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OAS accession Detail for 0242882
| << previous | |revision: 13 |
| accessions_id: | 0242882 | archive |
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| Title: | NCCOS Assessment: Modeling at-sea density of marine birds to support renewable energy planning on the Pacific Outer Continental Shelf of the contiguous United States (NCEI Accession 0242882) |
| Abstract: | This dataset provides seasonal spatial rasters of predicted long-term (1980-2017) density of 33 individual species and 13 taxonomic groups of marine birds throughout the Pacific Outer Continental Shelf (OCS) and adjacent waters off the contiguous United States at 2-km spatial resolution. Two indications of the uncertainty associated with the model predictions are also provided: 1) seasonal spatial layers indicating areas with no survey effort and 2) seasonal spatial rasters of the precision of predicted density of each species/group characterized as its coefficient of variation (CV). Predicted density should always be considered in conjunction with these two indications of uncertainty. This dataset also includes spatial rasters of environmental predictor variables that were used in the predictive modeling. |
| Date received: | 20211020 |
| Start date: | 19800101 |
| End date: | 20171231 |
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| West boundary: | -131 |
| East boundary: | -117.1 |
| North boundary: | 49 |
| South boundary: | 29.8 |
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| Submitting institution: | US DOC; NOAA; NOS; National Centers for Coastal Ocean Science |
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| Supplementary information: | Methods: This analysis relied mainly on two types of data: counts of marine birds at sea from sighting surveys and information about the environment in the Pacific OCS region. Sighting datasets were provided by various state and federal agencies, academic institutions, and private organizations. Available spatial information describing the environment in the Pacific OCS region 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 or taxonomic group 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 Leirness et al. (2021). |
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| Metadata version: | 13 |
| Keydate: | 2021-11-08 21:31:09+00 |
| Editdate: | 2024-09-10 15:23:29+00 |