The 23 samples from the sewage outlet of Xinghai Bathing Beach were sequenced in the V4-V5 region of the bacterial 16S rRNA gene by high-throughput sequencing. The number of optimized sequences obtained was 44695–53707 and clustered with 97% similarity to obtain a total of 2815 different OTUs. The OTU range between each site was between 500–1459. The highest value of OTU appeared in the C4 station, indicating higher bacterial diversity in the sewage in the winter. The A8 station 1000 m away from the sewage outlet had the least OTUs (500) in summer, and the number in winter was significantly higher than in summer.
This study statistically analyzed the alpha diversity of all samples, including OTUs, Chao, Shannon, and Simpson diversity. The coverage of all samples was above 99%, indicating that the sequencing depth can reflect the alpha diversity of sampling points. ANOVA analysis results showed that Chao and ACE at the sewage outlet were large (Fig. 2A, Fig. 2B), indicating that the species richness was higher. In addition, the Chao and ACE indexes had a decreasing trend as the distance from the sewage outlet increased, indicating that the closer the sewage outlet, the more the OTUs in the community. Similarly, the Shannon index (Fig. 2C) was highest at the sewage outlet, which decreased with the increase in distance from the outlet. The Simpson index was negatively correlated with other diversity indexes (Fig. 2D), and the value at the sewage outlet was large, indicating a high community diversity. α-diversity results showed that community diversity was inversely proportional to the distance of the sewage outlet.
β-diversity was used to analyze the content of various groups and calculate the difference between samples through Bray-Curtis. Both the presence or absence of OTU and the abundance were considered in the analysis. Figure 3A showed that there were significant differences in microbial diversity with different seasons, and Fig. 3B showed that the difference between summer and winter was the highest. However, the sewage outlet (B1) diversity in summer was more similar to winter.
Distribution of microbial community structure
The OTUs of the bacterial community in the water samples from the sewage outlets of Xinghai Bathing Beach in different seasons were clustered to obtain the main 11 phyla, 49 classes, 77 orders, 109 families, 181 genera, and 198 species. The phyla level analysis of the bacterial community structure in the sewage is shown in the Fig. 4A. The bacteria in different seasons were composed of 36 phyla, including Proteobacteria, Bacteroidetes, Actinobacteria, Cyanobacteria, among which the four dominant bacteria were: Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes. The Proteobacteria (70.9%) dominated the sewage outlet seawater samples, followed by the Bacteroidetes (20.7%), Actinomycetes (2.7%), and Firmicutes (2.1%), and the other phyla were lower than 2%. In the three seasons, the relative abundance of the Bacteroidetes initially decreased and then increased with a decrease in temperature. In contrast, the relative abundance of Actinobacteria increased first and lowered with a decrease in temperature. The relative abundance of Firmicutes in autumn was much lower in summer and winter.
A total of 32 phyla were detected in summer, and the relative abundances of Nitrospirae and Acidobacteria closely related to sewage treatment in summer were 0.01% and 0.06%, respectively. The unique bacteria phyla in summer were Omnitrophica, the relative abundance being much higher than the other two seasons. Nitrospirae, Saccharibacteria, Yanofskybacteria, Hydrogenedentes were not detected in the summer. The rare bacteria phyla in autumn were Actinobacteria (Fig. 4A). Besides, Magasanikbacteria, Chlamydiae, Aminicenantes, Nomurabacteria, and Yanofskybacteria were not detected in sewage compared with the other two seasons. The rare bacteria phyla in winter were Deinococcus, compared with the two seasons. Most bacteria phyla were detected in sewage in winter, indicating that the highest microbial diversity in sewage from sewage outlets is in winter.
A total of 77 bacterial orders (including the unclassified parts) were detected in 23 samples. The main orders are shown in Fig. 4B. Flavobacteriales occupied the prominent position at all stations (average relative abundance 18.5%), followed by Rhodobacterales (18.1%), Oceanospirillales (16.6%), and Alteromonadales (11.7%). Through longitudinal seasonal comparison, it was found that the relative abundance of Alteromonas in each station in autumn was significantly lower than the other two seasons. The relative abundance of Alteromonas in station A8 in summer was the highest (38.3%), whereas Rhodobacter was the lowest (6.6%).
The detailed composition of each in-situ bacterial community was revealed by heat maps (Fig. 5). At the genus level, the most detectable genus among the 23 samples at the Xinghai sewage outlet was Glaciecola of the Alteromonadaceae family, with an average relative abundance of 8.2%. Its relative abundance was higher in summer and winter, and lower in autumn. The second detectable genus was Marinobacterium of Oceanospirillaceae, with an average relative abundance of 6.8%. From the perspective of longitudinal seasons, the average relative abundance of Synechococcus from Cyanobacteria in autumn was significantly higher than in the summer and winter. The average relative abundance of Lentibacter from Rhodobacterales in summer and winter was significantly higher than the autumn. The relative abundance of Fictibacillus in different seasons (especially autumn and winter) at each station was low (0.3%). The unclassified genera account for 43.7% of all sequences.
