100+ datasets found
  1. r

    Data from: Distribution modeling and gap analysis of shorebird conservation...

    • scholarship.libraries.rutgers.edu
    zip
    Updated Aug 10, 2023
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    Richard G. Lathrop; Daniel M. Merchant (2023). Distribution modeling and gap analysis of shorebird conservation in northern Brazil [Dataset]. https://scholarship.libraries.rutgers.edu/esploro/outputs/dataset/Distribution-modeling-and-gap-analysis-of/991031794681504646
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    zip(376771196 bytes)Available download formats
    Dataset updated
    Aug 10, 2023
    Dataset provided by
    Remote Sensing
    Authors
    Richard G. Lathrop; Daniel M. Merchant
    Time period covered
    2023
    Area covered
    Brazil, North Region
    Dataset funded by
    National Fish and Wildlife Foundationhttp://www.nfwf.org/
    Description

    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.

  2. a

    PrepareRastersforMaxent

    • hub.arcgis.com
    Updated Jan 8, 2015
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    University of Nevada, Reno (2015). PrepareRastersforMaxent [Dataset]. https://hub.arcgis.com/content/11bf7e689c92413f8d31933b3e1f56b1
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    Dataset updated
    Jan 8, 2015
    Dataset authored and provided by
    University of Nevada, Reno
    Description

    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

  3. w

    U.S. Geological Survey Gap Analysis Program Species Distribution Models

    • data.wu.ac.at
    • datadiscoverystudio.org
    • +1more
    esri rest
    Updated Jun 8, 2018
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    Department of the Interior (2018). U.S. Geological Survey Gap Analysis Program Species Distribution Models [Dataset]. https://data.wu.ac.at/schema/data_gov/MzhkZjU2Y2EtZWQ0MS00YzQ1LTk1MGItZDk0NDBkMGY1ZmNh
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    esri restAvailable download formats
    Dataset updated
    Jun 8, 2018
    Dataset provided by
    Department of the Interior
    Area covered
    f97360f2bf2d00d14cf2cb1c1fab3f8035a3fdab
    Description

    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].

  4. a

    Predicted Geographical Distribution of Harriotta Raleighana (Demersal Fish)

    • hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Harriotta Raleighana (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/8609bff7406a4af1b24e87886d53cbca
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  5. a

    Predicted Geographical Distribution of Dalatias Licha (Demersal Fish)

    • hub.arcgis.com
    • doc-deptconservation.opendata.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Dalatias Licha (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/2877556c17394e05b2e39b7570097e5c
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  6. a

    Predicted Geographical Distribution of Caesioperca Lepidoptera (Demersal...

    • doc-marine-data-deptconservation.hub.arcgis.com
    • doc-deptconservation.opendata.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Caesioperca Lepidoptera (Demersal Fish) [Dataset]. https://doc-marine-data-deptconservation.hub.arcgis.com/documents/87779f1275844f79aaae7a09174db952
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  7. a

    Predicted Geographical Distribution of Sigmops Spp. (Demersal Fish)

    • hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Sigmops Spp. (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/6a9522ce91f04ba6885be8a1996e4450
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  8. a

    Predicted Geographical Distribution of Nemadactylus Douglasii (Demersal...

    • doc-deptconservation.opendata.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Nemadactylus Douglasii (Demersal Fish) [Dataset]. https://doc-deptconservation.opendata.arcgis.com/documents/3e67ab0ca7fa4cd282502732ee32954b
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  9. a

    Predicted Geographical Distribution of Rosenblattia Robusta (Demersal Fish)

    • hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Rosenblattia Robusta (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/70f6066ce82f4da7b21c1cf303d2f359
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  10. a

    Predicted Geographical Distribution of Micromesistius Australis (Demersal...

