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Hidden Markov Models: The Best Models for Forager Movements?

Author

Listed:
  • Rocio Joo
  • Sophie Bertrand
  • Jorge Tam
  • Ronan Fablet

Abstract

One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (∼1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines) for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%), significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance.

Suggested Citation

  • Rocio Joo & Sophie Bertrand & Jorge Tam & Ronan Fablet, 2013. "Hidden Markov Models: The Best Models for Forager Movements?," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-12, August.
  • Handle: RePEc:plo:pone00:0071246
    DOI: 10.1371/journal.pone.0071246
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    Cited by:

    1. Lamonica, Dominique & Drouineau, Hilaire & Capra, Hervé & Pella, Hervé & Maire, Anthony, 2020. "A framework for pre-processing individual location telemetry data for freshwater fish in a river section," Ecological Modelling, Elsevier, vol. 431(C).
    2. Floriane Cardiec & Sophie Bertrand & Matthew J Witt & Kristian Metcalfe & Brendan J Godley & Catherine McClellan & Raul Vilela & Richard J Parnell & François le Loc’h, 2020. "“Too Big To Ignore”: A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    3. Woillez, Mathieu & Fablet, Ronan & Ngo, Tran-Thanh & Lalire, Maxime & Lazure, Pascal & de Pontual, Hélène, 2016. "A HMM-based model to geolocate pelagic fish from high-resolution individual temperature and depth histories: European sea bass as a case study," Ecological Modelling, Elsevier, vol. 321(C), pages 10-22.
    4. Boyd, Charlotte & Punt, André E. & Weimerskirch, Henri & Bertrand, Sophie, 2014. "Movement models provide insights into variation in the foraging effort of central place foragers," Ecological Modelling, Elsevier, vol. 286(C), pages 13-25.

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