title: Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model.
abstract: Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012-April 2014), along with associated remotely sensed habitat data, in the lower Mid-Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA. We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [SST], salinity, primary productivity). We expected that avian bottom-feeders would respond primarily to static covariates that characterize seafloor variability, and that surface-feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species-specific and community-level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom-feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface-feeders associated significantly with two dynamic covariates (SST, primary productivity). However, other cases revealed significant relationships between static covariates and surface-feeders (e.g., distance to shore) and between dynamic covariates and bottom-feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.
authors.affiliation: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, 27695, USA. hgoyert@uidaho.edu.
authors.forename: Holly F
authors.initials: HF
authors.lastname: Goyert
authors.affiliation: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Beth
authors.initials: B
authors.lastname: Gardner
authors.affiliation: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Rahel
authors.initials: R
authors.lastname: Sollmann
authors.affiliation: Department of Biology, College of Staten Island, City University of New York, Staten Island, New York, 10314, USA.
authors.forename: Richard R
authors.initials: RR
authors.lastname: Veit
authors.affiliation: Biodiversity Research Institute, Portland, Maine, 04103, USA.
authors.forename: Andrew T
authors.initials: AT
authors.lastname: Gilbert
authors.affiliation: Biodiversity Research Institute, Portland, Maine, 04103, USA.
authors.forename: Emily E
authors.initials: EE
authors.lastname: Connelly
authors.affiliation: Biodiversity Research Institute, Portland, Maine, 04103, USA.
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: Williams
mesh_terms: D063147:Animal Distribution; D000818:Animals; D046091:Charadriiformes / Q000502:physiology*; D003247:Conservation of Natural Resources; D008954:Models, Biological; D011156:Population Density
keywords: Bayesian; foraging ecology; marine spatial planning; multi-species models; offshore wind energy development; seabird conservation