Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China

Jianxin Yang, Jian Gong and Wenwu Tang
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Jianxin Yang: Department of Land Resource Management, School of Public Administration, China University of Geosciences (Wuhan), 388 Lumo Road, Hongshan District, Wuhan 430074, China
Jian Gong: Department of Land Resource Management, School of Public Administration, China University of Geosciences (Wuhan), 388 Lumo Road, Hongshan District, Wuhan 430074, China
Wenwu Tang: Center for Applied Geographic Information Science, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Sustainability, 2019, vol. 11, issue 5, 1-23

Abstract: Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I , Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.

Keywords: spatial prioritization; threats; biodiversity benefits; land use; spatial clustering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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