38952 New_York_Hardbottom_Predictions Data Set Published / External 37192 ny_spatialplan Project Completed 2012-05 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. 568 Theme ISO 19115 Topic Category environment Theme ISO 19115 Topic Category oceans Theme Geospatial Platform OceanCommunity Theme NOS Data Explorer Topic Category Bathymetry/Topography Theme New York Theme Seafloor Theme bathymetry/topography Theme environment Theme hard bottom Theme hardbottom Theme prediction Theme spatial planning Theme uncertainty Temporal Long-term average determined by input data Spatial Long Island Spatial Mid-Atlantic Spatial New York Bight Spatial New York Offshore Planing Area Spatial Northwest Atlantic Ocean National Centers for Coastal Ocean Science Silver Spring MD Data Set None Planned raster digital data The final map of the "hard bottom occurrence likelihood index" consisted of a logistic transformation of MaxEnt's raw output to produce a smooth index between 0 and 1 (this is the default output of the MaxEnt program). It is related to the probability of occurrence, but is not strictly a probability (Elith et al., 2011). It should be considered an index of the relative likelihood of hard bottom occurrence, rather than a strict measure of the probability of encountering hard bottom. For more information see 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. 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. CCMA credits these people for deriving this dataset: M. Poti and B.P. Kinlan 40997 Hard Bottom Occurrence Published / External Completed Hard Bottom Occurrence Likelihood | Description Source: NOAA Converted from FGDC using 'fgdc_to_inport_xml_entity.pl' script. Data Steward 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Distributor 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Metadata Contact 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Point of Contact 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Principal Investigator 2012-05 Person Menza, Charles Charles.menza@noaa.gov 1305 East-West Hwy Silver Spring MD 20910 240-533-0372 301-713-4388 201205 -75 -69 42 37 Discrete 2012-05 Yes 3 Area No Row 443 Column 496 Vertical 1 Unclassified Contact Distributor for instructions on how to acquire dataset.; Data not yet available online. 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) https://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip Offline Data zip file Zip https://coastalscience.noaa.gov/projects/detail?key=80 Online Resource 2015-05-27 Date that the source FGDC record was last modified. 2017-04-05 Converted from FGDC Content Standard for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details. 2017-09-13 Partial upload of Spatial Info section only. 2017-11-01 Replaced entire Lineage section to populate new Source Contribution field. 2018-02-08 Partial upload of Positional Accuracy fields only. 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. 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. All users should independently analyze the dataset according to their own needs and standards to determine data usability. Bottom Type Descriptions from Hydrographic Surveys database National Oceanic and Atmospheric Administration (NOAA)/National OceanService (NOS)U.S. Coast and Geodetic Survey 2011-04-21 Discrete 2011-04-21 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 USGS 2011-04-21 Discrete 2011-04-21 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 Nature Conservancy (TNC) 2011-04-21 Discrete 2011-04-21 Surficial sediment survey data was used as input to a spatial model. | Source Geospatial Form: vector digital data | Type of Source Media: online 1 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 usSEABED Atlantic Coast Offshore Surficial Sediment Data Release 40997 Entity gov.noaa.nmfs.inport:38952 Tyler Christensen 2017-04-05T12:50:03 SysAdmin InPortAdmin 2025-05-15T19:16:08 2018-02-08 National Centers for Coastal Ocean Science NCCOS 1002 Public No No 2018-02-08 1 Year 2019-02-08