The complexity and uncertainty of land use and environmental factors
pose challenges to the management decisions of ecological restoration
and conservation.We integrated the mixed-cell CA model and Bayesian
belief networks to develop an innovative method for optimizing ecosystem
services under different land development scenarios, including
consideration of the uncertainty and variability of factors.The southern
region of Sichuan Province, China, was selected as an example.We first
established three development scenarios between 2015 and 2035, namely,
natural development scenario (NDS), ecological protection scenario
(EPS), and cultivated land protection scenario (CLPS).The MCCA model was
utilized to simulate the land use pattern in 2035 under different
scenarios.We then construced a BBN-based model to investigate the carbon
sequestration, grain supply, soil conservation, habitat quality, and
water yield in 2035 under uncertain scenarios.After the sensitivity
analysis and evaluation of the model, we determined the state
combination of influential factors at various ecosystem service levels
and the ecological restoration and conservation key areas.The obtained
result showed that the key influencing factors impacting the ecosystem
services level included NPP, Slope, forestland and ET, and the state
combination corresponding to the highest level of ecosystem services was
predominantly distributed in regions with the highest NPP, the highest
Slope, the highest forestland area and low ET.Based on this finding, we
proposed some suggestions for ecological restoration and conservation of
key areas.This model considers uncertainties and is capable of providing
scientific recommendations on restoration and conservation; therefore,
it can serve as an effective tool to assist stakeholders in making
decisions.
Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips
