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Article

Changes in Bacterial Community Structure in Reservoir Sediments before and after the Flood Season

by
Xianting He
,
Min Chen
,
Luxin Zhou
,
Wenyan He
,
Ning Liao
and
Youcai Tuo
*
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(9), 946; https://doi.org/10.3390/d15090946
Submission received: 1 July 2023 / Revised: 2 August 2023 / Accepted: 19 August 2023 / Published: 22 August 2023
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Abstract

:
Bacterial communities are important components of reservoir ecosystems, participating in and determining the material–energy transformations within reservoirs. The intense material–energy transport during the flood season can cause perturbations to the stratified environment and material distribution within the reservoir, with the bacterial community being the most sensitive indicator of these changes. In this study, we analyzed sediments from four representative sampling sites before and after the flood season in a seasonally stratified reservoir and compared the diversity and composition of bacterial communities before and after the flood season using 16S rRNA high-throughput sequencing technology. The results showed that the bacterial community structure was different before and after flood season, and the bacterial abundance and α diversity were slightly higher before flood season than after flood season, and the relative abundance of bacteria was relatively low, and the dominant genera were not obvious. After flood season, the dominant genera were mainly Acinetobacter, Flavobacterium, Pseudomonas, Arthrobacter, and Massilia, all of which were aerobic denitrifying bacteria with strong denitrification ability. It is clear that the reservoir bacterial community structure changes significantly between flood seasons and plays a key role in later stages of aquatic ecology restoration. These results provide a new way of interpreting the dynamic changes in reservoir aquatic ecology.

1. Introduction

During the flood season, rivers transport a large amount of sediment into reservoirs, and with intense dynamic disturbance and high nutrient inflow, reservoir water stratification, microorganisms, and chemicals in the water body are modified to a significant extent [1,2,3]. These processes can affect different aspects of ecological services within reservoir systems, including material cycling, energy exchange, and bacterial community diversity, Doering et al. [4] demonstrated that flood disturbances can directly affect the microbial community or initially modify the environment, thereby influencing the microbial community structure. In a study by Lin et al. [5], it was found that there is a high abundance of nitrites and nitrates in the water during the flood season, and the content of nitrogen-fixing bacteria was relatively high throughout the process. Raymond et al. [3] found that the flood season severely complicated the hydrological conditions and increased the organic carbon content. Similarly, Steichen et al. [6] observed a dynamic process of increasing and then decreasing organic carbon content throughout extreme floods. Therefore, it is crucial to study how floods affect the function of reservoir ecosystems, which will provide a useful reference for the management of ecological safety in reservoir water. However, these dynamic processes have not yet been systematically and fully investigated, and further reference information from different perspectives is urgently needed.
Sediment is particularly important in reservoir ecosystems, especially after the flood season, and the intense input of exogenous materials during the flood season can have an extensive impact on sediment, which is usually the recipient of allochthonous materials [7,8]. Sediment is also a key site for the cycling of biogenic elements such as carbon, nitrogen, and organic phosphorus matter in the water column, as well as the retention of nutrient compounds in the upper reaches [9,10]. Further, it has been found that there is significant seasonal variation in the contents of organic matter and nitrifying bacteria in river sediments [11,12,13]. It is speculated that the changes in the responses of material transformation processes at the sediment interface may be an important process due to the effect of high inflow during the flood season [14,15]. However, fewer studies have been conducted on the effects of incoming flood water on material and energy transformations in sediments.
Because ecological processes in aquatic systems are complex and difficult to track in isolation, looking for a strong indicator is often helpful and allows one to obtain at least a glimpse of the specific changes that occur in reservoir systems before and after the flood season. Bacterial communities are directly or indirectly involved in material transformation and energy exchange in the sediment environment [16,17]. Due to the sedimentation of organic matter, nutrient elements, and chemical pollutants, the total biomass and taxonomic abundance of microorganisms in the sediment are much higher than those found in the corresponding water bodies [8,18]. The remarkable community diversity and genetic diversity of microorganisms [19,20,21] make them particularly sensitive to changes in their external environment [22]. Moreover, bacterial community diversity, distribution characteristics, and community structure form an important basis and prerequisite for understanding the mechanism by which aquatic ecosystem structures are maintained [23]. Most studies on microbial community structure during the flood season found that bacterial abundance and diversity were significantly higher during flood periods, such as the study by Carvalho et al. [24]. The study by Yue et al. [25] showed that elements such as n and p were important variables influencing the absolute abundance of ammonia oxidizers and denitrifying bacteria in the water column, and Carney et al.’s [26,27] study revealed that nutrients delivered by floods significantly stimulated microbial growth and supported substrate-controlled bacterial community succession. However, little is known about the structure of sediment microbial communities and the effects of flooding on microbial ecological functions.
In this study, sediments from four typical sample sites of a reservoir before and after the flood season were analyzed and compared by high-throughput sequencing techniques to assess the differences in sediment bacterial community structure. This study aimed to determine the difference of microbial community structure in reservoir sediments before and after the flood season, and to make preliminary and simple predictions on the changes of ecological impact that may be affected by bacteria community, which is significant for studying the reservoir water ecology. The results of this study can provide new insights into the microbial research on reservoir systems.

