reptiles USGS Metadata Identifier USGS:682f942dd4be027fab6e2415 Common geographic areas United States No access constraints. Please see 'Distribution Information' for details. These data are marked with a Creative Common CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. 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 mailing

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Fort Collins CO 80526 US 360-701-6466 jprevey@usgs.gov The gridded values presented here were calculated based on publicly available data on nonnative species locations and data on human transportation corridors and population centers. These data are intended to provide information on possible factors that could influence invasion risk. No formal tests were performed to assess accuracy of the data. We checked to ensure that the data match the details reported in the metadata and that all values fell within expected ranges. Data represent invasion risk factors across the contiguous United States. U.S. Census Bureau, U.S. Department of Commerce 2020 vector digital data https://www.arcgis.com/home/item.html?id=d8e6e822e6b44d80b4d3b5fe7538576d Digital and/or Hardcopy 2020 publication date US Census We used census populated place (CPP) polygon centroids to calculate a least-cost surface to find the shortest travel time between each pixel and any nearby CPP. U.S. Geological Survey 2023 vector digital data https://www.sciencebase.gov/catalog/item/4f70b1f4e4b058caae3f8e16 Digital and/or Hardcopy 2014 2023 publication date NTD Road, highway, and trail data from this data source were used to create the travel cost surface for the human transport risk layer. Global Biodiversity Information Facility 2023 spreadsheet https://www.gbif.org/ Digital and/or Hardcopy 1981 2023 observed GBIF Occurrence data for nonnative taxa spatial layers were aggregated from this source. EDDMapS, University of Georgia 2023 tabular digital data https://www.eddmaps.org/ Digital and/or Hardcopy 1981 2023 observed EDDMapS Occurrence data for nonnative taxa spatial layers were aggregated from this source. U.S. Geological Survey 2023 spreadsheet https://nas.er.usgs.gov/ Digital and/or Hardcopy 1981 2023 observed NAS Occurrence data for nonnative taxa spatial layers were aggregated from this source. U.S. Geological Survey 2018 raster digital data https://apps.nationalmap.gov/downloader/ Digital and/or Hardcopy 2018 ground condition NED Elevation data from the 30 meter resolution elevation dataset were used to calculate slope values and approximate travel speed values for walking for the human transport risk layer. U.S. Census Bureau, U.S. Department of Commerce 2022 vector digital data https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.2020.html Digital and/or Hardcopy 2022 publication date TIGER Road and highway data from this data source were used to create the travel cost surface for the human transport risk layer. To calculate and visualize risk associated with human transport of non-native species, we developed a ‘human transport risk’ layer by incorporating data on the proximity to human population centers with the speed of movement of humans across the landscape. To produce this layer of human transport risk across the contiguous United States, we utilized information from a travel cost/resistance layer and polygons of census populated places (CPP) with their associated population estimate (US Census). The travel cost surface layer was made with road, highway (TIGER), trail (NTD), and slope (NED) data by relating approximate travel speed values (Theobald 2005) to road types, trails, and slope values from . The final cost layer reports the inverse speed values, such that high values relate to high movement resistance and therefore low travel speed. We stratified all CPP polygon centroids into 10 cumulative population classes, such that the next most populous cities are included in each class. We created a cumulative least-cost surface to find the shortest travel time between each pixel and any nearby CPP polygon centroid using the gdistance package (van Etten, 2017) in the statistical program R (R Core Team, 2023). This calculation was repeated for each population class, and the natural log of the resulting layers were added together to visualize the cumulative influence of all human populations in the surrounding area on each raster cell. The purpose of this method was to ensure that grid cells with large surrounding populations and small travel costs were considered to be at higher human transport risk than cells surrounded by small populations and large travel costs. Method references R Core Team. 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. Theobald, D.M., 2005. GIS concepts and ArcGIS methods, 2ns Edition. Fort Collins, Colorado: Conservation Planning Technologies. p. 302. van Etten, J., 2017. R Package gdistance: Distances and Routes on Geographical Grids. J. Stat. Soft. 76, 1–21. https://doi.org/10.18637/jss.v076.i13 US Census NTD NED TIGER 2024 To estimate invasion risk associated with proximity to known presence of non-native species, we obtained non-native species presence data from 1980 onwards from the Global Biodiversity Information Facility (GBIF ), Early Detection and Distribution Mapping System (EDDMapS), and the Nonindigenous Aquatic Species (NAS) database. Occurrence data were aggregated across all sources, then divided by species type into taxa groups that had a sufficient number of observations (>20,000) and spatial coverage across the country, and were deemed relevant to managers: amphibians, fishes, invertebrates, mammals, mollusks, plants, and reptiles. Within each taxa group, numeric values contained in each raster cell represent the number of non-native species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. GBIF EDDMapS NAS 2024 Raster Grid Cell 2489 5665 1 0.011166735833983468 0.01116673583398347 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 human_transport_risk.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the relative proximity to human population centers and the speed of movement across the landscape. High values indicate low travel costs and closer distance to population centers, thus, a higher potential for the human transport of non-native species Producer Defined 0.0 100.0 amphibian_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native amphibian species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 fish_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native fish species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 invertebrate_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native invertebrate species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 mammal_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native mammal species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 mollusk_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native mollusk species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 plant_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native plant species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 reptile_richness.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell represent the number of non-native reptile species with known occurrences within pixel cells, with an inverse distance buffer around cells with at least one observation. Producer Defined 0.0 100.0 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. Digital Data https://doi.org/10.5066/P13MG455 None 20250827 FORT Data Management U.S. Geological Survey, Fort Collins Science Center FORT Data Management mailing

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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