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.
Spatiotemporal dynamics of animal contests arise from effective forces between contestants
