Implementing source-sink models for management recommendations

In applied conservation and land management, decisions must often be made concerning what resources to improve, what resources to let go, or where restoration efforts should be targeted. Such decisions are ideally based on empirical data, however considerable challenges emerge when population dynamics are variable in space and time. In this study, we demonstrate how Monte Carlo simulation modeling can be used to incorporate temporal variation in spatially structured population dynamics to identify the most critical habitat patches, the most expendable habitat patches, and regions of the landscape that, if restored, will likely provide the greatest benefit to the metapopulation.