Effects of water stratification and mixing on microbial community structure in a subtropical deep reservoir

Abstract

Microorganisms play pivotal roles within aquatic ecosystems, affecting their structure, functioning and services. However, little is known about the effects of water stratification and mixing on the aquatic microbial community dynamics in subtropical reservoirs. In this study, we explored vertical and seasonal patterns of microbial diversity in the Dongzhen Reservoir (southeast China). Quantitative PCR, quantitative RT-PCR, and 454 pyrosequencing were used for an in-depth characterization of the bacterial community across time (every three months for one year) and space (five different water depths). Our results indicated that thermal and oxygen stratification shaped the phylogenetic composition of microbial communities in the reservoir. There were significant differences in physical, chemical and microbiological parameters between epilimnion and hypolimnion (P < 0.05). The RNA: DNA ratios were significantly lower in epilimnion and metalimnion but rapidly increased in hypolimnion (P < 0.05), suggesting that microorganisms were more active at low temperatures, low dissolved oxygen concentrations and high TN/TP ratios. Redundancy analysis and pathway analysis revealed a complex interplay of various environmental and biological factors by explaining the spatiotemporal variations in bacterial communities. Adaptive reservoir management strategies should consider carefully the effects of water stratification and mixing, together with the distribution patterns of aquatic microorganisms.

Figure 1. (A–D) Temperature, dissolved oxygen (DO), pH and chlorophyll-a profiles of Dongzhen Reservoir during one year.

The bottom of the reservoir is shown in grey.

Figure 2. Number of 16S rDNA and 16S rRNA copies per liter and RNA: DNA ratio from different water depths (0 m, 10 m, 20 m, 26 m, 33 m) and different seasons (winter, spring, summer, autumn) in Dongzhen Reservoir.

Error bars indicate standard errors of the three replicates samples.

Figure 3

(A) Phylum-level (and Actinobacteria class level) changes in different bacterial composition of Dongzhen Reservoir. Relative abundances of phylotyped reads are shown to their closest match in the Ribosomal Database Project. (B) Cluster analysis of bacterial communities based on the Bray-Curtis similarity of 454 pyrosequencing profiles. The right legend shows color-coding indicating the top 20 most abundant genera in Dongzhen Reservoir.

Figure 4

(A) RDA bioplot of the distribution of bacterial community and environmental variables in Dongzhen Reservoir. Only statistically significant environmental variables are shown according to Monte Carlo permutation test (P < 0.05). (B) Variation partitioning of bacterial communities into the statistically significant environmental variables.

Figure 5. Final path with structural equation modeling.

The significant value for the Chi-square test is given, assessing whether the model is significantly different from the corresponding correlation matrix. The goodness-of-fit index and Bentler CFI (Comparative Fit Index) indicate the goodness of fit of the model, the best fit would result in a value of 1. The Bayesian Information Criterion (BIC) is another measure of the goodness-of-fit and it is minimized to obtain a better model fit. Numbers on each arrow indicate partial correlation coefficients associated with each causal relationship, and arrow thickness is also proportional to the partial correlation value.