整合电路理论和网络建模,识别生态系统固碳服务流网络

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Chen Qu , Jia Xu , Wen Li , Yucen Zhai , Yiting Wang , Baozhu Liu , Shaoning Yan
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引用次数: 0

摘要

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Integrating circuit theory and network modeling to identify ecosystem carbon sequestration service flow networks
Current methods for mapping ecosystem service flows often fail to accurately capture the diverse biogeographical environments associated with these service flows when visualizing the structural features of ecosystem service flow networks. Taking the provinces of Liaoning, Jilin, and Heilongjiang in China as examples, we combined circuit theory and network model approaches to map ecosystem carbon sequestration service flow networks. We examined how network structure influences differences in the supply and demand for carbon sequestration services. Combining circuit theory and network models can be used to effectively map the flow of carbon sequestration services in ecosystems, showcasing its ability to represent these processes. From 2000 to 2020, the disparity between the supply and demand of carbon sequestration services has consistently grown, accompanied by a growing spatial imbalance in the distribution of supply and demand areas. The supply sources of carbon sequestration services have significantly declined while the demand sources have steadily increased. The length of the carbon flow corridors decreased sharply before stabilizing. There has been a continuous increase in the number of deficit nodes and disrupted edges within carbon sequestration service flow networks. The response of carbon sequestration services to landscape patterns and network topology indicators showed a nonlinear relationship, exhibiting a threshold effect. The findings have provided strategic insights for allocating and managing carbon resources at the regional level.
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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