Mapping ecosystem service clusters and exploring their driving mechanisms in karst peak-cluster depression regions in China
•Trade-offs and synergies between ecosystem services were evaluated.•Self-organizing map was used to identify ecosystem services bundles.•Ecosystem service interrelationships are influenced by natural and social factors.•Our findings can form the basis of ecological environmental management policies...
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Published in | Ecological indicators Vol. 158; p. 111524 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.01.2024
Elsevier |
Subjects | |
Online Access | Get full text |
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Abstract | •Trade-offs and synergies between ecosystem services were evaluated.•Self-organizing map was used to identify ecosystem services bundles.•Ecosystem service interrelationships are influenced by natural and social factors.•Our findings can form the basis of ecological environmental management policies.
Understanding the intricate interrelationships among ecosystem services (ESs) and exploring their driving mechanisms are prerequisites for their efficient management. The southwestern karst region is a typical ecologically fragile area and a crucial component for establishing an ecological security barrier in southern China. We aimed to evaluate the relationship between ESs and the concept of ecosystem service bundles (ESBs) and explore the distribution of ESs and their interrelationships. Using methods, such as Spearman's correlation and spatial autocorrelation analysis, this study focused on the karst peak-cluster depression region in southwestern China to analyse the trade-offs and synergistic effects among 12 ESs. Additionally, this study used the self-organizing map to identify ESBs and used the Random Forest model to explore the potential driving factors influencing the distribution patterns of ESs. The results indicated a spatial clustering effect among the 12 ESs, whereby provisioning and cultural services had the most significant clustering effect (Moran's I > 0.8). A total of 25 pairs of ESs exhibited synergistic effects, and 31 pairs showed trade-off effects. Food production showed a trade-off with both regulatory and supporting services, whereas ESs within the same category tended to exhibit synergistic effects. The distribution and interrelationships of ESs are influenced by natural and social factors, such as climate, topography, and human activities, resulting in the formation of six ESBs. This study enriches the understanding of the relationship of ESs and will contribute to the coordinated development of resources and the environment in the karst peak-cluster depression region of southwestern China. |
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AbstractList | Understanding the intricate interrelationships among ecosystem services (ESs) and exploring their driving mechanisms are prerequisites for their efficient management. The southwestern karst region is a typical ecologically fragile area and a crucial component for establishing an ecological security barrier in southern China. We aimed to evaluate the relationship between ESs and the concept of ecosystem service bundles (ESBs) and explore the distribution of ESs and their interrelationships. Using methods, such as Spearman's correlation and spatial autocorrelation analysis, this study focused on the karst peak-cluster depression region in southwestern China to analyse the trade-offs and synergistic effects among 12 ESs. Additionally, this study used the self-organizing map to identify ESBs and used the Random Forest model to explore the potential driving factors influencing the distribution patterns of ESs. The results indicated a spatial clustering effect among the 12 ESs, whereby provisioning and cultural services had the most significant clustering effect (Moran's I > 0.8). A total of 25 pairs of ESs exhibited synergistic effects, and 31 pairs showed trade-off effects. Food production showed a trade-off with both regulatory and supporting services, whereas ESs within the same category tended to exhibit synergistic effects. The distribution and interrelationships of ESs are influenced by natural and social factors, such as climate, topography, and human activities, resulting in the formation of six ESBs. This study enriches the understanding of the relationship of ESs and will contribute to the coordinated development of resources and the environment in the karst peak-cluster depression region of southwestern China. •Trade-offs and synergies between ecosystem services were evaluated.•Self-organizing map was used to identify ecosystem services bundles.•Ecosystem service interrelationships are influenced by natural and social factors.