Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Abstract An ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies.