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• W2892087628 abstract "A comprehensive understanding of the relationships among ecosystem services (ES) is important for landscape management, decision-making and policy development, but interactions among multiple ES remain under-researched. In particular, earlier studies often did not clearly distinguish between supply, flow and demand. Furthermore, the underlying mechanisms in complex socio-ecological systems remain less examined. In this study, we therefore aimed to assess interactions among eight key ES, adopting a multistep approach. For all ES, we mapped ES supply, flow and demand at the municipality level in the Alpine Space area. We applied correlation analysis and cluster analysis in order to analyse the linkages between ES and to identify bundles of ES. We used random forest analysis to explain the distribution of the ES bundles and to identify important drivers based on socio-ecological variables (e.g. land use/cover, climate, topography and population density). Our results demonstrate that trade-offs and synergies varied greatly for supply, flow and demand. We identified five ES bundles, distinguishing hotspots of ES supply and demand. Twelve socio-ecological variables correctly predicted the membership of 81% of the municipalities to the ES bundles. Our results suggest that a limited number of socio-ecological variables can explain the majority of the distribution of ES bundles in the landscape. Considering the spatial relationships between mountain regions and their surrounding lowlands, regional and transnational governance frameworks need to connect areas of multiple ES supply to areas of ES demand, and should account for the different levels and types of ES relationships." @default.
• W2892087628 created "2018-09-27" @default.
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• W2892087628 date "2019-02-01" @default.
• W2892087628 modified "2023-10-18" @default.
• W2892087628 title "Integrating supply, flow and demand to enhance the understanding of interactions among multiple ecosystem services" @default.
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• W2892087628 doi "https://doi.org/10.1016/j.scitotenv.2018.09.235" @default.
• W2892087628 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30257232" @default.
• W2892087628 hasPublicationYear "2019" @default.
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