State-space models for bio-loggers: A methodological road map

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Standard

State-space models for bio-loggers: A methodological road map. / Jonsen, I.D.; Basson, M.; Bestley, S.; Bravington, M.V.; Patterson, T.A.; Pedersen, Martin Wæver; Thomson, R.; Thygesen, Uffe Høgsbro; Wotherspoon, S.J.

In: Deep-Sea Research. Part 2: Topical Studies in Oceanography, Vol. 88-89, 2012, p. 34-46.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Harvard

Jonsen, ID, Basson, M, Bestley, S, Bravington, MV, Patterson, TA, Pedersen, MW, Thomson, R, Thygesen, UH & Wotherspoon, SJ 2012, 'State-space models for bio-loggers: A methodological road map' Deep-Sea Research. Part 2: Topical Studies in Oceanography, vol 88-89, pp. 34-46., 10.1016/j.dsr2.2012.07.008

APA

Jonsen, I. D., Basson, M., Bestley, S., Bravington, M. V., Patterson, T. A., Pedersen, M. W., ... Wotherspoon, S. J. (2012). State-space models for bio-loggers: A methodological road map. Deep-Sea Research. Part 2: Topical Studies in Oceanography, 88-89, 34-46. 10.1016/j.dsr2.2012.07.008

CBE

Jonsen ID, Basson M, Bestley S, Bravington MV, Patterson TA, Pedersen MW, Thomson R, Thygesen UH, Wotherspoon SJ. 2012. State-space models for bio-loggers: A methodological road map. Deep-Sea Research. Part 2: Topical Studies in Oceanography. 88-89:34-46. Available from: 10.1016/j.dsr2.2012.07.008

MLA

Vancouver

Author

Jonsen, I.D.; Basson, M.; Bestley, S.; Bravington, M.V.; Patterson, T.A.; Pedersen, Martin Wæver; Thomson, R.; Thygesen, Uffe Høgsbro; Wotherspoon, S.J. / State-space models for bio-loggers: A methodological road map.

In: Deep-Sea Research. Part 2: Topical Studies in Oceanography, Vol. 88-89, 2012, p. 34-46.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{9742cef152194174a3d9abbe3477a6a6,
title = "State-space models for bio-loggers: A methodological road map",
publisher = "Pergamon",
author = "I.D. Jonsen and M. Basson and S. Bestley and M.V. Bravington and T.A. Patterson and Pedersen, {Martin Wæver} and R. Thomson and Thygesen, {Uffe Høgsbro} and S.J. Wotherspoon",
year = "2012",
doi = "10.1016/j.dsr2.2012.07.008",
volume = "88-89",
pages = "34--46",
journal = "Deep-Sea Research. Part 2: Topical Studies in Oceanography",
issn = "0967-0645",

}

RIS

TY - JOUR

T1 - State-space models for bio-loggers: A methodological road map

A1 - Jonsen,I.D.

A1 - Basson,M.

A1 - Bestley,S.

A1 - Bravington,M.V.

A1 - Patterson,T.A.

A1 - Pedersen,Martin Wæver

A1 - Thomson,R.

A1 - Thygesen,Uffe Høgsbro

A1 - Wotherspoon,S.J.

AU - Jonsen,I.D.

AU - Basson,M.

AU - Bestley,S.

AU - Bravington,M.V.

AU - Patterson,T.A.

AU - Pedersen,Martin Wæver

AU - Thomson,R.

AU - Thygesen,Uffe Høgsbro

AU - Wotherspoon,S.J.

PB - Pergamon

PY - 2012

Y1 - 2012

N2 - Ecologists have an unprecedented array of bio-logging technologies available to conduct in situ studies of horizontal and vertical movement patterns of marine animals. These tracking data provide key information about foraging, migratory, and other behaviours that can be linked with bio-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological “road map” for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods for location and/or behavioural state estimation from different tracking data types. Finally, we outline key areas where the methodology is advancing, and where it needs further development

AB - Ecologists have an unprecedented array of bio-logging technologies available to conduct in situ studies of horizontal and vertical movement patterns of marine animals. These tracking data provide key information about foraging, migratory, and other behaviours that can be linked with bio-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological “road map” for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods for location and/or behavioural state estimation from different tracking data types. Finally, we outline key areas where the methodology is advancing, and where it needs further development

U2 - 10.1016/j.dsr2.2012.07.008

DO - 10.1016/j.dsr2.2012.07.008

JO - Deep-Sea Research. Part 2: Topical Studies in Oceanography

JF - Deep-Sea Research. Part 2: Topical Studies in Oceanography

SN - 0967-0645

VL - 88-89

SP - 34

EP - 46

ER -