Dataset Overview | National Centers for Environmental Information (NCEI)

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.

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].

Dataset Identifiers

ISO 19115-2 Metadata

gov.noaa.nodc:0282806

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Download Data	HTTPS (download) Navigate directly to the URL for data access and direct download. FTP (download) These data are available through the File Transfer Protocol (FTP). FTP is no longer supported by most internet browsers. You may copy and paste the FTP link to the data into an FTP client (e.g., FileZilla or WinSCP).
Distribution Formats	CSV GeoTIFF
Ordering Instructions	Contact NCEI for other distribution options and instructions.
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

NCEI Dataset Landing Page
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Descriptive Information
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Associated Resources

Buhl‐Mortensen, L., Vanreusel, A., Gooday, A. J., Levin, L. A., Priede, I. G., Buhl‐Mortensen, P., Gheerardyn, H., King, N. J., & Raes, M. (2010). Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Marine Ecology, 31(1), 21–50. https://doi.org/10.1111/j.1439-0485.2010.00359.x
- https://doi.org/10.1111/j.1439-0485.2010.00359.x
  related journal article
Influence of hard bottom morphology on fish assemblages of the continental shelf off Georgia, southeastern USA
- https://www.ingentaconnect.com/content/umrsmas/bullmar/2009/00000084/00000003/art00002
  related journal article
Data synthesis and predictive modeling of deep-sea coral and hardbottom habitats offshore of the southeastern U.S.: guiding efficient discovery and protection of sensitive benthic areas
- https://espis.boem.gov/Final%20Reports/BOEM_2022-038.pdf
  related journal article
Quattrini, A. M., Ross, S. W., Sulak, K. J., Necaise, A. M., Casazza, T. L., & Dennis, G. D. (2004). MARINE FISHES NEW TO CONTINENTAL UNITED STATES WATERS, NORTH CAROLINA, AND THE GULF OF MEXICO. Southeastern Naturalist, 3(1), 155–172. https://doi.org/10.1656/1528-7092(2004)003[0155:mfntcu]2.0.co;2
- https://doi.org/10.1656/1528-7092(2004)003[0155:mfntcu]2.0.co;2
  related journal article
Benthic habitat mapping and assessment in the Wilmington-East Wind Energy Call Area
- https://repository.library.noaa.gov/view/noaa/8438
  related journal article
NOAA NCEI Bathymetric Data Viewer
- https://www.ncei.noaa.gov/maps/bathymetry/
  NCEI project webpage
NOAA NCEI Coastal Digital Elevation Models (DEMs)
- https://www.ngdc.noaa.gov/mgg/coastal/coastal.html
  NCEI project webpage
NOAA NCEI U.S. Coastal Relief Model (CRM) – Southeast Atlantic
- https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ngdc.mgg.dem:305/html
  NCEI project webpage
NOAA NCEI U.S. Coastal Relief Model (CRM) – Florida and East Gulf of Mexico
- https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ngdc.mgg.dem:307/html
  NCEI project webpage
General Bathymetric Chart of the Oceans (GEBCO)
- https://www.gebco.net/data_and_products/historical_data_sets/#gebco_2019
  GEBCO project webpage
dbSEABED: Information Integration System for Marine Substrates.
- https://instaar.colorado.edu/~jenkinsc/dbseabed/
  dbSEABED project webpage
NASA Ocean Color SeaWiFS, Aqua MODIS, Terra MODIS, VIIRS. https://oceancolor.gsfc.nasa.gov/
- https://oceancolor.gsfc.nasa.gov/
  NASA project webpage
HYCOM + NCODA Global 1/12° Reanalysis.
- https://www.hycom.org/dataserver/gofs-3pt0/reanalysis
  HYCOM project webpage
Poti, Matthew; Henkel, Sarah K.; 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 Benthic Macrofauna, 2016-10-01 to 2020-09-30 (NCEI Accession 0276535). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/h9kg-0897.
- https://doi.org/10.25921/h9kg-0897
  associated dataset
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). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/5e50-g162.
- https://doi.org/10.25921/5e50-g162
  associated dataset
Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic
- https://coastalscience.noaa.gov/project/characterizing-spatial-distributions-of-deep-sea-corals-and-hardbottom-habitats-in-the-u-s-southeast-atlantic/
  National Centers for Coastal Ocean Science (NCCOS) project page

