Book chapter
Geolocating fish using Hidden Markov Models and Data Storage Tags
Section for Population- and Ecosystem Dynamics, National Institute of Aquatic Resources, Technical University of Denmark1
National Institute of Aquatic Resources, Technical University of Denmark2
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark3
Department of Informatics and Mathematical Modeling, Technical University of Denmark4
Geolocation of fish based on data from archival tags typically requires a statistical analysis to reduce the effect of measurement errors. In this paper we present a novel technique for this analysis, one based on Hidden Markov Models (HMM's). We assume that the actual path of the fish is generated by a biased random walk.
The HMM methodology produces, for each time step, the probability that the fish resides in each grid cell. Because there is no Monte Carlo step in our technique, we are able to estimate parameters within the likelihood framework. The method does not require the distribution to be Gaussian or belong to any other of the usual families of distributions and can thus address constraints from shorelines and other nonlinear effects; the method can and does produce bimodal distributions.
We discuss merits and limitations of the method, and perspectives for the more general problem of inference in state-space models of animals. The technique can be applied to geolocation based on light, on tidal patterns, or measurement of other variables that vary with space. We illustrate the method through application to a simulated data set where geolocation relies on depth data exclusively.
Language: | English |
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Publisher: | Springer |
Year: | 2009 |
Pages: | 277-293 |
Series: | Reviews: Methods and Technologies in Fish Biology and Fisheries and Reviews: Methods and Technologies in Fish Biology and Fisheries |
Journal subtitle: | Volume 8 Reviews: Methods and Technologies in Fish Biology and Fisheries |
ISBN: | 1282292323 , 1402096399 , 1402096402 , 6612292326 , 9048181763 , 9781282292321 , 9781402096396 , 9781402096402 , 9786612292323 and 9789048181766 |
ISSN: | 15713075 |
Types: | Book chapter |
DOI: | 10.1007/978-1-4020-9640-2_17 |
ORCIDs: | Thygesen, Uffe Høgsbro , Pedersen, Martin Wæver and Madsen, Henrik |