Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns

Type Article
Date 2017-09
Language English
Author(s) Heerah Karine1, Woillez MathieuORCID1, Fablet Ronan2, Garren Francois1, Martin Stephane1, de Pontual HeleneORCID1
Affiliation(s) 1 : IFREMER, Sci & Technol Halieut, F-29280 Plouzane, CS, France.
2 : Univ Bretagne Loire, IMT Atlantique, F-29238 Brest, France.
Source Movement Ecology (2051-3933) (Biomed Central Ltd), 2017-09 , Vol. 5 , N. 20 , P. 1-15
DOI 10.1186/s40462-017-0111-3
WOS© Times Cited 7
Keyword(s) Fourier transform, Non negative matrix factorization, Classification, Animal behaviour, European sea bass, Movement ecology, Diurnal and tidal cycles, Biologging, Data storage tags
Abstract

Background

Movement pattern variations are reflective of behavioural switches, likely associated with different life history traits in response to the animals’ abiotic and biotic environment. Detecting these can provide rich information on the underlying processes driving animal movement patterns. However, extracting these signals from movement time series, requires tools that objectively extract, describe and quantify these behaviours. The inference of behavioural modes from movement patterns has been mainly addressed through hidden Markov models. Until now, the metrics implemented in these models did not allow to characterize cyclic patterns directly from the raw time series. To address these challenges, we developed an approach to i) extract new metrics of cyclic behaviours and activity levels from a time-frequency analysis of movement time series, ii) implement the spectral signatures of these cyclic patterns and activity levels into a HMM framework to identify and classify latent behavioural states.

Results

To illustrate our approach, we applied it to 40 high-resolution European sea bass depth time series. Our results showed that the fish had different activity regimes, which were also associated (or not) with the spectral signature of different environmental cycles. Tidal rhythms were observed when animals tended to be less active and dived shallower. Conversely, animals exhibited a diurnal behaviour when more active and deeper in the water column. The different behaviours were well defined and occurred at similar periods throughout the annual cycle amongst individuals, suggesting these behaviours are likely related to seasonal functional behaviours (e.g. feeding, migrating and spawning).

Conclusions

The innovative aspects of our method lie within the combined use of powerful, but generic, mathematical tools (spectral analysis and hidden Markov Models) to extract complex behaviours from 1-D movement time series. It is fully automated which makes it suitable for analyzing large datasets. HMMs also offer the flexibility to include any additional variable in the segmentation process (e.g. environmental features, location coordinates). Thus, our method could be widely applied in the bio-logging community and contribute to prime issues in movement ecology (e.g. habitat requirements and selection, site fidelity and dispersal) that are crucial to inform mitigation, management and conservation strategies.

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