Elsevier

Ecological Modelling

Volume 321, 10 February 2016, Pages 10-22
Ecological Modelling

A HMM-based model to geolocate pelagic fish from high-resolution individual temperature and depth histories: European sea bass as a case study

https://doi.org/10.1016/j.ecolmodel.2015.10.024Get rights and content
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Highlights

  • We develop a HMM-based model allowing the undersea geolocation of pelagic fish.

  • Fish geolocation is performed only from high-resolution temperature-depth DSTs.

  • All model parameters are estimated using an efficient SEM algorithm.

  • The reconstruction for both synthetic and real data is robust.

  • We report reconstructions of sea bass migratory patterns within the Bay of Biscay.

Abstract

Numerous methods have been developed to geolocate fish from data storage tags. Whereas demersal species have been tracked using tide-driven geolocation models, pelagic species which undertake extensive migrations have been mainly tracked using light-based models. Here, we present a new HMM-based model that infers pelagic fish positions from the sole use of high-resolution temperature and depth histories. A key contribution of our framework lies in model parameter inference (diffusion coefficient and noise parameters with respect to the reference geophysical fields—satellite SST and temperatures derived from the MARS3D hydrodynamic model), which improves model robustness. As a case study, we consider long time series of data storage tags (DSTs) deployed on European sea bass for which individual migration tracks are reconstructed for the first time. We performed a sensitivity analysis on synthetic and real data in order to assess the robustness of the reconstructed tracks with respect to model parameters, chosen reference geophysical fields and the knowledge of fish recapture position. Model assumptions and future directions are discussed. Finally, our model opens new avenues for the reconstruction and analysis of migratory patterns of many other pelagic species in relatively contrasted geophysical environments.

Keywords

Fish movement
Archival tagging
Migration
Population structure
Hidden Markov Model (HMM)
State-space model

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