Catherine S. Jarnevich Demetra A. Williams Keana S. Shadwell Peder Engelstad Ian S. Pearse Janet S. Prevey Linnea Fraser Shelby K. Leclare Richard D. Inman Cameron Reimer 20260122 tabular and raster digital data https://doi.org/10.5066/P13AJ46S This is a dataset containing the potential distribution of 54 invasive terrestrial plant species. We developed habitat suitability models for invasive plant species selected by Department of Interior and USDA Forest Service land management agencies and other managers. We applied the modeling workflow described in Jarnevich et al. (2024: https://doi.org/10.3897/neobiota.96.134842), adapted to global data. We developed a global library of environmental variables known to physiologically limit plant distributions (Young et al. 2025 Table S1: 10.1002/eap.70010) and relied on human input based on natural history knowledge to narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling (SAHM 2.2.2, Morisette et al., 2013). We constructed weighted ensembles using up to 5 model algorithms for each species. We also created an integrated map that combines three binary threshold maps that has converted the continuous values to suitable/unsuitable, ranging from inclusive to restrictive. This data bundle contains tabular outputs including a species metadata file, the merged data sets used to create models, response curve data, variable importance information, and model assessment metrics. There is a child item containing three raster files for each species representing the following: 1) Occurrence suitability - Continuous value ensemble 2) Restricted occurrence suitability - Continuous value ensemble with restricted environmental conditions* 3) Integrated binary map that reflects three thresholds: a) 0.01 – first percentile threshold applied to continuous occurrence suitability b) 0.05 – fifth percentile threshold applied to continuous occurrence suitability b) 0.1 – tenth percentile threshold applied to continuous occurrence suitability *Restricted environmental conditions = only display areas where environmental characteristics are inside the range of the values used to develop the model. For example, a location with a minimum winter temperature of 12 C would be outside the range of -10 to 10 C used in model development. The bundle documentation files are: 1) 'INHABIT_global_V1_project_metadata.xml' (this file) which contains the project-level metadata. 2) Species_model_information.csv contains information on specific model changes of each species from tuning algorithm parameters to ensure model quality. 3) Merged_dataset.csv contains the merged data set used to create the models, including location and associated environmental data, for each species. 4) Response_curves.csv is the tabular information need to produce response curves for each predictor retained in each of the models produced for each species. 5) Variable_importance.csv is the tabular summaries indicating predictor importance for each of the models produced for each species. 6) Assessment_metrics.csv is the tabular summaries of assessment metrics for each model algorithm or model ensemble for each species. 7) Rcode.zip is a zipped file of the R scripts used to prep the data included in the merged dataset and code to make the derived raster output. 8) Child item with XX_YY.tif where XX is the species and YY is the raster type explained above. These data will be integrated into the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: Acacia mearnsii (black wattle), Acaena novae-zelandiae (biddy-biddy), Agave sisalana (Sisal hemp), Anchusa arvensis (Small bugloss), Brassica elongata (Elongated mustard), Bromus tectorum (Cheatgrass), Caragana arborescens (Siberian pea shrub), Carrichtera annua (Wards weed), Cecropia schreberiana ssp. antillarum (pumpwood), Cenchrus polystachios (Mission grass), Centaurea iberica (Iberian starthistle), Chloris divaricata (Spreading windmill-grass), Cirsium arvense (Creeping Thistle), Citrullus colocynthis (Gourd), Cotoneaster integerrimus (Cotoneaster), Crataegus laevigata (Smooth hawthorn), Crepis tectorum (Narrow leaf hawksbeard), Cynodon nlemfuensis (African bermudagrass), Distimake cissoides (Roadside woodrose), Echium plantagineum (Paterson's curse), Enneapogon cenchroides (Soft feather pappusgrass), Eragrostis echinochloidea (african lovegrass), Fallopia bohemica (Bohemian knotweed), Fallopia japonica (Japanese knotweed), Fallopia sachalinensis (Giant knotweed), Galega officinalis (goat's-rue), Hieracium aurantiacum (Orange hawkweed), Hiptage benghalensis (Hiptage), Impatiens glandulifera (Himalayan balsam), Jacobaea vulgaris (Tansy ragwort), Leucanthemum vulgare (Oxeye daisy), Lythrum virgatum (Wand loosestrife), Matthiola parviflora (stock), Melilotus albus (White sweet clover), Mikania micrantha (mile-a-minute weed), Nardus stricta (matgrass), Persicaria wallichii (Himalayan Knotweed), Phalaris arundinacea (Reed canarygrass), Phragmites australis (Phragmites), Pinus pinaster (cluster pine), Prunus padus (Bird cherry), Prunus virginiana (Choke cherry), Pseudosasa japonica (arrow bamboo), Pteris tripartita (Giant brake fern), Savignya parviflora (Small-flowered Savignya), Scleria microcarpa (tropical nutrush), Senecio linearifolius (fireweed groundsel), Sonchus arvensis (Perennial sow thistle), Sorbus aucuparia (European mountain ash), Spathodea campanulata (african tulip tree), Taraxacum officinale (Dandilion), Trifolium repens (White clover), Vicia cracca (Bird vetch), and Volutaria tubuliflora (desert knapweed). To provide information and documentation on the potential distribution of potentially invasive plant species in the United States. 19800101 20240611 date of source data Complete None planned Globe -179.7501 179.7042 82.7193 -55.9930 ISO 19115 Topic Category biota None Species Distribution Modeling VisTrails SAHM habitat suitability USGS Thesaurus invasive species biogeography modeling habitat suitability indices USGS Metadata Identifier USGS:685ebd93d4be025490e9e6cf Common geographic areas Globe None Acacia mearnsii Acaena novae-zelandiae Agave sisalana Anchusa arvensis Brassica elongata Bromus tectorum Caragana arborescens Carrichtera annua Cecropia schreberiana Cenchrus polystachios Centaurea iberica Chloris divaricata Cirsium arvense Citrullus colocynthis Cotoneaster integerrimus Crataegus laevigata Crepis tectorum Cynodon nlemfuensis Echium plantagineum Eragrostis echinochloidea Euryops multifidus Fallopia japonica Fallopia sachalinensis Galega officinalis Hieracium aurantiacum Hiptage benghalensis Impatiens glandulifera Jacobaea vulgaris Leucanthemum vulgare Lythrum virgatum Melilotus albus Mikania micrantha Nardus stricta Persicaria wallichii Phalaris arundinacea Phragmites australis Pinus pinaster Prunus padus Prunus virginiana Pseudosasa japonica Pteris tripartita Scleria microcarpa Sonchus arvensis Sorbus aucuparia Spathodea campanulata Taraxacum officinale Trifolium repens Vicia cracca Kingdom Plantae Subkingdom Viridiplantae Infrakingdom Streptophyta Superdivision Embryophyta Division Tracheophyta Subdivision Polypodiophytina Class Polypodiopsida Subclass Polypodiidae Order Polypodiales Suborder Pteridineae Family Pteridaceae Subfamily Pteridoideae Genus Pteris Species Pteris tripartita Subdivision Spermatophytina Class Pinopsida Subclass Pinidae Order Pinales Family Pinaceae Genus Pinus Species Pinus pinaster Class Magnoliopsida Superorder Caryophyllanae Order Caryophyllales Family Polygonaceae Genus Persicaria Species Persicaria wallichii Genus Fallopia Species Fallopia sachalinensis Species Fallopia japonica Superorder Asteranae Order Lamiales Family Bignoniaceae Genus Spathodea Species Spathodea campanulata Order Ericales Family Balsaminaceae Genus Impatiens Species Impatiens glandulifera Order Asterales Family Asteraceae Genus Taraxacum Species Taraxacum officinale Genus Sonchus Species Sonchus arvensis Genus Mikania Species Mikania micrantha Genus Leucanthemum Species Leucanthemum vulgare Genus Jacobaea Species Jacobaea vulgaris Genus Hieracium Species Hieracium aurantiacum Genus Euryops Species Euryops multifidus Genus Crepis Species Crepis tectorum Genus Cirsium Species Cirsium arvense Genus Centaurea Species Centaurea iberica Order Boraginales Family Boraginaceae Genus Echium Species Echium plantagineum Genus Anchusa Species Anchusa arvensis Superorder Lilianae Order Poales Family Cyperaceae Genus Scleria Species Scleria microcarpa Family Poaceae Genus Pseudosasa Species Pseudosasa japonica Genus Phragmites Species Phragmites australis Genus Phalaris Species Phalaris arundinacea Genus Nardus Species Nardus stricta Genus Eragrostis Species Eragrostis echinochloidea Genus Cynodon Species Cynodon nlemfuensis Genus Chloris Species Chloris divaricata Genus Cenchrus Species Cenchrus polystachios Genus Bromus Species Bromus tectorum Order Asparagales Family Asparagaceae Genus Agave Species Agave sisalana Superorder Rosanae Order Myrtales Family Lythraceae Genus Lythrum Species Lythrum virgatum Order Malpighiales Family Malpighiaceae Genus Hiptage Species Hiptage benghalensis Order Cucurbitales Family Cucurbitaceae Genus Citrullus Species Citrullus colocynthis Order Brassicales Family Brassicaceae Genus Carrichtera Species Carrichtera annua Genus Brassica Species Brassica elongata Order Rosales Family Urticaceae Genus Cecropia Species Cecropia schreberiana Family Rosaceae Genus Sorbus Species Sorbus aucuparia Genus Prunus Species Prunus virginiana Species Prunus padus Genus Crataegus Species Crataegus laevigata Genus Cotoneaster Species Cotoneaster integerrimus Genus Acaena Species Acaena novae-zelandiae Order Fabales Family Fabaceae Genus Vicia Species Vicia cracca Genus Trifolium Species Trifolium repens Genus Melilotus Species Melilotus albus Genus Galega Species Galega officinalis Genus Caragana Species Caragana arborescens Genus Acacia Species Acacia mearnsii 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. Catherine S Jarnevich U.S. Geological Survey, Fort Collins Science Center Ecologist mailing

2150 Centre Avenue Bldg C

Fort Collins CO 80526 US 970-226-9439 jarnevichc@usgs.gov Funding to support global INHABIT has come from the Bipartisan Infrastructure Law: Ecosystem Restoration Activity 6: Invasive Species, and this work contributes to the National Early Detection and Rapid Response Framework, from USDA Bipartisan Infrastructure Law, and USGS Core Science Systems. INHABIT is also logistically supported by USGS Core Science Systems and the Integrated Taxonomic Information System (ITIS). The U.S. Geological Survey Fort Collins Science Center participated in the project. Created using version 2.2.2 of the VisTrails/SAHM package (Morisette et al., 2013), and version 4.4.0 of R, including "taxize", "CoordinateCleaner", "enmSdmX" packages. Citations: Scott Chamberlain, Eduard Szoecs, Zachary Foster, Zebulun Arendsee, Carl Boettiger, Karthik Ram, Ignasi Bartomeus, John Baumgartner, James O'Donnell, Jari Oksanen, Bastian Greshake Tzovaras, Philippe Marchand, Vinh Tran, Maëlle Salmon, Gaopeng Li, and Matthias Grenié. (2020) taxize: Taxonomic information from around the web. R package version 0.9.98. https://github.com/ropensci/taxize Zizka A, Silvestro D, Andermann T, Azevedo J, Duarte Ritter C, Edler D, Farooq H, Herdean A, Ariza M, Scharn R, Svanteson S, Wengstrom N, Zizka V, Antonelli A (2019). “CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases.” _Methods in Ecology and Evolution_, -7. doi:10.1111/2041-210X.13152 <https://doi.org/10.1111/2041-210X.13152>, R package version 3.0.1, <https://github.com/ropensci/CoordinateCleaner>. Smith A, Murphy S, Henderson D, Erickson K (2023). “Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth.” _Global