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Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: insights from machine learning

Guilford, T.; Meade, J.; Willis, J.; Phillips, Richard A.; Boyle, D.; Roberts, S.; Collett, M.; Freeman, R.; Perrins, C.M.. 2009 Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: insights from machine learning. Proceedings of the Royal Society of London, Series B, 276 (1660). 1215-1223. 10.1098/rspb.2008.1577

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Abstract/Summary

The migratory movements of seabirds (especially smaller species) remain poorly understood, despite their role as harvesters of marine ecosystems on a global scale and their potential as indicators of ocean health. Here we report a successful attempt, using miniature archival light loggers (geolocators), to elucidate the migratory behaviour of the Manx shearwater Puffinus puffinus, a small (400 g) Northern Hemisphere breeding procellariform that undertakes a trans-equatorial, trans-Atlantic migration. We provide details of over-wintering areas, of previously unobserved marine stopover behaviour, and the long-distance movements of females during their pre-laying exodus. Using salt-water immersion data from a subset of loggers, we introduce a method of behaviour classification based on Bayesian machine learning techniques. We used both supervised and unsupervised machine learning to classify each bird's daily activity based on simple properties of the immersion data. We show that robust activity states emerge, characteristic of summer feeding, winter feeding and active migration. These can be used to classify probable behaviour throughout the annual cycle, highlighting the likely functional significance of stopovers as refuelling stages.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1098/rspb.2008.1577
Programmes: BAS Programmes > Global Science in the Antarctic Context (2005-2009) > DISCOVERY 2010 - Integrating Southern Ocean Ecosystems into the Earth System
ISSN: 0962-8452
Additional Keywords: geolocator tracking technology, tracking pelagic seabirds, Bayesian machine learning, migration, Manx shearwater, spatial ecology
NORA Subject Terms: Marine Sciences
Zoology
Ecology and Environment
Date made live: 02 Nov 2010 09:54
URI: http://nora.nerc.ac.uk/id/eprint/10978

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