Prioritizing spatially aggregated cost-effective sites in natural reserves to mitigate human-induced threats

In this study, we illustrate a spatially explicit prioritization framework that integrates cost effectiveness analysis (CEA) and spatial clustering statistics to help stakeholders identify threat mitigation actions that are both spatially aggregated and cost-effective. This framework estimates expected biodiversity benefits in alternative sites, and evaluates associated costs using non-monetized proxy. We compared local autocorrelation-based clustering statistics, including local Moran’s <i>I</i>, Getis-Ord Gi*, and AMOEBA, to quantify the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced more cost-effective threat mitigation sites, but these sites may be dispersed in space. Spatial clustering methods could identify spatially contiguous management sites with only minor loss in cost effectiveness. These spatially contiguous sites substantially contribute to reducing actual costs related to given biodiversity benefits. Integrating CEA with spatial clustering statistics provides stakeholders with solid support for prioritizing threat management actions that are both cost-effective and aggregated in space.