Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China.
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China.
J Environ Manage. 2023 Oct 15;344:118400. doi: 10.1016/j.jenvman.2023.118400. Epub 2023 Jun 16.
Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.
人口增长和相关的生态空间占用对区域生态安全和社会稳定构成了巨大威胁。在中国，“生态保护红线”（ECR）作为一项国家政策被提出，以解决空间不匹配和管理矛盾，禁止城市化和工业建设。然而，在 ECR 内仍然存在不友好的人类干扰活动（如耕种、采矿和基础设施建设），对生态稳定和安全构成了巨大威胁。在本文中，提出了一种贝叶斯网络（BN）-GIS 概率模型，以空间和定量的方式解决区域尺度上 ECR 面临的人类干扰风险。贝叶斯模型整合了多种人类活动、ECR 的生态受体及其暴露关系，用于计算人类干扰风险。然后引入案例学习方法地理信息系统（GIS），根据变量的空间属性训练 BN 模型，以评估风险的空间分布和相关性。该方法应用于中国江苏省 2018 年划定的 ECR 人类干扰风险评估。结果表明，大多数 ECR 处于低或中人类干扰风险水平，而连云港市的一些饮用水源和森林公园则处于最高风险。敏感性分析结果表明 ECR 脆弱性，特别是对耕地的脆弱性，对人类干扰风险的贡献最大。这种空间概率方法不仅可以提高模型的预测精度，还有助于决策者确定如何确定政策设计和保护干预的优先次序。总的来说，它为后来的 ECR 调整以及区域尺度上的人类干扰风险监督和管理奠定了基础。
