A Data-Inspired Framework to Simulate Collective Behaviour of Predator-Prey Systems

The collective escape of prey from predators is a classic example of adaptive behavior in animal groups. Across species, individual prey have evolved a large repertoire of evasive maneuvers to evade predators, and predators have evolved a series of hunting strategies to overcome them. With recent technological advances, empirical data on collective escape and cooperative hunting are increasingly becoming available, and large variation in the collective dynamics of different predator-prey systems is apparent. Computational models to link rules of individual behavior and collective patterns through self-organization are needed to understand this variation, but system-specific characteristics are rarely included in agent-based models of collective behavior. To tackle this challenge, we introduce a framework named DaNCES, that uses individual-based state machines to model spatiotemporal dynamics of collective predator-prey interactions. We implement it in a highly optimized software and pair it with an R package to facilitate its use. A synthetic agent in our framework can switch its behavior between continuous and discrete reactions through a dynamic Markov chain, depending on its local information. The flexibility and structure of our software allow substantial changes in a model with minimal code alterations, supporting the link between empirical data and theory. Overall, DaNCES is a valuable tool toward a standardized methodology to identify the underlying mechanisms of collective escape and cooperative hunting across species, enabling the link between individual behavior and eco-evolutionary dynamics, and offering bio-inspired solutions for autonomous artificial swarms.