Data from: A unifying framework for quantifying the nature of animal interactions

Collective phenomena, whereby agent-agent interactions determine spatial patterns, are ubiquitous in the animal kingdom. On the other hand, movement and space use are also greatly influenced by the interactions between animals and their environment. Despite both types of interaction fundamentally influencing animal behaviour, there has hitherto been no unifying framework for the models proposed in both areas. Here, we construct a general method for inferring population-level spatial patterns from underlying individual movement and interaction processes, a key ingredient in building a statistical mechanics for ecological systems. We show that resource selection functions, as well as several examples of collective motion models, arise as special cases of our framework, thus bringing together resource selection analysis and collective animal behaviour into a single theory. In particular, we focus on combining the various mechanistic models of territorial interactions in the literature with step selection functions, by incorporate interactions into the step selection framework and demonstrating how to derive territorial patterns from the resulting models. We demonstrate the efficacy of our model by application to a population of insectivore birds in the Amazon rainforest.

由个体间交互（agent-agent interactions）决定空间格局的集体现象，在动物界中广泛存在。另一方面，动物的运动模式与空间利用方式，也深受其与所处环境之间的交互作用影响。尽管这两类交互均从根本上塑造动物行为，但迄今为止，针对这两个领域所提出的各类模型，尚未形成统一的理论框架。本研究构建了一种通用方法，可从底层的个体运动与交互过程中推导得到种群水平的空间格局，这是构建生态系统统计力学（statistical mechanics）的关键一环。我们证明，资源选择函数（resource selection functions）以及若干集体运动模型案例，均可作为本框架的特例，由此将资源选择分析与动物集体行为研究整合为统一理论。具体而言，本研究聚焦于将文献中各类领地交互（territorial interactions）机制模型与步选择函数（step selection functions）相结合：通过将交互作用纳入步选择框架，并展示如何从所得模型中推导得到领地空间格局。我们通过将模型应用于亚马逊雨林中的食虫鸟类种群，验证了本模型的有效性。