1. Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied.
2. Here, we bridge this gap by introducing a method for describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open-source software and demonstrate the broad ecological relevance of this distribution.
3. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins Cebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location-specific encounter probability.
4. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package.

中文翻译：

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从动物追踪数据估计遭遇位置分布

1. 长期以来，生态学家一直对将个人行为与更高层次的过程联系起来很感兴趣。对于能动的物种，这种“升级”取决于任何给定的运动策略在最大化遇到积极因素和最小化遇到负面因素的情况下的程度。尽管遭遇事件对广泛的生态过程很重要，但遭遇理论并没有跟上动物追踪或运动建模的发展。此外，现有的工作主要集中在动物运动与遭遇率之间的关系上，而个体运动与环境中遭遇事件的空间位置之间的关系仍然明显不足。
2. 在这里，我们通过引入一种描述在家庭范围内移动的长期遭遇位置概率的方法来弥合这一差距，称为遭遇条件分布 (CDE)。然后我们推导出该分布以及置信区间，将其统计估计器实施到开源软件中，并证明该分布的广泛生态相关性。
3. 我们首先使用模拟数据来展示我们的估计器如何提供渐近一致的估计。然后，我们证明了这种方法在生物系统中经常发生的三种基于模拟的场景中的普遍效用：（a）家庭范围与邻居重叠的个体群体；(b) 一对个体在他们的家园之间有严格的领土边界；(c) 捕食者的栖息地范围很大，包括多个猎物个体的栖息地。使用来自巴拿马巴罗科罗拉多岛的白脸卷尾猴 Cebus capucinus和昏昏欲睡的蜥蜴Tiliqua rugosa 的GPS 数据， 在南澳大利亚邦迪进行跟踪，然后我们展示了如何使用 CDE 来估计领土边界的位置、识别关键资源、量化竞争或掠夺性互动的潜力和/或识别直接由位置引起的任何行为变化——特定的相遇概率。
4. CDE 使研究人员能够更好地了解相互作用的个体群体的动态。值得注意的是，这项工作中开发的一般估计框架直接建立在家庭范围估计之外，不需要专门的数据收集协议。此方法现在可通过ctmm R包公开获得。