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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abstract: 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.
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.
credit: Related Funding Agency: US DOI, Bureau of Ocean Energy Management (BOEM)
credit: Related Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
credit: NCCOS Partners: Changming Dong, University of California
credit: NCCOS Partners: Andrew Moore, University of California, Santa Cruz
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title: 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.
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supplementalInformation: 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)
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description: 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..
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