4.7 Article

Using ecosystem service bundles to detect trade-offs and synergies across urban-rural complexes

Journal

LANDSCAPE AND URBAN PLANNING
Volume 136, Issue -, Pages 110-121

Publisher

ELSEVIER
DOI: 10.1016/j.landurbplan.2014.12.006

Keywords

Driver; Geographic factors; Scaling; Socio-economic factors; Spatial pattern; Urbanization

Funding

  1. National Science Foundation of China [31370354, 31270377, 71303217]

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Understanding the relationships among ecosystem services is crucial for ecosystem-based management (EBM). In urbanized areas, improving human well-being increasingly depends on the services provided by human-dominated or artificial ecosystems. However, there is little understanding of the relationships among multiple ecosystem services of human-dominated ecosystems. Based on the methodology of ecosystem service bundles (sets of ecosystem services that repeatedly appear together), we conducted a case study in 22 urban-rural complexes across the Yangtze River Delta, the most developed area of China, to explore the spatial distributions, synergies, and trade-offs of multiple ecosystem services. Results showed that: (1) most of the selected ecosystem services were spatially clumped across the study area, and the distribution of ecosystem services contributes to the formation of scale effect in economy for the provision of services; (2) ecosystem service bundles aggregated spatially at the city cluster scale, implying the aggregate feature of bundles might be scale free; (3) trade-offs occurred mainly between landdependent services (such as crop production and water supply), while synergies occurred mainly between land-independent services (such as manufactured products and students of higher education), and there were no significant relationships between landdependent and land-independent services; (4) compared with geographic factors, socio-economic factors were the major drivers in the formation of ecosystem service bundles in the heavy urbanized region of the Yangtze River Delta. The study could improve the understanding of the relationships among multiple ecosystem services from local to regional scales. (C) 2014 Elsevier B.V. All rights reserved.

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