Ecology & Biogeography_, *32*, -13. doi:10.1111/geb.13628 <https://doi.org/10.1111/geb.13628>. Nicholas E. Young Catherine S. Jarnevich Helen R. Sofaer Ian Pearse Julia Sullivan Peder Engelstad Thomas J. Stohlgren 20200309 publication PLOS ONE vol. 15, issue 3 n/a Public Library of Science (PLoS) ppg. e0229253 https://doi.org/10.1371/journal.pone.0229253 Peder Engelstad Catherine S. Jarnevich Terri Hogan Helen R. Sofaer Ian S. Pearse Jennifer L. Sieracki Neil Frakes Julia Sullivan Nicholas E. Young Janet S. Prevéy Pairsa Belamaric Jillian LaRoe 20220208 publication PLOS ONE vol. 17, issue 2 n/a Public Library of Science (PLoS) ppg. e0263056 https://doi.org/10.1371/journal.pone.0263056 Catherine S. Jarnevich Peder Engelstad Demetra A. Williams Keana S. Shadwell Cameron J. Reimer Grace C. Henderson Janet S. Prevey Ian S. Pearse 2024 Version 4 publication United States U.S. Geological Survey https://doi.org/10.5066/P14HNEJF Catherine S. Jarnevich Peder Engelstad Demetra Williams Keane Shadwell Cameron Reimer Grace Henderson Janet S. Prevey Ian S. Pearse 20241125 publication Neobiota 96:261-278 Europe Pensoft https://doi.org/10.3897/neobiota.96.134842 Nicholas E. Young Demetra A. Williams Keana S.Shadwell Ian S.Pearse Catherine S. Jarnevich 20250304 publication Ecological applications 35(2): e70010 United States Ecological Society of America https://doi.org/10.1002/eap.70010 Jeffrey T. Morisette Catherine S. Jarnevich Tracy R. Holcombe Colin B. Talbert Drew Ignizio Marian K. Talbert Claudio Silva David Koop Alan Swanson Nicholas E. Young 20130125 publication Ecography vol. 36, issue 2 n/a Wiley ppg. 129-135 https://doi.org/10.1111/j.1600-0587.2012.07815.x R Core Team 2025 application/service Vienna, Austria R Foundation for Statistical Computing https://www.R-project.org Before ensembling layers, we examined model outputs for quality. We ensured that the training and test Area Under the Curve (AUC) were greater than 0.7. We also visually assessed the complexity of response curves to identify overfitting. With evidence of overfitting based on those criteria, we explored alternative model-specific tuning parameters to decrease overfitting while maintaining good performance. We also ensured that the train and test Continuous Boyce Index (CBI) values were greater than 0.5. Model algorithms for each species that did not meet this threshold were assessed individually and when deemed appropriate, subsequently dropped. After layers were ensembled, maps were visualized for consistency and ecological plausibility. We used the hashtag method to split our data into non-random, spatially-sampled cross-validation and independent test data to evaluate model performance. This methodology uses a hashtag shape (#) overlaid on the 99% binary kernel density estimate (KDE) of the occurrence points. Observation points falling within the buffered hashtag are assigned to the test split (and withheld for model evaluation) with data desired ratio of 70% train/ 30% test. We evaluated location data for accuracy including taxonomic and spatial coordinates. All known synonyms and U.S. Department of Agriculture (USDA) Plants Database acronyms were collected (excluding subspecies, variants, and hybrids) using the Integrated Taxonomic Information System (ITIS; www.itis.gov) as an authoritative taxonomy in the R library ‘taxize’. We filtered observations by observation type (observation or specimen only), observation date (1980 to present), and coordinate uncertainty (≤ 30 m). Furthermore, we cleaned the species data and corresponding geographic coordinates by flagging potentially erroneous coordinates that are known common issues in biological collection databases using the R package “CoordinateCleaner”. This included removing records from within country capitals, within country centroids and within a 100m radius of known biodiversity institutions (which include research centers, universities, herbaria, museums, zoos, and botanical gardens). We also removed records with coordinates that fall into the oceans, coordinates with latitude and longitudes equal to zero, and species with equal latitude and longitude coordinates. We checked the entire dataset for duplicate records. Locations were compared with reported distributions. Data sets are 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. We filtered our dataset to coordinates with ≤ 30 m accuracy and visually screened our data for positional accuracy. We filtered our dataset to coordinates with ≤ 30 m accuracy and visually screened our data for positional accuracy. Dirk Nikolaus Karger Olaf Conrad Jürgen Böhner Tobias Kawohl Holger Kreft Rodrigo Wilber Soria-Auza Niklaus E. Zimmermann H. Peter Linder Michael Kessler 2018 raster digital data n/a EnviDat https://doi.org/10.16904/envidat.228.v2.1 https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav2%2F&prefix=%2F Digital and/or Hardcopy 1981 2010 ground condition CHELSA Potential predictors derived from this dataset include bioclim data (bio2, bio3, bio7, bio12, bio13, bio14, bio15, bio16, bio17, bio18, bio19). J. T. Abatzoglou S. Z. Dobrowski S. A. Parks K. C. Hegeqisch 2018 raster digital data https://www.climatologylab.org/terraclimate.html https://app.climateengine.com/climateEngine Digital and/or Hardcopy 1981 2018 ground condition TerraClimate Potential predictors derived from this data include maximum summer temperature, mean minimum temperature of the 4 months after the coldest quarter, minimum winter temperature, and potential water deficit (PPT_ETo). Gabriel B Senay Stefanie K Kagone MacKenzie Friedrichs Naga M Velpuri James Rowland James Verdin 2017 raster digital data https://doi.org/10.5066/f72v2f1f https://earlywarning.usgs.gov/fews/product/460 Digital and/or Hardcopy 2003 2021 ground condition FEWS Potential predictors derived from this data