NOAA ESRI Grid - seafloor hardbottom occurrence predictions model in New York offshore planning area from Biogeography Branch

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.

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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.

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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)

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.