South Polar Skua Predicted At-Sea Density, U.S. West Coast

Overview: This dataset provides seasonal spatial rasters of predicted long-term (1980-2017) density throughout the Pacific Outer Continental Shelf (OCS) and adjacent waters off of the contiguous United States at 2-km spatial resolution. The maps represent model-derived spatial predictions of long-term average density, in units of individuals per km2. The maps do not provide predictions of the actual number of individuals of a given species or taxonomic group that would be expected in a given area; they only indicate where a given species/group may be more or less abundant.

Here, the predicted densities are displayed as a pair of layers per species group and season. To match the publication’s maps and illustrate the difference in magnitude of predicted densities, the original density values were log-transformed to create logarithmic scale predicted density maps. A second layer displays untransformed predicted density values in units of individuals per km2; this can be queried to access the publication’s original bird density values. The values displayed on the scale bars for both layers are formatted to reflect densities on the original scale and match the publication.

Appropriate Use and Caveats: The predicted density values shown on the maps are inherently biased and should not be interpreted as the actual number of individuals expected in a given area. The amount of bias can be assumed to be constant within a given map, but heterogeneous among maps. Therefore, predicted density values are comparable within the same map (i.e. same species and season), but values from separate maps (i.e. separate species and/or seasons) are not comparable. Although predicted densities are shown at 2-km resolution, interpretation of the maps presented to inform spatial planning is more reliable at scales of 10–100 km.

The log-transformed predicted density layers are useful for visualizing the spatial variation in a species' seasonal distribution and indicate where a species may be more or less abundant.

The untransformed predicted density layers should not be used to visually assess where a species may be more or less abundant. These layers are "turned off" by default. However, the predicted density values from these layers may be useful to compare relative differences in predicted densities within the same map (i.e. same species and season).

Background: Marine birds have the potential to be affected by human activities in the ocean environment such as offshore wind energy development. Understanding bird abundance, spatial distribution, and density are necessary for scoping and siting new offshore wind energy projects and for minimizing environmental impacts. The at-sea distributions of birds are known to be influenced by behavior, such as foraging, as well as environmental factors, such as prey distribution. Marine birds can be adversely affected by offshore wind energy via collisions with turbines or displacement from normal migration routes, foraging areas, or resting areas, so avoidance in terms of siting infrastructure is of high importance. High-resolution spatial models of marine bird distributions along the West Coast provide critical information for renewable energy siting and allow users to evaluate potential environmental effects of management actions.

This project was conducted to inform BOEM’s renewable energy planning in the Pacific OCS Region. Seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the study region were developed. 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. For a complete description of the methods see Leirness et al. (2021), and download accompanying data from the following archive: https://doi.org/10.25921/xqf2-r853.