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OAS accession Detail for 0176682
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Title: NCCOS Assessment: Modeling At-Sea Density of Marine Birds to Support Atlantic Marine Renewable Energy Planning from 1978-2016 (NCEI Accession 0176682)
Abstract: This dataset provides seasonal spatial rasters of median predicted long-term (1978-2016) relative density of 47 marine bird species throughout the US Atlantic Outer Continental Shelf (OCS) and adjacent waters at a 2-km spatial resolution. Three indications of the uncertainty associated with the model predictions are also provided: 1) seasonal spatial layers indicating areas with no survey effort, 2) seasonal spatial rasters of the precision of predicted relative density of each species characterized as its coefficient of variation (CV), and 3) seasonal spatial rasters of the precision of predicted relative density of each species characterized as its 90% confidence interval. Predicted relative density should always be considered in conjunction with these three indications of uncertainty. Suggested symbology class breaks and labels for mapping predicted relative density and its CV are also included. Finally, this dataset also includes spatial rasters of environmental predictor variables that were used in the predictive modeling.
Date received: 20181002
Start date: 19780101
End date: 20161005
Seanames: Gulf of Maine, Mid-Atlantic Bight, North Atlantic Ocean
West boundary: -83
East boundary: -63.1
North boundary: 44.8
South boundary: 23.8
Observation types: model output
Instrument types:
Datatypes: BIRDS
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: This analysis relied mainly on two types of data: counts of marine birds at sea from sighting surveys and information about the U.S. Atlantic OCS environment. Sighting datasets were provided by USGS and USFWS (Northwest Atlantic Seabird Catalog) and by CWS-ECCC (ECSAS database). Available spatial information describing the environment of U.S. Atlantic OCS and adjacent waters was compiled and synthesized by NCCOS. Environmental data came from a range of sources including remote sensing datasets and an ocean model dataset. Spatial environmental variables were characterized as spatial rasters, with dynamic variables represented by seasonal long-term climatologies. Spatial predictive modeling was applied to the sighting data to account for spatial and temporal heterogeneity in survey effort, platform, and protocol. An ensemble machine-learning technique, component-wise boosting of hierarchical zero-inflated count models, was used to relate the counts of each species to the environmental predictor variables while accounting for survey heterogeneity and the aggregated nature of sightings. The modeling technique allowed for complex non-linear relationships between response and predictor variables and interacting effects among predictors. Bootstrapping was used to derive estimates of the uncertainty in model predictions. For a complete description of the methods see Winship et al. (2018)
Availability date:
Metadata version: 4
Keydate: 2018-10-10 17:31:45+00
Editdate: 2024-09-10 20:22:19+00