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Volume 98, Issue 7 p. 1932-1944
Article

Estimation and simulation of foraging trips in land-based marine predators

Théo Michelot

Corresponding Author

Théo Michelot

University of Sheffield, Sheffield, UK

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Roland Langrock

Roland Langrock

Bielefeld University, Bielefeld, Germany

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Sophie Bestley

Sophie Bestley

Australian Antarctic Division, Department of Environment, Kingston, Tasmania, Australia

Institute for Marine and Antarctic Studies, Hobart, Tasmania, Australia

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Ian D. Jonsen

Ian D. Jonsen

Macquarie University, Sydney, New South Wales, Australia

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Theoni Photopoulou

Theoni Photopoulou

Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

University of Cape Town, Rondebosch, South Africa

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Toby A. Patterson

Toby A. Patterson

CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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First published: 04 May 2017
Citations: 49
Corresponding Editor: Jeffrey S. Shima.

Abstract

The behavior of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are accompanied by many empirical studies that seek to segment tracks in some useful way, as well as theoretical studies of optimal foraging strategies. However, relatively few studies have detailed statistical methods for inferring behaviors in central place foraging trips. In this paper we describe an approach based on hidden Markov models, which splits foraging trips into segments labeled as “outbound”, “search”, “forage”, and “inbound”. By structuring the hidden Markov model transition matrix appropriately, the model naturally handles the sequence of behaviors within a foraging trip. Additionally, by structuring the model in this way, we are able to develop realistic simulations from the fitted model. We demonstrate our approach on data from southern elephant seals (Mirounga leonina) tagged on Kerguelen Island in the Southern Ocean. We discuss the differences between our 4-state model and the widely used 2-state model, and the advantages and disadvantages of employing a more complex model.