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.

Export Data Access API A Series of models describing Fauna habitat suitability and occurrence as probability. More information: https://portal.spatial.nsw.gov.au/portal/home/item.html?id=78e6ae3d34aa45d2b8118fd0308d6459 Metadata Portal Metadata InformationContent TitleFauna Species occupancy and Distribution Baselines in NSW RFA RegionsContent TypeOtherDescriptionA Series of models describing Fauna habitat suitability and occurrence as probability.Initial Publication Date06/08/2022Data Currency01/01/2000Data Update FrequencyOtherContent SourceFile TypeTIFFAttributionData Theme, Classification or Relationship to other DatasetsAccuracySpatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentContent LineageData ClassificationUnclassifiedData Access PolicyOpenData QualityTerms and ConditionsCreative CommonsStandard and SpecificationData CustodianNatural Resources CommissionPoint of Contactnrc@nrc.nsw.gov.auData AggregatorData DistributorAdditional Supporting InformationTRIM Number

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 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.

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 invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions 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 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. Each species folder contains the potential distribution of the species and all raster layers were produced using VisTrails:SAHM [SAHM 2.1.2]. Each of the 8 rasters represent the following: 1) MPP - minimum predicted presence threshold 2) 0.01 - one percentile threshold 3) 0.1 - ten percentile threshold 4) MaxSS - maximum sensitivity plus specificity threshold 5) MPP - minimum predicted presence threshold with Restricted Environmental Conditions 6) 0.01 - one percentile threshold with Restricted Environmental Conditions 7) 0.1 - ten percentile threshold with Restricted Environmental Conditions 8) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions These rasters 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: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Amur honeysuckle (Lonicera maackii), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua ), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Beefsteak mint (Perilla frutescens), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur buttercup (Ranunculus testiculatus), Burning bush (Euonymus alatus), Camelthorn (Alhagi maurorum), Canada thistle (Cirsium arvense), Cereal rye (Secale cereale), Cheatgrass (Bromus tectorum), Chinaberry (Melia azedarach), Chinese holly (Ilex cornuta), Chinese privet (Ligustrum sinense), Chinese tallowtree (Triadica sebifera), Chinese wisteria (Wisteria sinensis), Chocolate vine (Akebia quinata), Clasping pepperweed (Lepidium perfoliatum), Cogongrass (Imperata cylindrica), Common crupina (Crupina vulgaris), Common gorse (Ulex europaeus ), Common reed (Phragmites australis), Common tansy (Tanacetum vulgare), Coral ardisia (Ardisia crenata), Crape myrtle (Lagerstroemia indica), Creeping bentgrass (Agrostis stolonifera), Creeping buttercup (Ranunculus repens), Crested wheatgrass (Agropyron cristatum), Crown vetch (Securigera varia), Dalmatian toadflax (Linaria dalmatica), Diffuse knapweed (Centaurea diffusa), Dyer's woad (Isatis tinctoria), English holly (Ilex aquifolium), English ivy (Hedera helix), European beachgrass (Ammophila arenaria ), False brome (Brachypodium sylvaticum), Field brome (Bromus arvensis), Fountaingrass (Pennisetum setaceum), French broom (Genista monspessulana), Fuller's teasel (Dipsacus fullonum), Garlic mustard (Alliaria petiolata), Giant knotweed (Fallopia sachalinensis), Hairy cat's ear (Hypochaeris radicata), Halogeton (Halogeton glomeratus),

We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions 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 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. Each species folder contains the potential distribution of the species and all raster layers were produced using VisTrails:SAHM [SAHM 2.1.2]. Each of the 8 rasters represent the following: 1) MPP - minimum predicted presence threshold 2) 0.01 - one percentile threshold 3) 0.1 - ten percentile threshold 4) MaxSS - maximum sensitivity plus specificity threshold 5) MPP - minimum predicted presence threshold with Restricted Environmental Conditions 6) 0.01 - one percentile threshold with Restricted Environmental Conditions 7) 0.1 - ten percentile threshold with Restricted Environmental Conditions 8) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions These rasters 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: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Amur honeysuckle (Lonicera maackii), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua ), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Beefsteak mint (Perilla frutescens), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur buttercup (Ranunculus testiculatus), Burning bush (Euonymus alatus), Camelthorn (Alhagi maurorum), Canada thistle (Cirsium arvense), Cereal rye (Secale cereale), Cheatgrass (Bromus tectorum), Chinaberry (Melia azedarach), Chinese holly (Ilex cornuta), Chinese privet (Ligustrum sinense), Chinese tallowtree (Triadica sebifera), Chinese wisteria (Wisteria sinensis), Chocolate vine (Akebia quinata), Clasping pepperweed (Lepidium perfoliatum), Cogongrass (Imperata cylindrica), Common crupina (Crupina vulgaris), Common gorse (Ulex europaeus ), Common reed (Phragmites australis), Common tansy (Tanacetum vulgare), Coral ardisia (Ardisia crenata), Crape myrtle (Lagerstroemia indica), Creeping bentgrass (Agrostis stolonifera), Creeping buttercup (Ranunculus repens), Crested wheatgrass (Agropyron cristatum), Crown vetch (Securigera varia), Dalmatian toadflax (Linaria dalmatica), Diffuse knapweed (Centaurea diffusa), Dyer's woad (Isatis tinctoria), English holly (Ilex aquifolium), English ivy (Hedera helix), European beachgrass (Ammophila arenaria ), False brome (Brachypodium sylvaticum), Field brome (Bromus arvensis), Fountaingrass (Pennisetum setaceum), French broom (Genista monspessulana), Fuller's teasel (Dipsacus fullonum), Garlic mustard (Alliaria petiolata), Giant knotweed (Fallopia sachalinensis), Hairy cat's ear (Hypochaeris radicata), Halogeton (Halogeton glomeratus),