Improved Understanding of Trade-Offs and Synergies in Ecosystem Services via Fine Land-Use Classification and Multi-Scale Analysis in the Arid Region of Northwest China

Abstract

Ecosystem services (ESs) serve as a fundamental cornerstone for upholding global biodiversity and promoting human well-being. ESs trade-off and synergy are supposed to be significantly affected by climate change (CC) and land use/cover change (LULC). However, the limited availability of finely classified future land-use data and integrated landscape change models incorporating climate change scenarios has hindered our understanding of the trade-off and synergistic patterns and controls of ESs at multiple scales, particularly in arid areas. Here, a future multi-scenario ESs trade-off/collaborative assessment framework (SD-PLUS-InVEST model) for multi-scale conversion and refined land-use classification was developed by coupling the patch-generated land-use simulation (PLUS) model, system dynamics (SD) model, InVEST model, geographically weighted regression (GWR) model, optimal parameter geographical detector (OPGD) model, and structural equation model (SEM). The four ESs, namely carbon storage (CS), habitat quality (HQ), water conservation (WC), and soil conservation (SC), were assessed. Further, multi-scale ESs were evaluated under different climate change and development scenarios (i.e., the SSP1-2.6 and ecological protection scenario, SSP1-2.6-EP; SSP2-4.5 and natural development scenario, SSP2-4.5-ND; SSP5-8.5 and economic growth scenario, SSP5-8.5-EG). The results demonstrated that the arid region of northwest China (ANWC) was experiencing a significant and continuous warming trend accompanied by increased humidity. There will be a significant decrease in the areas occupied by paddy fields, natural forests, and permanent glaciers among the 24 LULC types. Conversely, there will be a substantial increase in dry land, high-coverage grassland, and urban construction land areas. According to the SSP1-2.6-EP, SSP2-4.5-ND, and SSP5-8.5-EG scenarios, the comprehensive land-use dynamic degrees were estimated to reach 2.58%, 4.08%, and 4.74%, respectively. The LULC resulting from CC exacerbates the differences in the four ESs of ANWC. In particular, CS and HQ experience significant reductions in 2100. Conversely, WC and SC show notable increases during the same period. The changes in CS, HQ, WC, and SC reach 11.36 × 108 m3, 1735.25 × 108 t, −1.29 × 108 t, and −0.009, respectively. The four ESs of CS, HQ, WC, and SC in ANWC display a synergistic relationship. This synergy is influenced by the heterogeneous spatial distribution of CS, HQ, WC, and SC, with the strongest synergy observed between CS and HQ and the weakest between CS and WC. Interestingly, the distribution differences in ESs synergy were amplified at watershed, county, and grid scales in mountainous areas, with the most significant detection differentiation occurring at the grid scale. Furthermore, the detection of spatial heterogeneity in the four ESs can be attributed to various factors. These factors include the drought index (q = 0.378), annual average precipitation (q = 0.375), economic density (q = 0.095), vegetation coverage (q = 0.262), and soil bulk density (q = 0.077). Our results highlight the importance of CC in influencing ESs. The spatial variations in ESs trade-offs and coordination at different scales, particularly the pronounced differences observed in mountainous areas, underscore the need to prioritize the conservation of arid mountainous regions in terms of future policy making.