Hidden Markov modelling of movement data from fish

Publication: ResearchPh.D. thesis – Annual report year: 2010

Standard

Hidden Markov modelling of movement data from fish. / Pedersen, Martin Wæver; Madsen, Henrik (Supervisor); Thygesen, Uffe Høgsbro (Supervisor).

Kgs. Lyngby, Denmark : Technical University of Denmark (DTU), 2010. (IMM-PHD-2010-243).

Publication: ResearchPh.D. thesis – Annual report year: 2010

Harvard

Pedersen, MW, Madsen, H & Thygesen, UH 2010, Hidden Markov modelling of movement data from fish. Ph.D. thesis, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark. IMM-PHD-2010-243

APA

Pedersen, M. W., Madsen, H., & Thygesen, U. H. (2010). Hidden Markov modelling of movement data from fish. Kgs. Lyngby, Denmark: Technical University of Denmark (DTU). (IMM-PHD-2010-243).

CBE

Pedersen MW, Madsen H, Thygesen UH 2010. Hidden Markov modelling of movement data from fish. Kgs. Lyngby, Denmark: Technical University of Denmark (DTU). (IMM-PHD-2010-243).

MLA

Pedersen, Martin Wæver, Henrik Madsen, and Uffe Høgsbro Thygesen Hidden Markov modelling of movement data from fish Kgs. Lyngby, Denmark: Technical University of Denmark (DTU). 2010. (IMM-PHD-2010-243).

Vancouver

Pedersen MW, Madsen H, Thygesen UH. Hidden Markov modelling of movement data from fish. Kgs. Lyngby, Denmark: Technical University of Denmark (DTU), 2010. (IMM-PHD-2010-243).

Author

Pedersen, Martin Wæver; Madsen, Henrik (Supervisor); Thygesen, Uffe Høgsbro (Supervisor) / Hidden Markov modelling of movement data from fish.

Kgs. Lyngby, Denmark : Technical University of Denmark (DTU), 2010. (IMM-PHD-2010-243).

Publication: ResearchPh.D. thesis – Annual report year: 2010

Bibtex

@phdthesis{db175f6a4a8d4b87b3e57385d17e3118,
title = "Hidden Markov modelling of movement data from fish",
publisher = "Technical University of Denmark (DTU)",
author = "Pedersen, {Martin Wæver} and Henrik Madsen and Thygesen, {Uffe Høgsbro}",
year = "2010",
series = "IMM-PHD-2010-243",

}

RIS

TY - BOOK

T1 - Hidden Markov modelling of movement data from fish

A1 - Pedersen,Martin Wæver

AU - Pedersen,Martin Wæver

A2 - Madsen,Henrik

A2 - Thygesen,Uffe Høgsbro

ED - Madsen,Henrik

ED - Thygesen,Uffe Høgsbro

PB - Technical University of Denmark (DTU)

PY - 2010

Y1 - 2010

N2 - Movement data from marine animals tagged with electronic tags are becoming increasingly diverse and plentiful. This trend entails a need for statistical methods that are able to filter the observations to extract the ecologically relevant content. This dissertation focuses on the development and application of hidden Markov models (HMMs) for analysis of movement data from sh. The main contributions are represented by six scientific publications. Estimation of animal location from uncertain and possibly indirect observations is the starting point of most movement data analyses. In this work a discrete state HMM is employed to deal with this task. Specifically, the continuous horizontal plane is discretised into grid cells, which enables a state-space model for the geographical location to be estimated on this grid. The estimation model for location is extended with an additional state representing the behaviour of the animal. With the extended model can migratory and resident movement behaviour be related to geographical regions. For population inference multiple individual state-space analyses can be interconnected using mixed effects modelling. This framework provides parameter estimates at the population level and allows ecologists to identify individuals that deviate from the rest of the tagged population. The thesis also deals with geolocation on state-spaces with complicated geometries. Using an unstructured discretisation and the finite element method tortuous shore line geometries are closely approximated. This furthermore enables accurate probability densities of location to be computed. Finally, the performance of the HMM approach in analysing nonlinear state space models is compared with two alternatives: the AD Model Builder framework and BUGS, which relies on Markov chain Monte Carlo estimation.

AB - Movement data from marine animals tagged with electronic tags are becoming increasingly diverse and plentiful. This trend entails a need for statistical methods that are able to filter the observations to extract the ecologically relevant content. This dissertation focuses on the development and application of hidden Markov models (HMMs) for analysis of movement data from sh. The main contributions are represented by six scientific publications. Estimation of animal location from uncertain and possibly indirect observations is the starting point of most movement data analyses. In this work a discrete state HMM is employed to deal with this task. Specifically, the continuous horizontal plane is discretised into grid cells, which enables a state-space model for the geographical location to be estimated on this grid. The estimation model for location is extended with an additional state representing the behaviour of the animal. With the extended model can migratory and resident movement behaviour be related to geographical regions. For population inference multiple individual state-space analyses can be interconnected using mixed effects modelling. This framework provides parameter estimates at the population level and allows ecologists to identify individuals that deviate from the rest of the tagged population. The thesis also deals with geolocation on state-spaces with complicated geometries. Using an unstructured discretisation and the finite element method tortuous shore line geometries are closely approximated. This furthermore enables accurate probability densities of location to be computed. Finally, the performance of the HMM approach in analysing nonlinear state space models is compared with two alternatives: the AD Model Builder framework and BUGS, which relies on Markov chain Monte Carlo estimation.

BT - Hidden Markov modelling of movement data from fish

T3 - IMM-PHD-2010-243

T3 - en_GB

ER -