include actual evapotranspiration. Charlene DiMiceli Mark Carroll Robert Sohlberg Do-Hyung Kim Maggi Kelly John Townshend 2015 raster digital data n/a NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/modis/mod44b.006 Digital and/or Hardcopy 2000 2020 ground condition MODIS/Terra VCF Potential predictors include the percent bareground standard deviation (2000-2020) and percent tree cover (2000-2016). Christina M. Kennedy James R. Oakleaf David M. Theobald Sharon Baruch‐Mordo Joseph Kiesecker 20190110 raster digital data Global Change Biology vol. 25, issue 3 n/a Wiley ppg. 811-826 https://doi.org/10.1111/gcb.14549 Digital and/or Hardcopy 2016 ground condition gHM Potential predictors include global human modification. Laura Poggio Luis M. de Sousa Niels H. Batjes Gerard B. M. Heuvelink Bas Kempen Eloi Ribeiro David Rossiter 20210614 raster digital data SOIL vol. 7, issue 1 n/a Copernicus GmbH ppg. 217-240 https://doi.org/10.5194/soil-7-217-2021 https://files.isric.org/soilgrids/latest/data_aggregated/1000m/ Digital and/or Hardcopy 2021 ground condition SoilGrids250m 2.0 Potential predictors include: bulk density, clay, nitrogen, pH, sand, and soil organic carbon, all at a 0-5cm depth. Wei Shangguan Yongjiu Dai Qingyun Duan Baoyuan Liu Hua Yuan 20140214 raster digital data Journal of Advances in Modeling Earth Systems vol. 6, issue 1 n/a American Geophysical Union (AGU) ppg. 249-263 https://doi.org/10.3334/ornldaac/1304 http://globalchange.bnu.edu.cn/research/soilw Digital and/or Hardcopy 20140214 ground condition LAIRG Potential predictors include calcium carbonate at a depth of 0-2.3m. J.D. PELLETIER P.D. BROXTON P. HAZENBERG X. ZENG P.A. TROCH G. NIU Z.C. WILLIAMS M.A. BRUNKE D. GOCHIS 2016 raster digital data n/a ORNL Distributed Active Archive Center https://doi.org/10.3334/ornldaac/1304 Digital and/or Hardcopy 2015 ground condition ORNL DAAC Potential predictors include soil and sedimentary deposit thickness. Valerio Barbarossa Mark A.J. Huijbregts Arthur H.W. Beusen Hylke E. Beck Henry King Aafke M. Schipper 20180327 raster digital data Scientific Data vol. 5, issue 1 n/a Springer Science and Business Media LLC https://doi.org/10.1038/sdata.2018.52 Digital and/or Hardcopy 1960 2015 ground condition FLO1K Potential predictors include mean annual streamflow. Esri 20141203 raster digital data n/a n/a https://usgs.maps.arcgis.com/home/item.html?id=46cbfa5ac94743e4933b6896f1dcecfd Digital and/or Hardcopy 2014 ground condition Esri Potential predictors include distance to water. Jean-François Pekel Andrew Cottam Noel Gorelick Alan S. Belward 20161207 raster digital data Nature vol. 540, issue 7633 n/a Springer Science and Business Media LLC ppg. 418-422 https://doi.org/10.1038/nature20584 Digital and/or Hardcopy 1984 2021 ground condition JRC Potential predictors include a summed monthly water recurrence layer. Robert Kennedy Zhiqiang Yang Noel Gorelick Justin Braaten Lucas Cavalcante Warren Cohen Sean Healey 20180501 publication Remote Sensing vol. 10, issue 5 n/a MDPI AG ppg. 691 https://doi.org/10.3390/rs10050691 Digital and/or Hardcopy 1985 2020 ground condition LandTrendr Used to derive Normalized Difference Moisture Index (NDMI) median and NDMI standard deviation. Louis Giglio Christopher Justice Luigi Boschetti David Roy 2021 raster digital data n/a NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/modis/mcd64a1.061 Digital and/or Hardcopy 20010101 20211201 ground condition MCD64A1.061 Potential predictors include burn frequency. Giuseppe Amatulli Sami Domisch Mao-Ning Tuanmu Benoit Parmentier Ajay Ranipeta Jeremy Malczyk Walter Jetz 2018 raster digital data n/a Nature's Scientific Data Sci Data 5, 180040 https://www.earthenv.org/topography https://doi.org/10.1038/sdata.2018.40 Digital and/or Hardcopy 2010 ground condition GMTED2010 Predictors include roughness, elevation, and Compound Topographic Index (CTI). The University of Georgia - Center for Invasive Species and Ecosystem Health 20201108 tabular digital data EDDMapS. 2021. Early Detection & Distribution Mapping System. The University of Georgia - Center for Invasive Species and Ecosystem Health. Available online at http://www.eddmaps.org/; last accessed November 8, 2021. http://www.eddmaps.org/ Digital and/or Hardcopy 19800101 20240611 observed EDDMAPS Species location data Global Biodiversity Information Facility 20240611 tabular digital data Derived dataset GBIF.org, accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2024-06-11. Filtered export of GBIF occurrence data. https://doi.org/10.15468/dl.seyatj https://doi.org/10.15468/dl.yhmjak Digital and/or Hardcopy 19800101 20240611 observed GBIF Species location data USDI-BLM 20210101 tabular digital data https://gbp-blm-egis.hub.arcgis.com/pages/aim Digital and/or Hardcopy 19800101 20230314 observed AIM Lotic Species location data Evelyn M. Beaury Catherine S. Jarnevich Ian Pearse Annette E. Evans Nathan Teich Peder Engelstad Jillian LaRoe Bethany A. Bradley 20230630 publication n/a Springer Science and Business Media LLC https://doi.org/10.1007/s10530-023-03118-z Digital and/or Hardcopy 19800101 20230630 observed Plant Abundance Dataset (Beaury) Species location data 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 publication Ecology vol. 104, issue 3 n/a Wiley https://doi.org/10.1002/ecy.3947 Digital and/or Hardcopy 19800101 20230112 observed SPCIS Species location data NatureServe 20230419 tabular digital data http://www.imapinvasives.org Digital and/or Hardcopy 19800101 20230419 observed iMapInvasives Species location data LANDFIRE Earth Resources Observation and Science Center (EROS) U.S. Geological Survey 20210805 LF Remap raster digital data Sioux Falls, SD Earth Resources Observation and Science Center (EROS), U.S. Geological Survey https://www.landfire.gov Digital and/or Hardcopy 2016 ground condition Landfire