Linking ecosystem service supply and demand to landscape ecological risk for adaptive management: The Qinghai-Tibet Plateau case

生态系统服务 适应性管理 环境资源管理 风险管理 风险评估 生态学 地理 生态系统 环境科学 业务 计算机科学 财务 计算机安全 生物
作者
Yuanxin Liu,Mingyue Zhao
出处
期刊:Ecological Indicators [Elsevier]
卷期号:146: 109796-109796 被引量:9
标识
DOI:10.1016/j.ecolind.2022.109796
摘要

Clarifying the spatiotemporal variations in ecosystem service (ES) supply and demand helps to understand natural-social coupled systems, and comprehensive landscape ecological risk (LER) assessment is the basis for risk warning. However, it is still a huge challenge to incorporate ES supply and demand into ecological adaptive management. In this study, we defined and identified ES supply and demand risk (ESSDR), and integrated it into LER assessment to develop a comprehensive ecological risk framework. Using InVEST model and multi-source data, this study explicitly quantified the spatiotemporal variations of ESSDR of soil retention (ESSDRI_SR), carbon sequestration (ESSDRI_CS), water yield (ESSDRI_WY), LER of Qinghai Province in the Qinghai-Tibet Plateau during 2010–2020. The results indicated that all ESSDRs and LER showed spatial heterogeneity. Among the ESSDR areas, the low risk areas accounted for the highest proportion, with ESSDRI_CS, ESSDRI_SR and ESSDRI_WY accounting for 4.83%, 14.84% and 12.45%, respectively. The area of very high and high LER decreased by 1.5% and 5.45% from 2010 to 2020, reaching 19.05% and 22.74%, respectively. The comprehensive ecological risk assessment showed that over 60% of Qinghai is designated as having ecological risks. However, the region with the most risk co-occurrence (risk group 4) accounted for 0.11% of Qinghai's area. At last, adaptive suggestions were proposed for risk management and ecological conservation. This research provides and illustrates an innovative method for comprehensive ecological risk assessment, which could substantially enhance the scientific foundation on which ecological risk assessment is based and policy-making that follow compared to traditional LER framework.
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