    • hub.arcgis.com
    • doc-deptconservation.opendata.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Micromesistius Australis (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/b61e6f30b1054f4a9b3f2f4bb0fa3b4d
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  11. a

    Predicted Geographical Distribution of Mora Moro (Demersal Fish)

    • doc-deptconservation.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Mora Moro (Demersal Fish) [Dataset]. https://doc-deptconservation.opendata.arcgis.com/items/e064cf3811d14d92a8b7a48cbe03a579
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  12. a

    Predicted Geographical Distribution of Bassanago Hirsutus (Demersal Fish)

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Bassanago Hirsutus (Demersal Fish) [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/documents/9fc4ee4818f240029a88b55d4a4e4424
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  13. a

    Predicted Geographical Distribution of Arhynchobatis Asperrimus (Demersal...

    • doc-marine-data-deptconservation.hub.arcgis.com
    • hub.arcgis.com
    Updated Jan 1, 2020
    + more versions
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    DOC_admin (2020). Predicted Geographical Distribution of Arhynchobatis Asperrimus (Demersal Fish) [Dataset]. https://doc-marine-data-deptconservation.hub.arcgis.com/items/cd82b3fe696840aaa0902e9af6c526d9
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  14. a

    Predicted Geographical Distribution of Coryphaenoides Mcmillani (Demersal...

    • hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Coryphaenoides Mcmillani (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/b688f883c3ce40238bc811284423144e
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  15. a

    Predicted Geographical Distribution of Pseudolabrus Miles (Demersal Fish)

    • doc-deptconservation.opendata.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Pseudolabrus Miles (Demersal Fish) [Dataset]. https://doc-deptconservation.opendata.arcgis.com/documents/48981657562e4ffcbf5dd0f8e8fd2a6b
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  16. a

    Predicted Geographical Distribution of Nemadactylus Douglasii (Reef Fish)

    • doc-deptconservation.opendata.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Nemadactylus Douglasii (Reef Fish) [Dataset]. https://doc-deptconservation.opendata.arcgis.com/documents/b8cba8291dc345b3ad8cb9565da381e8
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  17. a

    Predicted Geographical Distribution of Simenchelys Parasitica (Demersal...

    • hub.arcgis.com
    • doc-marine-data-deptconservation.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Simenchelys Parasitica (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/6ae7002e30b146718e76a49c45d94265
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  18. a

    Predicted Geographical Distribution of Hyperoglyphe Antarctica (Demersal...

    • hub.arcgis.com
    • doc-deptconservation.opendata.arcgis.com
    • +2more
    Updated Dec 17, 2021
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    DOC_admin (2021). Predicted Geographical Distribution of Hyperoglyphe Antarctica (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/1bc4dde7c5f24937850be84ad49bf691
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    Dataset updated
    Dec 17, 2021
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  19. a

    Predicted Geographical Distribution of Latridopsis Ciliaris (Demersal Fish)

    • doc-marine-data-deptconservation.hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Latridopsis Ciliaris (Demersal Fish) [Dataset]. https://doc-marine-data-deptconservation.hub.arcgis.com/items/8d6ab97b9fa940fa984599f64339d5be
    Explore at:
    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

  20. a

    Predicted Geographical Distribution of Plagiogeneion Rubiginosum (Demersal...

    • hub.arcgis.com
    • doc-deptconservation.opendata.arcgis.com
    Updated Jan 1, 2020
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    DOC_admin (2020). Predicted Geographical Distribution of Plagiogeneion Rubiginosum (Demersal Fish) [Dataset]. https://hub.arcgis.com/documents/f8bdb6ca6afc4f9d9d30c15f52137385
    Explore at:
    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    DOC_admin
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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

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Link copied
Close
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Richard G. Lathrop; Daniel M. Merchant (2023). Distribution modeling and gap analysis of shorebird conservation in northern Brazil [Dataset]. https://scholarship.libraries.rutgers.edu/esploro/outputs/dataset/Distribution-modeling-and-gap-analysis-of/991031794681504646

Data from: Distribution modeling and gap analysis of shorebird conservation in northern Brazil

Related Article
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zip(376771196 bytes)Available download formats
Dataset updated
Aug 10, 2023
Dataset provided by
Remote Sensing
Authors
Richard G. Lathrop; Daniel M. Merchant
Time period covered
2023
Area covered
Brazil, North Region
Dataset funded by
National Fish and Wildlife Foundationhttp://www.nfwf.org/
Description

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.

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