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Marine Ecology Progress Series

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MEPS 391:165-182 (2009)  -  DOI:

Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review

Ewan D. Wakefield1,*, Richard A. Phillips1, Jason Matthiopoulos2

1British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET, UK
2NERC Sea Mammal Research Unit, Gatty Marine Laboratory, University of St. Andrews, Fife KY16 8LB, UK

ABSTRACT: Colonial seabirds are relatively easy to observe, count, measure and manipulate, and consequently have long been used as models for testing ecological hypotheses. A combination of animal tracking and satellite imagery has the potential to greatly inform such efforts, by allowing seabird–environment interactions to be observed remotely. We review how this can be achieved by applying innovative statistical techniques to quantify habitat use and preferences. Seabird movements are now observable at scales of meters using GPS loggers, and up to several years using light-based geolocation, while satellite remote sensing systems, at resolutions of km, are capable of characterizing the millions of km2 of habitat that are accessible to seabirds. Physical forcing and biological processes result in a hierarchical, patchy distribution of prey. Hence, analyses of seabird movements should be conducted at appropriate scales. Variation in habitat accessibility should also be considered: this declines with distance from the colony during the breeding season, when seabirds are central place foragers, and may be limited in the nonbreeding period by migration corridors that are defined by wind patterns. Intraspecific competition can further modify spatial usage, leading to spatial segregation of birds foraging from different colonies. We recommend that spatial usage be modeled as a function of habitat preference, accessibility and, potentially, competition. At the population level, this is currently best achieved using an empirical approach (e.g. using mixed-effects generalized additive models). At the individual level, more mechanistic models (e.g. state–space models) are more appropriate and have the advantage of modeling location errors explicitly.

KEY WORDS: Satellite tracking · GPS · Platform transmitter terminal · Geolocator · Spatial models · Generalized additive model · State space models · Albatross

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Cite this article as: Wakefield ED, Phillips RA, Matthiopoulos J (2009) Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review. Mar Ecol Prog Ser 391:165-182.

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