Analysis of Ecosystem Service Contribution and Identification of Trade-Off/Synergy Relationship for Ecosystem Regulation in the Dabie Mountains of Western Anhui Province, China
Muyi Huang,
Qilong Wang,
Qi Yin (),
Weihua Li,
Guozhao Zhang,
Qiaojun Ke and
Qin Guo
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Muyi Huang: School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
Qilong Wang: College of Management, Sichuan Agricultural University, Chengdu 611130, China
Qi Yin: College of Management, Sichuan Agricultural University, Chengdu 611130, China
Weihua Li: School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
Guozhao Zhang: School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
Qiaojun Ke: School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
Qin Guo: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230601, China
Land, 2023, vol. 12, issue 5, 1-22
Abstract:
The study of tradeoffs/synergies among ecosystem services (ESs) is highly significant for land-use planning and regional ecosystem optimization. Land-use change and topographic factors have important implications for ESs. Strengthening the comparative analysis of the capacity of ESs provided by different land-use types in specific regions, studying the topographic gradient effects of ecosystem service trade-offs/synergies with slope changes, and identifying the dominate trade-off/synergy relationship among ESs will help us to carry out ecosystem regulation according to local conditions through land-use layout optimization at a fine scale. Our research site was located in the Dabie Mountains of western Anhui Province, China (DBM), where, based on the InVEST software, R language, self-organizing maps (SOM), and GeoDA, the temporal and spatial variations of five typical ESs, including food supply, soil retention, water yield, carbon storage, and biodiversity maintenance from 2005 to 2020, were analyzed, and spatial distributions of the different ESs clusters were also recognized by using the SOM method. Moreover, the impacts of land-use type and slope on ESs, and the characteristics of trade-offs/synergies among the five ESs, were discussed. Results showed, firstly, that the total values of ESs showed a changing trend of “three increases and two decreases” from 2005 to 2020. Among the ESs, food supply, soil retention and water yield showed upward trends, with annual growth rates of 2.83%, 6.50% and 2.98%, respectively, whereas carbon storage and biodiversity maintenance showed downward trends, with annual decline rates of 0.03% and 0.07%, respectively. Second, the results showed that the Moran’s I index of the total ESs was 0.3995 in 2005 and 0.4305 in 2020, respectively, indicating that they had significant spatial clustering characteristics. The Low-Low clustering regions with reduced changes were mainly in the central and northern parts of the study area, whereas the High-High clustering regions with increased changes were found distributed mainly in the south of the study area. Thirdly, it was found that cropland and woodland were the main contributors to the total amounts of ESs, but the supply capacity of ESs per unit area of woodland was the largest, constituting nearly 1/3 of the total supply capacity of the ESs. Last, the slope effect on trade-offs and synergies was significant between typical ecosystem service pairs in the study area; trade-offs were the main relationships between the pairs of ESs in the study area, which accounted for nearly 60% of all types of trade-offs/synergies during the 15 years. In addition, the spatial distributions of the trade-offs/synergies between ESs pairs were visualized clearly, and the six ES bundles were identified by using the SOM method at the township administrative scale. The identification of ecosystem service bundles is of great significance for the division of ecological functional zones and ecological regulation in the DBM.
Keywords: ecosystem services; trade-offs and synergies; spatiotemporal heterogeneity; land use; SOM; DBM (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)
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