NCCOS Assessment: Modeling At-Sea Density of Marine Birds to Support Atlantic Marine Renewable Energy Planning from 1978-2016 (NCEI Accession 0176682)

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)

Submission Package ID: RM4YYU