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bioRxiv · 10.1101/444794

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

Patin, R.·Etienne, M.-P.·Lebarbier, E.·Chamaille-Jammes, S.·Benhamou, S.
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Abstract

O_LIRecent 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.\nC_LIO_LIWe 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.\nC_LIO_LIAlthough 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.\nC_LIO_LIUsing 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 zebras small scale movements and an elephants large scale movements, to illustrate the identification of various movement modes and of home range shifts, respectively.\nC_LI

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Patin, R., Etienne, M.-P., Lebarbier, E., Chamaille-Jammes, S., Benhamou, S.. 2018-10-17. Identifying stationary phases in multivariate time-series for highlighting behavioural modes and home range settlements. https://doi.org/10.1101/444794

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