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MEPS prepress abstract   -  DOI: https://doi.org/10.3354/meps13887

Foraging distribution of breeding northern fulmars is predicted by commercial fisheries

J. H. Darby*, S. De Grissac, G. E. Arneill, E. Pirotta, J. J. Waggitt, L. Börger, E. Shepard, D. Cabot, E. Owen, M. Bolton, E. W. J. Edwards, P. M. Thompson, J. L. Quinn, M. Jessopp

*Corresponding author:

ABSTRACT: Habitat-use and distribution models are essential tools of conservation biology. For wide-ranging species, such models may be challenged by the expanse, remoteness and variability of their habitat, and are often compounded by their mobility. In marine environments, direct observations and sampling are usually impractical over broad regions, and instead remotely sensed proxies of prey availability are often used to link species abundance or foraging behaviour to areas that are expected to provide food consistently. One source of food consumed by many marine top predators is fisheries waste, however habitat-use models rarely account for this interaction. We assessed the utility of commercial fishing effort as a covariate in foraging habitat models for northern fulmars (Fulmarus glacialis), a species known to exploit fisheries waste, during their summer breeding season. First, we investigated the prevalence of fulmar-vessel interactions using concurrently tracked fulmars and fishing vessels. We infer that over half of our study individuals associate with fishing vessels while foraging, mostly with trawl type vessels. We then used Hidden Markov models to explain the spatio-temporal distribution of putative foraging behaviour as a function of a range of covariates. Persistent commercial fishing effort was a significant predictor of foraging behaviour, and more important than commonly-used environmental covariates retained in the model. This study demonstrates the effect of commercial fisheries on the foraging distribution and behaviour of a marine top predator and supports the idea that, in some systems, incorporating human activities into distribution studies can improve model fit substantially.