•Our findings can form the basis of ecological environmental management policies. Understanding the intricate interrelationships among ecosystem services (ESs) and exploring their driving mechanisms are prerequisites for their efficient management. The southwestern karst region is a typical ecologically fragile area and a crucial component for establishing an ecological security barrier in southern China. We aimed to evaluate the relationship between ESs and the concept of ecosystem service bundles (ESBs) and explore the distribution of ESs and their interrelationships. Using methods, such as Spearman's correlation and spatial autocorrelation analysis, this study focused on the karst peak-cluster depression region in southwestern China to analyse the trade-offs and synergistic effects among 12 ESs. Additionally, this study used the self-organizing map to identify ESBs and used the Random Forest model to explore the potential driving factors influencing the distribution patterns of ESs. The results indicated a spatial clustering effect among the 12 ESs, whereby provisioning and cultural services had the most significant clustering effect (Moran's I > 0.8). A total of 25 pairs of ESs exhibited synergistic effects, and 31 pairs showed trade-off effects. Food production showed a trade-off with both regulatory and supporting services, whereas ESs within the same category tended to exhibit synergistic effects. The distribution and interrelationships of ESs are influenced by natural and social factors, such as climate, topography, and human activities, resulting in the formation of six ESBs. This study enriches the understanding of the relationship of ESs and will contribute to the coordinated development of resources and the environment in the karst peak-cluster depression region of southwestern China. Understanding the intricate interrelationships among ecosystem services (ESs) and exploring their driving mechanisms are prerequisites for their efficient management. The southwestern karst region is a typical ecologically fragile area and a crucial component for establishing an ecological security barrier in southern China. We aimed to evaluate the relationship between ESs and the concept of ecosystem service bundles (ESBs) and explore the distribution of ESs and their interrelationships. Using methods, such as Spearman's correlation and spatial autocorrelation analysis, this study focused on the karst peak-cluster depression region in southwestern China to analyse the trade-offs and synergistic effects among 12 ESs. Additionally, this study used the self-organizing map to identify ESBs and used the Random Forest model to explore the potential driving factors influencing the distribution patterns of ESs. The results indicated a spatial clustering effect among the 12 ESs, whereby provisioning and cultural services had the most significant clustering effect (Moran's I > 0.8). A total of 25 pairs of ESs exhibited synergistic effects, and 31 pairs showed trade-off effects. Food production showed a trade-off with both regulatory and supporting services, whereas ESs within the same category tended to exhibit synergistic effects. The distribution and interrelationships of ESs are influenced by natural and social factors, such as climate, topography, and human activities, resulting in the formation of six ESBs. This study enriches the understanding of the relationship of ESs and will contribute to the coordinated development of resources and the environment in the karst peak-cluster depression region of southwestern China. |
ArticleNumber | 111524 |
Author | Yuan, Chuan Xie, Jing Zhang, Xuemao Xie, Binggeng Li, Junhan Zhou, Kaichun Xiao, Jianyong |
Author_xml | – sequence: 1 givenname: Xuemao surname: Zhang fullname: Zhang, Xuemao email: xmzhang0217@163.com organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China – sequence: 2 givenname: Binggeng surname: Xie fullname: Xie, Binggeng email: xbgyb1961@163.com organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China – sequence: 3 givenname: Kaichun surname: Zhou fullname: Zhou, Kaichun email: zhoukaichun@hunnu.edu.cn organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China – sequence: 4 givenname: Junhan surname: Li fullname: Li, Junhan email: junhanl@163.com organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China – sequence: 5 givenname: Chuan surname: Yuan fullname: Yuan, Chuan email: yuanchuan01zg@163.com organization: School of Life Sciences, Southwest University, Chongqing 400715, China – sequence: 6 givenname: Jianyong surname: Xiao fullname: Xiao, Jianyong email: converxiao@126.com organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China – sequence: 7 givenname: Jing surname: Xie fullname: Xie, Jing email: jingxie3861@163.com organization: School of Geographic Sciences, Hunan Normal University, Changsha 410081, China |
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Snippet | •Trade-offs and synergies between ecosystem services were evaluated.•Self-organizing map was used to identify ecosystem services bundles.•Ecosystem service... Understanding the intricate interrelationships among ecosystem services (ESs) and exploring their driving mechanisms are prerequisites for their efficient... |
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SubjectTerms | algorithms autocorrelation China climate Ecosystem service bundles Ecosystem service management ecosystem services ecosystems food production humans karsts Random Forest model topography Trade-offs and synergies |
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Title | Mapping ecosystem service clusters and exploring their driving mechanisms in karst peak-cluster depression regions in China |
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