摘要:社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具，能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足；（2）应集成多种情景模拟和景观效应分析方法，完善现有SES模型的理论方法基础；（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能，具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用，能更好地支持跨学科集成模型开发与应用。



Abstract:Social-ecological system (SES) simulation model can be used as an effective tool for integrating landscape patterns and policy research in interdisciplinary fields, and it also can address complex feedback mechanism between social-ecological effects of landscape patterns and relevant landscape-based policy decision. The research progress of SES models was analyzed in the field of forest-agriculture landscape patterns, using bibliometric analysis and literature review (including 3091 papers from dataset of Web of Science) for identifying integrated research trend of the social-ecological methods from 2000 to 2019. The main conclusions are as follow: 1) A conceptual framework was developed for integrating forest-agriculture landscape patterns and social-ecological processes. Using this framework, we found that most of existing models in cases were insufficient to analyze the complex feedback relationship between landscape processes and socioeconomic decision, and the major type in this feedbacks was one-way impacts of land use on its ecological consequences. 2) A comparison of typical categories in social-ecological models was conducted by using literature review, based on dimensions of feedbacks, landscape, spatiotemporal scale, uncertainty and complexity. We address that there is an urgent need to develop diverse methods of scenario-based simulation and integrated landscape effects, for improving theoretical and methodological knowledge of 4 SES models, including empirical statistical, system simulation, pattern optimization, and agent-based model (ABM). Specially, the ABM model have better performance to reflecting socio-ecological feedback for its capability of bottom-up modeling and making-decision functions. 3) We provided a framework for elaborating complex conceptual links between potential pathways in social-ecological system models. Base on the framework, we suggested that the SES models can be promoted by several available pathways, such as, developing scenario-based simulation, improving the theoretical fundaments of decision making in changes of landscape patterns, strengthening model uncertainty analysis, reducing model complexity and integrating qualitative and quantitative data. Especially, we addressed that the integration of pattern optimization model and agent-based model should be given to a priority level for development of integrated modeling research, as both models can be used to synthesize the characterization of major socio-ecosystem processes by using spatiotemporal and explicitly spatially methods. The results of this study is useful for generating a comprehensive understanding for the role of forest-agriculture landscape pattern in complex social-ecological systems at multi-scales. Furtherly, the inductive discussion and conclusions can be used to support the development and application of integrated social-ecological models in related emerging interdisciplinary fields, such as sustainability science, ecological economics and land system science.





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