The effective management and conservation of fish population is achieved by understanding the behavior of anglers and creating appropriate incentives for sustainability. However, it is often difficult to predict the behavior of anglers in recreational fisheries because multiple factors influence their behavior. Consequently, fishery managers are often forced to make management decisions without a good understanding of their effects on the behavior of recreational anglers. Here, a model that links fish population dynamics to anglers’ behavioral dynamics was built. The anglers’ decision model takes the discrete choice approach, in which, anglers first decide whether they go fishing based on distance to fishing sites and expected catch of red drum and spotted seatrout, which are the two of the main recreational fishing targets in the Gulf of Mexico. Then, if they decide to go fishing, they determine the location to go fishing based on the distance and expected catch. The biological models are in the form of vector autoregressive models (VARMs). These two models are linked by incorporating the relative abundance of two species of fish predicted by the biological models as the expected catch in the angler’s decision model.
Results/Conclusions
The decisions by anglers are affected by the distance to fishing locations and expected catch of red drum and spotted seatrout, which are simulated with the biological models. The coupled model predicts that under the level of fluctuation in the abundance of the two species experienced in the past 35 years, the number of trips that might be taken by anglers’ fluctuates noticeably. This fluctuation is magnified as the cost of travel decreases because the anglers can travel long distance to seek better fishing conditions. On the other hand, as the cost of travel increases, their preference to fish in nearby areas increases. Because the distances to fishing locations do not change over time, the anglers’ decisions also become constant over time. The model demonstrates the importance of incorporating anglers’ decision processes in understanding the changes in a fishing effort level. The model in this study can be used for improving management and conservation of fish and potentially other populations.