喀斯特
复杂网络
恢复生态学
偏移量(计算机科学)
生态学
功能生态学
生态网络
生态系统服务
计算机科学
环境科学
环境资源管理
地理
生物
生态系统
万维网
考古
程序设计语言
作者
Kexin Huang,Peng Li,Xiaohui Wang,Wei Deng,Ying Liu
标识
DOI:10.1016/j.jclepro.2022.135512
摘要
Ecological networks (ENs) are important for maintaining regional ecological security and improving ecosystem service capacity. Therefore, it is important to consider spatial topological relationships and the need for carbon neutrality in the optimization of ENs. The optimization of EN structures and functions should be a systematic multi-objective process. This study combined landscape ecology, the complex network model, and spatial analysis technology, proposing an ecological barrier–topological feature–carbon offset-based (BTC-based) model research framework to deeply analyze and optimize ENs. First, the EN was preliminarily identified using the circuit theory model and the ecological barrier areas were simulated. Then, based on the complex network model, an undirected and unweighted complex network was established and its topological structural characteristics were analyzed. By constructing the estimation model for the carbon offset rate, its spatial distribution characteristics were calculated and interpreted. Finally, the BTC-based model was used to optimize the EN. Based on this model, Guizhou Province (a typical karst region in China) was taken as the research area for this study. The results showed that ecological improvement and degradation coexisted, and measures must be taken to optimize the EN. Based on the 2018 EN, the areas that required optimization were identified. Additionally, a restoration strategy was proposed for edge addition optimization for ecological corridors to promote the ecological protection and quality improvement of ecological sources and corridors. This approach would ensure the accurate implementation of ecological restoration projects and the effective management of ecosystems. The model proposed in this study also provides methodological support for EN optimization in other types of ecological regions.
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