Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour.
J R Soc Interface
The use of miniature data loggers is rapidly increasing our understanding of the movements and habitat preferences of pelagic seabirds. However, objectively interpreting behavioural information from the large volumes of highly detailed data collected by such devices can be challenging. We combined three biologging technologies—global positioning system (GPS), saltwater immersion and time–depth recorders—to build a detailed picture of the at-sea behaviour of the Manx shearwater (Puffinus puffinus) during the breeding season. We used a hidden Markov model to explore discrete states within the combined GPS and immersion data, and found that behaviour could be organized into three principal activities representing (i) sustained direct flight, (ii) sitting on the sea surface, and (iii) foraging, comprising tortuous flight interspersed with periods of immersion. The additional logger data verified that the foraging activity corresponded well to the occurrence of diving. Applying this approach to a large tracking dataset revealed that birds from two different colonies foraged in local waters that were exclusive, but overlapped in one key area: the Irish Sea Front (ISF). We show that the allocation of time to each activity differed between colonies, with birds breeding furthest from the ISF spending the greatest proportion of time engaged in direct flight and the smallest proportion of time engaged in foraging activity. This type of analysis has considerable potential for application in future biologging studies and in other taxa.
|Title:||Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour.|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > CoMPLEX - Maths and Physics in the Life Sciences and Experimental Biology
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