2. Materials and Method

2.1. Study Site and Sample Collection

Zipingpu Reservoir is located in the upper reaches of the Min River in a subtropical monsoon climate zone in southwest China. The reservoir is mainly used for irrigation and water supply, as well as for flood control and power generation. The maximum water depth exceeds 120 m, and the reservoir capacity is approximately 9.8 × 108 m3 [28]. The annual average natural runoff of the river is approximately 148 × 108 m3, which is approximately 15 times the reservoir capacity, and on average, the flood season is from June to September every year [29,30]. The reservoir usually develops thermal stratification in late spring and is offset in autumn, and bottom hypoxia was reported to occur during the stratification period [28].
The sampling points cover the entire reservoir along the river longitudinally, while sediment samples are taken from the thalweg point horizontally. In June 2021 and October 2021, two sets of sediment samples with an inner diameter of 11.2020 cm and a length of 125 cm were repeatedly collected at four sampling points in the Zipingpu Reservoir area using a low-disturbance sampler. After collecting a sample using a siphon, the samples were transported to the laboratory on ice within 24 h and frozen at −18° for storage. The samples collected at the confluence of the tributary (30°59′40″ N, 103°28′54″ E) were named 6-S and 10-S, those collected at the entrance of the reservoir area (31°1′24″ N, 103°31′17″ E) were named 6-M and 10-M, those collected at the cross-reservoir bridge (31°1′17″ N, 103°32′42″ E) were named 6-K and 10-K, and those collected at the dam (31°2′16″ N, 103°34′27″ E) were named 6-B and 10-B. The June samples were named in the form of “6-S/M/K/B”, and the October samples were named in the form of “10-S/M/K/B”. The specific location information is shown in Figure 1.

2.2. DNA Extraction and PCR Amplification

Total genomic DNA samples were extracted using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s instructions. The DNA was then quantified by a Nanodrop NC2000 spectrophotometer, and the quality of the extracted DNA was detected by 1.2% agarose gel electrophoresis. PCR amplification of the bacterial 16S rRNA gene V3–V4 region was performed using a published forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The PCR components contained 5 µL of buffer (5×), 0.25 µL of Fast pfu DNA Polymerase (5 U/μL), 2 µL (2.5 mM) of dNTPs, 1 μL (10 µM) of each forward and reverse primer, 1 μL of DNA template, and 14.75 µL of ddH2O.

2.3. Illumina NovaSeq Sequencing and Bioinformatics Data Processing

PCR amplified products were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreendsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Then, amplicons were pooled in equal amounts, and paired-end 2 × 250 bp sequencing was performed using the Illumina NovaSeq PE250 platform with a NovaSeq 6000 SP Reagent Kit (500 cycles). The raw sequence data were demultiplexed using the demux plugin followed by primer cutting with the cutadapt plugin [31]. The reads were then quality filtered, denoised, merged, and had chimeras removed using the DADA2 plugin. After completing denoising for all libraries, feature amplicon sequence variant and ASV tables were merged, and singleton ASVs were removed. The sequence numbers for each sample at each step were uploaded to the Supplemental Material (File name: Table S1). The rarefaction depth was set to 95% of the smallest sample’s sequence size, and 74,617 sequences were obtained for each sample after rarefaction, and rarefied data have also been added to the Supplemental Material (File name: Table S2). The alpha-diversity and beta diversity metrics (weighted UniFrac) were estimated using the diversity plugin with samples that were rarefied to 74,617 sequences per sample. Taxonomy was assigned to ASVs using the classify-sklearn naïve Bayes taxonomy classifier against the SILVA Release 132 Database.

2.4. Statistical Analysis

Sequence data analyses and alpha diversity indices were performed on ASV table using QIIME2 (2019.4) [32]. UPGMA clustering analysis to show the between-habitat diversity of the sample bacterial community was performed on the weighted UniFrac distance matrix algorithm using the UCLUST function of the R Stat package [33]. A genus-level bacterial abundance clustering heatmap to show the trends in the relative abundance of bacteria was created using the pheatmap [34] package in R software. To quantify the extent of variation in composition of bacterial community between the presented samples while minimizing the loss of bacterial divergence information, Principal component analysis (PCA) was performed using the vegan [35] package in R software [36]. Linear discriminant analysis effect size (LEfse) was performed on samples using Canoco for Windows 4.5 software [37] to show the hierarchical taxonomic distribution of significantly enriched biomarkers in each group of samples from the community and their degree of importance. One-way ANOVA was performed on the microbial communities before and after the flood season using SPSS statistical analysis software to finally identify the species that play a key role around the flood season. In all data analyses, the lowest level assigned was genus level and only two decimal places were retained.

3. Results

3.1. Taxonomic Classification Unit Count

Figure 2 shows that the proportion of genus- and family-level identifications was approximately 50%, while phylum-level classifications accounted for a much smaller proportion of the identified samples. Furthermore, the discrimination of sequencing results was high.
Figure 3 shows the top 20 genera in relative abundance before and after the flood season. Acinetobacter, Flavobacterium, Rhodoferax, Massilia, Pseudomonas, and Arthrobacter were among the 20 most abundant genera in all the samples. Each sample contained Acinetobacter and Rhodoferax. Sample 6-S contained mainly Trichococcus, Flavobacterium, Rhodoferax, and Erysipelothrix, and the four genera were relatively evenly represented. Meanwhile, sample 6-M contained mainly Flavobacterium, Geobacter, and Arthrobacter. Sample 6-M mainly contained Flavobacterium and Geobacter. Sample 6-K mainly contained Flavobacterium, Rhodoferax, and Erysipelothrix. Sample 6-B showed a similar composition to sample 6-K, mainly containing Flavobacterium, Rhodoferax, and Erysipelothrix. The relative abundance of bacteria in the June samples was all below 4%. Sample 10-S contained mainly Arthrobacter (4%) and Sphingomonas (4%), sample 10-M mainly contained Acinetobacter (21%), Flavobacterium (18%), Pseudomonas (11%), Massilia (11%), and Janthinobacterium (6%), sample 10-K mainly contained Acinetobacter (17%), Flavobacterium (11%), Massilia (5%), and Janthinobacterium (4%), and sample 10-B mainly contained Acinetobacter (12%), Paenisporosarcina (5%), and Flavobacterium (4%).
The changes in the dominant bacterial phyla before and after the flood season can be observed from Figure 4a,b. In the June samples (Figure 4a), the dominant phylum was Proteobacteria (56%), followed by Firmicutes (17%), Bacteroidetes (12%), and Actinobacteria (7%), and the other phyla accounted for less than 4% of the samples. In the October sample (Figure 4b), the dominant phylum was Proteobacteria (49%), followed by Bacteroidetes (14%), Actinobacteria (10%), Firmicutes (8%), and Cyanobacteria (4%).
At the genus level, there was an obvious change in the bacterial composition in the samples at the two time points, consistent with the results shown in Figure 3. In June (Figure 4c), the relative abundance of Rhodoferax was 4%, Geobacter was 2%, Flavobacterium was 2%, Erysipelothrix was 2%, and Syntrophus was 1%. The bacterial community was relatively homogeneous without obvious dominant populations.
However, the October samples (Figure 4d) were dominated by Acinetobacter at 13%, Flavobacterium at 8%, Massilia at 5%, Pseudomonas at 4%, and Janthinobacterium at 3%. The influx of water during the flood season particularly increased the abundances of Acinetobacter and Flavobacterium in the sediment. Acinetobacter abundance was very low in each sample in June, although it was the most prominent bacterial group with the greatest change in relative abundance before and after the flood season.

