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