Renewable resource management with environmental prediction

Variations in environmental conditions affect renewable resource growth. The ability to predict such variations is improving, providing scope for improved management. We generalize a common stochastic stock recruitment model to explore how optimal management changes with environmental prediction. We obtain three main results. First, while it might seem that a prediction of adverse future conditions should lead to more conservative management, the opposite may be true. Second, optimal management requires only a one-period-ahead forecast, suggesting Forecast accuracy is more important than forecast lead time. Finally, we derive conditions on environmental fluctuations guaranteeing positive optimal harvest in every period.