Species location data Bethany Bradley Annette Evans Catherine S Jarnevich Evelyn Beaury Peder S Engelstad Nathan B Teich Jillian M Laroe 20240426 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p14vvres Digital and/or Hardcopy 19800101 20240426 observed Bradley Abund Species location data Species location data was acquired from online occurrence data repositories including Global Biodiversity Information Facility (GBIF; https://doi.org/10.15468/dl.seyatj, https://doi.org/10.15468/dl.yhmjak) and the Early Detection and Distribution Mapping System (EDDMapS), as well as from the Standardized Plant Community with Introduced Status database (SPCIS), a plant abundance dataset from Bradley et al. 2023, iMapInvasives database, and the LANDFIRE reference database. AIM data were obtained directly from the BLM by request. See the Logical Consistency Report section for how species location data were further filtered and cleaned to ensure data quality. Occurrence data were spatially thinned to 1 location per 5 kilometers (km) to avoid issues of autocorrelation using the R package “enmSdmX” (Smith et al., 2023) using occ-prep-bg-functions.R. These thinned data were then inputs to VisTrails Software for Assisted Habitat Modeling (SAHM, Morisette et al., 2013) workflows to create each habitat suitability layer. Predictor layers were derived from a multitude of sources (see "Source Inputs" and Young et al. 2025 for more details) that capture long term trends in climatic and environmental conditions. References: Morisette, J. T., Jarnevich, C. S., Holcombe, T. R., Talbert, C. B., Ignizio, D., Talbert, M. K., ... & Young, N. E. (2013). VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography, 36(2), 129-135. https://doi.org/10.1111/j.1600-0587.2012.07815.x. Smith A, Murphy S, Henderson D, Erickson K (2023). “Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth.” _Global Ecology & Biogeography_, *32*, -13. doi:10.1111/geb.13628 <https://doi.org/10.1111/geb.13628>. GBIF EDDMAPS SPCIS iMapInvasives Landfire AIM Lotic Plant Abundance Dataset (Beaury) Bradley Abund CHELSA TerraClimate FEWS MODIS/Terra VCF gHM SoilGrids250m 2.0 LAIRG ORNL DAAC FLO1K Esri JRC LandTrendr MCD64A1.061 GMTED2010 20240611 The observation data locations were used to generate background samples based on the target background method of background data generation. Species distribution models (SDMs) were fit following modified methods from Jarnevich et al. (2024, https://doi.org/10.3897/neobiota.96.134842) using the SAHM (version 2.2.2, Morisette et al., 2013) in the VisTrails software. For species with >143 spatially thinned occurrences, ~70% of occurrences were used to train the model and the other ~30% were withheld to evaluate the model. All models were fit using n-fold spatial cross validation (where n is 8 or 9). The model algorithms included Random Forest (RF), Multiple Adaptive Regression Splines (MARS), Maxent (v 3.4.1), Generalized Linear Model (GLM), and Boosted Regression Trees (BRT). See Jarnevich et al. (2024, https://doi.org/10.3897/neobiota.96.134842) for additional details. SAHM produces several standard outputs for each model including assessment metrics for training and test data, rasters that map predicted habitat suitability across geographic space, metrics of predictor importance (change in Area Under the Curve (AUC) values when predictor values are permuted between presence and background locations), and response curves describing how relative habitat suitability varies across the range of values for each predictor. Finally, we assessed model performance using the continuous Boyce index (CBI) calculated for each of the models per species to determine overall performance. We used the calculated CBI metric for each combination of model algorithm plus background approach to determine inclusion in the ensemble step (Step 3). Individual models with CBI values of less than 0.50 were assessed and dropped if deemed appropriate. See Species_model_information.csv for whether an algorithm was excluded from a species' ensemble map. References: Morisette, J. T., Jarnevich, C. S., Holcombe, T. R., Talbert, C. B., Ignizio, D., Talbert, M. K., ... & Young, N. E. (2013). VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography, 36(2), 129-135. https://doi.org/10.1111/j.1600-0587.2012.07815.x. 20240618 We created maps of continuous habitat suitability for each species (raster layer 1). These maps represent a weighted ensemble of the algorithms and background approaches used to generate the models (up to 5), weighted according to their continuous Boyce index (CBI) values. They represent habitat suitability on a 0-100 scale, where lower values represent less predicted suitability for that species and higher values represent greater predicted suitability. These maps were also restricted based on environmental conditions, meaning areas where environmental conditions are outside the environmental range of location points used to build the model are masked from the map (raster layer 2). 1) Occurrence suitability - Continuous value ensemble 2) Restricted occurrence suitability - Continuous value ensemble with restricted environmental conditions* *Restricted environmental conditions = only display areas where environmental characteristics are inside the range of the values used to develop the model. For example, a location with a minimum winter temperature of 12 C would be outside the range of -10 to 10 C used in model development. 