Evaluating evasion strategies in zebrafish larvae

Abstract

An effective evasion strategy allows prey to survive encounters with predators. Prey are generally thought to escape in a direction that is either random or serves to maximize the minimum distance from the predator. Here, we introduce a comprehensive approach to determine the most likely evasion strategy among multiple hypotheses and the role of biomechanical constraints on the escape response of prey fish. Through a consideration of six strategies with sensorimotor noise and previous kinematic measurements, our analysis shows that zebrafish larvae generally escape in a direction orthogonal to the predator’s heading. By sensing only the predator’s heading, this orthogonal strategy maximizes the distance from fast-moving predators, and, when operating within the biomechanical constraints of the escape response, it provides the best predictions of prey behavior among all alternatives. This work demonstrates a framework for resolving the strategic basis of evasion in predator–prey interactions, which could be applied to a broad diversity of animals.

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

“…6D) for which these robotic systems would exhibit success rates exceeding 95%. To probe these predictions numerically, we employed a three-link fish model [64, 87] and developed, based on the RL-inspired amplification and attenuation strategies, controllers that mapped local flow signals to body deformations, much like Braitenberg’s vehicles that linked signal intensity to wheel rotation. Numerical tests indicate success of the three-link fish in tracking a traveling wave signal (Suppl.…”

Mapping Spatial Patterns to Energetic Benefits in Groups of Flow-coupled Swimmers

“…An understanding of how the spatial arrangement of individuals within a group influences their cost of locomotion can provide insights into the evolution of social structures, resource allocation, and overall fitness of each individual in cooperative activities such as foraging, mating, and evasion [13][14][15][16][17][18][19]. It could also guide the design of bio-inspired engineering systems and algorithms that steer groups of entities, such as swarms of autonomous robotic vehicles, underwater or in flight, that collaborate to achieve a desired task while minimizing energy consumption and improving the overall system efficiency [20][21][22][23][24].…”