Larval zebrafish minimize energy consumption during hunting via adaptive movement selection.

Animals must balance behavioral goals with the energetic costs of movement. How such trade-offs are implemented algorithmically during natural behavior, and how they adapt to changing energetic constraints, remains poorly understood. Here we show that larval zebrafish adjust their motor strategies during prey hunting to minimize energy expenditure. Using high-speed video tracking and 3D computational fluid dynamics simulations, we quantified the energy costs (in Joules) of different movement types across three developmental stages. While the structure of discrete movement types ("bouts") remained stable with age, their selection probabilities changed depending on their energetic cost, such that more expensive bouts were always used less frequently. This relationship was conserved across development despite shifts in the relative costs of different bouts. A reinforcement learning model trained to catch prey while minimizing energy use reproduced this relationship, demonstrating that it is an energetically optimal strategy. Furthermore, when fish were reared in a high-viscosity environment that altered energetic costs, they readapted their movement probabilities to preserve the same energy-based control rule, demonstrating active optimization rather than a predetermined developmental program. These findings reveal how goal-directed animals can dynamically regulate movement selection to minimize energetic costs, even under changing environmental and biomechanical conditions. Our results provide a quantitative link between biomechanics, development, behavior, and computational control theory in a freely moving animal.