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

dc.contributor.authorPatin, Remi
dc.contributor.authorEtienne, Marie‐Pierre
dc.contributor.authorLebarbier, Emilie
dc.contributor.authorChamaillé-Jammes, Simon
dc.contributor.authorBenhamou, Simon
dc.date.accessioned2019-11-21T06:31:46Z
dc.date.issued2020-01
dc.descriptionThe code of the method is publicly available as an R package (cran.r‐proje ct.org/packa ge=segcl ust2d ). The data used in the examples (24‐hr GPS track of a plains zebra and GPS track of an African el-ephant recorded for >2.5 years) are available on Dryad: https: //doi. org/10.5061/dryad.2j63369 (Patin, Etienne, Lebarbier, Chamaillé‐Jammes, & Benhamou, 2019).en_ZA
dc.description.abstractRecent advances in biologging open promising perspectives in the study of 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. characterized 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. We introduce a new segmentation‐clustering method we called segclust2d (available as a r package at cran.r-project.org/package=segclust2d). It can segment bivariate (or more generally multivariate) time series and possibly cluster the various segments obtained, corresponding to different phases assumed to be stationary. This method is easy to use, as it only requires specifying a minimum segment length (to prevent over‐segmentation), based on biological rather than statistical considerations. This method can be applied to bivariate piecewise time series of any nature. We focus here on two types of time series related to animal movement, corresponding to (a) at large scale, series of bivariate coordinates of relocations, to highlight temporary home ranges, and (b) at smaller scale, bivariate series derived from relocations data, such as speed and turning angle, to highlight different behavioural modes such as transit, feeding and resting. Using computer simulations, we show that segclust2d can rival and even outperform previous, more complex methods, which were specifically developed to highlight changes of movement modes or home range shifts (based on hidden Markov and Ornstein–Uhlenbeck modelling), which, contrary to our method, usually require the user to provide relevant 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 how various movement modes and home range shifts, respectively, can be identified.en_ZA
dc.description.departmentZoology and Entomologyen_ZA
dc.description.embargo2020-09-20
dc.description.librarianhj2019en_ZA
dc.description.sponsorshipThe Grant ANR‐16‐CE02‐0001‐01 of the French ‘Agence Nationale de la Recherche', and the Zone Atelier program of the CNRS.en_ZA
dc.description.urihttp://wileyonlinelibrary.com/journal/janeen_ZA
dc.identifier.citationPatin R, Etienne M‐P, Lebarbier E, Chamaillé‐Jammes S, Benhamou S. Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements. Journal of Animal Ecology 2020;89:44–56. https://doi.org/10.1111/1365‐2656.13105.en_ZA
dc.identifier.issn0021-8790 (print)
dc.identifier.issn1365-2656 (online)
dc.identifier.other10.1111/1365-2656.13105
dc.identifier.urihttp://hdl.handle.net/2263/72361
dc.language.isoenen_ZA
dc.publisherWileyen_ZA
dc.rights© 2019 The Authors. Journal of Animal Ecology © 2019 British Ecological Society. This is the pre-peer reviewed version of the following article : Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements. Journal of Animal Ecology, vol. 89, no. 1, pp. 44-56, 2020, doi : 10.1111/1365-2656.13105. The definite version is available at : wileyonlinelibrary.com/journal/jane.en_ZA
dc.subjectArea‐concentrated searchingen_ZA
dc.subjectClusteringen_ZA
dc.subjectForagingen_ZA
dc.subjectHome rangeen_ZA
dc.subjectMigrationen_ZA
dc.subjectMovement ecologyen_ZA
dc.subjectSegmentationen_ZA
dc.subjectTransiten_ZA
dc.titleIdentifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlementsen_ZA
dc.typePostprint Articleen_ZA

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