20240710 Suitable habitat was classified for each weighted ensemble using the one percentile threshold (0.01), five percentile threshold (0.05), and ten percentile threshold (0.1) using integrated-binary-process.R. Using these thresholds, we created binary maps for the weighted ensemble map. We then combined all three thresholds into a single combined map (integrated binary map). This combined map displays areas of environmental extrapolation (value of -1), areas of no suitability (value of 0), areas of suitability as determined by applying the first percentile (value of 1), areas of suitability as determined by applying the fifth percentile (value of 5), and areas of suitability as determined by applying the tenth percentile (value of 10). 3) Integrated binary map that reflects three thresholds: a) 0.01 – first percentile threshold applied to model group ensemble b) 0.05 – fifth percentile threshold applied to model group ensemble b) 0.1 – tenth percentile threshold applied to model group ensemble 20240710 Raster Grid Cell 14995 33705 1 Eckert_IV 0.0 0.0 0.0 0.0 0.0 0.0 row and column 1000.0 1000.0 meters WGS_1984 WGS 84 6378137.0 298.257223563 Species_model_information.csv Comma Separated Value (CSV) file containing data. Producer Defined Extent Defines the targeted study extent for the species Producer Defined Global Model extent global to be targeted for the entire US Producer defined Alaska Model extent targeted to Alaska and species may be native to the contiguous US Producer defined scientific_name Species identity flag in the format Genus species Producer Defined Scientific name for analyzed species in the format Genus species common_name Species identity flag as common name associated with the scientific name in scientific_name Producer Defined Common name for analyzed species area_thresh Area threhold parameter used to define the hashtag shape that defined the test data area Producer Defined NA Not applicable, indicating a hashtag shape was not used to define test data Producer defined 0.13 0.48 index lifeform U.S. Department of Agriculture plant growth habit and duration associated with the scientific name in species_scientific from USDA plants database Producer Defined tree tree growth habit Producer defined shrub shrub growth habit Producer defined short-lived forb forb growth habit with annual duration Producer defined long-lived forb forb growth habit with biennial or perennial duration Producer defined long-lived graminoid graminoid growth habit with biennial or perennial duration Producer defined vine vine growth habit Producer defined short-lived graminoid graminoid growth habit with annual duration Producer defined train_pts Number of training points used to fit the species' model Producer Defined Not enough points Not enough occurrence data to train a model Producer defined 101 7092 count test_pts Number of test points used to assess the species' model Producer Defined 0 3153 count n_splits Number of training data splits for the species Producer Defined NA No splits due to low sample size Producer defined 7 9 count bg_pts Number of background points used to fit the species' model Producer Defined NA No background points generated for species Producer defined 5298 10004 count model_changes Freeform text to describe any model algorithm changes for the species' model Producer Defined << empty cell >> No Data Producer defined Freeform text to describe any model algorithm specific changes for the species, where brt is boosted regression tree, glm is generalized linear model, mars is multivariate adaptive regression spline, rf is random forest, and kde is kernel density estimate. removed_algorithms List of model algorithms removed for the species Producer Defined NA Not applicable, indicating all model algorithms were retained to generate the ensemble Producer defined List of models dropped from consideration in the final model ensemble for the specified species, where brt is boosted regression tree, glm is generalized linear model, mars is multivariate adaptive regression spline, rf is random forest, and kde is kernel density estimate min_cbi Minimum continuous boyce index for any algorithm included in the ensemble for the species Producer Defined NA No Data Producer defined 0.28 0.99 index max_cbi Maximum continuous boyce index for any algorithm included in the ensemble for the species Producer Defined NA No Data Producer defined 0.71 1.0 index ensemble_cbi Continuous boyce index calculated for the ensemble for the species using the training data from train_pts and parenthetically using test data from test_pts Producer Defined NA No Data Producer defined 0.42 (NA) 1 (1) index threshold Calculated threshold values for the species used to discritize ensemble suitability values Producer Defined NA No Data Producer defined Calculated threshold value for three different threshold rules including first, fifth, and tenth Merged_dataset.csv Comma Separated Value (CSV) file containing data. Producer Defined scientific_name Species identity flag in the format Genus species Producer Defined Scientific name for analyzed species in the format Genus species set Identified if the data were used to fit the model (train) or assess the model (test) Producer Defined train Locations used to train the model Producer defined test Locations used to assess (test) the model Producer defined Split Spatial cross-validation split value assigned to the point Producer Defined test No split because set = test Producer defined 1 10 index X X coordinate in World Eckert IV (ESRI:54012) Producer Defined -16542152 16673370 Y Y coordinate in World Eckert IV (ESRI:54012) Producer Defined -6575360 8269558 responseBinary Flag indicating if presence or background location Producer Defined 1 Occurrence location Producer defined -9998 Background location Producer defined Bulk_density_0_5cm Bulk density (fine earth) in cg/cm³ at depth 0-5cm. Producer Defined NA No data Producer defined 178.5 1710.8 kg / m^3 Burn_Frequency The number of years with a recorded burn between 2001 and 2021, calculated from MODIS. Producer Defined 0 21 Count Soil_calcium_carbonate_content Total soil calcium carbonate between the depths of 0-2.3m. Producer