Anticipating adaptation: a mechanistic approach for linking policy and stock status to recreational angler behavior

Abstract

We use techniques from economic recreation demand modeling to develop a mechanistic model of individual recreational fishing behavior and estimate it using license-frame survey data. By consistently integrating individuals’ seasonal decisions of where, whether, and how much to fish, the model generates predictions of aggregate indicators such as angler-days and fishing mortality as phenomena arising from individual behavior. We use the model to simulate alternative future scenarios by altering policy variables or measures of fishing quality, such as catch rates. The mechanistic nature of the model incorporates anglers’ adaptive behavior to these stimuli, generating scenarios that are likely more robust to shifts in the decision context than many commonly used phenomenological models. We utilize the model to examine the sensitivity of total catch and catch per unit effort (CPUE) to changes in fish stocks, revealing substantial nonlinearities in this relationship. We also simulate total fishing trips, participation, CPUE, and total catch for a seasonal fishing permit versus a per-trip fee, finding dramatic differences across the two policies that call into question the wisdom of permit fees as management tools.