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Diurnal changes in bacterial communities in oxic surface and hypoxic middle seawater layers of the Changjiang River Estuary
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Yan Huang1, Lei Yuan1, Yingping Fan1, Habib U Rehman Jakhrani1, Jianxin Wang1, *
Acta Oceanologica Sinica | 2021, 40(4) : 92 - 106
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Acta Oceanologica Sinica | 2021, 40(4): 92-106
• Marine Biology •
Diurnal changes in bacterial communities in oxic surface and hypoxic middle seawater layers of the Changjiang River Estuary
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Yan Huang1, Lei Yuan1, Yingping Fan1, Habib U Rehman Jakhrani1, Jianxin Wang1, *
Affiliations
  • 1 Marine Microorganism Ecological & Application Laboratory, Zhejiang Ocean University, Zhoushan 316022, China
Published: 2021-04-25 doi: 10.1007/s13131-021-1778-2
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The Changjiang River Estuary (CRE) in the East China Sea suffers from seasonal hypoxia in summer. The vertical distributions and seasonal changes of microbial communities in the CRE were well documented. However, little is known about the diurnal changes of bacterial communities in the hypoxic zone of the CRE. Here, 16S rRNA gene analysis was used to explore the changes of bacterial communities in the oxic surface and hypoxic middle seawater layers during 24 h in the CRE. Significant differences between the hypoxic and oxic layers were observed: the phyla Cyanobacteria, Bacteroidetes and Acidimicrobiia were enriched in the oxic layer, whereas the phylum SAR406 and the class Deltaproteobacteria were more abundant in the hypoxic layer. In addition, some subtle diurnal variations of the bacterial relative abundance were found in both two layers. The relative abundance of Synechococcus increased at night, and this change was more obvious in the hypoxic layer. The similar trend was also found in some phototrophic and several heterotrophic bacteria, such as Rhodobacteraceae, OM60 and Flavobacteriaceae. Their relative abundances peaked at 16:00 in the oxic layer, while the relative abundances peaked at around 7:00 and decreased until 13:00 in the hypoxic layer. Together, the results of the present study suggest that some photosynthetic bacteria and several heterotrophic bacteria have similar diurnal variations implying the light and physicochemical heterogeneity in the course of a day are important for bacterial diurnal changes in the CRE.

bacterial communities  /  diurnal changes  /  hypoxic zone  /  the Changjiang River Estuary
Yan Huang, Lei Yuan, Yingping Fan, Habib U Rehman Jakhrani, Jianxin Wang. Diurnal changes in bacterial communities in oxic surface and hypoxic middle seawater layers of the Changjiang River Estuary[J]. Acta Oceanologica Sinica, 2021 , 40 (4) : 92 -106 . DOI: 10.1007/s13131-021-1778-2
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1 Introduction
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Marine hypoxic zones are regions with dissolved oxygen (DO) concentration below 2 mg/L (Diaz, 2001; Rabalais and Turner, 2001; Chen et al., 2007; Chi et al., 2017). Hypoxia has become an environmental problem of public concern, because of its damage of the structure and function of ecosystems and its increasing number (Conley et al., 2011). For example, hypoxia usually destroys coastal ecosystems and affects fisheries through food web interactions (Zhu et al., 2011). In addition, hypoxia can change the natural redox conditions, then impacts the material cycles (Turner et al., 2008; Bianchi and Allison, 2009).
The Changjiang River Estuary (CRE) located offshore from the mouth of the Changjiang River is a region that has complex hydrological conditions (Yang et al., 2012). It is also strongly influenced by water masses and ocean currents. The CRE and its adjacent waters are attacked by the Changjiang Diluted Water (CDW), the Taiwan Warm Current (TWC), terrestrial riverine runoff and the Kuroshio branch in summer and suffer from eutrophication for decades (Zhang et al., 1999). Interestingly, seasonal hypoxia (up to 10 000 km2) is easily formed in near-bottom water in the region off the CRE in summer (Li et al., 2002; Zhu et al., 2011), which may be caused by freshwater input, thermal warming and the decomposition of deposited organic matter from rivers mediated by microorganisms (Diaz and Rosenberg, 2008; Lohrenz et al., 2008; Grenz et al., 2010).
Low DO or hypoxia, can cause significant disturbances on the composition of microbial loop and changes in biogeochemical cycles (Naqvi et al., 2000), resulting from the indispensable role of oxygen in aerobic metabolism (Sato et al., 2016). How bacterial communities respond to hypoxia is vital for understanding the structure and function of estuarine ecosystems. Up to now, several studies on bacterial communities in the CRE hypoxic zones have been reported (Liu et al., 2012; Ye et al., 2016; Wu et al., 2019). Distinct microbial community differences were observed between hypoxic and oxic zones. For example, Alphaproteobacteria, SAR406 and Deltaproteobacteria were more abundant taxonomic groups in the hypoxic zones (Wu et al., 2019). Recently, a study explored the temporal distribution of bacterial communities of seawater in the CRE hypoxic area in June, August and October (Liu et al., 2012). They found that Gammaproteobacteria, Cytophaga–Flavobacteriia–Bacteroides (CFB), Deltaproteobacteria, Cyanobacteria and Firmicutes are the dominant groups, and seasonal environmental heterogeneity may be the reason for the shift of bacterial communities in the three months. However, knowledge of variations in bacterial communities during a short-term period (e.g., 1 d) in the CRE is still scarce. Only the diurnal variations of environmental factors and phytoplankton distributions have been studied in the CRE (Gao and Song, 2005; Gao et al., 2007; Yan et al., 2012; Lou and Hu, 2014).
There have been indications that bacteria are influenced by short-term fluctuation of growth conditions (e.g., light, temperature and nutrients) in marine waters (Suyama et al., 2002; Ruiz-González et al., 2012; García et al., 2018). Thus far, circadian rhythms of algal growth, bacterial gene transcription and gene expression have been well studied in open ocean sites (Vaulot et al., 1995; Gilbert et al., 2010; Ottesen et al., 2013, 2014), seafloor slopes (Fuhrman et al., 1985) and coastal waters (Gilbert et al., 2010; Ottesen et al., 2014). Like most phytoplankton, phototrophic Cyanobacteria was mostly studied with regards to diurnal changes in various aspects. For example, Lefort and Gasol (2014) found that Synechococcus grew during the light period and divided at night, whereas Prochlorococcus displayed the opposing pattern in winter in the coastal waters of the Northwest Mediterranean Sea. In addition, anaerobic photosynthetic bacteria, performing photosynthesis with sulfide, hydrogen or organic substrates, were reported to have higher viability of cells under light conditions than in dark conditions without nutrients (Suyama et al., 2002). Photosynthetic release and excretion by grazing activities are considered to be the main sources of dissolved organic matter (DOM) for most marine bacteria in oceanic environments (Suyama et al., 2002), and the generation of DOM is usually synchronized with the circadian cycle (Johan et al., 1990), which may lead to some heterotrophic groups showing corresponding variations.
In view of close associations within microbial loop (Porter, 1996), different preferences for organic substrates or phytoplankton species (Pinhassi et al., 2004; Alonso-Sáez and Gasol, 2007), and different sensitivities to sunlight or phototrophic abilities (Béjà et al., 2000; Kolber et al., 2000; Alonso-Sáez et al., 2006), this study assumes that bacterial groups are associated with the environmental conditions and might display different amplitudes and rhythms in their diurnal variations in the oxic and hypoxic layers in the CRE. In this study, the assumption by exploring the variation of bacterial community composition and diversity using 16S rRNA gene analysis was tried to be examine.
2 Materials and methods
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2.1 Sample collection
During a summer cruise in July 2016, a total of 18 seawater samples were collected every three hours from 16:00 (July 22, 2016) to 16:00 (July 23, 2016) at the same geographical location (Station D3, 30°59.39′N, 122°46.26′E) in CRE (Fig. 1). Seawaters from the oxic surface layer (1.95–2.1 m in depth, abbreviated as S) and the hypoxic middle layer (9.95–10.0 m in depth, abbreviated as M) were respectively collected at each time node (Table 1). For each sample, 2 L seawater was collected using Niskin bottles mounted to an SBE32 CTD (Sea-Bird Electronics, USA). In order to remove large organisms and particles, each water sample was pre-filtered through a filter with 3 μm pore size (with a gentle vacuum pressure less than 33.3 kPa) and then filtered through a 0.22 μm filter (Millipore Corporation, USA) to collect the free-living microbial cells. Samples were temporarily stored at –20°C on board, then were transferred to –80°C in the laboratory until the extraction of DNA. The samples were grouped according to the sampling time into two groups: Day (6:00–18:00, abbreviated as D) and Night (18:00–24:00 and 0:00–6:00, abbreviated as N) (Ottesen et al., 2014; García et al., 2018). Each sample was named using the abbreviation of the layer, followed by the sampling time number. For example, the S19 sample was taken at 19:00 from the oxic surface seawater. The S16_1 represents the surface sample collected at 16:00 on the first day.
2.2 Physicochemical measurements of seawater
For each sample, the physicochemical parameters of seawater, including depth, salinity and temperature, were documented using the CTD in situ. Other parameters of seawater, including the concentrations of nitrate (${{\rm {NO}}_3^-} $), nitrite (${\rm {NO}}_2^- $), chemical oxygen demand (COD), silicate (${{\rm {SiO}}_3^{2-}} $), phosphate (${{\rm {PO}}_4^{3-}} $) and DO, were measured according to the General Administration of Quality Supervision Inspection and Quarantine and Standardization Administration of China (2007) prior to chemical parameter analysis (Wu et al., 2019). ${{\rm {NO}}_3^-} $ was determined using the copper-cadmium column reduction method. ${\rm {NO}}_2^- $ was determined using the diazo coupling method. COD was determined using the alkaline potassium permanganate method. ${{\rm {SiO}}_3^{2-}} $ was determined using the silicon molybdenum blue method. ${{\rm {PO}}_4^{3-}} $ was determined using the phosphorus molybdenum blue method. The DO value was determined using the iodometric method. According to the definition of hypoxia, the middle layer samples could be defined as hypoxic (DO≤2 mg/L), whereas the surface samples could be defined as oxic (DO>2 mg/L).
2.3 Microbial sampling and DNA extraction
The DNA of all the samples was extracted using a FastDNA spin kit for soil (MP Biomedicals, USA), according to the manufacturer’s instruction. The DNA extracts were quantified using a NanoDrop ND2000 (Thermo Fisher Scientific, USA) and were subsequently submitted to Biozeron Co. (China) for 16S rRNA gene amplification (V4–V5 region) using dual-indexed bacterial barcoded primer pairs 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′) (Stubner, 2002). Before performing PCR, the barcode sequences were linked to the primers during primer synthesis. The PCR was performed under the following program: denaturation at 95°C for 3 min, followed by 35 cycles of amplification (95°C for 30 s, 55°C for 30 s and 72°C for 30 s) and a final extension of 10 min at 72°C. Finally, purified PCR products were sequenced on the Illumina HiSeq platform. The raw sequence data in this study were deposited in GenBank under BioProject PRJNA632685 (BioSample Accessions: SRR11787864–SRR11787881).
2.4 16S rRNA gene data processing and analysis
The raw reads FASTQ files were processed and analyzed using the QIIME v1.8.0 bioinformatics program (Caporaso et al., 2010). The original paired reads were joined with fast length adjustment of short reads with the default settings (Magoč and Salzberg, 2011). Reads were assigned to each sample based on their unique barcodes. Chimeric sequences were detected using the UCHIME module of the USEARCH program (Edgar et al., 2011). The full dataset (n=18) contained 655 588 clean reads. After filtering the chimeric reads, the sequences with 97% similarity were clustered into operational taxonomic units (OTUs) using the pick_open_reference_otus.py script in QIIME (Caporaso et al., 2010). The taxonomy composition of OTUs was determined according to the Greengenes database (13_8_release) (DeSantis et al., 2006). Some taxa annotated as “Chloroplast” “Eukaryota” “Archaea” and “Mitochondria” were filtered from the OTU table using filter_taxa_from_otu_table.py script in QIIME (Caporaso et al., 2010).
