Scale-dependent responses of ecosystem service trade-offs to urbanization in Erhai Lake Basin, China.

Urbanization is an important factor affecting ecosystem services (ESs) and their trade-offs. However, little is known about the responses of ES trade-offs to urbanization at different scales. Here, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was used to evaluate water yield (WY), water purification (WP), carbon storage (CS), and habitat quality (HQ) in Erhai Lake Basin using earth observation data, and the percentage of urban land (PUL), population density (POP), gross domestic product (GDP), and night light index (NLI) were used as urbanization indicators. We quantified the ES trade-offs using the root mean square error and analyzed spatiotemporal changes in urbanization indicators, ESs, and their trade-offs. Finally, we characterized the relationship between urbanization and ES trade-offs using correlation analysis and curve regression at the grid and town scales. From 2000 to 2020, values of PUL/GDP/NLI/POP were high in the south and low in the north; specifically, they were 15, 8, 2, and 0.42 times higher in the south than in the north, respectively. The urban expansion area in the Erhai Basin from 2000 to 2020 resulted in a 123.24% and 77.03% increase in WY and WP, respectively, and a 32.38% and 100% decrease in CS and HQ, respectively. The trade-offs between WY and CS and between WY and HQ increased, and other ES trade-offs decreased. Urbanization was significantly correlated with most ES trade-offs at the grid scale, but not at the town scale. There was a significant positive correlation between all urbanization indicators and the trade-off between CS and WP (p < 0.05), and the magnitude of the correlation increased with scale. The relationship between ES trade-offs and urbanization was mostly U-shaped and inverted U-shaped at the grid scale, but N-shaped and inverted N-shaped at the town scale. This study provides information that could be used for multi-scale urban planning.