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Dataset Overview | National Centers for Environmental Information (NCEI)

NCCOS Assessment: U.S. West Coast Cross-Shelf Habitat Suitability Modeling of Deep-Sea Corals and Sponges, 2016-10-01 to 2020-09-30 (NCEI Accession 0276883)

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This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals and sponges offshore of the continental U.S. West Coast to 1200 m depth. It includes raster datasets at 200 x 200 m spatial resolution depicting the mean of the predicted relative habitat suitability, the coefficient of variation of the predicted relative habitat suitability, the classified mean relative habitat suitability, and the ‘robust high’ habitat suitability prediction for each of 31 taxa of deep-sea corals and 15 taxa of sponges and raster datasets at 200 x 200 m spatial resolution depicting the number of taxa of deep-sea corals associated with hard substrate that have ‘high’ habitat suitability or ‘robust high’ habitat suitability at each grid cell. The data collection also includes raster datasets at 200 x 200 m spatial resolution depicting each of the 66 spatial environmental predictor variables considered for fitting the models.
  • Cite as: Poti, Matthew; Henkel, Sarah; Bizzarro, Joseph J.; Hourigan, Thomas F.; Clarke, M. Elizabeth; Whitmire, Curt E.; Powell, Abigail; Yoklavich, Mary M.; Bauer, Laurie; Winship, Arliss J.; Coyne, Michael; Gillett, David J.; Gilbane, Lisa; Christensen, John; Jeffrey, Christopher F.G. (2023). NCCOS Assessment: U.S. West Coast Cross-Shelf Habitat Suitability Modeling of Deep-Sea Corals and Sponges, 2016-10-01 to 2020-09-30 (NCEI Accession 0276883). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/5e50-g162. Accessed [date].
gov.noaa.nodc:0276883
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Distribution Formats
  • GeoTIFF
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Distributor NOAA National Centers for Environmental Information
+1-301-713-3277
NCEI.Info@noaa.gov
Dataset Point of Contact NOAA National Centers for Environmental Information
ncei.info@noaa.gov
Time Period 2016-10-01 to 2020-09-30
Spatial Bounding Box Coordinates
West: -125.75885
East: -117.12375
South: 31.99803
North: 47.75512
Spatial Coverage Map
General Documentation
Associated Resources
Publication Dates
  • publication: 2023-03-15
Data Presentation Form Digital table - digital representation of facts or figures systematically displayed, especially in columns
Dataset Progress Status Complete - production of the data has been completed
Historical archive - data has been stored in an offline storage facility
Data Update Frequency As needed
Supplemental Information
Submission Package ID: J1KUMA

Methods:
Records of deep-sea coral and sponge occurrence (presences) were obtained from the NOAA National Database for Deep-Sea Corals and Sponges (McGuinn et al. 2020). Maximum entropy (Maxent) models were generated to estimate relationships between occurrence and spatial environmental predictor variables depicting seafloor topography, substrate, oceanography, and geography. Models were used to predict and map habitat suitability for each deep-sea coral and sponge taxon across the entire study area. Variability in model predictions was also mapped for each taxon to provide a measure of the level of confidence in model predictions. For more details, see Poti et al. (2020).

File Information
Total File Size: 20.8 GB total, 648 files in 17 folders (unzipped), 1.71 GB (zipped)
Data File Format(s): GeoTiff .TIF (and ancillary files .TIF.AUX.XML, .VAT.CPG, .VAT.DBF)
Data File Compression: .zip
Data File Resolution: 200 x 200 meters
GIS Projection: oblique Mercator coordinate system (origin = 39°N 125°W, azimuth =
75°, scale = 0.9996, datum = WGS84)
Purpose The Pacific Outer Continental Shelf (OCS) Region includes an extensive area offshore of California, Oregon, and Washington for which the U.S. Department of the Interior Bureau of Ocean Energy Management (BOEM) oversees the responsible development of energy and mineral resources. Information about the distribution of sensitive biota in the region, including deep-sea corals and sponges, is critical for making environmentally sound decisions about managing those activities and developing mitigation measures to avoid or minimize impacts on marine environments and organisms. To meet this critical information need, BOEM partnered with the US Department of Commerce National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) National Centers for Coastal Ocean Science (NCCOS) and the NOAA National Marine Fisheries Service (NMFS) to develop habitat suitability models predicting spatial distributions of suitable habitat for deep-sea corals and sponges. The objectives of this study were to collect and compile observations of deep-sea coral and sponge occurrence from survey data, to identify and derive datasets depicting environmental characteristics that might be correlated with the occurrence of these organisms, and to predict and map the spatial distributions of suitable habitat across the study area for selected taxa. In addition, preliminary ground-truthing of the deep-sea coral and sponge models was conducted using survey data collected in partnership with NOAA NMFS, BOEM, the US Geological Survey (USGS), and others. Products include geospatial datasets and maps to meet BOEM’s environmental assessment, management, and decision-making needs, as well as to support coastal and ocean management efforts by other local, state, and federal agencies.
Use Limitations
  • accessLevel: Public
  • Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
Dataset Citation
  • Cite as: Poti, Matthew; Henkel, Sarah; Bizzarro, Joseph J.; Hourigan, Thomas F.; Clarke, M. Elizabeth; Whitmire, Curt E.; Powell, Abigail; Yoklavich, Mary M.; Bauer, Laurie; Winship, Arliss J.; Coyne, Michael; Gillett, David J.; Gilbane, Lisa; Christensen, John; Jeffrey, Christopher F.G. (2023). NCCOS Assessment: U.S. West Coast Cross-Shelf Habitat Suitability Modeling of Deep-Sea Corals and Sponges, 2016-10-01 to 2020-09-30 (NCEI Accession 0276883). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/5e50-g162. Accessed [date].
Cited Authors
Principal Investigators
Collaborators
Contributors
Resource Providers
Points of Contact
Publishers
Acknowledgments
  • Related Funding Agency: US DOI, Bureau of Ocean Energy Management (BOEM)
  • Related Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
  • NCCOS Partners: Changming Dong, University of California
  • NCCOS Partners: Andrew Moore, University of California, Santa Cruz
Theme keywords NODC DATA TYPES THESAURUS NODC OBSERVATION TYPES THESAURUS WMO_CategoryCode
  • oceanography
Global Change Master Directory (GCMD) Science Keywords NCCOS Research Data Type
  • NCCOS Research Data Type > Derived Data Product
  • NCCOS Research Data Type > Geospatial
  • NCCOS Research Data Type > Model
NCCOS Research Priority
  • NCCOS Research Priority > Marine Spatial Ecology
NCCOS Research Topic
  • NCCOS Research Topic > Ecological and Biogeographic Assessments
  • NCCOS Research Topic > Habitat Mapping
Provider Keywords
  • Habitat Suitability
Data Center keywords NODC COLLECTING INSTITUTION NAMES THESAURUS NODC SUBMITTING INSTITUTION NAMES THESAURUS Global Change Master Directory (GCMD) Data Center Keywords
Instrument keywords Provider Instruments
  • Models/Analyses > Data Analysis > Environmental Modeling
Place keywords NODC SEA AREA NAMES THESAURUS Global Change Master Directory (GCMD) Location Keywords NCCOS Research Location
  • NCCOS Research Location > Region > Pacific Ocean
  • NCCOS Research Location > U.S. States and Territories > California
  • NCCOS Research Location > U.S. States and Territories > Oregon
  • NCCOS Research Location > U.S. States and Territories > Washington
Provider Place Names
  • Channel Islands National Marine Sanctuary
  • Coastal Ocean
  • Continental Shelf
  • Cordell Bank National Marine Sanctuary
  • Eastern Pacific
  • Greater Farallones National Marine Sanctuary
  • Monterey Bay National Marine Sanctuary
  • Olympic Coast National Marine Sanctuary
  • Southern California Bight
Project keywords NCCOS Project Keywords
  • NCCOS Project: Characterizing Potential Distributions of Deep-Sea Corals, Sponges, and Macrofauna Offshore of the U.S. West Coast
Keywords NCEI ACCESSION NUMBER
Use Constraints
  • Cite as: Poti, Matthew; Henkel, Sarah; Bizzarro, Joseph J.; Hourigan, Thomas F.; Clarke, M. Elizabeth; Whitmire, Curt E.; Powell, Abigail; Yoklavich, Mary M.; Bauer, Laurie; Winship, Arliss J.; Coyne, Michael; Gillett, David J.; Gilbane, Lisa; Christensen, John; Jeffrey, Christopher F.G. (2023). NCCOS Assessment: U.S. West Coast Cross-Shelf Habitat Suitability Modeling of Deep-Sea Corals and Sponges, 2016-10-01 to 2020-09-30 (NCEI Accession 0276883). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/5e50-g162. Accessed [date].
Access Constraints
  • Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
Fees
  • In most cases, electronic downloads of the data are free. However, fees may apply for custom orders, data certifications, copies of analog materials, and data distribution on physical media.
Lineage information for: dataset
Processing Steps
  • 2023-03-15T18:16:15Z - NCEI Accession 0276883 v1.1 was published.
Output Datasets
Lineage information for: dataset
Processing Steps
  • Parameter or Variable: Habitat Suitability (calculated); Units: N/A; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: Records of deep-sea coral and sponge occurrence (presences) were obtained from the NOAA National Database for Deep-Sea Corals and Sponges (McGuinn et al. 2020). Maximum entropy (Maxent) models were generated to estimate relationships between occurrence and spatial environmental predictor variables depicting seafloor topography, substrate, oceanography, and geography. Models were used to predict and map habitat suitability for 31 taxa of deep-sea corals and 15 taxa of sponge taxon across the entire study area. For 22 taxa of deep-sea corals considered to be associated with hard substrate, maps of habitat suitability were combined to create maps depicting the number of taxa of deep-sea corals associated with hard substrate that have ‘high’ habitat suitability or ‘robust high’ habitat suitability at each grid cell. For more details, see Poti et al. (2020).; Data Quality Method: Non-parametric bootstrapping was used to estimate variability (uncertainty) in predicted habitat suitability. The mean habitat suitability from 100 bootstrapped model predictions was selected as the best estimate of habitat suitability. The coefficient of variation (CV) of the 100 bootstrapped predictions was calculated as a measure of variability in model predictions at each grid cell. To allow comparisons of predicted habitat suitability across taxa, the mean habitat suitability was classified into a map with four habitat suitability classes (‘very low’, ‘low’, ‘medium’, ‘high’) using a series of breakpoints identified through receiver operating curve analysis. A map of ‘robust high’ habitat suitability identified grid cells for which all 100 bootstrapped predictions were classified in the high habitat suitability class. Maps of habitat suitability were reviewed by study authors and collaborators with expertise in the ecology of deep-sea corals and sponges. In addition, data collected by study authors and collaborators was used to demonstrate how models could be validated. See Poti et al. (2020) for details..
Last Modified: 2024-06-10T13:27:17Z
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