For the diversity calculations, the OTU table was subsampled (rarefied) by randomly removing pseudo-random sequences using the single_rarefaction.py script in QIIME (Caporaso et al., 2010). The sequence number after subsampling was 20 653 in each sample. Then the subsampled OTU table was used to calculate the alpha diversity of each sample using the alpha_diversity.py script in QIIME (Caporaso et al., 2010). The diversity and richness indexes, including observed OTUs, Shannon and Chao1 (Lee and Chao, 1994), are shown in Table A1. To compare the diversities and richness of bacterial communities in different groups (DS, NS, DM and NM) named by the sampling time (day or night) and water layers (surface or middle), alpha diversity indexes were analyzed using the function ggsignif in the ggplot2 package (Wickham and Chang, 2009). The beta diversity was explored with the Bray-Curtis dissimilarity index using the function vegdist of the vegan package in R (Bray and Curtis, 1957; Oksanen et al., 2010).
2.5 Statistical analysis
The statistical analyses were performed in the R statistical environment (Oksanen et al., 2010). A heatmap was plotted to visualize the Spearman correlations among the environmental factors using the function ggcorplot in the ggplot2 package (Wickham and Chang, 2009). The alpha diversity indexes were presented as the means ± standard deviation (SD), and the Wilcoxon test was used to make pairwise comparisons (nonparametric data). To estimate the similarity among the samples, the data of the bacterial OTUs was processed using non-metric multidimensional scaling. Detrended correspondence analysis was used to select a linear multivariate redundancy analysis (RDA) or a unimodal ordination method (Canonical Correspondence Analysis) (ter Braak and Smilauer, 2002). Given the length of the first axis of the detrended correspondence analysis (1.058 SD<4 SD), RDA combined with a Monte Carlo permutation test (999 permutations) was probably the most suitable method to analyze the influence of environmental variations on microbial communities. Most of the collinear factors were eliminated, leaving six factors: depth, ${{\rm {NO}}_3^-} $, ${{\rm {NO}}_2^-} $, COD, ${{\rm {PO}}_4^{3-}} $ and pH. Hellinger was used to standardize the OTU data, and the dissimilarity in environmental factors was log-transformed. Analysis of similarity (ANOSIM) was used to test the dissimilarity of environmental factors, which was calculated based on the Bray-Curtis distance. Spearman indexes were used to investigate the correlation among environmental factors or between the environmental factors and bacterial community composition at the family level.
3 Results
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3.1 Environmental variations in seawater
The environmental factors that described the geographical location and the water properties of the studied site were summarized in Table 1. Briefly, the salinity of the oxic samples was distinctly lower than that of the hypoxic ones (p<0.001). Inversely, the concentrations of ${{\rm {NO}}_3^-} $ and DO in the surface seawater were distinctly higher than those in the middle water (p<0.001). Notably, the temperature, and the ${{\rm {NO}}_3^-} $, ${{\rm {NH}}_4^+} $ and ${{\rm {PO}}_4^{3-}} $ concentrations in Sample M22 were the lowest among the hypoxic samples. In the hypoxic layer, only the concentrations of ${{\rm {NO}}_3^-} $ (p<0.05) and temperature (p<0.05) were significantly different between day and night, while there was no distinct difference (p>0.05) in the oxic samples. To evaluate the relationships among these variables, correlation coefficients (Spearman) and significances were determined (Fig. A1). ${{\rm {NO}}_3^-} $ was positively correlated to temperature (r=0.91, p<0.05) and DO (r=0.91, p<0.05). Meanwhile, salinity was distinctly negatively related to ${{\rm {NO}}_3^-} $ (r=–0.87, p<0.05) and temperature (r=–0.94, p<0.05).
3.2 Alpha and beta diversity of bacterial communities
In total, 655 588 bacterial sequences with high quality were obtained from all the samples. Briefly, the observed OTUs index of the bacteria ranged from 323 to 468; the Chao1 index of the bacteria ranged from 438.11 to 645.75; the Shannon index of the bacteria ranged from 5.05 to 5.75 (Table A1). Chao1 index indicates the extent of the community richness. Here, Chao1 index for DS was significantly lower than DM (p<0.01, Wilcoxon test; Fig. 2a), which indicated in the daytime bacterial community had lower richness in the oxic layer than that in the hypoxic layer. The Shannon index represented the degree of community diversity. There was no significant difference in the Shannon index between each two groups (p>0.05, Wilcoxon test; Fig. 2b), except between DS and NM (p=0.016, Fig. 2b). Overall, the bacterial richness and diversity in the two layers were quite different, whereas there were no obvious differences on the bacterial richness and diversity of each water layer during 24 h. Beta diversity was measured to capture changes in community composition across different environments. Patterns in microbial community structure among samples were collectively examined using ANOSIM and non-metric multidimensional scaling based on the Bray-Curtis distance ordinations of the OTUs (Singh et al., 2015). As shown in Fig. 3a, the oxic and hypoxic groups were separated by the first axis. ANOSIM also portrayed the significant difference in the bacterial community structure between the depth groups (r=0.410 5, p=0.004). However, there was no significant difference observed according to the time (r=–0.012 7, p=0.462; Fig. 3b). These results indicated that there were more significant variations in community structure across depths than between day and night.
3.3 Composition of bacterial communities
In all samples, the dominant phyla were Proteobacteria (52.9%–75.2%), Bacteroidetes (9.7%–26.2%), Actinobacteria (5.1%–13.1%) and SAR406 (2.0%–9.3%) (Figs 4a and c). The percentages of Bacteroidetes (p<0.05), Actinobacteria (p<0.05) and Cyanobacteria (p=0.113) in the surface samples were slightly higher than those in the seawater samples of middle layer. However, the percentage of SAR406 (p=0.006) in the oxic samples was noticeably lower than in the hypoxic ones. It was also found that the relative amount of Cyanobacteria (p=0.122) in the daytime group was higher than that in the night group. In contrast, the relative amounts of SAR406 (p=0.181 2), ZB3 (p<0.05) and Verrucomicrobia (p<0.1) displayed the opposite trend. Notably, compared with each individual sample, Cyanobacteria (4.9%), ZB3 (3.1%) and Chloroflexi (1.4%) showed the highest relative abundances in S16_1, S22 and M22 samples, respectively.
At the class level, Alphaproteobacteria (17.7%–42.7%), Gammaproteobacteria (17.3%–37.5%), Flavobacteriia (9.0%–24.1%) and Acidimicrobiia (4.9%–20.9%) were the most abundant clades (Figs 4b and d). More pronounced fluctuations in bacterial community composition were observed at the class taxonomic level. In particular, for AB16 (p<0.01) and Deltaproteobacteria (p<0.01), their percentages in the oxic layer were distinctly lower than those in the hypoxic layer. In addition, Flavobacteriia (p<0.05), Acidimicrobiia (p<0.05), Betaproteobacteria (p=0.27) and Synechococcophycideae (p=0.1) were more abundant in the oxic samples. The percentage of AB16 (p=0.17) was slightly higher in the night samples than that in the daytime ones. In contrast, higher relative abundances of Alphaproteobacteria (p=0.05), Betaproteobacteria (p=0.25), Flavobacteriia (p<0.1) and Synechococcophycideae (p=0.13) were observed in daytime samples. As mentioned above, Synechococcophycideae, which is affiliated to Cyanobacteria, was the most abundant class in S16_1. Notably, compared with other samples, S22 and M22 contained relatively high percentage of BS119, which belongs to the ZB3 clade.