Defined NA No data Producer defined 0 55.6 Percent Annual_precipitation Annual Precipitation, calculated by summing the precipitation values for all twelve months of the year. Producer Defined 8 65535 mm Precipitation_of_wettest_month Precipitation of Wettest Month. It is calculated by identifying the month with the highest cumulative precipitation total, and takes the precipitation value for that month. Producer Defined 2 27140 mm Precipitation_of_driest_month Precipitation of Driest Month. It is calculated by identifying the month with the lowest cumulative precipitation total, and takes the precipitation value for that month. Producer Defined 0 13902 mm Precipitation_seasonality Precipitation seasonality is a measure of the variation in monthly precipitation totals over the course of the year. This index is the ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation (aka coefficient of variation) and is expressed as a percentage Producer Defined 35 2229 mm Precipitation_of_wettest_quarter Precipitation of wettest quarter approximates total precipitation that prevails during the wettest season. The three consecutive months with the highest cumulative precipitation total are identified, and then the precipitation values are summed for all three months. Producer Defined 5 64338 mm Precipitation_of_driest_quarter Precipitation of driest quarter approximates total precipitation that prevails during the driest season. The three consecutive months with the lowest cumulative precipitation total are identified, and then the precipitation values are summed for all three months. Producer Defined 0 46016 mm Summer_precipitation Precipitation of warmest quarter approximates total precipitation that prevails during the warmest season. It is calculated by identifying the warmest quarter of the year (the average temperatures of each month in the quarter are summed; the quarter with the highest value is selected), and the precipitation values for the three months in this quarter are then summed. Producer Defined 0 64338 mm Winter_precipitation Precipitation of Coldest Quarter. It is calculated by identifying the coldest quarter of the year (the average temperatures of each month in the quarter are summed; the quarter with the lowest value is selected), and the precipitation for the three month in this quarter are then summed. Producer Defined 0 58317 mm Mean_Monthly_Temp_Range Mean diurnal range is the mean of all the averaged, monthly temperature extremes whose inputs are monthly mean maximum and minimum temperatures. It is calculated by finding the difference between the maximum and minimum temperature for each month, and then averaging these values, which can inform the relevance of temperature fluctuation to different species. Producer Defined 5 187 °C Isothermality Isothermality quantifies how much the day-to-night temperatures oscillate relative to the summer-to-winter oscillations. Inputs include monthly mean maximum and monthly mean minimum temperatures. Producer Defined 60 943 °C Annual_Temp_Range Temperature annual range is a measure of temperature variation over a given period calculated by subtracting minimum temperature of coldest month (BIO 6) from maximum temperature of warmest month (BIO5). This can inform whether species distributions are affected by ranges of extreme climatic conditions. Producer Defined 21 699 °C Soil_clay_content Clay content (0-2 µm) mass fraction (percent) at depth 0-5cm Producer Defined NA No data Producer defined 1.124 71.212 Percent Compound_topographic_index Compound topographic index Producer Defined 9488 35041 index * 1000 Distance_to_water Euclidean distance to nearest freshwater waterbody in meters. Producer Defined 0 751500 Meters ETa_fall Actual evapotranspiration in the 2 months prior to the coldest quarter. Producer Defined NA No data Producer defined 0 49956 mm *100 (integer) ETa_spring_3_months Actual evapotranspiration in the 3 months after to the coldest quarter. Producer Defined NA No data Producer defined 0 96884 mm *100 (integer) ETa_Summer Actual evapotranspiration during the warmest quarter. Producer Defined NA No data Producer defined 0 105872 mm *100 (integer) ETa_spring_4_months Actual evapotranspiration in the 4 months after to the coldest quarter. Producer Defined NA No data Producer defined 0 122404 mm *100 (integer) ETa_growing_season Actual evapotranspiration during the growing season (warmest 7 months). Producer Defined NA No data Producer defined 0 201500 mm *100 (integer) ETa_not_summer Actual evapotranspiration throughout the year except the summer months (coldest quarter + 2 months prior + 4 months after). Producer Defined NA No data Producer defined 0 230100 mm *100 (integer) Human_modification_index The global Human Modification map (HM) provides a cumulative measure of human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global datasets with a median year of 2016. Producer Defined NA No data Producer defined 0 0.99418 index Median_moisture_index Normalized Difference Moisture Index (NDMI) median between April 1st and September 30th spanning 1985 to 2020 derived from Landsat 5, 7, and 8. Producer Defined NA No data Producer defined -714 1009 Index Variation_in_moisture_index Normalized Difference Moisture Index (NDMI) standard deviation between April 1st and September 30th spanning 1985 to 2020 derived from Landsat 5, 7, and 8. Producer Defined NA No data Producer defined 0 491 Index Nitrogen_0_5cm Nitrogen content in cg/kg at depth 0-5cm Producer Defined NA No data Producer defined 12.83 2742.13 cg / kg Bare_ground_variation Standard deviation of percent cover of non-vegetated land from 2000-2020. Producer Defined 0 41 % SD * 10 Soil_pH Soil pH x 10 in water at depth 0.05 m. Producer Defined NA No data Producer defined 37 96.77 index (pH * 10) annual_ETo Annual potential water deficit. Producer Defined NA No data Producer defined -29651 1797 mm Warmest_quarter_ETo Potential water deficit of the warmest quarter. Producer Defined NA No data Producer defined -8069 2325 mm Terrain_roughness Topographic roughness based on GMTED2010 Producer Defined 0 1498 m Soil_sand_content Sand content in g/kg at depth 0-5cm Producer Defined NA No data Producer defined 0.60 97.64 g / kg Soil_organic_carbon_content Soil organic carbon content (fine earth fraction) in g/kg at depth 0-5cm. Producer Defined 1.005 460.305 g / kg Soil_thickness Soil and sedimentary deposit depth (0-50m). Producer Defined NA No data Producer defined 0 50 cm Mean_Max_Summer_Temp Mean maximum temperature of the 3 warmest months (i.e., summer). Producer Defined NA No data Producer defined -1358 4664 °C * 100 Mean_Min_Spring_Temp Mean minimum temperature of the 4 months after the coldest quarter (i.e., spring). Producer Defined NA No data Producer defined -3164 2794 °C * 100 Mean_Min_Winter_Temp Mean minimum temperature of the 3 coldest months (i.e., winter). Producer Defined NA No data Producer defined -4772 2499 °C * 100 Tree_Cover Mean percent tree cover between 2000-2016. Producer Defined NA No data Producer defined 0 8633 Percent * 100 Summed_monthly_water_recurrence The Monthly Recurrence collection contains 12 images: monthly measures of the seasonality of water based on the occurrence values detected in that month over all years. Producer Defined 0 1200 Summed percent Assessment_metrics.csv Comma Separated Value (CSV) file containing data. Producer Defined scientific_name Species identity flag in the format Genus species Producer Defined Scientific name for analyzed species in the format Genus species algorithm Algorithm used to generate the model being assessed Producer Defined BRT Boosted regression tree Producer defined GLM Generalized linear model Producer defined MARS Multivariate adaptive regression splines Producer defined RF Random forest Producer defined Maxent Maxent Producer defined masked ensemble Continuous value ensemble (of all algorithms) with restricted environmental conditions Producer defined unmasked ensemble Continuous value ensemble (of all algorithms) with unrestricted environmental conditions Producer defined metric Model evaluation metrics Producer Defined AUC Area Under the Curve - Receiver Operator Curve Producer defined AUC-PR AUC - Precision Recall Producer defined correlation coefficient correlation coefficient Producer defined percent correctly classified percent correctly classified Producer defined sensitivity sensitivity Producer defined kappa Kappa Index Producer defined true skill statistic true skill statistic Producer defined CBI Continuous Boyce Index Producer defined type Whether a metric was evaluated on test, training, or cross-validation data Producer Defined train training data Producer defined cross-validation average across cross-validation splits Producer defined test test data Producer defined value Value of model evaluation metric Producer Defined Possible value ranges differ based on the metric defined in the 'metric' column Variable_importance.csv Comma Separated Value (CSV) file containing data. Producer Defined scientific_name Species identity flag in the format Genus species Producer Defined Species identity flag in the format Genus species algorithm Algorithm used to generate the model the variable importance values are for Producer Defined BRT Boosted regression tree Producer defined GLM Generalized linear model Producer defined MARS Multivariate adaptive regression splines Producer defined RF Random forest Producer defined Maxent Maxent Producer defined predictor Name of predictor that variable importance value is associated with for the model specified by the model_type and algorithm columns Producer Defined Predictor variables defined as columns in Merged_dataset AUC_difference Calculated change in AUC for the model when predictor values are permutated between presence and background Producer Defined -0.407 0.77 split Cross-validation split number or train for all training data with which the variable importance values is associated Producer Defined Split number (1 to 9) or full set (train) of data points used to calculate variable importance Response_curves.csv Comma Separated Value (CSV) file containing data. Producer Defined scientific_name Species identity flag in the format genus species Producer Defined Scientific name for the species the response curve data represents algorithm Algorithm used to generate the model the response curve respresents Producer Defined RF Random forest Producer defined Maxent Maxent Producer defined MARS Multivariate adaptive regression splines Producer defined GLM Generalized linear model Producer defined BRT Boosted regression tree Producer defined predictor Name of predictor that variable importance value is associated with for the model specified by the algorithm column Producer Defined Predictor variables defined as columns in Merged_dataset predictor_value Value associated with the predictor defined in the 'predictor' column Producer Defined Valid ranges are specific to the predictor defined in the 'predictor' and match the range in the model training data for the species in 'Merged_dataset.csv'. suitability_value Relative habitat suitability, where higher values indicate greater suitability relative to lower values, associated with the specified predictor_value for the scientific_name Producer Defined 0.107 0.243 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. 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/P13AJ46S None 20260122 FORT Data Management USGS Fort Collins Science Center FORT Data Management mailing address

2150 Centre Avenue Bldg C

Fort Collins CO 80526 US 970-226-9100 fortdatamanagement@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999