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, 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.

本研究提出了一个空间显性优先规划框架，该框架集成了成本效益分析（Cost Effectiveness Analysis, CEA）与空间聚类统计方法，以帮助利益相关方识别兼具空间集聚性与成本效益的威胁缓解措施。该框架可估算备选站点的预期生物多样性收益，并采用非货币化替代指标评估相关成本。我们对比了基于局部自相关的聚类统计量，包括局部莫兰指数（local Moran’s I）、盖蒂斯-奥德Gi*（Getis-Ord Gi*）以及AMOEBA，以量化给定预算下已识别站点的空间集聚程度。研究发现，CEA方法可生成成本效益更优的威胁缓解站点，但此类站点在空间上往往呈离散分布。空间聚类方法则可识别出空间连续的管理区域，仅会造成轻微的成本效益损失。此类空间连续的管理区域可大幅降低与既定生物多样性收益相关的实际成本。将CEA与空间聚类统计方法相结合，可为利益相关方优先开展兼具成本效益与空间集聚性的威胁管理行动提供坚实支撑。