FN Archimer Export Format
PT J
TI Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns
BT 
AF HEERAH, Karine
   WOILLEZ, Mathieu
   FABLET, Ronan
   GARREN, Francois
   MARTIN, Stephane
   DE PONTUAL, Helene
AS 1:1;2:1;3:2;4:1;5:1;6:1;
FF 1:PDG-RBE-STH-LBH;2:PDG-RBE-STH-LBH;3:;4:PDG-RBE-STH-LBH;5:PDG-RBE-STH-LBH;6:PDG-RBE-STH-LBH;
C1 IFREMER, Sci & Technol Halieut, F-29280 Plouzane, CS, France.
   Univ Bretagne Loire, IMT Atlantique, F-29238 Brest, France.
C2 IFREMER, FRANCE
   IMT ATLANTIQUE, FRANCE
SI BREST
SE PDG-RBE-STH-LBH
IN WOS Ifremer jusqu'en 2018
   DOAJ
   copubli-france
   copubli-univ-france
IF 3.286
TC 14
UR https://archimer.ifremer.fr/doc/00402/51400/51974.pdf
LA English
DT Article
DE ;Fourier transform;Non negative matrix factorization;Classification;Animal behaviour;European sea bass;Movement ecology;Diurnal and tidal cycles;Biologging;Data storage tags
AB 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. 
PY 2017
PD SEP
SO Movement Ecology
SN 2051-3933
PU Biomed Central Ltd
VL 5
IS 20
UT 000411412800001
BP 1
EP 15
DI 10.1186/s40462-017-0111-3
ID 51400
ER

EF