A heatmap that exhibited the relative abundance of the major classified families (the top 30 families) was summarized in Fig. 5. It shows the relative abundance of each family in each sample and highlights the differences in the two seawater layers and different time nodes. The relative abundances of Pelagibacteraceae (p=0.38), Rhodospirillaceae (p=0.01) and A714017 (p<0.01) were relatively high in hypoxic samples, whereas OCS115 (p<0.05), Flavobacteriaceae (p<0.1), Cryomorphaceae (p<0.05) and OM60 (p=0.05) showed the reverse trend. In both layers, Pelagibacteraceae were more abundant in the daytime, whereas the abundances of Flavobacteriaceae and Rhodobacteraceae were relatively high from 16:00 to 19:00. Synechococcus and Prochlorococcus, which are affiliated with the Cyanobacteria group, exhibited low ratios in all samples (Fig. 6). The relative abundance of Prochlorococcus was unexpectedly low. The changes in the relative abundances of Synechococcus and Prochlorococcus in the oxic layer were not correlated (r=0.009 2, p>0.1). In contrast, the trends in relative abundances of the two genera were significantly correlated in the hypoxic samples (r=0.52, p<0.05). Synechococcus species were more abundant in the oxic samples than those in the hypoxic samples. In both oxic and hypoxic layers, the percentage of Synechococcus was relatively low at 1:00. Synechococcus presented some differences over the course of the 24 h period in the two layers. In the oxic water, the concentration of Synechococcus at 16:00 (4.89%) was highest, then decreased until 4:00 (1.89%). Reversely, the percentage of Synechococcus peaked at M10 (10:00; 14.57%) in the hypoxic samples. The diel changes of the genus Synechococcus and the families Flavobacteriaceae, Rhodobacteraceae and OM60 were displayed in Fig. 7. In the oxic samples, Rhodobacteraceae correlated with Synechococcus and Flavobacteriaceae (r>0.5, p<0.1). In the hypoxic samples, Rhodobacteraceae and OM60 displayed similar trends to Synechococcus with respect to the diel cycle (r≥0.45), except for Flavobacteriaceae. The diel changes in the hypoxic zone were more stable than those in the oxic zone. It was found that the relative abundances of these three families (Flavobacteriaceae, Rhodobacteraceae and OM60) decreased in the evening (16:00–1:00), increased to maximum values at 7:00, then decreased to minimum values at 13:00, before increasing again. In view of close associations within microbial loop, correlation coefficients of the relative abundance between Synechococcus and the top 29 families (except Synechococcaceae) were summarized in Table A2. In the oxic layer, Synechococcus was correlated with HTCC2188 (r<–0.6, p<0.1), Nitrospinaceae (r<–0.7, p<0.05), and Rhodobacteraceae (r>0.5, p<0.1). In the hypoxic layer, Synechococcus was correlated with OM60 (r>0.45, p=0.18), wb1_P06 (r>0.6, p<0.1), A714017 (r<–0.6, p<0.1) and C111 (r>0.6, p=0.05).
3.4 The correlation between bacterial communities and environmental variations
RDA was used to analyze variation of the community structure at the OTU level as a function of environmental factors including ${{\rm {NO}}_3^-} $, ${{\rm {NO}}_2^-} $, COD and ${{\rm {PO}}_4^{3-}} $ concentrations, depth and pH. Collectively, these data explained 40% of the variation in community structure (Fig. 8). On the horizontal axis (RDA1, 30% of constrained variability), the most influential constraining variable was depth (biplot score, 0.88) followed by ${{\rm {NO}}_3^-} $ (biplot score, –0.68). On the vertical axis (RDA2, 10% of constrained variability), the most influential constraining variable is ${{\rm {PO}}_4^{3-}} $ (biplot score, –0.40), followed by COD (biplot score, –0.26). The permutest function results showed that six factors (depth, ${{\rm {NO}}_2^-} $, ${{\rm {NO}}_3^-} $, pH, COD and ${{\rm {PO}}_4^{3-}} $) were significantly related to the variation in bacterial community composition (p<0.05). However, this result did not represent each factor’s contribution. The function envfit was used to measure each factor onto an ordination. The depth, salinity, temperature and DO were significantly related to the bacterial community structure (r>0.45, p<0.05). However, ${{\rm {PO}}_4^{3-}} $ and ${{\rm {NO}}_2^-} $ had little effect on the bacterial community (r<0.2, p>0.1). In addition, the RDA1 axis clearly separated the oxic and hypoxic samples.
