Spatial heterogeneity of ecosystem services trade-offs among ecosystem service bundles in an alpine mountainous region: A case- study in the Qilian Mountains, Northwest China

In mountainous areas with diverse climate, topography, and socio-economic status, ecosystem services (ES) trade-offs may differ largely in space. However, few researches have spatially identified the trade-offs and influencing factors of ES in different regions. We selected the Qilian Mountains (QLM) as a study area and eight key ES [grain production (GP), livestock production (LP), water yield (WY), carbon sequestration (CS), water erosion prevention (WAEC), wind erosion prevention (WIEC), habitat quality (HQ), and sightseeing (SS)] were evaluated through spatially explicit models. K-means clustering was used to identify the ES bundles. Trade-offs between ES were identified using factor analysis. Natural and human drivers that influence the spatial differentiation of ES were explored through redundancy analysis. The results showed the eight ES had obvious spatial heterogeneity and could be clustered into three bundles. In the entire QLM, there were obvious synergies between most ES, especially GP, LP, CS, and SS. However, several trade-offs existed in different bundles (e.g., HQ with GP, LP, CS, and SS in bundle 1; HQ with GP, LP, and SS in bundle 2; WIEC with WY and WAEC in bundle 3). Natural factors were the main factors affecting the spatial differentiation of ES, among which vegetation coverage, elevation, precipitation, and slope have higher explanatory degrees, but differences among three bundles were great. The influence of human factors on the provisioning and cultural services was relatively high in local areas. This study can provide references for formulating regional development strategies in the QLM and other mountainous regions.

Automated summary

This study assessed the trade-offs and influencing factors of ecosystem services (ES) in the Qilian Mountains, a mountainous region with diverse climate and topography. The results showed significant spatial heterogeneity in the eight key ES evaluated, which could be clustered into three bundles. Natural factors, such as vegetation coverage, elevation, precipitation, and slope, were found to be the main drivers of ES differentiation, while human factors had a relatively high influence on provisioning and cultural services in local areas.