Bacterial OTUs with relative abundances greater than 0.1% were defined as abundant taxa; those with relative abundances below 0.01% were defined as rare taxa, and those with relative abundances between 0.01 and 0.1% were moderate OTUs (Jiao and Lu, 2020 (link); Yang L. et al., 2022 (link)). After all data passed the preliminary Shapiro–Wilk test (p > 0.05), a t-test was used to assess the differences between the parameters (Hou et al., 2019 (link)). Partial least squares discriminant analysis (PLS-DA) with variable importance (VIP) values was used to identify the difference in soil metabolites (Darnaud et al., 2021 (link)). The PLS-DA model was validated with a permutation test (n = 200), R2 data are larger than Q2 data, and the intercept of Y and Q2 was less than 0 (R2 intercept = 0.9576, Q2 intercept = −0.3811), which means the model was fit and not overfitting (Chen et al., 2021 (link)). The alpha diversity indices including Chao1 and inverse Simpson index (or InvSimpson for short) were calculated by the R program package “vegan” (Oksanen et al., 2007 ). Nonmetric multidimensional scaling (NMDS) based on Bray–Curtis distance was conducted by the “vegan” package.
The network analysis was inferred using the SparCC-based algorithm Fastspar with a bootstrap procedure repeated 100 times, and only strong (r > 0.6) and significant (p < 0.01) correlations between OTUs were retained (Watts et al., 2019 (link)). The network nodes with high closeness (or betweenness) centrality value were identified as keystone hubs in the network: the network hubs were highly connected, both in general and within a module; the module hubs were highly connected within a module, the connectors provided links among multiple modules, and the peripherals had few links to other species (Poudel et al., 2016 (link)). Variation partitioning analysis (VPA) was used to assess the impact of environmental factors on bacterial communities using the “vegan” package (Wang et al., 2019 (link)). Before the performance of VPA, we assessed the collinearity of the variables by calculating the variance inflation factor (VIF). The factors were included in the VPA analyses, only when the collinearity VIF < 10 (Liao et al., 2019 (link)). The predictors of bacterial richness were identified via random forest modeling using the “randomForest” package in R (Liaw and Wiener, 2002 ). The importance of each predictor was determined by the increase in the mean square error (InMSE) and was averaged over 5,000 trees (Xu et al., 2018 (link)).
The network analysis was inferred using the SparCC-based algorithm Fastspar with a bootstrap procedure repeated 100 times, and only strong (r > 0.6) and significant (p < 0.01) correlations between OTUs were retained (Watts et al., 2019 (link)). The network nodes with high closeness (or betweenness) centrality value were identified as keystone hubs in the network: the network hubs were highly connected, both in general and within a module; the module hubs were highly connected within a module, the connectors provided links among multiple modules, and the peripherals had few links to other species (Poudel et al., 2016 (link)). Variation partitioning analysis (VPA) was used to assess the impact of environmental factors on bacterial communities using the “vegan” package (Wang et al., 2019 (link)). Before the performance of VPA, we assessed the collinearity of the variables by calculating the variance inflation factor (VIF). The factors were included in the VPA analyses, only when the collinearity VIF < 10 (Liao et al., 2019 (link)). The predictors of bacterial richness were identified via random forest modeling using the “randomForest” package in R (Liaw and Wiener, 2002 ). The importance of each predictor was determined by the increase in the mean square error (InMSE) and was averaged over 5,000 trees (Xu et al., 2018 (link)).
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