Publication Dates	publication: 2023-10-13
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: YBN85R 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	Poti, Matthew US DOC; NOAA; NOS; National Centers for Coastal Ocean Science Goyert, Holly F. CSS, Inc. and NCCOS Salgado, Enrique J. CSS, Inc. and NCCOS Bassett, Rachel CSS, Inc. and NCCOS Coyne, Michael CSS, Inc. and NCCOS Winship, Arliss J. CSS, Inc. and NCCOS Etnoyer, Peter US DOC; NOAA; NOS; National Centers for Coastal Ocean Science Hourigan, Thomas F. US DOC; NOAA; National Marine Fisheries Service (NMFS); Deep Sea Coral Research and Technology Program (DSCRTP) Coleman, Heather M. US DOC; NOAA; National Marine Fisheries Service (NMFS); Deep Sea Coral Research and Technology Program (DSCRTP) Christensen, John US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
Principal Investigators	Matthew Poti US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS) John Christensen US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS)
Collaborators	Rachel Bassett CSS, Inc. and NCCOS Heather Coleman US DOC; NOAA; National Marine Fisheries Service (NMFS); Deep Sea Coral Research and Technology Program (DSCRTP) Michael Coyne CSS, Inc. and NCCOS Peter Etnoyer US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS) Holly Goyert CSS, Inc. and NCCOS Thomas Hourigan US DOC; NOAA; National Marine Fisheries Service (NMFS); Deep Sea Coral Research and Technology Program (DSCRTP) Enrique Salgado CSS, Inc. and NCCOS Arliss Winship CSS, Inc. and NCCOS Sandra D. Brooke Florida State University (FSU) Jason Chaytor US DOI; US Geological Survey (USGS) Erik Cordes Temple University (TU) Stephanie Farrington Florida Atlantic University (FAU) Harbor Branch Oceanographic Institute (HBOI) Scott Harris College of Charleston Christopher Jenkins University of Colorado - Boulder Martha Nizinski US DOC; NOAA; National Marine Fisheries Service (NMFS); National Systematics Laboratory John Reed Florida Atlantic University (FAU) Harbor Branch Oceanographic Institute (HBOI) Carolyn Ruppel US DOI; United States Geological Survey (USGS) George Sedberry South Carolina Department of Natural Resources (SCDNR) Timothy Snoots South Carolina Department of Natural Resources (SCDNR) Geoffrey Timms South Carolina Department of Natural Resources (SCDNR)
Contributors	US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS) College of Charleston Temple University (TU) South Carolina Department of Natural Resources (SCDNR) Florida State University (FSU)
Resource Providers	US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS)
Points of Contact	Matthew Poti US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS) NCCOS Data Manager US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS)
Publishers	NOAA National Centers for Environmental Information
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

Theme keywords	NODC DATA TYPES THESAURUS biological data CHLOROPHYLL A CORAL CORAL - SPECIES IDENTIFICATION CURRENT SPEED DISSOLVED OXYGEN HABITAT HABITAT - BENTHIC REMOTE SENSING REFLECTANCE SALINITY seafloor topography SEDIMENT - GRAIN SIZE SEDIMENTS SEDIMENTS - PERCENT COVER species abundance SUBSTRATE DESCRIPTION water depth WATER TEMPERATURE NODC OBSERVATION TYPES THESAURUS model output survey - coral reef WMO_CategoryCode oceanography Global Change Master Directory (GCMD) Science Keywords EARTH SCIENCE > BIOLOGICAL CLASSIFICATION EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH EARTH SCIENCE > OCEANS > MARINE SEDIMENTS EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN EARTH SCIENCE > OCEANS > OCEAN CIRCULATION > OCEAN CURRENTS EARTH SCIENCE > OCEANS > OCEAN OPTICS > REFLECTANCE EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY EARTH SCIENCE > SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES > COASTAL LANDFORMS > CORAL REEFS 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 Occupancy Probability
Data Center keywords	NODC COLLECTING INSTITUTION NAMES THESAURUS College of Charleston Florida State University South Carolina Department of Natural Resources Temple University US DOC; NOAA; NOS; National Centers for Coastal Ocean Science NODC SUBMITTING INSTITUTION NAMES THESAURUS US DOC; NOAA; NOS; National Centers for Coastal Ocean Science Global Change Master Directory (GCMD) Data Center Keywords DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce SCDNR > South Carolina Department of Natural Resources
Instrument keywords	Provider Instruments Models/Analyses > Data Analysis > Environmental Modeling
Place keywords	NODC SEA AREA NAMES THESAURUS Coastal Waters of Florida South Atlantic Ocean Global Change Master Directory (GCMD) Location Keywords OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN OCEAN > ATLANTIC OCEAN > SOUTH ATLANTIC OCEAN NCCOS Research Location NCCOS Research Location > Region > Atlantic Ocean NCCOS Research Location > U.S. States and Territories > Delaware NCCOS Research Location > U.S. States and Territories > Florida NCCOS Research Location > U.S. States and Territories > Georgia NCCOS Research Location > U.S. States and Territories > Maryland NCCOS Research Location > U.S. States and Territories > North Carolina NCCOS Research Location > U.S. States and Territories > South Carolina NCCOS Research Location > U.S. States and Territories > Virginia 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 Keywords NCCOS Project: Characterizing Spatial Distributions of Deep-Sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic
Keywords	NCEI ACCESSION NUMBER 0282806

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.
Output Datasets	NCEI Accession 0282806 v1.1 NCEI Accession 0282806 v1.1 (download) published 2023-10-13T22:44:08Z

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..

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: 2024-04-10T23:31:50Z
For questions about the information on this page, please email: ncei.info@noaa.gov

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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