This data bundle contains the final outputs from a VisTrails/SAHM workflow to model the potential distribution of 5 rare plants (Aliciella formosa, Sclerocactus cloverae, Townsendia gypsophila, Astragalus ripleyi, and Cymopterus spellenbergii) in northern New Mexico. These models utilized field data of spatially thinned occurrence locations and random background locations or random plus absence locations for the 5 species. Predictors included but were not limited to soil characteristics, topography, percent tree cover, bare ground, and continuous heat-insolation load index rasters. Details about both occurrence data and predictor inputs are included in the associated manuscript and Source Info section of this metadata. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2], and combined these to create an ensemble of models for each species. For more information on the model creation process and interpretation of the final maps, see "Process Step" section. The bundle documentation files are: 1) 'NMrareplant_SDM_project_metadata.xml' (this file) which contains FGDC metadata describing the archive bundle. 2) Ensemble geotiff for each of the 5 modeled species: 'Code_vX_HML.tif' where code is the first two letters of the genus and species and X is the iteration of the final model product. 3) Tailored raster predictor layers not otherwise publicly available that were used during the modeling process and their corresponding metadata

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.

This raster presents the final outputs from a VisTrails/SAHM workflow to model the potential distribution of Zuni fleabane (Erigeron rhizomatus) in northwestern New Mexico. These models utilized field data of spatially thinned occurrence locations and random background locations. We included a suite of predictors related to soils, topography, vegetation cover, and geology. Details about both occurrence data and predictor inputs are included in the associated manuscript and Source Info section of this metadata. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2] (Morisette et al., 2013), and combined these to create an ensemble of models. The resulting raster depicts potential suitability ranging from 0 - Unsuitable to 4 - highest suitability based on the total number of models in agreement of suitability. For more information on the model creation process and interpretation of the final map, see "Process Step" section.

Here we present the map of probable suitable habitat for Alkali mariposa-lily (Calochortus striatus). The data indicate both how many models predicted each location to be suitable for the species, and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. This product can be used to inform future conservation, planning, and management actions in the California desert. Complete methods and other additional information are provided in the article associated with this data release (Reese and others, 2019).

This is a dataset containing the input and output data used in the analysis of best practices of invasive plant species distribution modeling (Young et al. 2024). We developed habitat suitability models for 13 invasive plant species at a variety of geographic ranges and different invasion stages and modeling strategies to assess the impact of predictor quality, thinning resolution, and geographic range of occurrence points on model performance. We developed a library of environmental variables at both the global scale and at the scale of the contiguous United States known to physiologically limit plant distributions (Young et al. 2024, Table S1) 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 used the Continuous Boyce Index (CBI) as a metric to assess model performance. This data bundle contains the merged data sets used to create the models, including location and associated environmental data, for each species, invasion stage, and modeling strategy, grouped by predictor set. In this data bundle, we have also included a dataframe of CBI values for each species, invasion stage, and modeling strategy, used in our analyses. The species include Ailanthus altissima, Alliaria petiolata, Brassica tournefortii, Cenchrus ciliaris, Chondrilla juncea, Cirsium vulgare, Dioscorea bulbifera, Imperata cylindrica, Lonicera maackii, Lysimachia nummularia, Microstegium vimineum, Pueraria montana, and Ranunculus testiculatus.

This is a dataset containing the input and output data used in the analysis of best practices of invasive plant species distribution modeling (Young et al. 2024). We developed habitat suitability models for 13 invasive plant species at a variety of geographic ranges and different invasion stages and modeling strategies to assess the impact of predictor quality, thinning resolution, and geographic range of occurrence points on model performance. We developed a library of environmental variables at both the global scale and at the scale of the contiguous United States known to physiologically limit plant distributions (Young et al. 2024, Table S1) 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 used the Continuous Boyce Index (CBI) as a metric to assess model performance. This data bundle contains the merged data sets used to create the models, including location and associated environmental data, for each species, invasion stage, and modeling strategy, grouped by predictor set. In this data bundle, we have also included a dataframe of CBI values for each species, invasion stage, and modeling strategy, used in our analyses. The species include Ailanthus altissima, Alliaria petiolata, Brassica tournefortii, Cenchrus ciliaris, Chondrilla juncea, Cirsium vulgare, Dioscorea bulbifera, Imperata cylindrica, Lonicera maackii, Lysimachia nummularia, Microstegium vimineum, Pueraria montana, and Ranunculus testiculatus.

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.

Here we present the map of potential suitable habitat for Alkali mariposa-lily (Calochortus striatus). The data indicate both how many models predicted each location to be potentially suitable for the species and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. These data are intended to be used only to target future plant surveys in areas where new occurrences are most likely to benefit future habitat modelling efforts. Complete methods and other additional information are provided in the article associated with this data release (Reese and others, 2019).