## 3 Results and Discussion
### Microbial Succession during Black Rice Wine Fermentation  
The primary drivers of microbial community succession in the BRW brewing process comprise both non-biological and biological elements. These non-biological components involve temperature as well as metabolic byproducts such as reducing sugars, total acidity, ethanol, pH, and total anthocyanins, which can be regarded as critical environmental factors [16; 26]. RDA was used to further analyze the relationship between key microorganisms and environmental factors (Figure 3a) [27]. The findings indicated a uniform link between microbial and environmental factors during all BRWs' brewing processes, where  
Figure 2: Relative abundance of microbial composition of genus (**a**,**b**) and Venn diagram of OUTs (**c**,**d**) in different Xiaoqus. _Pediococcus_ and _Wickerhamomyces_ had a negative correlation with temperature, reducing sugars, and total anthocyanins for all samples, but were positively associated with alcohol and total acidity. It had been observed that temperature can influence the fermentation process, particularly when the temperature drops to 20 \({}^{\circ}\)C in the later stages of alcohol fermentation. This could lead to a decrease in the physiological activity of _Pediococcus_[28], thus avoiding the total acids of the fermentation broth from increasing significantly, and consequently preventing the occurrence of wine rancidity. The _Wickerhamomyces_ in the Q1 and Q3 samples and the unclassified_f_Metschnikowiaceae in Q2 displayed an inverse relationship with total acid and alcohol content, while exhibiting a positive correlation with reducing sugars. _Enterobacter_ demonstrated a distinct response to environmental factors in the samples from Sichuan Dazhu Xiaoqu and Jiangxi Yingtan Xiaoqu: a negative correlation with alcohol and total acid was observed in Sichuan Dazhu, whereas only a negative correlation with alcohol and a positive correlation with total acid was seen in Jiangxi Yingtan. Figure 3: Microbial succession during black rice wine fermentation. (**a**) RDA analysis based on relative abundance of dominant genus, the red arrow signifies environmental factors, and the blue arrow symbolizes microorganisms; (**b–d**) represent microbial interaction network of Q1, Q2, and Q3, respectively, constructed using [PERSON]’s correlation ( \(|\,r\,|>0.8\), \(p<0.05\)). The reason for this could be that microbial succession was affected not only by environmental conditions but also by the interactions between microorganisms (biological factors). By utilizing [PERSON]'s analysis, microbial network correlations between the species located in different Xiaoqus were evaluated (Figure 3b-d). In the Q1, Q2, and Q3 samples, 44, 42, and 46 nodes, respectively, were identified, along with 69, 67, and 78 pairs of correlations (\(\left|\,\mathrm{r}\right|>0.8\), \(p<0.05\)). The correlation between the bacterial and fungal communities was represented by 18, 9, and 18 pairs, respectively, while the bacterial and fungal communities each had 41, 38, and 41 pairs and 10, 20, and 19 pairs of correlations, respectively. The findings suggested a more intricate correlation between bacterial communities than fungi communities in different regions of Xiaoqu, and were in agreement with [PERSON]'s research results [16]. Furthermore, the interactions between bacterial and fungal communities were less significant than those within their respective communities, likely due to habitat filtration that restricted the magnitude of such interactions [29]. A high abundance of fungal strains (Q1: _Saccharomyces_Q2: unclassified_f_Metschnikowiaceae, and Q3: _Saccharomyces_) found in the samples exhibited a competitive dynamic with other fungal strains such as _Wickerhamomyces_ and _Saccharomyces_.