The Ocean Archive System searches our original datasets as they were submitted to us, not individual points or profiles. If you want to search and retrieve ocean profiles in a common format, or objectively analyzed fields, your better option may be to use one of our project applications. See: Access Data
OAS accession Detail for 0244006, meta_version: 7. Current meta_version is: 11
| << previous | |revision: 7| | next >> |
| accessions_id: | 0244006 | archive |
|---|---|
| Title: | NCCOS assessment: Predicting deep-sea coral habitats within the Papahānaumokuākea Marine National Monument, Hawaii (NCEI Accession 0244006) |
| Abstract: | This data collection contains geospatial data from spatial predictive models that were developed for 22 deep-sea coral and sponge (DSCS) taxa within the Papahānaumokuākea Marine National Monument (PMNM) from depths of 100–3,500 m. It includes raster datasets at 360 x 360 m spatial resolution depicting the predicted probability of occurrence for each of these taxa and a raster dataset at 360 x 360 m spatial resolution depicting the predicted taxonomic richness. These predictions provide a baseline for the potential distribution of these vulnerable and ecologically significant communities in the northwestern Hawaiian Islands (NWHI), and will support management planning, permitting, exploration and sanctuary designation efforts by the Monument. The data collection also includes raster datasets at 360 x 360 m spatial resolution depicting each of the 44 spatial environmental predictor variables considered for fitting the models. |
| Date received: | 20211102 |
| Start date: | 20190701 |
| End date: | 20211231 |
| Seanames: | North Pacific Ocean, Papahānaumokuākea Marine National Monument |
| West boundary: | 177 |
| East boundary: | -160 |
| North boundary: | 31.977425 |
| South boundary: | 19.120667 |
| Observation types: | GIS product, model output |
| Instrument types: | |
| Datatypes: | biological data, CORAL |
| Submitter: | |
| Submitting institution: | US DOC; NOAA; NOS; National Centers for Coastal Ocean Science |
| Collecting institutions: | US DOC; NOAA; NOS; National Centers for Coastal Ocean Science |
| Contributing projects: | |
| Platforms: | |
| Number of observations: | |
| Supplementary information: | Submission Package ID: ALAPGA Methods: Records of DSCS occurrence were obtained from the NOAA National Database for Deep-Sea Corals and Sponges (McGuinn et al. 2020) for remotely operated vehicle (ROV) surveys conducted by the NOAA Ship Okeanos Explorer and OET’s E/V Nautilus and surveys conducted using HURL’s submersibles Pisces IV and Pisces V. Information in the video logs for HURL submersible dives was used to revise spatial positions of records from HURL submersible dives and to obtain additional records of DSCS occurrence not included in the National Database. Spatial predictive models were generated for 22 DSCS taxa to estimate the relationships between occurrence (presence-absence) and spatial environmental predictor variables depicting depth and seafloor topography, oceanography, and geography. The models were then used to predict and map the probability of occurrence for each taxon across the study area. Relevant DSCS taxa were selected to meet the needs of the PMNM and spanned a range of depths, habitat types, ecological significance, as well as economic and cultural importance. Presence-absence of each taxon was assigned at the level of the model grid cell using the records of DSCS occurrence. A measure of survey effort was also assigned to each grid cell using the navigation data or video log from each survey represented in the records. Three statistical modeling approaches were used: Boosted Regression Trees (BRTs), Generalized Additive Models (GAMs), and maximum entropy (Maxent). Predictions from these different models were combined to create one ensemble prediction for each DSCS taxon. The ensemble predictions for the 22 DSCS taxa were summed to estimate DSCS richness across the study area. Variability in model predictions was also mapped for each taxon to provide a measure of the confidence in model predictions. At each step in the process, local partners and experts reviewed draft methods and maps to ensure the final products were accurate, meaningful and useful. For more details, see Poti et al. (2022). File Information: Total File Size: 4.67 GB in 314 files, 3 folders (unzipped) / 2.05 GB (zipped) Data File Format(s): GeoTiff .TIF (and ancillary files .TFW, .TIF.AUX.XML) Data File Compression: None Data File Resolution: 360x360 meters GIS Projection: Hotine_Oblique_Mercator (Projected) WGS 1984 [WKID 4326] (Geographic) |
| Availability date: | |
| Metadata version: | 7 |
| Keydate: | 2021-12-09 21:33:00+00 |
| Editdate: | 2022-02-08 15:17:33+00 |