Data from: Estimating encounter location distributions from animal tracking data

Abstract1. Ecologists have long been interested in linking individual behavior 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 modeling. 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: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) 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 specialised data collection protocols. This method is now openly available via the ctmm R package.

1. 生态学家长期以来一直致力于将个体行为与更高层级的生态过程关联起来。对于运动性物种而言，这一向上尺度转换过程取决于特定运动策略在最大化遭遇正向因素、最小化遭遇负向因素方面的表现优劣。尽管遭遇事件对诸多生态过程均具有重要意义，但遭遇理论的发展却未能跟上动物追踪与运动建模领域的进展。此外，现有研究主要聚焦于动物运动与遭遇率之间的关联，而个体运动与环境中遭遇事件的空间位置之间的关系，则长期显著未得到充分探索。
2. 本研究通过引入一种用于描述家域（home range）内运动时的长期遭遇位置概率的方法，填补了这一研究空白，该方法被称为遭遇条件分布（conditional distribution of encounters，CDE）。随后，我们推导了该分布及其置信区间的数学形式，将其统计估计器集成至开源软件中，并论证了该分布广泛的生态学应用价值。
3. 我们首先通过模拟数据验证了所提估计器可生成渐近一致估计结果。随后，我们针对生物系统中常见的三类基于模拟的场景，验证了该方法的普适性：其一，家域与邻近个体存在重叠的种群；其二，家域间存在刚性领地边界的成对个体；其三，家域覆盖多个猎物个体家域的捕食者。借助采集自巴拿马巴罗科罗拉多岛的白面卷尾猴（Cebus capucinus）GPS追踪数据，以及澳大利亚南澳州邦迪地区的睡蜥（Tiliqua rugosa）GPS追踪数据，我们进一步展示了CDE可用于估算领地边界位置、识别关键资源、量化竞争或捕食交互的潜在可能性，以及识别由位置特异性遭遇概率直接引发的行为变化。
4. CDE可帮助研究者更好地理解交互个体种群的动态过程。值得注意的是，本研究提出的通用估计框架直接基于家域估计方法拓展而来，无需专门化的数据采集协议。目前，该方法已通过ctmm R软件包公开上线。