Séminaire

The European sea bass is a fish of high commercial value both for professional and commercial fisheries. The sea bass northern stock has been recognized as over exploited by the European commission since 2015. In order to improve our management strategies, it is crucial to increase our knowledge on their ecology at sea. Animal movement patterns are reflective of behavioural switches, and are likely associated with different life history traits in response to the animals’ abiotic and biotic environment. Detecting these behavioural switches and periodicities can provide rich information on the underlying processes driving these movement patterns (e.g. ressource availability, physiological constraints etc). However, extracting these signals from movement time-series requires tools that objectively describe and quantify these behaviours. To address these challenges, we developed a robust but flexible approach to i) extract new metrics of cyclic behaviours and activity levels from a time-frequency analysis of movement time series, ii) implement spectral signatures into a HMM framework to identify and classify latent behavioural states. Applying our method to 40 high-resolution European sea bass depth time series, we demonstrated that the fishes occupied different parts of the water column and had different activity levels according to environmental cycles. The animals tended to exhibit tidal rhythms while less active, whereas diurnal rhythms were observed while the animals were more active and deeper in the water column. The presence of different behaviours were well defined and appeared at similar times throughout the annual cycle amongst individuals, suggesting these behaviours are likely related to seasonal functional behaviours (e.g. feeding, migrating and spawning).

The innovative aspects of our method lie within the combined use of powerful, but generic, mathematical tools (spectral analysis and Hidden Markov Models) to identify and classify behavioural states. It relies on objective criterion and is fully automated which makes it suitable for analyzing large datasets. In addition, HMMs offer the flexibility to include any variables (e.g. environmental features such as water temperature) in the segmentation process. 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, interactions with human activities, site fidelity and dispersal) that are crucial to inform mitigation, management and conservation strategies.

jbclod@locean-ipsl.upmc.fr