In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intra and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side by side movement modes. Besides distance, the method includes factors such as sex, age, time of day, or season that cause frequencies of movement modes to deviate from random. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion circumferences. Our empirical data were GPS relocation time series collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data analysis were used to illustrate the types of behavioral assessment that our methodology reveals. Our methodology facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which is becoming increasingly ubiquitous as telemetry and related technologies improve. Our method should open up a whole new vista of behavioral-interaction type analyses to movement and behavioral ecologists.

中文翻译：

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GPS定位数据解析双胞胎时空行为的相对运动方法

在本文中，我们介绍了一种新的方法来分类和计算种内和种间二元组的运动模式的频率，特别着重于距离介导的进场，后退，跟随和并排运动模式。除了距离之外，该方法还包括诸如性别，年龄，一天中的时间或季节之类的因素，这些因素会导致运动模式的频率偏离随机性。我们使用模拟和经验数据来演示和验证我们的方法。我们的模拟数据是从具有吸引力和排斥周长的相对运动，偏向随机行走（RM-BRW）模型获得的。我们的经验数据是从纳米比亚的埃托沙国家公园的非洲大象那里收集的GPS定位时间序列。仿真数据主要用于验证我们的方法，而经验数据分析用于说明我们的方法论揭示的行为评估的类型。我们的方法使用GPS重新定位数据来促进对二元组机车相互作用的自动化，无观察者偏见的分析，随着遥测技术和相关技术的改进，该问题变得越来越普遍。我们的方法应该为运动和行为生态学家打开全新的行为互动类型分析视野。