Spatial relationships between ecosystem services and socioecological drivers across a large-scale region: A case...

Understanding the relationships between ecosystem services (ES) and their underlying socioecological drivers is essential for forming the efficient management decisions of ecosystems. We use a large watershed area as a case-study to analyze trade-offs/synergies and bundles of ESs and identify the associated socioecological variables (SEVs). This study assessed the supply of 7 ES indicators, namely, three provisioning services (crop production, livestock production, and industrial production), three regulating services (water conservation, soil conservation, and carbon sequestration), and one cultural service (recreation), across 65 municipalities in the Yellow River Basin (YRB) in China. We analyzed the paired trade-offs/synergies using Spearman's coefficient and identified the ES bundles (ESBs) by applying principal component analysis and K-means clustering. Subsequently, we detected the SEVs that affect the ES supply using the geo-detector model and characterized the associations between ESBs and socioecological clusters according to the spatial overlap. The results demonstrated that the synergies between ESs substantially exceeded the trade-offs, among which the strongest synergies were between the crop production and the livestock production, and both responded strongly to the cropland and the population density. Trade-offs were identified between provisioning services and soil conservation. Municipalities were grouped into three ESBs in the YRB. The ESB, which was dominated by provisioning ESs, was associated with areas where cropland, precipitation and socioeconomic conditions were all important, and the regulation of ESB was linked to regions with distinct ecological characteristics. We also identified an ESB that was dominated by carbon sequestration, as determined by extensive grassland and bare land. The land use/land cover strongly affected the characteristics of the ESBs. The findings can be used by land managers to identify areas in which ESs are dominant, to determine the associations of these compositions of the ESs with SEVs, and to support the formulation of optimal ES management in large-scale basins.