Data from: Extracting spatio-temporal patterns in animal trajectories: an ecological application of sequence analysis methods

Digital tracking technologies have considerably increased the amount and quality of animal trajectories, enabling the study of habitat use and habitat selection at a fine spatial and temporal scale. However, current approaches do not yet explicitly account for a key aspect of habitat use, namely the sequential variation in the use of different habitat features. To overcome this limitation, we propose a tree-based approach that makes use of sequence analysis methods, derived from molecular biology, to explore and identify ecologically relevant sequential patterns in habitat use by animals. We applied this approach to ecological data consisting of simulated and real trajectories from a roe deer population (Capreolus capreolus), expressed as ordered sequences of habitat use. We show that our approach effectively captured spatio-temporal patterns of sequential habitat use by roe deer. In our case study, individual sequences were clustered according to the sequential use of the elevation gradient (first order) and of open/closed habitats (second order). We provided evidence for several behavioural processes, such as migration and daily alternating habitat use. Some unexpected patterns, such as homogeneous sequences of use of open habitat, could also be identified. Our findings advocate the importance of dealing with the sequential nature of movement data. Approaches based on sequence analysis methods are particularly useful and effective since they allow exploring temporal patterns of habitat use in a synthetic and visually captive manner. The proposed approach represents a useful and effective way to classify individual movement behaviour across populations and species. Ultimately, this method can be applied to explore the temporal scale of ecological processes based on movement.数字追踪技术极大提升了动物运动轨迹数据的数量与质量，使得研究者能够在精细的时空尺度下开展栖息地利用（habitat use）与栖息地选择（habitat selection）研究。然而，现有研究方法尚未明确考量栖息地利用的一个关键维度，即不同栖息地特征的利用顺序差异。 为克服这一局限，我们提出一种基于树结构的研究方法，该方法借助源自分子生物学的序列分析（sequence analysis）手段，探索并识别动物栖息地利用中具有生态学意义的序列模式。我们将该方法应用于一组生态数据，该数据包含狍（roe deer, Capreolus capreolus）种群的模拟与真实运动轨迹，以栖息地利用的有序序列形式呈现。结果表明，我们的方法能够有效捕捉狍的栖息地利用序列的时空模式。在本案例研究中，个体序列依据海拔梯度（一阶）与开放/封闭栖息地（二阶）的利用顺序进行聚类。我们的研究为多种行为过程提供了实证依据，例如迁徙以及昼夜交替的栖息地利用模式。此外还可识别出部分意外的模式，例如开放栖息地利用的均质序列。 本研究结果凸显了考量运动数据序列属性的重要性。基于序列分析方法的研究路径尤为实用高效，因为其能够以综合且直观可视的方式探索栖息地利用的时间模式。所提出的方法为跨种群与跨物种的个体运动行为分类提供了一种高效实用的路径。最终，该方法可用于基于运动数据探索生态过程的时间尺度。