Structural Modeling of Marine Reserves with Bayesian Estimation

Structural models can assess the effectiveness of fishery management prospectively and retrospectively. However, when only fishery-dependent data are available, structural econometric models are highly nonlinear in the parameters, and maximum likelihood and other extremum-based estimators can fail to converge. As a solution to these estimation challenges, we adapt Bayesian econometric methods to estimate a dynamic structural model of marine reserve formation. Using simulated data, we find that our approach is able to recover structural biological and economic parameters that classical estimation procedures fail to recover. We apply the approach to real data from the Gulf of Mexico reef-fish fishery. We test the effects of the Steamboat Lumps Marine Reserve on population growth and catchability for gag, a species of grouper. We find that after four years, the reserve has neither produced statistically significant losses in sustainable yield nor statistically significant gains in biological production.