## 3 Results
### Multiple Factors Influence the Metabolite Profiles of Wine  
In addition to the regional patterns of soil and grape must microbiota, environmental indicators of wine-producing regions also exhibited significant differences, including soil properties and weather conditions in vineyards during the growing season (April to October 2022). To elucidate the role of microbial diversity in wine regionality, a random forest model was employed to rank the importance of biotic predictors (soil and must microbial diversity) and abiotic predictors (soil and weather parameters) influencing wine regionality. Additionally, PLS-PM was used to assess whether the relationship between microbial diversity and wine regionality remains significant when accounting for multiple factors simultaneously. The random forest model (R\({}^{2}\) = 0.9614, \(p\) < 0.01) identified must fungal diversity, soil fungal diversity, and weather conditions as significant predictors of  
Figure 3: Composition and diversity of microbiota in must from different regions. Note: Composition of fungal (**a**) and bacterial (**b**) communities in must (Top 10 relative abundances; statistical significance was determined at \(p\) < 0.05); PCa analysis based on Bray-Curtis and weighted UniFrac distances revealed the distribution patterns of fungal (**c**) and bacterial (**d**) samples from different regions in grape must.  
wine regionality. Among these, fungal diversity in grape must had the greatest impact on wine regionality, followed by weather conditions and then soil fungal diversity. In contrast, bacterial diversity (in both grape must and soil) and soil properties had no significant influence on wine regionality, as evidenced by the increase in the mean square error (MSE) (Figure 5).  
Figure 4: LEFse analysis identified differentially abundant fungal (**a**) and bacterial (**b**) taxa during fermentation.  
Figure 5: Key predictors of wine regionality. Note: The mean predictor importance from the random forest model is represented by the percentage increase in the mean squared error (MSE) associated with climate, soil properties, and microbial diversity (Shannon index) in the context of wine regionality. Significance levels: *, \(p<0.05\); **, \(p<0.01\).  
In the PLS-PM model, the Root Mean Square Error of Approximation (RMSEA) was 0.001, and the Goodness of Fit (GOF) index was 0.901, indicating an excellent overall model fit. The model explained 98.2% of the variance in the regional distribution patterns of wine metabolite profiles (Figure 6a). The model results showed that must fungal diversity had the strongest significant direct positive effect on wine metabolite profiles (R = 0.867, \(p\) < 0.001; Figure 6a). Secondly, weather conditions, especially TEM Avg, RHU Avg and GST Avg, had a significant direct positive effect on wine metabolite profiles (R = 0.752, \(p\) < 0.01); moreover, these weather variables also indirectly influenced wine metabolite profiles through their strong effects on soil and must microbial diversity (e.g., PRE Avg, RHU Avg, TEM Avg) (Figure 6a). Finally, soil fungal diversity had a significant direct positive effect on the wine metabolite profiles (R = 0.619, \(p\) < 0.05; Figure 6a). In addition to the aforementioned factors, must bacterial diversity also had a direct positive effect on wine metabolite profiles, although this effect was not statistically significant (R = 0.531, \(p\) > 0.05); soil properties and soil bacterial diversity exerted weaker negative effects on wine metabolite profiles (Rsoil properties = \(-\)0.322, \(p\) > 0.05; Rsoil bacterial diversity = \(-\)0.112, \(p\) > 0.05) (Figure 6a).