Various geospatial data sets have been packaged in an ArcGIS Pro .aprx. The user will need the ArcGIS Pro software to access and view the data. For more information on ArcGIS Pro go to https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview. Note that Metadata for various geospatial data files can be accessed by selecting View Metadata within ArcGISPro.

Maxent software (http://www.cs.princeton.edu/~schapire/maxent) is frequently used for presence-only species distribution modeling. Maxent requires, however, that input ASCII raster files be aligned with one another and have the same spatial extent. This tool pre-processes raster data in preparation for Maxent modeling to ensure that all rasters have the same extent, same cell size, and aren't missing data. There are two version of this geoprocessing modeling. The advanced version is for the ArcGIS Advanced license. The basic version is the the ArcGIS Advanced license. Both versions require Spatial Analyst. The difference between the two is that the advanced version creates a polygon shapefile that shows the difference between the template raster and the processed raster. Ideally, this should generate a polygon with empty output, but if it doesn't you can use it to diagnose problems. The tool first resamples the raster, then uses a focalmean (3x3 and 5x5) to fill gaps, and mosaics the resampled, 3x3, and 5x5 rasters together, and converts to ASCII.Recommended citation format: Dilts, T.E. (2015) Prepare Rasters for Maxent Tool for ArcGIS 10.1. University of Nevada Reno. Available at: http://www.arcgis.com/home/item.html?id=11bf7e689c92413f8d31933b3e1f56b1

GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were based on land cover data of ecological systems and--when applicable for the given taxon--on ancillary variables such as elevation, hydrologic characteristics, human avoidance characteristics, forest edge, ecotone widths, etc. Distribution models were generated using a python script that selects model variables based on literature cited information stored in a wildlife habitat relationship database (WHRdb); literature used includes primary and gray publications. Distribution models are 30 meter raster data and delimited by GAP species ranges. Distribution model data were attributed with information regarding seasonal use based on GAP regional projects (NWGAP, SWReGAP, SEGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/ GAP used the best information available to create these species distribution models; however GAP seeks to improve and update these data as new information becomes available. Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Distribution Models. Available: http://gapanalysis.usgs.gov. Accessed [date].

Download Model Uncertainty for Predicted Geographical Distribution of Harriotta Raleighana (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Harriotta raleighana (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Harriotta Raleighana (DemersalFish)) Number of taxa records: 5611 Statistical model performance: Good (TSS = 0.75) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Dalatias Licha (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Dalatias licha (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Dalatias Licha (DemersalFish)) Number of taxa records: 2294 Statistical model performance: Good (TSS = 0.76) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Caesioperca Lepidoptera (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Caesioperca lepidoptera (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Caesioperca Lepidoptera (DemersalFish)) Number of taxa records: 86 Statistical model performance: Good (TSS = 0.84) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Sigmops Spp. (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Sigmops spp. (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Sigmops Spp. (DemersalFish)) Number of taxa records: Statistical model performance: (TSS = ) Expert evaluation of predicted geographical distribution: , Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Nemadactylus Douglasii (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Nemadactylus douglasii (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Nemadactylus Douglasii (DemersalFish)) Number of taxa records: 101 Statistical model performance: Good (TSS = 0.91) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Rosenblattia Robusta (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Rosenblattia robusta (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Rosenblattia Robusta (DemersalFish)) Number of taxa records: 168 Statistical model performance: Good (TSS = 0.8) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Micromesistius Australis (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Micromesistius australis (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Micromesistius Australis (DemersalFish)) Number of taxa records: 1650 Statistical model performance: Good (TSS = 0.85) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Mora Moro (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Mora moro (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Mora Moro (DemersalFish)) Number of taxa records: 6049 Statistical model performance: Good (TSS = 0.82) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Bassanago Hirsutus (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Bassanago hirsutus (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Bassanago Hirsutus (DemersalFish)) Number of taxa records: 2702 Statistical model performance: Moderate (TSS = 0.71) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Arhynchobatis Asperrimus (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Arhynchobatis asperrimus (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Arhynchobatis Asperrimus (DemersalFish)) Number of taxa records: 74 Statistical model performance: Good (TSS = 0.92) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Coryphaenoides Mcmillani (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Coryphaenoides mcmillani (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Coryphaenoides Mcmillani (DemersalFish)) Number of taxa records: 120 Statistical model performance: Good (TSS = 0.86) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Pseudolabrus Miles (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Pseudolabrus miles (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Pseudolabrus Miles (DemersalFish)) Number of taxa records: 349 Statistical model performance: Good (TSS = 0.9) Expert evaluation of predicted geographical distribution: 3, Somewhat accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Nemadactylus Douglasii (Reef Fish) on Subtidal Rocky Reefs around New Zealand DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Nemadactylus douglasii (reef fish) on subtidal rocky reefs in New Zealand using ensemble Species Distribution Modelling (Bootstrapped Boosted Rregression Tree and Random Forest models). Predictions originally described in Smith et al., 2013 and updated in Lundquist et al., 2020. Spatial predictions generated for all reef habitat (defined by DOC national rocky reef layer). Number of taxa records: 62 Statistical model performance: Good (TSS = 0.88) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 250m

Download Model Uncertainty for Predicted Geographical Distribution of Simenchelys Parasitica (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Simenchelys parasitica (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Simenchelys Parasitica (DemersalFish)) Number of taxa records: 313 Statistical model performance: Good (TSS = 0.81) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Hyperoglyphe Antarctica (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Hyperoglyphe antarctica (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Hyperoglyphe Antarctica (DemersalFish)) Number of taxa records: 842 Statistical model performance: Good (TSS = 0.81) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Latridopsis Ciliaris (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Latridopsis ciliaris (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Latridopsis Ciliaris (DemersalFish)) Number of taxa records: 315 Statistical model performance: Good (TSS = 0.83) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 1km

Download Model Uncertainty for Predicted Geographical Distribution of Plagiogeneion Rubiginosum (Demersal Fish) DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographic distribution of Plagiogeneion rubiginosum (demersal fish) using ensemble Species Distribution Modelling (Bootstrapped Boosted Regression Tree and Random Forest models) described in Lundquist et al., 2020. Spatial predictions generated for all geographic areas within the EEZ to depths of 2500m (areas considered to have adequate sample coverage). Associated spatially explicit uncertainty predictions are available for this taxa (see Model Uncertainty for Predicted Geographical Distribution of Plagiogeneion Rubiginosum (DemersalFish)) Number of taxa records: 244 Statistical model performance: Good (TSS = 0.82) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 1km