[Multi-disciplinary knowledge integration and its technical approaches in the integrated ecology of macroecosystem science].
摘要
The development of contemporary macroecosystem sciences requires to comprehensively understand the process mechanism and model mechanism of large-scale macroecosystem structure and function, spatial variation, and dynamic evolution, to realize quantitative simulation, scientific assessment, prediction and early warning of ecosystem change and its impacts on human well-being, and to serve the utilization, protection, regulation, and management of ecosystems. Therefore, a new research field of large-scale integrated ecology of macroecosystem science (IEMES) is emerging. Based on the systematic analysis of the basic theories, approaches and key technologies of integrated ecology of macroecosystem science, the following basic understandings have been formed: 1) IEMES takes macroecosystem at regional, continental, and global scales as the research object, and adopts the methods and technologies of multidisciplinary knowledge integration. It is aimed to solve the major resource and environmental problems during the development of human society, such as food security, resource security, ecological security, and environmental security. 2) The basic scientific and technological tasks of IEMES are to understand the basic structural and functional properties of macroecosystem, monitor the changes of ecosystem state, explain the spatiotemporal evolution of ecosystem, uncover the mechanism of ecosystem operation and maintenance process, quantitatively evaluate the functional state and service capacity of ecosystem, predict the dynamic evolution and geographical pattern of ecosystem, provide early warning of ecosystem changes and ecological environmental disasters. 3) It is needed to reconstruct the theory and methodology system of "multi-source data analysis, multi-model simulation, multi-disciplinary knowledge fusion" and develop key technology of multi-disciplinary knowledge fusion of "multi-scale observation, multi-method verification, multi-process fusion, and cross-scale si