Attracted to Fish: A Gravity-Based Model of Purse-Seine Vessel Behaviour.

This paper presents a gravity-based behavioral algorithm designed to simulate the dynamic decision-making processes of purse seine fishers in the Eastern Pacific Ocean. The algorithm captures the complex interplay between fishers’ actions, environmental conditions, and regulatory constraints. It comprises two core strategies: an action strategy and a destination strategy. The action strategy involves selecting the most favorable course of action based on estimated values and preferences, while the destination strategy uses gravity fields to determine attractive ocean cell locations. These fields are modulated by real-time circumstances, guiding fishers toward areas of high value. Calibration against real-world data from the Inter-American Tropical Tuna Commission (IATTC) observer database is ongoing, with a focus on achieving accurate representation of action frequencies and species-specific catch per action type. Initial calibration results highlight the need for further refinement. While still a work in progress, this algorithm provides a robust foundation for capturing the intricate dynamics of purse seine fishing, adapting to evolving conditions, and informing policy evaluations. Future enhancements include adaptive fishing strategies and incorporating fleet-level interactions for a more comprehensive understanding of fishing behaviors.