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

Mathieu Woillez, Ronan Fablet, Tran-Thanh Ngo, Maxime Lalire, Pascal Lazure and Hélène de Pontual

Ecological Modelling, 2016, vol. 321, issue C, 10-22

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 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:321:y:2016:i:c:p:10-22

DOI: 10.1016/j.ecolmodel.2015.10.024

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