Individual based population inference using tagging data

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Abstract

A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark, DTU Informatics, Building 321
Publication statusPublished - 2010
SeriesIMM-Technical Report-2010-11

Cite this

Pedersen, M. W., Thygesen, U. H., Baktoft, H., & Madsen, H. (2010). Individual based population inference using tagging data. Kgs. Lyngby: Technical University of Denmark, DTU Informatics, Building 321. IMM-Technical Report-2010-11
Pedersen, Martin Wæver ; Thygesen, Uffe Høgsbro ; Baktoft, Henrik ; Madsen, Henrik. / Individual based population inference using tagging data. Kgs. Lyngby : Technical University of Denmark, DTU Informatics, Building 321, 2010. (IMM-Technical Report-2010-11).
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Pedersen, MW, Thygesen, UH, Baktoft, H & Madsen, H 2010, Individual based population inference using tagging data. IMM-Technical Report-2010-11, Technical University of Denmark, DTU Informatics, Building 321, Kgs. Lyngby.

Individual based population inference using tagging data. / Pedersen, Martin Wæver; Thygesen, Uffe Høgsbro; Baktoft, Henrik; Madsen, Henrik.

Kgs. Lyngby : Technical University of Denmark, DTU Informatics, Building 321, 2010. (IMM-Technical Report-2010-11).

Research output: Book/ReportReport

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AB - A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population.

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Pedersen MW, Thygesen UH, Baktoft H, Madsen H. Individual based population inference using tagging data. Kgs. Lyngby: Technical University of Denmark, DTU Informatics, Building 321, 2010. (IMM-Technical Report-2010-11).