Exploring ecosystem services trade-offs using the Bayesian belief network model for ecological restoration decision-making: A case study in Guizhou Province, China

Abstract

• A Bayesian belief network (BBN) model is developed to determine the driving factors regarding trade-off changes. • The multi-scenario scheme of ES optimization is obtained by visualizing the key state of key variables. • Suggestions for ecological restoration were given, and a good way is to adjust the combination of the key state of key variables. The relationships between trade-off and synergy in ecosystem services (ESs) is essential for eco-environmental management and restoration in Karst regions, and many researchers have focused heavily on this. However, to date, and to the best of our knowledge, we still lack an adequate understanding of the driving factors of relationship changes and the optimization of the spatial pattern of ESs in this region. In this study, a Bayesian belief network (BBN) model was developed to link three major ESs—net primary productivity (NPP), water yield (WY), and soil conservation (SC)—and determine the driving factors regarding trade-off changes. Furthermore, the multi-scenario scheme of ES optimization was obtained and suggestions are provided in this paper for ecological restoration and decision-making in Guizhou Province, China. The results revealed that the relationships of regional ESs were mainly synergistic, but the synergistic relationship was weakening, accompanied by a trade-off trend during the study period. The area ratios with an enhancement of trade-offs in NPP and SC, SC and WY, and NPP and WY were 17.9%, 28.6%, and 26.6%, respectively, which indicates a significant uncoordinated changes between WY and the two other ESs. The ES relationships in this region are underpinned by multiple factors. Among them, water resources are the primary natural factor that restricts the concerted development of ESs, and the afforestation area (AA) is the main human factor that lowers the trade-offs among ESs. The superiority and distribution scope of ecological restoration were recognized through a multi-scenario analysis of the BBN model. In terms of the superiority of ecological restoration, it was concluded that scenario 3 > scenario 2 > scenario 1; in terms of the restoration area, it was scenario 1 > scenario 2 > scenario 3. Duyun City, in the south of Guizhou Province, as well as surrounding counties, were regions with a high probability of trade-off occurrences, and they could be regions for future ES optimization. Based on the driving factors and the occurrence probability of trade-offs, the ecological restoration area was identified, and it was found the ESs trade-off in this region can be decreased by adjusting a combination of the key variables’ key states. This study provides insight towards achieving a mechanistic understanding and spatial expression of the driving forces underpinning trade-offs/synergies, in turn ensuring the accurate implementation of ecological restoration and the effective management of karst ecosystems.