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Big data analyses reveal patterns and drivers of the movements of southern elephant seals

Authors :
Jorge P. Rodríguez
Juan Fernández-Gracia
Michele Thums
Mark A. Hindell
Ana M. M. Sequeira
Mark G. Meekan
Daniel P. Costa
Christophe Guinet
Robert G. Harcourt
Clive R. McMahon
Monica Muelbert
Carlos M. Duarte
Víctor M. Eguíluz
Source :
Scientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
Publication Year :
2017
Publisher :
Nature Portfolio, 2017.

Abstract

Abstract The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with “big data”, that require no ‘a priori’ assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for “big data” techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
7
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.9100f5122b96446eb26a59e66f604623
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-017-00165-0