DOI: 10.5061/dryad.2j63369
title: Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements

description: 1. Recent advances in bio-logging 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. 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 (available as a R
 package at cran.r-project.org/package=segclust2d). It can segment bi- (or
 more generally multi-) variate 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. 3. 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 (i) at large
 scale, series of bivariate coordinates of relocations, to highlight
 temporary home ranges, and (ii) 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. 4. Using
 computer simulations, we show that segclust2d 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 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. 15-Aug-2019
description: 24-h GPS track of a
 plains zebra (Equus quagga)GPS data from a plain
 zebra at 8min sampling interval, in Hwange National Park. Data include :
 coordinates (x and y) in UTM zone 35 south, date (in GMT+2), angle in
 radian, distance between successive location and its smoothed counterpart
 (dist and dist_smoothed), speed between successive location and its
 smoothed counterpart (speed and speed_smoothed), angle at constant radius
 and its absolute value (spatial_angle and abs_spatial_angle), persistance
 and rotation speed.dryad_zebra.csvGPS track of an African Elephant (Loxodonta africana), recorded for &gt; 2.5 yearsGPS data from an African elephant in Hwange National Park, data resampled approximately daily. Data include coordinates in UTM zone 35 south as well as date in GMT+2dryad_elephant.csv