3.2. Diversity of Bacterial Community

3.2.1. Indices of Bacterial Diversity in Sediment at Different Locations

The bacterial diversity and richness of the samples collected at different locations and two time points are shown in Table 1. The high coverage of bacterial communities in each sample (99%) indicates great sequencing depth and reliable data, suggesting that the samples represent the true bacterial communities in the sediment. The Simpson, Shannon, and Chao1 indices were used to analyze and assess diversity within habitats. The Chao1 richness index was relatively low at the bridge over the reservoir at both time points, while index values were relatively high and similar at the other sites. Overall, bacterial abundance, Shannon index, and Chao1 index were slightly higher in June than in October, indicating a slight decrease in both overall abundance and uniformity of bacterial composition following the flood season. However, together with the Simpson index, the results indicate that bacterial communities were highly diverse during both periods.

3.2.2. Comparison of Similarity between Samples

The tree diagram of the unweighted pair group method with arithmetic mean (UPGMA) (Figure 5) provides an intuitive representation of the diversity among habitats based on the samples. It can be observed that the four samples collected in June have a comparatively high similarity, with the June samples being in close proximity to 10-K and 10-B. On the whole, there are some differences between samples before the flood season and samples after the flood season. In addition, the similarity between the four samples of 10-S, 10-M, 10-K, and 10-B and the June samples increases in sequential order.

3.3. Relative Abundance Distribution Trend

The relative abundance heatmap (Figure 6) depicts the differences in bacterial community composition between samples and demonstrates the trends in bacterial abundance distribution across the samples. Figure 6 shows that samples 6-K and 6-B belonged to the same branch before the flood season, exhibiting high similarity in their community structures. However, the similarity with the community structure of 6-M decreased, leading to the independent branching of 6-M. This difference in community structure also explains the substantial difference between 6-B and 6-S. Similarly, the post-flood samples 10-K and 10-M were in the same branch, indicating high similarity in their community structures. However, the similarity with the community structure of 10-B decreased, suggesting an independent branch. The community structures of 10-K and 10-S differed significantly.

