Description: The National Database of Deep Sea Coral Observations has been developed to serve the needs of natural resource managers, scientific researchers, and the public.
Description: This dataset represents depth uncertainty predictions from a bathymetric model developed for the New York offshore spatial planning area. The model also includes spatially-explicit depth estimates represented in another raster dataset. Uncertainty is defined as the standard error of depth estimates from model output. The model used to derive depth estimates and corresponding uncertainty builds on previous predictive bathymetric modeling approaches in the region (e.g. Calder, 2006), provides a continuous gridded bathymetric surface, and allows users to view and explore spatial variation in the vertical accuracy of depth predictions. The spatial resolution of the model is identical to the National Oceanic and Atmospheric Administration's (NOAA) Coastal Relief Model (CRM; horizontal resolution approximately 83.8 m) in the study area and was built from the same database of hydrographic survey points.
Description: This dataset represents depth predictions from a bathymetric model developed for the New York offshore spatial planning area. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. The model used to derive depth estimates builds on previous predictive bathymetric modeling approaches in the region (e.g. Calder, 2006), provides a continuous gridded bathymetric surface, and allows users to view and explore spatial variation in the vertical accuracy of depth predictions. The spatial resolution of the model is identical to the National Oceanic and Atmospheric Administration's (NOAA) Coastal Relief Model (CRM; horizontal resolution approximately 83.8 m) in the study area and was built from the same database of hydrographic survey points.
Description: A group containing Sediment layers: Mean Grain Size error, Mean Grain Size, Hardbottom Occurance Likelihood, and Predicted Sediment Texture
Description: This dataset represents sediment size prediction uncertainty from a sediment spatial model developed for the New York offshore spatial planning area. The model also includes spatially-explicit mean grain size estimates represented in another raster dataset. The predictive model of mean grain size was developed building upon the data compilation and analytical framework laid out by Goff et al. (2008).
Description: This dataset represents sediment size predictions from a sediment spatial model developed for the New York offshore spatial planning area. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. The predictive model of mean grain size was developed building upon the data compilation and analytical framework laid out by Goff et al. (2008).
Description: 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.
Description: This dataset represents sediment composition class predictions from a sediment spatial model developed for the New York offshore spatial planning area. The predictive spatial model of mean grain size was developed building upon the data compilation and analytical framework laid out by Goff et al. (2008) and Poppe et al. (2005).
Description: This dataset represents relative seabird abundance predictions from spatial models developed for the New York offshore spatial planning area. This raster was derived from multiple individual species season models. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. Raster values represent the sum of the predicted relative abundance (individuals sighted per km per 15 minutes) for all modeled species and groups over all seasons in which they were modeled. Abundance was treated as zero for all seasons in which a species or group was not modeled.
Description: This dataset represents seabird diversity predictions from spatial models developed for the New York offshore spatial planning area. This raster was derived from multiple individual species season models. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. Raster values represent the annual predicted seabird Shannon diversity index. The Shannon index incorporates both presence and relative abundance. Species and groups that were not modeled in any season did not contribute to index calculation.
Description: This dataset represents relative seabird abundance predictions from spatial models developed for the New York offshore spatial planning area. This raster was derived from multiple individual species season models. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. Raster values represent a dimensionless number scaled between 0 and 1, where values closer to 0 indicate greater certainty. The relative uncertainty value at each location is the same for all hotspot quantities (abundance, richness, and diversity index), because it is a function of the underlying trend and spatial model uncertainty for each species/group.
Description: This dataset represents seabird species richness, or number of species, predictions from spatial models developed for the New York offshore spatial planning area. This raster was derived from multiple individual species season models. The model also includes spatially-explicit uncertainty estimates represented in another raster dataset. Raster values represent the annual predicted number of seabird species.