A Study on Combating Illegal Trade in Green Sea Turtles based on a Multi-Model Strategy

Abstract

This study addresses the long-standing and increasingly complex issue of global illegal wildlife trade, proposing a system solution aimed at monitoring and combating this trade, with a special emphasis on the protection of green sea turtles. The ARIMA Predict Model was utilized to demonstrate that increased enforcement efforts and higher operational expenses are significantly associated with reduced volumes of illegal wildlife trade. An illegal trade risk model was developed, employing SLSQP to enhance accuracy, constrained by budget allocation, maximum RFID reader capacity, and device failure rates. Furthermore, the Sea Turtle Movement Model was crafted by examining seasonal behaviors, agent-based movement patterns, temperature preferences, stochastic elements, and simulation time steps, enabling the delineation of migratory paths of sea turtles across different seasons. Ant Colony Optimization (ACO) was applied to optimize patrol routes. The findings underscored the necessity for additional governmental support, encompassing legal authorization, environmental access, international collaboration, and financial backing. The project’s effectiveness was assessed using the Risk Reduction Coefficient Model and Monte Carlo simulations, which significantly reduced illegal trade by 34.43%, with projections indicating continued declines over the subsequent five years.