Geolocating Fish Using Hidden Markov Models and Data Storage Tags

  • Uffe Høgsbro ThygesenEmail author
  • Martin Wæver Pedersen
  • Henrik Madsen
Part of the Reviews: Methods and Technologies in Fish Biology and Fisheries book series (REME, volume 9)


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.


Fish migrations Geolocation uncertainty Hidden Markov Model State-space models 


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Uffe Høgsbro Thygesen
    • 1
    Email author
  • Martin Wæver Pedersen
    • 2
  • Henrik Madsen
    • 2
  1. 1.Institute for Aquatic Resources, Technical University of DenmarkCharlottenlundDenmark
  2. 2.Institute for Informatics and Mathematical Modelling, Technical University of Denmark2800 Kgs. LyngbyDenmark

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