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DOI10.1016/j.ecolind.2022.108569
Exploring ecosystem services trade-offs using the Bayesian belief network model for ecological restoration decision-making: A case study in Guizhou Province, China
Peng, Li; Chen, Tiantian; Deng, Wei; Liu, Ying
通讯作者Chen, TT
来源期刊ECOLOGICAL INDICATORS
ISSN1470-160X
EISSN1872-7034
出版年2022
卷号135
英文摘要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 tradeoffs/synergies, in turn ensuring the accurate implementation of ecological restoration and the effective management of karst ecosystems.
英文关键词Ecosystem services (ESs) Trade-off/synergy Driving factors Bayesian belief network (BBN) model Ecological restoration
类型Article
语种英语
开放获取类型gold
收录类别SCI-E ; SSCI
WOS记录号WOS:000761353400001
WOS关键词KARST ROCKY DESERTIFICATION ; GREEN PROGRAM ; SYNERGIES ; DYNAMICS ; IMPACT ; PROJECTS ; BUNDLES ; GROWTH ; GRAIN
WOS类目Biodiversity Conservation ; Environmental Sciences
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/392299
推荐引用方式
GB/T 7714
Peng, Li,Chen, Tiantian,Deng, Wei,et al. Exploring ecosystem services trade-offs using the Bayesian belief network model for ecological restoration decision-making: A case study in Guizhou Province, China[J],2022,135.
APA Peng, Li,Chen, Tiantian,Deng, Wei,&Liu, Ying.(2022).Exploring ecosystem services trade-offs using the Bayesian belief network model for ecological restoration decision-making: A case study in Guizhou Province, China.ECOLOGICAL INDICATORS,135.
MLA Peng, Li,et al."Exploring ecosystem services trade-offs using the Bayesian belief network model for ecological restoration decision-making: A case study in Guizhou Province, China".ECOLOGICAL INDICATORS 135(2022).
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