Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel;
Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz 78315, Germany.
Proc Natl Acad Sci U S A. 2021 Dec 7;118(49). doi: 10.1073/pnas.2106269118.
Competition among animals for resources, notably food, territories, and mates, is ubiquitous at all scales of life. This competition is often resolved through contests among individuals, which are commonly understood according to their outcomes and in particular, how these outcomes depend on decision-making by the contestants. Because they are restricted to end-point predictions, these approaches cannot predict real-time or real-space dynamics of animal contest behavior. This limitation can be overcome by studying systems that feature typical contest behavior while being simple enough to track and model. Here, we propose to use such systems to construct a theoretical framework that describes real-time movements and behaviors of animal contestants. We study the spatiotemporal dynamics of contests in an orb-weaving spider, in which all the common elements of animal contests play out. The confined arena of the web, on which interactions are dominated by vibratory cues in a two-dimensional space, simplifies the analysis of interagent interactions. We ask whether these seemingly complex decision-makers can be modeled as interacting active particles responding only to effective forces of attraction and repulsion due to their interactions. By analyzing the emergent dynamics of "contestant particles," we provide mechanistic explanations for real-time dynamical aspects of animal contests, thereby explaining competitive advantages of larger competitors and demonstrating that complex decision-making need not be invoked in animal contests to achieve adaptive outcomes. Our results demonstrate that physics-based classification and modeling, in terms of effective rules of interaction, provide a powerful framework for understanding animal contest behaviors.
动物之间为争夺资源（尤其是食物、领地和配偶）而展开的竞争在生命的各个尺度上普遍存在。这种竞争通常通过个体之间的竞争来解决，这些竞争通常根据其结果来理解，尤其是这些结果如何取决于参赛者的决策。由于这些方法仅限于终点预测，因此无法预测动物竞赛行为的实时或实时空间动态。通过研究具有典型竞赛行为的系统，可以克服这一局限性，这些系统足够简单，可以进行跟踪和建模。在这里，我们提议使用这些系统构建一个理论框架，描述动物参赛者的实时运动和行为。我们研究了一种织网蜘蛛的竞赛时空动态，其中所有常见的动物竞赛元素都发挥了作用。蛛网的封闭竞技场，其相互作用主要由二维空间中的振动线索主导，简化了对相互作用的分析。我们想知道这些看似复杂的决策者是否可以被建模为仅对由于相互作用而产生的吸引力和排斥力的有效力做出反应的相互作用的活性粒子。通过分析“参赛者粒子”的突现动力学，我们为动物竞赛的实时动态方面提供了机械解释，从而解释了较大竞争者的竞争优势，并证明了在动物竞赛中不需要复杂的决策来实现适应性结果。我们的研究结果表明，基于物理的分类和建模，根据有效的相互作用规则，为理解动物竞赛行为提供了一个强大的框架。
