Estimating animal behaviour and residency from movement data

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Standard

Estimating animal behaviour and residency from movement data. / Pedersen, Martin Wæver; Patterson, Toby Alexander; Thygesen, Uffe Høgsbro; Madsen, Henrik.

In: Oikos, Vol. 120, 2011, p. 1281-1290.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Harvard

APA

CBE

MLA

Vancouver

Author

Pedersen, Martin Wæver; Patterson, Toby Alexander; Thygesen, Uffe Høgsbro; Madsen, Henrik / Estimating animal behaviour and residency from movement data.

In: Oikos, Vol. 120, 2011, p. 1281-1290.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{9eff48d842fa4b72b5af9984526c1df5,
title = "Estimating animal behaviour and residency from movement data",
author = "Pedersen, {Martin Wæver} and Patterson, {Toby Alexander} and Thygesen, {Uffe Høgsbro} and Henrik Madsen",
year = "2011",
doi = "10.1111/j.1600-0706.2011.19044.x",
volume = "120",
pages = "1281--1290",
journal = "Oikos",

}

RIS

TY - JOUR

T1 - Estimating animal behaviour and residency from movement data

A1 - Pedersen,Martin Wæver

A1 - Patterson,Toby Alexander

A1 - Thygesen,Uffe Høgsbro

A1 - Madsen,Henrik

AU - Pedersen,Martin Wæver

AU - Patterson,Toby Alexander

AU - Thygesen,Uffe Høgsbro

AU - Madsen,Henrik

PY - 2011

Y1 - 2011

N2 - We present a process-based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite-linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.

AB - We present a process-based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite-linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.

U2 - 10.1111/j.1600-0706.2011.19044.x

DO - 10.1111/j.1600-0706.2011.19044.x

JO - Oikos

JF - Oikos

VL - 120

SP - 1281

EP - 1290

ER -