To further identify the relationship between environmental factors and the bacterial community structure (the top 30 families), a heatmap was constructed based on agglomerative hierarchical clustering with complete linkage to provide an overview of the identified connections among the studied samples (Fig. 9). Depth, salinity, temperature, DO and ${{\rm {NO}}_3^-} $ were significantly correlated with some families. With respect to the bacterial communities, AEGEAN_112, Nitrospinaceae, Piscirickettsiaceae, A714017 and AEGEAN_185 were positively correlated with depth and salinity (r>0.52, p<0.05), but were negatively related to temperature and ${{\rm {NO}}_3^-} $ (r<–0.59, p<0.05). Flavobacteriaceae and Cryomorphaceae displayed the same results: the relative abundances were negatively related to depth and salinity (r<–0.49, p<0.05), but positively correlated with temperature and ${{\rm {NO}}_3^-} $ (r>0.46, p<0.05). Several families of the class Gammaproteobacteria were significantly related to most environmental factors. These families, including Piscirickettsiaceae, Alteromonadaceae, Thiohalorhabdaceae and Vibrionaceae, were negatively related to COD, ${{\rm {NO}}_2^-} $, DO, temperature and ${{\rm {NO}}_3^-} $ (r<–0.47, p<0.05). The families Methylophilaceae and OM60 clades were positively associated with DO and pH (r>0.60, p<0.01). A number of families were also found to be associated with depth, salinity and temperature. Combining the RDA with a Monte Carlo permutation test (Fig. 8), depth was linearly related to some factors (such as salinity, temperature and pH), and was a proxy for a number of other factors that potentially have more direct effects on the microorganisms. Light, pressure and other physical factors can all vary with depth, and the interaction of these factors can influence the ecological communities.
4 Discussion
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4.1 Comparison of bacterial communities between oxic and hypoxic layers
In our study, relatively distinct bacterial communities were observed between the oxic and hypoxic layers. In addition, the richness and diversity of bacteria in the hypoxic layers were greater than the oxic ones, as found in the northern Gulf of Mexico (Campbell et al., 2019) and northwestern Mediterranean Sea (Pommier et al., 2010). The relative abundances of some phyla, like Cyanobacteria, Bacteroidetes and Acidimicrobiia, were greater in the oxic layer (p<0.05), whereas the phylum SAR406 and the class Deltaproteobacteria were more abundant in hypoxic conditions (p<0.05), which were also observed abundant in the low oxygen (2 mg/L<DO<3 mg/L) water layers in the previous study of the CRE in July 2016 (Wu et al., 2019).
As expected, the groups enriched in the oxic layer were mostly phototrophic (Cyanobacteria) or aerobic bacteria (Acidimicrobiia and Flavobacteriia). Bacteroidetes, in particular the class Flavobacteriia, are specialized in the degradation of polysaccharides, which are the constituents of marine algae (for example, xylan in red algae, and laminarin in brown algae and diatoms) (de Jesus Raposo et al., 2013; Synytsya et al., 2015). Previous studies have found that diatoms are the dominant species of phytoplankton in summer in the CRE (Lin et al., 2008; Ye et al., 2016; Fan et al., 2019). Ye et al. (2016) ever found that the diatoms bloomed in August around 31°N, 123°E where is quite close to the sampling site of this study, indicating diatoms grow in this area. As the primary colonizers of marine phytoplankton, Flavobacteriia has been shown to be more abundant during phytoplankton blooms, especially in the coastal surface waters (Alonso et al., 2007; Teeling and Amann, 2012). A high proportion of Flavobacteriia share the characteristic of gliding motility, which allows them to grow on some algal cells and utilize phytoplankton-derived polysaccharides (Mann et al., 2013; McBride et al., 2009; Qin et al., 2010; Tang et al., 2017). The family Cryomorphaceae has been shown to exhibit great similarity to Flavobacteriaceae according to phenotype and genotype (Bernardet et al., 1996; Bowman, 2014). In addition, Cryomorphaceae is also associated with phytoplankton blooms (Pinhassi et al., 2004). Marine algae converts a substantial fraction of fixed carbon dioxide into various polysaccharides, which suggested that more marine carbon cycling may occur in the oxic layers (Cottrell and Kirchman, 2000; Isao et al., 1990; Richardson and Jackson, 2007).
