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

• Published in: Scientific Reports
• Main Authors: Rodríguez, Jorge P., Fernández-Gracia, Juan, Thums, Michele, Hindell, Mark A., Sequeira, Ana M.M., Meekan, Mark G., Costa, Daniel P., Guinet, Christophe, Harcourt, Robert G., McMahon, Clive R., Muelbert, Monica, Duarte, Carlos M., Eguíluz, Víctor M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: animal migration; scientific data; statistical physics; Southern Ocean; Elephant Seals; Mirounga leonina; Southern Elephant Seals
• Online Access: https://researchers.mq.edu.au/en/publications/6f78f372-5fe6-4bdb-8b3f-ef5acd763962; https://doi.org/10.1038/s41598-017-00165-0; https://research-management.mq.edu.au/ws/files/60320043/33725785.pdf; http://www.scopus.com/inward/record.url?scp=85024388580&partnerID=8YFLogxK

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.