Adaptive management has been advocated as a framework for resolving key uncertainties while managing complex ecological systems. It has been applied to many areas of ecology and conservation biology, particularly fisheries research and wildlife harvesting; however, few examples could be found in the literature where adaptive management has guided the management of metapopulations. In this study, we determined the value of learning about the colonization rate when managing a metapopulation under an adaptive management framework. The colonization rate of many species is difficult to measure and rarely known with certainty. As metapopulation dynamics are strongly dependent on colonization rate in most situations, uncertainty about this process can lead to considerable uncertainty about the optimal management approach. We developed a metapopulation model for the endangered growling grass frog (Litoria raniformis) in outer Melbourne, Australia and assessed three management approaches: adding new patches, adding area to existing patches and doing nothing. We use stochastic dynamic programming to find the optimal passive and active adaptive management strategy for this species with the goal of maximizing the long term persistence while learning about the colonization rate.
Results/Conclusions
The optimal management strategy for L. raniformis was found to be sensitive to the state of the metapopulation and belief in the colonization rate. Under a passive adaptive management strategy there was a threshold in the expected colonization rate, below which it was optimal to increase patch areas. Under an active adaptive management strategy, where management is implemented with the goal of resolving uncertainty to improve future management, it was optimal to add patches only when we were confident that the colonization rate is high. Optimizing current and future management of metapopulations is a key topic for ecology and conservation biology, because habitat fragmentation remains a significant threat to biodiversity around the globe. This research provides a framework for managing metapopulations in the face of uncertainty while learning about of the dynamics of these complex ecological systems.