Linking Empirical Modeling of Recreational Angler Behavior with Population Dynamics

Despite the growing impact of recreational fishing mortality on the health of many fish stocks, the integration of economic models of recreational angler behavior with population biological models of fish stocks has seen little progress.  While recreational demand models have been extensively applied to recreational fisheries, they have rarely been successfully integrated with biological models to evaluate the biological and economic costs and benefits of policy changes.  Building models that span the linkages between policy instruments, angler behavior and welfare, and fish mortality is essential for assessing the impacts of changes in management – particularly as real-world interest in adapting incentive-based instruments designed for commercial fisheries to the recreational context increases.

To address these gaps, we develop a bioeconomic model that explicitly models anglers’ discrete decisions of whether and where to fish along with the continuous decision of how much to fish using a consistent economic framework.  This approach, commonly known as the Kuhn-Tucker (KT) model in the econometric literature, avoids many of the awkward assumptions associated with the merging of discrete-choice demand models with population biological models.  We demonstrate the empirical potential of our framework by combining estimates of the KT demand model with calibrated biomass-dynamic equations to simulate the effects of alternative policy instruments for the management of Lake Michigan fisheries for lake trout and chinook salmon.  We demonstrate that capturing the feedbacks between fish stocks and regulations and adaptive angler behavior in response to these stimuli is important to accurately predicting the status of stocks subject to substantial recreational fishing mortality.