3.4. Identification of Key Differential Bacteria at the Genus Level

3.4.1. Quantitative Analysis of the Degree of Difference between Each Sample

The results of PCA analysis are shown in Figure 7. Figure 7a shows that the main genera causing differences in the samples were Acinetobacter, Flavobacterium, Pseudomonas, Arthrobacter, Massilia, and Janthinobacterium, with the largest contributors being Acinetobacter and Flavobacterium.
The distance between the samples in Figure 7b reflects the size of the differences in bacterial richness composition between the samples. The distribution of sample points in June was more concentrated, and the overall composition of bacterial richness was similar. The river channel at the confluence of the tributary was narrower (Figure 1), and the flow velocity at this site was much higher than at other sampling sites, making it difficult for microorganisms and exogenous materials to settle here during the flood season. As a result, the bacterial richness composition at this site after the flood season was more similar to that of the samples before the flood season. Hence, this sample lost its reference value and was not considered in this article. The largest distance between individual samples in October and the overall sample in June was for 10-M, followed by 10-K, and then 10-B. The closer the samples were to the front of the dam, the more similar the microbial bacterial richness composition was to that in June. This may be because, after the upstream water passed through the entrance of the reservoir area during the flood season and due to the rapid widening of the river channel and its entry into the reservoir area, the water flow velocity sharply decreased. Consequently, microorganisms and nutrients from upstream settled and were consumed rapidly. By the time the water reached the dam, the abundance of microorganisms and the content of various substances were greatly reduced, resulting in the final impact on the sediment gradually decreasing and causing the sample values to be closer to those recorded in June. This conjecture aligns with the results presented in Section 3.2

3.4.2. Comparison of Changes in Significantly Enriched Biomarkers before and after the Flood Season

LEfSe analysis was performed based on relative abundance at the genus level to obtain a branching bacteria taxonomic map (cladogram) and to show the hierarchical taxonomic distribution of significantly enriched biomarkers in each group of samples from the community. A histogram of the distribution of LDA values of significantly different bacteria was used to show the significantly enriched bacteria within each group and their degree of importance (p < 0.05, LDA ≥ 4). The distribution of LDA values of significantly different bacteria and their significance values (p < 0.05, LDA ≥ 4) within each group are shown.
Figure 8a shows that the biomarkers before the flood season were mainly Syntrophaceae, Geobacteraceae, Erysipelotrichaceae, Carnobacteriaceae, Bacteroidia, and Nitrosomonadales at the order level, while the post-flood biomarkers were Micrococcaceae, Pseudomonadaceae, and Moraxellaceae. Additionally, many dominant genera were affiliated with these biomarkers: Acinetobacter belonged to Moraxellaceae; Massilia and Janthinobacterium belonged to Oxalobacteraceae; Pseudomonas belonged to Pseudomonadaceae; and Arthrobacter belonged to Micrococcaceae.
Figure 8b shows that the main biomarkers after the flood season were Acinetobacter, Arthrobacter, Pseudomonas, and Moraxellaceae, which highly overlapped with the different bacteria in the principal component analysis and the dominant genera reported in the previous section.

3.4.3. Determination of the Significantly Divergent Genera

Combining the results of bacterial taxonomic composition, principal component analysis, and LefSe analysis, seven categories of dominant genera were selected for ANOVA at the phylum and genus level, and the results showed that there were significant differences between the dominant genera before and after the flood season (p < 0.05 for significant differences).
According to the results shown in Table 2, it can be concluded that the genera that differed before and after the flood season were Acinetobacter, Arthrobacter, Flavobacterium, Pseudomonas, and Massilia, with Acinetobacter showing the most significant difference (p = 0.0041).

