Learning about colonization when managing metapopulations under an adaptive management framework

Abstract

Adaptive management is a framework for resolving key uncertainties while managing complex ecological systems. Its use has been prominent in fisheries research and wildlife harvesting; however, its application to other areas of environmental management remains somewhat limited. Indeed, adaptive management has not been used to guide and inform metapopulation restoration, despite considerable uncertainty surrounding such actions. In this study, we determined how best to learn about the colonization rate when managing metapopulations under an adaptive management framework. We developed a mainland-island metapopulation model based on the threatened bay checkerspot butterfly (Euphydryas editha bayensis) and assessed three management approaches: adding new patches, adding area to existing patches, and doing nothing. Using stochastic dynamic programming, we found the optimal passive and active adaptive management strategies by monitoring colonization of vacant patches. Under a passive adaptive strategy, increasing patch area was best when the expected colonization rate was below a threshold; otherwise, adding new patches was optimal. Under an active adaptive strategy, it was best to add patches only when we were reasonably confident that the colonization rate was high. This research provides a framework for managing mainland-island metapopulations in the face of uncertainty while learning about the dynamics of these complex systems.

Optimal timing of biodiversity offsetting for metapopulations

“…For example, managers could delay management actions while compensation funds accrue interest but still learn about the colonization and extinction dynamics of a metapopulation by observing the turn-over rate of patches (Southwell et al 2016). Waiting may provide managers with the opportunity to resolve key uncertainties in metapopulation dynamics or restoration strategies that influence management decisions.…”

“…Waiting may provide managers with the opportunity to resolve key uncertainties in metapopulation dynamics or restoration strategies that influence management decisions. For example, managers could delay management actions while compensation funds accrue interest but still learn about the colonization and extinction dynamics of a metapopulation by observing the turn-over rate of patches (Southwell et al 2016). This would allow managers to update their estimates of the colonization and extinction parameters in the metapopulation model component of our framework, which would lead to more effective allocation of invested compensation funds.…”

Using Fourier series to estimate periodic patterns in dynamic occupancy models

“…In regard to application, PTMs can offer more actionable information compared to STMs or HTMs. For example, to optimize the management of metapopulations, it is critical to know when colonization rates are high, as doing so indicates when new habitat patches should be added (Southwell et al 2016). While STMs do account for temporal variability within a species' colonization rate, they cannot predict when colonization rates should be high (Fig.…”

State-Dependent Resource Harvesting with Lagged Information about System States

“…This is typically not true if there are more than a few alternative models because model state has to be carried along with system state in the optimization. Fortunately, we have found passive adaptive optimization to be a good approximation of the active form, often providing only slightly lower objective values [ 50 , 51 ].…”