gov.noaa.nmfs.inport:38952 eng UTF8 dataset National Centers for Coastal Ocean Science resourceProvider NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov pointOfContact 2025-05-15T19:16:08 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) 3 row 443 column 496 vertical 1 area 0 NOAA ESRI Grid - seafloor hardbottom occurrence predictions model in New York offshore planning area from Biogeography Branch New_York_Hardbottom_Predictions 2012-05 publication NOAA/NMFS/EDM 38952 https://www.fisheries.noaa.gov/inport/item/38952 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information Menza, Charles 240-533-0372 301-713-4388 1305 East-West Hwy Silver Spring MD 20910 Charles.menza@noaa.gov principalInvestigator https://coastalscience.noaa.gov/projects/detail?key=80 WWW:LINK-1.0-http--link Citation URL Online Resource download This dataset represents hard bottom occurrence predictions from a spatial model developed for the New York offshore spatial planning area. This model builds upon the data compilation and analytical framework laid out by Greene et al. (2010). The model also provides a continuous gridded prediction surface representing the likelihood of hard bottom occurrence. These hard bottom occurrence predictions were developed to identify critical habitat areas for benthic organisms (e.g., clams, corals, demersal fish), and plan of human activities such as selecting appropriate offshore construction sites, and planning sand/gravel mining operations. CCMA credits these people for deriving this dataset: M. Poti and B.P. Kinlan completed NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov pointOfContact NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov custodian notPlanned OceanCommunity theme Geospatial Platform Bathymetry/Topography theme NOS Data Explorer Topic Category New York Seafloor bathymetry/topography environment hard bottom hardbottom prediction spatial planning uncertainty theme Long-term average determined by input data temporal Long Island Mid-Atlantic New York Bight New York Offshore Planing Area Northwest Atlantic Ocean place DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2017-04-24 publication 8.5 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 22. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian ny_spatialplan project InPort otherRestrictions Cite As: National Centers for Coastal Ocean Science, [Date of Access]: NOAA ESRI Grid - seafloor hardbottom occurrence predictions model in New York offshore planning area from Biogeography Branch [Data Date Range], https://www.fisheries.noaa.gov/inport/item/38952. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. otherRestrictions Access Constraints: Data not yet available online. otherRestrictions Use Constraints: Please reference NOAA/NOS/NCCOS/CCMA/Biogeography Branch when utilizing these data in a report or peer reviewed publication. Additionally, knowledge of how this dataset has been of use and which organizations are utilizing it is of great benefit for ensuring this information continues to meet the needs of the management and research communities. Therefore, it is requested but not mandatory, that any user of this data supply this information to the Project Manager: Charles Menza (charles.menza@noaa.gov) otherRestrictions Distribution Liability: These data were prepared by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed in this report, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. Any views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Although all data have been used by NOAA, no warranty, expressed or implied, is made by NOAA as to the accuracy of the data and/or related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by NOAA in the use of these data or related materials. unclassified NOAA Data Management Plan (DMP) NOAA/NMFS/EDM 38952 https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/nccos/dmp/pdf/38952.pdf WWW:LINK-1.0-http--link NOAA Data Management Plan (DMP) NOAA Data Management Plan for this record on InPort. information crossReference grid eng; US environment oceans -75 -69 37 42 | Currentness: 201205 2012-05-01 false eng false Hard Bottom Occurrence 2017-04-17 publication zip file Zip NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov distributor https://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip WWW:LINK-1.0-http--link https://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip Offline Data download dataset Horizontal Positional Accuracy The horizontal positional accuracy of this raster depends on the source and resolution of the data samples. See the following report for more information: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. Completeness Report The following reference provides information on omissions, selection criteria, generalization, definitions used, and other rules used to derive the data set: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. Conceptual Consistency All users should independently analyze the dataset according to their own needs and standards to determine data usability. A maximum entropy (MaxEnt) model was used to predict the likelihood of hard bottom occurrence by combining the presence-only hard bottom point dataset with potential predictor variables (Phillips et al., 2006; Phillips and Dudik, 2008). This approach can be thought of as creating a "suitability map" for the presence of hard bottom patches, analogous to habitat suitability maps developed for organisms (Elith et al., 2011). A full description of the MaxEnt algorithm is beyond the scope of this document (see Elith et al., 2011). Briefly, MaxEnt produces an estimate of the relative likelihood of a feature's occurrence at each location in a specified grid, assuming that presences take on the most spatially random (uniform) distribution possible under the constraint that for each environmental predictor variable the expected value from the estimated distribution matches its observed mean (Elith et al., 2006; Phillips et al., 2006; Peterson et al., 2007). MaxEnt models are trained on a subset of the data and validated by testing predictions on remaining data. MaxEnt has been shown to perform well compared to other presence-only approaches (Elith et al., 2006; Phillips and Dudik, 2008), and is readily implemented using free, open-source software (Phillips et al., 2006, downloadable at http://www.cs.princeton.edu/~schapire/maxent/). See the following report for more information on this layer's lineage: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. 2012-05-01T00:00:00 Source Contribution: Surficial sediment survey data was used as input to a spatial model. | Source Geospatial Form: vector digital data | Type of Source Media: online Bottom Type Descriptions from Hydrographic Surveys database 2011-04-21 publication National Oceanic and Atmospheric Administration (NOAA)/National OceanService (NOS)U.S. Coast and Geodetic Survey 2011-04-21 Source Contribution: Surficial sediment survey data was used as input to a spatial model. | Source Geospatial Form: vector digital data | Type of Source Media: online usSEABED Atlantic Coast Offshore Surficial Sediment Data Release 2011-04-21 publication USGS 2011-04-21 Source Contribution: Surficial sediment survey data was used as input to a spatial model. | Source Geospatial Form: vector digital data | Type of Source Media: online usSEABED Atlantic Coast Offshore Surficial Sediment Data Release 2011-04-21 publication Nature Conservancy (TNC) 2011-04-21