4. Discussion

The bacterial community was relatively homogeneous before the flood season, and there was no obvious dominant genus. At this time, Rhodoferax (4%) belongs to the β-Proteobacteria class of facultative anaerobic denitrifying bacteria [38]. The growth of Rhodoferax was favored by the lower winter temperatures. Furthermore, Geobacter (2%) is a group of heterotrophic bacteria widely distributed in aqueous sediments, soils, and various subsurface anaerobic environments [39]. Geobacter can participate in the biogeochemical cycles of carbon, nitrogen, and iron in anaerobic environments through various pathways [39,40]. The bacteria Erysipelothrix (2%) belongs to the genus of facultative heterotrophic bacteria, which are widely distributed in nature and are also common in animal manure and around farms [41,42]. Their presence may be due to animal husbandry in the upper reaches of Zipingpu.
The dominant genera of bacteria after the flood season included Acinetobacter, Flavobacterium, Pseudomonas, and Massilia. The bacteria with the highest relative abundance belonged to Acinetobacter, which is an obligate heterotrophic aerobic type of bacteria, requiring only a small amount of nutrients to survive in large numbers, with an optimum temperature of 35 degrees Celsius [43,44]. Therefore, their outbreak growth period is from June to September, and Acinetobacter often appears in polluted rivers [45]. This bacteria can carry out nitrification and denitrification reactions at the same time and is a common microorganism in the sediment of water bodies. Flavobacterium is a common aerobic denitrifying bacteria that is an important factor in aquatic nitrogen cycles and the restoration of the water environment [46]. With the increase of nutrient concentration, the growth of flavobacterium is significantly stimulated [47], and it has been found that the relative abundance of Flavobacterium also increases during algal blooms [48]. Thus, the presence of many Flavobacterium may be related to the eutrophication of the water body [49,50]. Massilia, an aerobic denitrifying bacterium, is widely found in water, soil, and air environments and has various functions, such as participating in carbon and nitrogen cycles and phosphorus solubilization [51,52,53]. Pseudomonas and Acinetobacter belong to γ-Proteobacteria, have a high abundance in nutrient-rich water bodies, are typical aerobic denitrifying bacteria with strong nitrogen fixation capacity [54,55], and can play a role in water restoration [43]. Pseudomonas, Massilia, Flavobacterium, and Acinetobacter can remove nitrogen and degrade pollutants. According to respiration type, all the dominant genera in the Zipingpu Reservoir after the flood season except Rhodoferax are heterotrophic aerobic bacteria, and all of them have denitrification functions. In the flood season, the reoxygenation capacity of the reservoir is weaker at high temperatures than at low temperatures, the vertical distribution of DO is significant, the substrate oxygen consumption is strong, and the metabolism of organisms is fast [56]. The dissolved oxygen content in the deep layer of the water body is significantly lower than that in the surface layer. In addition, some studies have found that the concentration of dissolved oxygen has a significant impact on the nitrification process of denitrifying bacteria [57]. Aerobic denitrifying bacteria have potential applications in the remediation of reservoir pollutants [58].
Notably, our research uses metabarcoding data, which can qualitatively help us to identify the structure, function, and interrelationships of microbial communities, as well as their responses and changes under environmental conditions at different points in time. However, to deepen the discussion and quantitative evaluation of the differences in community structures, more environmental data and chemical analyses are needed.

5. Conclusions

This study found that the predominant genera in the sediment before and after the flood season were similar at the phylum level, but there were significant differences at the genus level. Before the flood season, there were only a few dominant genera with low relative abundance, whereas after the flood season, the abundance of dominant genera, including Acinetobacter, Flavobacterium, Massilia, Pseudomonas, and Arthrobacter, increased significantly. Sediment microbial communities also exhibited significant differences before and after the flood season, with anaerobic bacteria dominating before the flood season and heterotrophic aerobic denitrifying bacteria dominating after the flood season. In addition, the flood season was found to represent a turning point in the ecological cycle of the reservoir water, with increased richness and species diversity of the bacterial community. This period also provided favorable conditions for the survival and activity of denitrifying bacteria, which play a critical role in the ecological restoration of reservoirs after the flood season [59,60].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d15090946/s1; Table S1: sequence-number, Table S2: rarefied data.

Author Contributions

Conceptualization, M.C. and Y.T.; methodology, X.H. and M.C.; software, X.H.; formal analysis, X.H. and M.C.; investigation, X.H., L.Z., W.H. and N.L.; data curation, X.H. and L.Z.; writing—original draft preparation, X.H.; writing—review and editing, X.H., M.C. and Y.T.; visualization, X.H.; supervision, M.C.; funding acquisition, M.C. and Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Sichuan Science and Technology Program [grant numbers 2023NSFSC0285 and 2021YFQ0068].

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data may be provided upon reasonable request.

Acknowledgments

We would like to thank the reviewers for taking the necessary time and effort to review the manuscript. We extend our sincere gratitude to the researchers (Du) from Shiyanjia Lab (www.shiyanjia.com, accessed on 23 February 2022) for their assistance with the sequence analysis.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of the specific sampling points.
Figure 1. Locations of the specific sampling points.
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Figure 2. The number of microbial taxonomic units at each level.
Figure 2. The number of microbial taxonomic units at each level.
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Figure 3. Histogram of bacterial composition at the genus level before and after the flood season.
Figure 3. Histogram of bacterial composition at the genus level before and after the flood season.
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Figure 4. Histogram of abundance of top 5 bacterial composition in the phylum (a,b) and genus (c,d) before and after the flood season.
Figure 4. Histogram of abundance of top 5 bacterial composition in the phylum (a,b) and genus (c,d) before and after the flood season.
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Figure 5. UPGMA clustering tree diagram for all samples.
Figure 5. UPGMA clustering tree diagram for all samples.
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Figure 6. A heatmap of the relative abundances of the 50 genera with the largest mean abundances. The horizontal clustering trees indicate differences in community structure between two samples, and the vertical clustering trees indicate differences between two genera.
Figure 6. A heatmap of the relative abundances of the 50 genera with the largest mean abundances. The horizontal clustering trees indicate differences in community structure between two samples, and the vertical clustering trees indicate differences between two genera.
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Figure 7. Bacterial load plot (a) and sample two-dimensional ranking plot (b). Each point in the left panel represents a genera, and the horizontal and vertical coordinates of the points represent the magnitude of the contribution of the genera to the difference in the sample in these two dimensions, respectively.
Figure 7. Bacterial load plot (a) and sample two-dimensional ranking plot (b). Each point in the left panel represents a genera, and the horizontal and vertical coordinates of the points represent the magnitude of the contribution of the genera to the difference in the sample in these two dimensions, respectively.
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Figure 8. Taxonomic branching of bacteria (a) and histogram of the distribution of LDA values (b).
Figure 8. Taxonomic branching of bacteria (a) and histogram of the distribution of LDA values (b).
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Table 1. Diversity indices of different samples.
Table 1. Diversity indices of different samples.
Sample NameNumber of Valid SequencesCoverageChao1ShannonSimpson
6S135,70799%7950.3711.670.99
6M107,84099%8575.4811.970.99
6K121,77699%3237.5311.140.99
6B126,63299%7394.8511.940.99
10S124,84299%5041.5210.490.99
10M109,73899%6196.2510.030.99
10K129,28199%3519.538.720.99
10B128,02699%7269.0510.910.99
Table 2. ANOVA results for the dominant bacteria.
Table 2. ANOVA results for the dominant bacteria.
Classification of Differences in Dominant Bacteria p Value
Class LevelDeltaproteobacteria0.01
Gammaproteobacteria0.01
Coriobacteriia0.02
Clostridia0.09
Erysipelotrichia0.04
Betaproteobacteria0.55
Flavobacteriia0.13
Genus LevelAcinetobacter0.01
Arthrobacter0.01
Flavobacterium0.11
Janthinobacterium0.11
Massilia0.12
Pseudomonas0.27
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He, X.; Chen, M.; Zhou, L.; He, W.; Liao, N.; Tuo, Y. Changes in Bacterial Community Structure in Reservoir Sediments before and after the Flood Season. Diversity 2023, 15, 946. https://doi.org/10.3390/d15090946

AMA Style

He X, Chen M, Zhou L, He W, Liao N, Tuo Y. Changes in Bacterial Community Structure in Reservoir Sediments before and after the Flood Season. Diversity. 2023; 15(9):946. https://doi.org/10.3390/d15090946

Chicago/Turabian Style

He, Xianting, Min Chen, Luxin Zhou, Wenyan He, Ning Liao, and Youcai Tuo. 2023. "Changes in Bacterial Community Structure in Reservoir Sediments before and after the Flood Season" Diversity 15, no. 9: 946. https://doi.org/10.3390/d15090946

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