We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions This file specifically, 2) 'mergedDataset.csv', contains the merged data set used to create the models, including location coordinates and associated environmental covariate data values. The bundle documentation files are: 1) 'AbundOccur.xml' contains FGDC project-level metadata 2) 'mergedDataset.csv', which this metadata file specifically describes, contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv is the tabular summaries by management unit.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'AbundOccur.xml' contains FGDC project-level metadata 2) 'mergedDataset.csv', contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv, which this metadata file specifically describes, is the tabular summaries by management unit.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'AbundOccur.xml' contains FGDC project-level metadata 2) 'mergedDataset.csv', contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv, which this metadata file specifically describes, is the tabular summaries by management unit.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'AbundOccur.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv is the tabular summaries by management unit.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'AbundOccur.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv is the tabular summaries by management unit.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'AbundOccur.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv is the tabular summaries by management unit.

We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further 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 2.1.2]. We selected background samples using the target background approach, and took an alternative approach to construct model ensembles by combining first percentile and ten percentile threshold rules (suitability values associated with the lowest one percent and lowest ten percent of the training data) to categorize the continuous output from each algorithm into low (below the one percentile), moderate (between the one and ten percentile), and high (above the ten percentile) suitability. Finally, we summed these to create an ensemble. This data bundle contains the merged data sets used to create the models, the composite raster files for each abundance threshold associated with each species, tabular summaries by management unit (including each species/ composite type combination), and the occurrence points with their associated cover. The spatial data are organized in a separate folder for each species, each containing 5 rasters describing potential habitat suitability for the species at the different abundance thresholds. Each of the rasters represent the composite map (composite_abundX.tif) for each abundance threshold. The bundle documentation files are: 1) 'thresholded_abundance_project_metdata.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for all three species for each thresholded abundance. 3) XX.tif where XX is the raster type explained above (abundance threshold). 4) managementSummary.csv is the tabular summaries by management unit.

We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further 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 2.1.2]. We selected background samples using the target background approach, and took an alternative approach to construct model ensembles by combining first percentile and ten percentile threshold rules (suitability values associated with the lowest one percent and lowest ten percent of the training data) to categorize the continuous output from each algorithm into low (below the one percentile), moderate (between the one and ten percentile), and high (above the ten percentile) suitability. Finally, we summed these to create an ensemble. This data bundle contains the merged data sets used to create the models, the composite raster files for each abundance threshold associated with each species, tabular summaries by management unit (including each species/ composite type combination), and the occurrence points with their associated cover. The spatial data are organized in a separate folder for each species, each containing 5 rasters describing potential habitat suitability for the species at the different abundance thresholds. Each of the rasters represent the composite map (composite_abundX.tif) for each abundance threshold. The bundle documentation files are: 1) 'thresholded_abundance_project_metdata.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for all three species for each thresholded abundance. 3) XX.tif where XX is the raster type explained above (abundance threshold). 4) managementSummary.csv is the tabular summaries by management unit.

