Ecological Indicators (Nov 2022)
Trade-offs and driving factors of multiple ecosystem services and bundles under spatiotemporal changes in the Danjiangkou Basin, China
Abstract
The Danjiangkou Basin (DJKB) is an important water source area for the South-to-North Water Diversion Middle Route Project in China. A comprehensive understanding of various ecosystem service (ES) bundles and their socioecological driving factors in this area remains important for ecological management and decision-making. Eight representative ES types within the DJKB were analyzed: food supply (FS), carbon sequestration (CS), habitat provision (HP), nitrogen retention (NR), phosphorus retention (PR), soil retention (SR), water yield (WY), and outdoor recreation (OR). The Spearman correlation coefficient was used to reveal trade-offs and synergies between paired ESs at subwatershed, grid, and regional scales, in addition to exploring their relationship with socioecological drivers. Using Getis-Ord Gi* and superposition analysis, spatiotemporal hotspots were superimposed, and a distribution map was generated. Lastly, K-means cluster analysis was used to analyze the spatiotemporal dynamics of ES bundles (ESBs) in 2000, 2005, 2010, 2015, and 2020. The results revealed that: (1) from 2000 to 2020, the eight ESs displayed clear spatiotemporal variability at the subwatershed scale: FS increased by 40.55 %, NR and PR first decreased, then increased. (2) Differences in trade-offs and synergies between spatiotemporal ESs at subwatershed, grid, and regional scales were also observed. (3) Superpositioning of hotspots revealed that only 1.33 % of the basin provided six ES types (except FS and NR); whereas ∼ 50 % of the study area provided 1–2 ES types. (4) Among the nine socioecological driving factors selected in the study, their respective influences remained relatively stable in time. GDP, population density, elevation, and slope were the main driving factors of ES changes, while other factors, such as precipitation and forest land ratio, also maintained notable influences. Three ESBs were mapped at the subwatershed scale [ESB1 (ecological high-quality area), ESB2 (agricultural production area), and ESB3 (ecological equilibrium area)] and their spatiotemporal patterns were analyzed. Overall, this study revealed the variations in ESs and their trade-offs within the DJKB across various spatiotemporal scales, identified the spatial distribution of ESB hotspots, and revealed the relationship between ESs and their socioecological drivers. These, results strengthened our understanding of spatiotemporal variations in ESs and ESBs, while providing technical and theoretical support for the planning and implementation of future DJKB ecosystem policies.
