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)
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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.
purpose: Marine birds have the potential to be affected by human activities in the ocean environment such as offshore wind energy development. This project was a partnership between the Bureau of Ocean Energy Management (BOEM), the US Geological Survey (USGS), and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) through Inter-Agency Agreement Numbers M15PG00009 and M15PG00010 to develop maps of the spatial distributions of marine bird species on the Pacific OCS of the contiguous United States that can be used to inform marine spatial planning in the region and guide future data collection efforts. The analysis relied on 21 unique marine bird sighting datasets provided by the various state and federal agencies, academic institutions, and private organizations. This project was conducted to inform BOEM’s renewable energy policy decisions in the Pacific OCS region. Having the most up-to-date and comprehensive biogeographic information is an important part of BOEM’s process to identify and fill critical data gaps, and to assess the potential direct and indirect impacts of offshore renewable energy development on marine birds. Products from this assessment may also support coastal and ocean management efforts by other local, state and federal agencies working in the Pacific OCS region.
credit: Related Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science (NCCOS)
credit: Related Funding Agency: US DOI; Bureau of Ocean Energy Management (BOEM)
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title: Modeling the at-sea density of marine birds to support renewable energy planning on the Pacific Outer Continental Shelf of the contiguous United States
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supplementalInformation: Submission Package ID: 4LFC6T 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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description: Parameter or Variable: Predicted Density (calculated); Units: None; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: Predicted density (proportional to number of individuals / km2) of 33 individual marine bird species and 13 taxonomic groups (Table 4) in up to four seasons (spring: Mar-May; summer: Jun-Aug; fall: Sep-Nov; and winter: Dec-Feb) was modeled using habitat-based spatial predictive modeling. See Leirness et al. (2021) for more details.; Data Quality Method: A data re-sampling technique (non-parametric bootstrapping) was used to estimate the precision of predicted density. Median (50th percentile) bootstrapped predictions were chosen as the best estimate of predicted density. Precision was characterized by the coefficient of variation of bootstrapped predictions (‘CV’). Seasonal spatial layers indicating areas with no survey effort are provided as an additional indication of uncertainty in model predictions. All maps of predicted density and CV were reviewed by experts with experience and knowledge of marine birds in the study area. Experts were from a range of organizations including federal and state government agencies, academic institutions, non-profits, and consultants. Comments and feedback received from the review were incorporated into the project report. See Leirness et al. (2021) for more details..
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