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

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

1. 近年来，生物遥测记录（bio-logging）技术的进步为多尺度下的动物运动研究带来了极具前景的研究视角。当前，我们已能够以较高的时空分辨率，在大范围、长周期内记录动物定位数据与辅助数据的时间序列（time-series）——例如通过机载加速度计（accelerometers）获取的活动水平数据。这类时间序列通常呈现分段平稳（piecewise stationary）特性：动物会在不同的平稳阶段间切换，即特定时段内某些关键参数具有固定的均值与方差。准确识别这些平稳阶段的起止节点，是解析潜在运动过程动态的关键第一步。
2. 本文提出一种全新的分割聚类（segmentation-clustering）方法，命名为segclust2d，该方法可作为R包（R package）于cran.r-project.org/package=segclust2d获取。该方法可对双变量（乃至更一般的多变量（multivariate））时间序列进行分割，并可对得到的各片段进行聚类，这些片段对应于不同的假定平稳阶段。该方法操作简便，仅需基于生物学而非统计学考量指定最小片段长度，即可避免过度分割问题。
3. 本方法可应用于任意类型的双变量分段时间序列。本文重点关注两类与动物运动相关的时间序列：其一为大尺度下的定位点双变量坐标序列，用于识别临时家域（home ranges）；其二为小尺度下由定位数据衍生的双变量序列，例如移动速度与转向角，用于区分诸如移动行进、觅食与休憩等不同的行为模式。
4. 通过计算机模拟实验，本文证实segclust2d的性能可与此前专为识别运动模式变化或家域转移而开发的更为复杂的方法相媲美，甚至更胜一筹——这些传统方法多基于隐马尔可夫（Hidden Markov）与奥恩斯坦-乌伦贝克（Ornstein-Uhlenbeck）建模，与本方法不同的是，它们通常需要用户提供合理的初始参数猜测，才能获得理想效果。此外，本文通过两个实际案例验证了该方法的有效性：分别针对斑马的小尺度运动与大象的大尺度运动，展示了其如何分别识别各类运动模式与家域转移情况。
2019年8月15日