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Understanding the behaviour of animals in the wild is fundamental to conservation efforts. Advances in bio-logging technologies have offered insights into the behaviour of animals during foraging, migration and social interaction. However, broader application of these systems has been limited by device mass, cost and longevity. Here, we use information from multiple logger types to predict individual behaviour in a highly pelagic, migratory seabird, the Manx Shearwater (Puffinus puffinus). Using behavioural states resolved from GPS tracking of foraging during the breeding season, we demonstrate that individual behaviours can be accurately predicted during multi-year migrations from low cost, lightweight, salt-water immersion devices. This reveals a complex pattern of migratory stopovers: some involving high proportions of foraging, and others of rest behaviour. We use this technique to examine three consecutive years of global migrations, revealing the prominence of foraging behaviour during migration and the importance of highly productive waters during migratory stopover.

Original publication

DOI

10.1098/rsif.2013.0279

Type

Journal article

Journal

J R Soc Interface

Publication Date

06/07/2013

Volume

10

Keywords

behaviour, bio-logging, ethoinformatics, foraging, machine learning, migration, Animal Migration, Animals, Appetitive Behavior, Birds, Conservation of Natural Resources, Data Collection, Geographic Information Systems, Informatics, Islands, Models, Biological, Northern Ireland, Telemetry