Integrating constraint effects among ecosystem services and drivers on seasonal scales into management practices

• Published in: Ecological indicators Vol. 125; p. 107425
• Main Authors: Li, Shengkun, Li, Xiaobing, Dou, Huashun, Dang, Dongliang, Gong, Jirui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021; Elsevier
• Subjects: Interaction thresholds; Landscape management; Non-linear characteristics; Synergies; Trade-offs; Xilingol grassland; Pre; RUSLE; Sand; Alt; Interaction thresholds; NDVI; SLwater; NPP; Trade-offs; Slo; RWEQ; WS; SR; Clay; Non-linear characteristics; SLwind; Landscape management; DEM; ES; SOM; FVC; CASA; Xilingol grassland; Tem; Synergies
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2021.107425

•Three ecosystem services were quantified and mapped on seasonal scales.•The constraint effect of paired ecosystem services was determined by the constraint line method.•The constraint line types of the same ecosystem service pairs were unchanged in different seasons and the whole year.•Ecosystem management measures should be tailored to seasonal differences. A better understanding of ecosystem service relationships and drivers facilitates scientific decision-making and improves ecosystem-based management practices. In contrast to previous studies that analyzed the relationship between ecosystem services associated with a specific time scale (i.e., annual scale), ecosystem services supply and their relationships were quantified in different seasons in the study. Three ecosystem services (NPP, net primary productivity; SLwater, soil water erosion; SLwind, soil wind erosion) were evaluated in the Xilingol grassland of China from 2000 to 2017. We extracted the upper boundary lines of the scatter plots of paired ecosystem services to define the types of constraint effects between them. We identified the constraint effects of ten drivers on ecosystem services. The results showed that the supply of ecosystem services shows substantial seasonal variation. The exponential constraint effects between NPP and SLwind, and the logarithmic constraint effects between SLwater and SLwind both changed inversely and monotonously, which were similar to trade-offs. The direction of the hump-shaped constraint effects between NPP and SLwater was changed. In this case, the trade-offs or synergies may not effectively characterize their relationship. Based on the constraint effects among ecosystem services, the constraint relationship of the drivers on ecosystem services, and the thresholds, optimized management measures can be taken. When vegetation cover was lower than 9%, vegetation did little to reduce SLwind; when vegetation cover was 55% or above, the effect of vegetation on reducing SLwind basically reached the maximum. Thresholds of key factors should be monitored seasonally, which can help us issue an early warning of SLwater. Our study suggests that policy-makers should pay more attention to the intra-annual variations in ecosystem service interactions and take constraint effects among ecosystem services and drivers into consideration when making decisions so as to achieve grassland ecological sustainability with less ecosystem service degradation.