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NCCOS Assessment: Southeastern U.S. Predictive Modeling of Deep-Sea Corals and Hardbottom Habitats, 2016-10-01 to 2021-09-30 (NCEI Accession 0282806)

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This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals (DSCs) and hardbottom habitats offshore of the southeastern U.S. It includes a database (.csv text file) containing records of occurrence (presence-absence) for DSCs with associated measures of sampling effort and bottom type from 20 datasets comprised of data from visual field surveys conducted with underwater vehicles. It also includes raster datasets at 100 x 100 m spatial resolution depicting the median and coefficient variation of the predicted occurrence (occupancy probability) for 24 taxa of DSCs (23 genera, 1 family) and hardbottom habitats. Additional raster datasets depict the median and coefficient of variation of the predicted genus richness for the 23 genera of DSCs. The data collection also includes raster datasets at 100 x 100 m spatial resolution depicting each of the 62 spatial environmental predictors considered for fitting the models. For more information, see Poti et al. (2022). The project to compile this model took place between 2016 and 2021, however the model input data range from 2001-2018 and the model output covers the same timeframe.
  • Cite as: Poti, Matthew; Goyert, Holly F.; Salgado, Enrique J.; Bassett, Rachel; Coyne, Michael; Winship, Arliss J.; Etnoyer, Peter; Hourigan, Thomas F.; Coleman, Heather M.; Christensen, John (2023). NCCOS Assessment: Southeastern U.S. Predictive Modeling of Deep-Sea Corals and Hardbottom Habitats, 2016-10-01 to 2021-09-30 (NCEI Accession 0282806). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/8mvd-4x25. Accessed [date].
gov.noaa.nodc:0282806
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Distribution Formats
  • CSV
  • 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 2021-09-30
Spatial Bounding Box Coordinates
West: -83.000522
East: -71.568484
South: 23.816104
North: 38.329956
Spatial Coverage Map
General Documentation
Associated Resources
Publication Dates
  • publication: 2023-10-13
  • revision: 2024-09-19
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
In this accession, NCEI has archived multiple versions of these data. The latest (and best) version of these data has the largest version number.

Methods:

A database of DSC occurrences (presence-absence) with associated measures of sampling effort (i.e., area observed by a survey segment) and bottom type was compiled from 20 available datasets containing data from underwater visual surveys conducted between 2001 and 2018. Spatially-explicit hierarchical occupancy models were used to relate records of DSC and hardbottom habitat occurrence from the database to spatial environmental predictors characterizing the seafloor, oceanography, and geography of the study area in order to predict and map the estimated occurrence of 24 taxa of DSCs and hardbottom habitats across the study area. The models attempted to account for imperfect detection and thus standardize estimates of occurrence across taxa. Variability in model predictions was also mapped to provide a measure of the level of confidence in the model predictions. The models also predicted the genus richness for 23 genera of DSCs. For more details, see Poti et al. (2022).

File Information:

Total File Size: 90.6 GB total, 117 files in 3 folders (unzipped), 16.9 GB (zipped)

Data File Format(s): Flat text file .CSV GeoTiff .TIF ##Data File Compression: No compression

