3.5 Redundancy analysis (RDA) on bacterial community composition
Redundancy analysis method (RDA) was employed to investigate what environmental factors shifted the bacterial community structure and the classes’ relative abundance among treatments. Environmental variables included SOC, TN, AN, MBC, MBN, CMR, NMR and PNR, and attributes including ECe, pH, CEC and AP were not considered since no significant differences were observed across all soil samples (collected on June 10, 2018). Monte Carlo permutation was employed in RDA method to test the significance of soil chemical and microbial parameters in explaining variation in bacterial community structure. Figure 8 gives the environment-species relationship of RDA tests based upon bacterial community data matrix at the class level. Soil bacterial community distribution differed between the four treatments, suggesting that metabolic functions also vary depending on the conditions. The first axis explained 37.8% of the variation (p < 0.01), which was correlated with CMR, PNR and TN. It was indicated that the first axis to some extent may characterize the status of soil carbon and nitrogen metabolism (Figure 7A). The second axis explained 12.0% of the variation, which was correlated with SOC, MBC, MBN, AN and NMR. The second axis represented the status of soil carbon and nitrogen content. TN was the strongest factor (P = 0.016) that was correlated with the class distribution of bacterial community. CMR also showed significant correlations with community composition (P = 0.046), whereas the other factors were all not significant (Fig. 7A). The community structure ofAlphaproteobacteria , Planctomycetia and Nitrospirawas significantly influenced by PNR, and the community structure ofActinobacteria was significantly influenced by CMR. Also, SOC, MBC, MBN and AN had significant influence on the community structure ofDeltaproteobacteria , Gammaproteobacteria andBacteroidia . The above results indicated that the N fertilization shifted the environmental factors and the distribution of the bacterial community.
Figure 8
Spearman’s rank correlation results, showing the dependence between relative abundance of bacterial classes and the environmental factors, are shown in Table 7. Nitrospira showed significant positive correlation with SOC, TN, AN, MBN, CMR, NMR and PRN, whereasCytophagia exhibited significant negative correlation with SOC, TN, AN, MBC and NMR. Relative abundance of Gammaproteobacteriawas positively correlated with TN, MBC, MBN and NMR. Moreover, significant negative correlation between Anaerolineae and TN,Bacilli and SOC, Bacilli and MBC,Acidobacteria_Gp 10 and MBC was observed. Three classes showed a statistically significant dependence on the CMR of soil samples:Alphaproteobacteria , Actinobacteria and Nitrospira . Generally, Nitrospira exhibited significant positive correlation with most of the environmental factors, whereas Cytophagia showed significant negative correlation with most of the soil properties. No significant correlation between soil properties andDeltaproteobacteria , Betaproteobacteria ,Planctomycetia , Acidobacteria_Gp 6,Sphingobacteriia , Ignavibacteria andGemmatimonadetes was observed, indicating that these bacterial classes were independent from the environmental factors associated with soil carbon and nitrogen metabolism.
Table 7
Effect of N gradient on the relative abundances of the dominant bacterial classes was examined using regression analysis (Figure 9). The N fertilization rates was significantly positively correlated withAlphaproteobacteria (R2=0.270, p<0.05), Gammaproteobacteria(R2=0.375, p<0.01) and Nitrospira(R2=0.650, p<0.01), indicating that the relative abundance of this bacterial classes increased with the N fertilization rates. Significant negative correlation was observed between N gradient and Cytophagia (R2=0.425, p<0.01) and Bacilli (R2=0.261, p<0.05) and the indication was that the increase of N fertilization rates reduced the relative abundance of the above bacterial classes.
Figure 9