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Identifying stationary phases in multivariate time-series for highlighting behavioural modes and home range settlements

Rémi Patin, View ORCID ProfileMarie-Pierre Étienne, View ORCID ProfileÉmilie Lebarbier, View ORCID ProfileSimon Chamaillé-Jammes, View ORCID ProfileSimon Benhamou
doi: https://doi.org/10.1101/444794
Rémi Patin
1Centre d’Écologie Fonctionnelle et Évolutive, CNRS et Université de Montpellier, France
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Marie-Pierre Étienne
2Institut de recherche mathématique de Rennes, Université de Rennes, AgroCampusOuest, Rennes, France
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  • ORCID record for Marie-Pierre Étienne
Émilie Lebarbier
3Mathématiques et Informatique Appliquées, Agroparistech, Paris, France
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Simon Chamaillé-Jammes
1Centre d’Écologie Fonctionnelle et Évolutive, CNRS et Université de Montpellier, France
4LTSER France, Zone Atelier “Hwange”, Hwange National Park, Bag 62, Dete, Zimbabwe
5Mammal Research Institute, Department of Zoology & Entomology, University of Pretoria, Pretoria, South Africa
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Simon Benhamou
1Centre d’Écologie Fonctionnelle et Évolutive, CNRS et Université de Montpellier, France
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  • For correspondence: simon.benhamou@cefe.cnrs.fr
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Abstract

  1. Recent advances in bio-logging open promising perspectives in the study animal movements at numerous scales. It is now possible to record time-series of animal locations and ancillary data (e.g. activity level derived from on-board accelerometers) over extended areas and long durations with a high spatial and temporal resolution. Such time-series are often piecewise stationary, as the animal may alternate between different stationary phases (i.e. characterised by a specific mean and variance of some key parameter for limited periods). Identifying when these phases start and end is a critical first step to understand the dynamics of the underlying movement processes.

  2. We introduce a new segmentation-clustering method we called segclust2d. It can segment bi-(or more generally multi-) variate time-series and possibly cluster the various segments obtained, corresponding to phases assumed to be stationary. It is easy to use, as it only requires specifying the minimum length of a segment (to prevent over-segmentation) based on biological considerations.

  3. Although this method can be applied to time-series of any nature, we focus here on two-dimensional piecewise time-series whose phases correspond at small scale to the expressions of different behavioural modes such as transit, feeding and resting, as characterised by two joint metrics such as speed and turning angles or, at larger scale, to temporary home ranges, characterised by stationary distributions of bivariate coordinates.

  4. Using computer simulations, we show that segcust2d can rival and even outperform previous, more complex methods, which were specifically developed to highlight changes in movement modes or home range shifts (based on Hidden Markov or Ornstein-Uhlenbeck modelling, respectively), which, contrary to our method, require truly informative initial guesses to be efficient. Furthermore we demonstrate it on actual examples involving a zebra’s small scale movements and an elephant’s large scale movements, to illustrate the identification of various movement modes and of home range shifts, respectively.

Authors’ contributions. RP analysed the data and contributed to the coding of the statistical model, which was developed by MPE and EL. SC provided the tracking data. SB led the project, performed computer simulations, and wrote the first draft of the manuscript, except the part describing the model which was first contributed to by MPE, EL and RP. All authors contributed significantly to the final manuscript.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 17, 2018.
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Identifying stationary phases in multivariate time-series for highlighting behavioural modes and home range settlements
Rémi Patin, Marie-Pierre Étienne, Émilie Lebarbier, Simon Chamaillé-Jammes, Simon Benhamou
bioRxiv 444794; doi: https://doi.org/10.1101/444794
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Identifying stationary phases in multivariate time-series for highlighting behavioural modes and home range settlements
Rémi Patin, Marie-Pierre Étienne, Émilie Lebarbier, Simon Chamaillé-Jammes, Simon Benhamou
bioRxiv 444794; doi: https://doi.org/10.1101/444794

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