Using time-series similarity measures to compare animal movement trajectories in ecology
Using time-series similarity measures to compare animal movement trajectories in ecology
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DOI：
10.1007/s00265-019-2761-1
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发表时间：
2019-11-01
影响因子：
2.3
通讯作者：
Hamer, Keith C.
中科院分区：
文献类型：
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作者：
Cleasby, Ian R.;Wakefield, Ewan D.;Hamer, Keith C.
识别和理解运动数据模式是运动生态学的主要目标之一。通过量化运动轨迹的相似性，可以对不同的过程做出推断，从个体专业化到觅食策略的个体发育。然而，运动分析并不是生态学所独有的，其他领域已经开发了估计运动轨迹相似性的方法，但目前生态学家尚未充分利用。在这里，我们介绍五种常用的轨迹相似性度量：动态时间扭曲（DTW）、最长公共子序列（LCSS）、真实序列编辑距离（EDR）、弗雷切特距离和最近邻距离（NND），其中只有NND是生态学家常用的。我们通过使用 Ornstein-Uhlenbeck (OU) 模型模拟运动轨迹来研究每种措施的性能，在该模型中我们改变了以下参数：(1) 吸引点，(2) 对该点的吸引力强度，以及 (3) 添加到运动过程中的噪声或波动性，以确定哪些措施对此类变化最敏感。此外，我们还通过对大型生态数据集进行轨迹聚类，展示了如何使用繁殖北方塘鹅（Morus bassanus）的运动轨迹来应用这些措施。模拟表明，DTW 和 Frechet 距离对运动参数的变化反应最灵敏，并且能够区分我们尝试的所有不同参数组合。相比之下，NND 是试验中最不敏感的措施。当应用于我们的塘鹅数据集时，尽管基础计算存在差异，但五个相似性度量高度相关。个体内部和个体之间的轨迹聚类使我们能够轻松地可视化和比较大型数据集中随时间变化的空间使用模式。轨迹簇反映了鸟类离开群体的方位，并强调了众所周知的测深特征的使用。随着运动数据量和量化动物轨迹相似性的需求不断增长，这里描述的测量方法以及它们为其他研究领域提供的桥梁将在生态学中变得越来越有用。
Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Frechet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Frechet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology.