Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.

The effects of anthropogenic land use on the water quality of reservoir-based water sources are understudied. We trained a self-organizing map (SOM) to measure the spatial pattern of nutrients over the course of a year in 61 reservoirs located in eastern China. In the linear regression model and one-way analyses of variance, we found that water quality was influenced by period of the year (flood, low, and normal periods based on rainfall conditions) and reservoir altitude (plains vs. mountains). Our results indicated that land use metrics measured in aquatic–terrestrial interfaces significantly influenced the water quality of reservoirs. The land use intensity (LUI) and the proportion of construction land had a positive correlation with ammonia nitrogen (NH₃-N) and chemical oxygen demand (COD_Mn) concentrations, and redundancy analysis indicated that the percent of landscape (PLAND) represented by construction land was positively correlated with COD_Mn, NH₃-N, total phosphorus (TP), and total nitrogen (TN) concentrations. The proportion of cropland was not correlated with any water quality property except for COD_Mn concentration. The total explained variance for water quality was highest when the scale was large (the area defined by a 1500 m radius around the reservoir), indicating that management which ensures water safety should be carried out at this scale.