Geolocating fish using Hidden Markov Models and Data Storage Tags

Publication: Research - peer-reviewBook chapter – Annual report year: 2009

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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.
Original languageEnglish
Title of host publicationTagging and Tracking of Marine Animals with Electronic Devices : Volume 8 reviews: methods and technologies in fish biology and fisheries
EditorsJ.L. Nielsen
Number of pages452
VolumePart 2: Geolocation Methods
PublisherSpringer
Publication date2009
Pages277-293
ISBN (print)14-02-09639-9
DOIs
StatePublished
NameReviews: Methods and Technologies in Fish Biology and Fisheries
Number9
ISSN (Print)1571-3075
NameReviews: Methods and Technologies in Fish Biology and Fisheries
Volume9
ISSN (Print)1571-3075
CitationsWeb of Science® Times Cited: 6
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