Gurarie Eliezer, Bracis Chloe, Delgado Maria, Meckley Trevor D, Kojola Ilpo, Wagner C Michael
Department of Biology, University of Maryland, College Park, MD, 20742, USA.
School of Environmental and Forest Sciences, University of Washington, Seattle, WA, 98195, USA.
J Anim Ecol. 2016 Jan;85(1):69-84. doi: 10.1111/1365-2656.12379. Epub 2015 Jul 23.
Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.
运动数据为我们提供了一扇窗口——往往也是唯一的窗口——使我们得以了解野生动物行为生态学背后的认知、社会和生物过程。用于识别和解释不同运动行为模式的可靠方法至关重要，但由于运动数据复杂、多变量且具有依赖性，这一任务变得复杂起来。为回答类似问题，人们已开发出许多不同的运动探索性分析方法，而从业者常常不知如何为特定问题选择合适的工具。我们将四种方法应用于三条模拟轨迹和两条动物轨迹并进行比较，这四种方法分别是首次通过时间（FPT）、马尔可夫模型的贝叶斯划分（BPMM）、行为变化点分析（BCPA）以及拟合多状态随机游走（MRW），所涉及的动物轨迹分别是一条追踪了12小时的海七鳃鳗（Petromyzon marinus）和一条追踪了1年的狼（Canis lupus）。这些模拟分别改变了速度、曲折度和空间偏差，凸显了所有方法对模型误设的敏感性。未考虑运动变量自相关性的方法会导致虚假的变化点，而未完全考虑空间偏差的方法则会完全忽略方向上的变化。当应用于动物数据时，这些方法在运动行为结构上大致达成一致。然而，重要的差异反映了假设和输出性质的不同。重要的权衡存在于先验假设的强度（BCPA中低，MRW中高）、输出的复杂性（BCPA中高，BPMM和MRW中低）和解释潜力（MRW中最高）之间。动物轨迹分析为运动数据的探索性分析提出了一些一般原则，包括利用各种方法优势的途径。我们主张在拟合复杂运动模型之前，对运动进行细致入微的探索性分析。