We developed habitat suitability models for occurrence of three invasive riparian woody plant taxa of concern to Department of Interior land management agencies, as well as for three dominant native riparian woody taxa. Study taxa were non-native tamarisk (saltcedar; Tamarix ramosissima, Tamarix chinensis), Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus pumila) and native plains/Fremont cottonwood (Populus deltoides ssp. monilifera and ssp. wislizenii, Populus fremontii), narrowleaf cottonwood (Populus angustifolia), and black cottonwood (Populus balsamifera ssp. trichocarpa and ssp. balsamifera). We generally followed the modeling workflow developed in Young et al. 2020. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples: random (10,000 spatially-filtered (50-kilometer [km]) random background samples) and Salix (10,000 randomly-selected occurrence records of Salix spp.). We constructed model ensembles with the 5 models for each taxon (five algorithms) with each background method, as well as with all 10 models for each taxon (five algorithms by two background methods), for three different occurrence likelihood thresholds (1st percentile, 10th percentile, and MSS (maximum sensitivity and specificity)). We also used the model ensembles to identify major watersheds where each taxon was under-represented in occurrence records relative to predicted habitat suitability, to evaluate risk of undetected or future invasion. For each 6-digit hydrological unit (HUC6, USGS Watershed Boundary Dataset) within the study area, we calculated the difference between actual occurrence record density and the density of occurrence records that would be expected if occurrence records were distributed among watersheds in proportion to habitat suitability in MaxSS 10-model ensembles. This data bundle contains the merged data sets used to create the models, occurrence locations that were used for independent assessments of model accuracy (not used in model training), the raster files associated with each taxon, and tabular summaries of actual and expected occurrence record densities by HUC6. The spatial data are organized in a separate folder for each taxon, each containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background data and 1st percentile threshold 8) X10th_combined - ensemble of 10 models with random and Salix background data and 10th percentile threshold 9) MaxSS_combined - ensemble of 10 models with random and Salix background data and MaxSS threshold The bundle documentation files are: 1) 'RiparianSDMs_main.xml' (this file), which contains the project-level metadata 2) 'ModelTrainingData.csv' contains the merged data set used to create the models, including location and environmental data. 3) 'IndependentAssessmentData.csv' contains the data set used to assess accuracy of model predictions (occurrence locations not used for model training) 4) XX.tif where XX is the raster type explained above in taxa subfolders. 5) 'HUC6Summaries.csv' contains tabular summaries of actual and expected occurrence record densities by HUC6. 6) 'bison_citations.txt' contains the different data sources with occurrences from the BISON database.

We developed habitat suitability models for occurrence of three invasive riparian woody plant taxa of concern to Department of Interior land management agencies, as well as for three dominant native riparian woody taxa. Study taxa were non-native tamarisk (saltcedar; Tamarix ramosissima, Tamarix chinensis), Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus pumila) and native plains/Fremont cottonwood (Populus deltoides ssp. monilifera and ssp. wislizenii, Populus fremontii), narrowleaf cottonwood (Populus angustifolia), and black cottonwood (Populus balsamifera ssp. trichocarpa and ssp. balsamifera). We generally followed the modeling workflow developed in Young et al. 2020. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples: random (10,000 spatially-filtered (50-kilometer [km]) random background samples) and Salix (10,000 randomly-selected occurrence records of Salix spp.). We constructed model ensembles with the 5 models for each taxon (five algorithms) with each background method, as well as with all 10 models for each taxon (five algorithms by two background methods), for three different occurrence likelihood thresholds (1st percentile, 10th percentile, and MSS (maximum sensitivity and specificity)). We also used the model ensembles to identify major watersheds where each taxon was under-represented in occurrence records relative to predicted habitat suitability, to evaluate risk of undetected or future invasion. For each 6-digit hydrological unit (HUC6, USGS Watershed Boundary Dataset) within the study area, we calculated the difference between actual occurrence record density and the density of occurrence records that would be expected if occurrence records were distributed among watersheds in proportion to habitat suitability in MaxSS 10-model ensembles. This data bundle contains the merged data sets used to create the models, occurrence locations that were used for independent assessments of model accuracy (not used in model training), the raster files associated with each taxon, and tabular summaries of actual and expected occurrence record densities by HUC6. The spatial data are organized in a separate folder for each taxon, each containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background data and 1st percentile threshold 8) X10th_combined - ensemble of 10 models with random and Salix background data and 10th percentile threshold 9) MaxSS_combined - ensemble of 10 models with random and Salix background data and MaxSS threshold The bundle documentation files are: 1) 'RiparianSDMs_main.xml' (this file), which contains the project-level metadata 2) 'ModelTrainingData.csv' contains the merged data set used to create the models, including location and environmental data. 3) 'IndependentAssessmentData.csv' contains the data set used to assess accuracy of model predictions (occurrence locations not used for model training) 4) XX.tif where XX is the raster type explained above in taxa subfolders. 5) 'HUC6Summaries.csv' contains tabular summaries of actual and expected occurrence record densities by HUC6. 6) 'bison_citations.txt' contains the different data sources with occurrences from the BISON database.