Torres Leigh G, Orben Rachael A, Tolkova Irina, Thompson David R
Department of Fisheries and Wildlife, Marine Mammal Institute, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America.
Applied Math and Computer Science Departments, University of Washington, Seattle, Washington, United States of America.
PLoS One. 2017 Jan 3;12(1):e0168513. doi: 10.1371/journal.pone.0168513. eCollection 2017.
Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.
动物运动数据中行为状态的识别和分类可能很复杂，存在时间偏差、耗时、依赖尺度，并且在不同研究和分类群中缺乏标准化。大型运动数据集越来越普遍，因此需要有效的数据探索方法来适应每条轨迹的个体差异。我们提出了时空驻留（RST）方法，用于根据行为状态可按恒定尺度区域内占据的空间和时间量进行划分的概念，对运动数据中的行为模式进行分类。利用在恒定搜索半径内的驻留时间和驻留距离的归一化值，RST能够区分耗时的行为模式（如休息）、耗时且耗距离的行为模式（如区域限制搜索）和过境行为（短时间和短距离）。我们使用灰头信天翁（Thalassarche chrysostoma）的GPS轨迹来证明RST对行为模式进行分类并适应每条轨迹固有尺度和个体性的能力。接下来，我们评估RST相对于其他经典运动指标区分行为状态的能力。然后，我们对信天翁轨迹数据进行时间上的子采样，以说明RST对分辨率较低数据的响应。最后，我们使用来自四个具有不同生态、功能尺度、生态系统和数据类型的分类群的数据集评估RST的性能。我们得出结论，RST是一种强大、快速且灵活的方法，可用于对动物运动数据中的行为状态进行详细的探索性分析和元分析，因为它能够将距离和时间测量整合到一个行为分组的描述性指标中。鉴于收集到的动物运动数据量不断增加，实施一致的行为分类指标以实现高效和比较分析既及时又有用。总体而言，将RST应用于客观地探索和比较运动数据中的行为模式，可以增强我们对动物运动生态学在精细和广泛尺度上的理解。