Data File Resolution: 100 x 100 meters ##GIS Projection: oblique Mercator coordinate system (origin = 35°N 75°W, azimuth = 40°, scale = 0.9996, datum = NAD83)
Purpose The Bureau of Ocean Energy Management (BOEM) oversees the responsible development of offshore energy and mineral resources on the U.S. Outer Continental Shelf. Offshore energy projects often include activities that may physically disturb the seafloor and could negatively affect benthic biota. BOEM identified a need for information on the spatial distributions of sensitive benthic habitats, including deep-sea corals and hardbottom areas capable of supporting diverse benthic communities, offshore of the southeastern U.S. within the Atlantic Outer Continental Shelf region. Many deep-sea corals form complex three-dimensional structures that can increase local biodiversity by providing microhabitats for use by other species such as fishes, crustaceans, and echinoderms (Buhl-Mortensen et al. 2010). Areas with exposed hardbottom provide available surface for attachment of sessile invertebrates like deep-sea corals and may also be associated with greater diversity and abundance of large fish on the continental shelf offshore of the southeastern U.S. (Quattrini et al. 2004, Kendall et al. 2009, Taylor et al. 2016). Information on the spatial distributions of deep-sea corals and hardbottom habitats is needed to assess the potential impacts of activities that may disturb the seafloor and to develop mitigation measures to avoid or minimize these impacts. This study had two main objectives: (1) to compile a database of presence-absence observations of DSCs with associated measures of sampling effort and bottom type from available data collected by underwater visual surveys and (2) to develop predictive models that relate the occurrence (presence-absence) of deep-sea corals and hardbottom habitats to spatial environmental predictors in order to predict and map their potential spatial distributions offshore of the southeastern U.S.
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; Goyert, Holly F.; Salgado, Enrique J.; Bassett, Rachel; Coyne, Michael; Winship, Arliss J.; Etnoyer, Peter; Hourigan, Thomas F.; Coleman, Heather M.; Christensen, John (2023). NCCOS Assessment: Southeastern U.S. Predictive Modeling of Deep-Sea Corals and Hardbottom Habitats, 2016-10-01 to 2021-09-30 (NCEI Accession 0282806). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/8mvd-4x25. Accessed [date].
Cited Authors
Principal Investigators
Collaborators
Contributors
Resource Providers
Points of Contact
Publishers
Acknowledgments
  • Funding Agency: US DOI; Bureau of Ocean Energy Management (BOEM)
  • Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
Theme keywords NODC DATA TYPES THESAURUS NODC OBSERVATION TYPES THESAURUS WMO_CategoryCode
  • oceanography
Global Change Master Directory (GCMD) Science Keywords NCCOS Research Areas
  • Marine Spatial Ecology > Ecological and Biogeographic Assessments
  • Marine Spatial Ecology > Habitat Mapping
NCCOS Research Data Types
  • Derived Data Product
  • Geospatial
  • Model
Provider Keywords
  • Occupancy Probability
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 Regions of Study
  • U.S. States and Territories > Delaware
  • U.S. States and Territories > Florida
  • U.S. States and Territories > Georgia
  • U.S. States and Territories > Maryland
  • U.S. States and Territories > North Carolina
  • U.S. States and Territories > South Carolina
  • U.S. States and Territories > Virginia
  • Waterbodies > Atlantic Ocean
Provider Place Names
  • Coastal Ocean
  • Continental Shelf
  • Oculina Bank Habitat Area of Particular Concern
  • South Atlantic Bight
  • South Atlantic Deepwater Coral Habitat Areas of Particular Concern
  • South Atlantic Deepwater Snapper Grouper MPAs
Project keywords NCCOS Project Names
  • NCCOS Project: Characterizing Spatial Distributions of Deep-Sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic
Keywords NCEI ACCESSION NUMBER
Keywords Send2NCEI Submission Package ID
  • YBN85R
Use Constraints
  • Cite as: Poti, Matthew; Goyert, Holly F.; Salgado, Enrique J.; Bassett, Rachel; Coyne, Michael; Winship, Arliss J.; Etnoyer, Peter; Hourigan, Thomas F.; Coleman, Heather M.; Christensen, John (2023). NCCOS Assessment: Southeastern U.S. Predictive Modeling of Deep-Sea Corals and Hardbottom Habitats, 2016-10-01 to 2021-09-30 (NCEI Accession 0282806). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/8mvd-4x25. 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
  • 2023-10-13T22:44:08Z - NCEI Accession 0282806 v1.1 was published.
  • 2024-09-19T20:54:51Z - NCEI Accession 0282806 was revised and v2.2 was published.
    Rationale: Updates were received for this dataset. These updates were copied into the data/0-data/ directory of this accession. These updates may provide additional files or replace obsolete files. This version contains the most complete and up-to-date representation of this archival information package. All of the files received prior to this update are available in the preceding version of this accession.
Output Datasets
Lineage information for: dataset
Processing Steps
  • Parameter or Variable: Occupancy Probability (calculated); Units: none; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: A database containing presence-absence observations of DSCs with associated measures of sampling effort and bottom type was compiled from 20 datasets comprised of data from visual field surveys conducted with underwater vehicles between 2001 and 2018. The database records were integrated with spatial environmental predictors representing seafloor topography, substrate, oceanography, and geography in spatial predictive models. Spatially-explicit hierarchical occupancy models were developed in a Bayesian framework to predict and map the occurrence (occupancy probability) of 24 taxa of DSCs and hardbottom habitats across the study area. The models also predicted the genus richness for the 23 genera of DSCs included in the study. For more details, see Poti et al. (2022).; Data Quality Method: The models generated posterior probability distributions for occupancy probability and richness. The posterior median was selected as the best estimate of predicted occupancy probability and predicted genus richness. The posterior coefficient of variation (CV) was calculated as a measure of variability (i.e., confidence) in model predictions at each grid cell. Maps of predicted occurrence (occupancy probability) and predicted genus richness were reviewed by study authors and collaborators with expertise in the ecology of deep-sea corals and hardbottom habitats. See Poti et al. (2022) for details..
Last Modified: 2024-09-20T06:06:55Z
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