数据驱动的社会-经济-自然复合生态系统研究:尺度、过程及其决策关联

社会-经济-自然系统是人类赖以生存和发展的复合系统,而数据驱动下的系统研究为加强生态系统的认知提供了新的增值导向。在新的数据语境下,社会-经济-自然复合系统呈现出一些新的特征,研究对象逐渐从单一要素向多要素耦合的方向转变,使支撑量化研究的数据体系更多样化、数据来源更广泛化、数据表达更具可视化,并呈现出研究尺度逐渐扩大化、研究对象更精细化的特征。在对数据的识别、表达和可视化的过程中,既要加强对时间、空间、结构、数量和秩序的耦合,也要注重与决策制定和地方服务的结合。新时期复合生态系统的未来研究方向应该从关键科学问题及支撑技术、尺度作用和多要素耦合以及地方和全球治理的科技支撑等方面展开,在数据的不断革新下,加强对多源数据、长期监测和时间序列的认知仍是需要深入研究的课题。开展复合生态系统的数据驱动分析,不仅能为生态系统的服务及可持续发展提供技术支撑,增强数据的长效共享机制,同时可为实现决策制定和信息传播等方面提供更多的价值支持。

Data-driven study of complex socio-economic-natural ecosystems: Scales,processes and decision linkages

The social-economic-natural system is a complex system for human survival and development, and the data-driven system research provides a new value-added orientation to enhance the cognition of the ecosystem. Under the new data context, the social-economic-natural complex system shows new features. The research object is gradually changing from a single element to a multi-factor coupling direction, which makes the data system more diversified, data sources more extensive, data expression more visualized. The research scale shows the characteristics of gradually expanding, and the research object would be more detailed. In the process of data identification, expression and visualization, it is therefore necessary to strengthen the coupling of time, space, structure, quantity and order, as well as to focus on the integration with decision making and local services. The future research of complex ecosystems in the new era should be carried out in terms of key scientific issues and supporting technologies, the role of scale and multi-factor coupling, as well as scientific and technological support for local and global governance. Under the continuous innovation of data, strengthening the cognition of multi-source data, long-term monitoring and time series still needs to be studied in depth. Carrying out data-driven analysis of complex ecosystems not only provides technical support for ecosystem services and sustainable development and enhances the long-term data sharing mechanism, but also provides more value support for realizing decision making and information dissemination.