The effective management of natural resource populations such as fisheries or forests and the ecosystems in which they are embedded is a central goal of much work in both theoretical and applied ecological research. Management decisions must be made in an uncertain world, coping with errors in data, uncertainty in model parameters and even the choice of models used. Advances in areas such as risk management, optimization under uncertainty and adaptive management methods have offered a way forward in spite of such uncertainty. We have only to admit what we don’t know, and make the best decision based on the information at hand.
The realization that such complex systems can contain tipping points — thresholds at which the system can transition into a less desirable state rapidly and with little warning — poses a fundamental challenge to these approaches. Such approaches require that we know what we don’t know: We don’t use the best guess of a parameter; instead, we have a distribution of possible values it may take. As recent events in the economy have illustrated, estimating the distribution isn’t easy, and for complex systems with alternate stable states, a little bit more uncertainty can make a big difference. How then do we make the best decision based on the information available without exposing the system to these sudden crashes? How do we handle that uncertainty we just cannot parameterize — the unknown unknowns?
We combine methods from optimal control and stochastic dynamic programming with methods from ecological resilience and early warning signals to study both simulated and empirical examples of these systems.