Temporal and spatial distribution characteristics of pathogenic bacteria abundance
To explore the changes in the abundance of fecal pollution indicator bacteria (FIB) and pathogenic bacteria in the sewage from the Xinghai sewage outlet with the season and the distance from the sewage outlet, the abundances of E. coli, Enterococcus, Staphylococcus aureus, and fecal coliform were determined. Figure 6 showed the changing trends of the pathogenic bacteria in different seasons. At the same distance from the sewage outlet, S. aureus was the most common bacteria in seawater, followed by fecal coliforms and E. coli, while Enterococcus was least in number. Water temperature and the total number of E. coli was found to decrease with the months (Fig. 6A). Although Enterococcus showed similar trends, it was more affected by temperature, and a decrease in terms of abundance was evident (Fig. 6B). The fecal coliforms showed an initial decrease in trend, which then increased, with the lowest bacterial abundance in autumn (Fig. 6C). S. aureus was not significantly affected by temperature, and change in the pattern was not evident (Fig. 6D). Therefore, the seasonal change in temperature is an essential factor influencing the number of microorganisms in the sewage. Besides, the optimal temperature for the survival of different microorganisms varied. Monitoring the change in the bacterial load in the water samples from different stations at the sewage outlet every month showed a decreasing trend of bacteria as the distance from the sewage outlet increased. However, some stations, such as the S6 station in winter, had exceptional circumstances, where the number of enterococci significantly increased, quite different from other stations.
Correlation between four typical pathogenic bacteria and the microbial community
Pathogenic bacteria are abundant in the sea area adjacent to the sewage outfall. In this study, E. coli, Enterococcus, S. aureus, and fecal coliforms were cultured, quantified, and combined with the sequencing results. Spearman’s method was used to analyze the correlation between the four typical pathogenic bacterias and the different levels of community structure in sewage. At the phylum level, Tenericutes showed a significant positive correlation with S. aureus. Fecal coliforms were significantly positively correlated with Firmicutes and Bacteroidetes and negatively correlated with Tenericutes and Proteobacteria. Firmicutes and Bacteroidetes were also significantly positively correlated with E. coli, while Planctomycetes, Verrucomicrobia, Actinobacteria, and Marinimicrobia were significantly negatively correlated. Planctomycetes, Verrucomicrobia, Actinobacteria, and Marinimicrobia was found to have a strong negative correlation with Enterococcus (Fig. 7A).
At the order level (Fig. 7B), the bacteria that showed a significantly positive correlation with S. aureus were Cellvibrionales and Bdellovibrionales, while Selenomonadales and Aeromonadales were significantly negatively correlated.; Most bacteria showed a positive correlation with fecal coliforms, including Burkholderiales, Bacteroidales, Clostridiales, Aeromonadales, etc. The results showed that the negative correlation coefficient between each bacteria and the fecal coliform was small. Similarly, bacteria that showed a significant positive correlation with E. coli were mainly Oceanospirillales and Flavobacteriales, etc. In addition, Acidimicrobiales, Rickettsiales, Methylophilales, Cytophagales, Rhizobiales showed a large negative correlation coefficient with E. coli. Similarly, Enterococcus also had a significant negative correlation with Acidimicrobiales, Rickettsiales, and other bacteria, and a significant positive correlation with Oceanospirillales and Bacillus.
At the genus level, the bacteria with significant positive correlation with E. coli were Mesoflavibacter, Aquibacter, Salinihabitans, and Halioglobus, while Punieispirillum, Pelagibacter, and Actinomarina of Candidatus were significantly negatively correlated (Fig. 7C). In addition, Amylibacter, Balneola, and Fluviicola were also significantly negatively correlated. Similarly, Mesoflavibacter, Aquibacter, Salinihabitans, Halioglobu, Alteromonas, Erythrobacter, Psychrosphaera, etc. were all significantly positively correlated with Enterococcus, while bacteria with negative correlation with enterococci were similar to that of E. coli. The fecal coliforms were significantly positively correlated with Colwellia, Thiothrix, Hydrogenophaga, etc., and had a strong negative correlation with Synechococcus and Formosa. Fewer bacteria were correlated with S. aureus. Among them, bacteria with negative correlations include Lentibacter, Arcobacter, etc., and bacteria with a strong positive correlation were Synechococcus and Formosa.
Influence of environmental factors on microbial community
A total of 23 samples were collected in the three seasons, summer being the tourist season. Therefore, there were a large number of tourists in the sea near the sewage outlet. The surface seawater temperature varied greatly due to the three seasons, ranging from 25.2°C to 7.8°C, salinity (S) varied slightly with seasons, ranging from 29.83 to 32.11, dissolved oxygen (DO) ranged from 12.14 to 4.24, the pH ranged from 7.7 to 8.0. The conductivity (Cond) ranged from 32011 µS/m to 47408 µS/m, and the ORP ranged from − 74.1 NTU to 45.3 NTU (Table S1). In living cells, aerobic cell potential was high, and anaerobic cell potential was low. Enzyme activity, cell assimilation ability, as well as microbial growth and development, were also affected by redox potential.
OTU abundance and the measured environmental parameters were used for redundancy analysis (RDA) to determine the specific environmental factors that can explain the composition of the bacterial community in the sewage from the sewage outlet (Fig. 8). The first two axes explain 40.16% and 9.91% of the cumulative variance, respectively. The effects of environmental factors on community structure were significantly affected by seasonal changes. In summer, water temperature, electrical conductivity, and DO were the main influencing factors, among which water temperature and electrical conductivity showed a significant positive correlation with community structure, and DO showed a significantly negative correlation. In autumn, salinity, pH, and ORP were the main variables that showed a positive correlation with community structure. The influence of environmental factors on community structure in winter was opposite to that in summer. DO was the primary determinant with a significant positive correlation with bacterial community structure, and water temperature and electrical conductivity had a significant negative correlation. It was observed that with the change in seasons, the main environmental factors affecting the bacterial community structure in sewage were different. Thus, RDA cannot explain changes in bacterial community structure caused by unmeasured environmental variables and processes, such as virus lysis. These processes have also been proven to affect bacterial community composition.