Evaluating habitat, prey, and mesopredator associations in a community of marine birds

Abstract

Offshore wind energy development on the US Atlantic Continental Shelf has brought attention to the need for marine spatial planning efforts to reduce potential conflict between wind turbines and marine animals, including seabirds. We evaluated the effects of marine mammals, fishes, and habitat characteristics on the distribution and relative abundance of marine birds off the coast of Delaware, Maryland, and Virginia. From May 2012 to 2014, we collected line transect data from 14 shipboard surveys, and novel high-resolution digital videography data from 14 aerial surveys. We compiled five habitat covariates: three static (distance to shore, sea floor slope, and sediment grain size), and two dynamic (sea surface temperature, salinity). We additionally analysed two seabird community covariates: the density of observed marine mammals and detected fish. Using zero-altered models, we tested our hypothesis that plunge-diving seabird species would show positive associations with marine mammals. Our results provide statistical evidence that, alongside competition, facilitative interactions occur among pelagic communities, where subsurface predators improve the detectability and accessibility of prey to surface-feeding seabirds. This study highlights the importance of quantifying community and ecological influences on avian abundance, particularly in predicting the potential exposure of marine birds and mammals to offshore development.

Associations of non-breeding shearwater species on the northeastern Newfoundland coast

“…Environmental data were examined for differences among years using non-parametric Wilcoxon tests. The number of birds in the 4 km 2 bins contained a large number of zeros (> 70% of bins yr −1 ); thus, we used a zeroaltered (hurdle) negative binomial model (pscl package; Zeileis et al 2008) to determine the important predictor variables underlying the distribution of each shearwater species, similar to Nishizawa et al (2015) and Goyert et al (2018). The hurdle model is composed of 2 parts: a binomial-based part, which identifies the factors explaining presence (i.e.…”

“…Owing to this interannual variability (especially for GRSH), we were interested to examine how the predictor variables explaining the presence and number of GRSH and SOSH differed among years; thus, we ran separate models by year, as other similar studies have done (e.g. Ainley et al 2009, Goyert et al 2018. The by-year models were initially run with an interaction between the number of the other Shearwater × Prey density, but the interaction was not statistically significant; therefore, the interaction was omitted from the by-year analysis but was left in the full model (with year as a discrete random variable; see Table S1).…”

“…Ainley et al 2005, Amorim et al 2009, but some studies use direct measures of prey distribution and density (e.g. Ainley et al 2009, Goyert et al 2018. Although proxies of prey habitat can sometimes effectively predict predator species distribution when prey distribution is unknown (e.g.…”

The role of sand lances (Ammodytes sp.) in the Northwest Atlantic Ecosystem: A synthesis of current knowledge with implications for conservation and management

“…Interspecific interactions may provide additional opportunities for socially flexible and opportunistic predators. The pelagic realm is a dynamic environment where prey aggregations (e.g., bait balls) can attract multiple predators that engage in facilitative (e.g., commensal) or competitive feeding frenzies (Goyert et al, 2018; Goyert, Manne, & Veit, 2014). During these social interactions, predatory fishes (e.g., tunas) and marine mammals can drive prey upwards towards seabirds feeding at the air–sea interface (Safina, 1990; Veit & Harrison, 2017).…”

Marine bird response to forage fish during winter in subarctic bays

“…Further research efforts should explore overwinter survival of juvenile herring as climate conditions change and explore whether other forage fish species (such as pollock or capelin) can mediate variation in herring abundance for marine predators. Additional research should also incorporate community dynamics that have been found to influence predator–prey relationships in other systems, such as local enhancement and interactions with marine mammals (e.g., Anderwald, Evans, Gygax, & Hoelzel, 2011; Fauchald, 2009; Goyert et al, 2018; Viet & Harrison, 2017). Further understanding of these relationships will facilitate the identification of critical areas and time periods for marine birds and their prey and may increase our insight of the status of the marine ecosystem.…”