SAR406 (Marine Group A), also named as Marinimicrobia, is ubiquitous in many deep oceans (Rinke et al., 2013; Gordon and Giovannoni, 1996; Wright et al., 2012). The SAR406 clade is also particularly abundant in oxygen minimum zones (Gordon and Giovannoni, 1996; Schattenhofer et al., 2009; Hu et al., 2016), and permanently or seasonally stratified anoxic basins (Fuchs et al., 2005; Stevens and Ulloa, 2008; Martha et al., 2010). The relative abundance of SAR406 was higher in hypoxic samples (p=0.006). Additionally, the clade was significantly negatively associated with DO and positively associated with depth and salinity. Wright et al. (2014) suggested that the SAR406 clade has the ability to reduce, or possibly oxidize sulfur compounds by analyzing partial genome data of SAR406. Subsequently, Thrash et al. (2017) identified the metabolic potential of SAR406 using a coupled shotgun metagenomic and metatranscriptomic method and found complete pathways for sulfur reduction and nitrate reduction. Otherwise, SAR406 has also been shown to degrade complex carbohydrates via aerobic respiration (Rinke et al., 2013; Thrash et al., 2017). This result might explain why the SAR406 clade was also found in the oxic layer in the present study.
Gammaproteobacteria is also slightly more abundant in the hypoxic samples, and has been proven to be an adaptive group under hypoxic conditions (Devereux et al., 2015; Jessen et al., 2017). When compared with the oxic water layers (5–16 m) in the similar depth of our middle layer in the CRE, the class Gammaproteobacteria also existed in oxic layers (Dong et al., 2014). It indicated that some subgroups of Gammaproteobacteria might be anaerobe or tolerant to the hypoxic environments (He et al., 2019). Actually, Gammaproteobacteria was found to be the dominant group in some other anoxic environments, such as the Blue Hole (He et al., 2019) and the Baltic Sea (Broman et al., 2017). Another abundant group in the hypoxic layer was Deltaproteobacteria, which was frequently found in anaerobic conditions and associated with sulfate reduction (Jørgensen and Bak, 1991; Coleman et al., 1993; Füssel et al., 2017). These findings suggest that more sulfate reduction may occur in hypoxic zones. Similar results were also found in the Zhujiang River Estuary (Liu et al., 2015) and the Blue Hole (He et al., 2019).
4.2 The diel changes in bacterial communities in the oxic and hypoxic layers
The photic zone of aquatic habitats is subjected to strong physicochemical gradients (Haro-Moreno et al., 2018). Episodic forcing over short-time scales was deemed to cause changes in phytoplankton community structures (Guadayol et al., 2009). Changes in temperature, light, UV and nutrient availability are known to moderate the growth of photosynthetic organisms, and light has often been considered as the most important factor determining diel variability (Gao et al., 2019). Interestingly, in this study, the composition of bacterial communities at 22:00 was significantly different from other times (Figs 3 and 4). From the tide graph (Fig. A2), we found that 22:00 was the low tide period. A previous study suggested that high DOM concentrations and increased prey activities could occur at low tide (Chauhan et al., 2009), which will alter the bacterial abundance in seawater. In addition, some other uncontrollable factors, e.g., viral lysis and physicochemical conditions, are also likely to contribute to shifts in the bacterial composition (Rappé et al., 2000; Bouvier and Del Giorgio, 2007). This study also found the bacterial communities were different at 16:00 in both layers, which may be caused by some environmental factors. Similar conditions occurred in other studies (Pernthaler and Pernthaler, 2005; Wang et al., 2013). Environmental microbial samples are greatly influenced by a variety of uncontrolled factors, so it is normal that different bacterial composition occurs in different days at the same time if it weakly affects the overall trend of the community structure. Here, the relative abundances of some phototrophic bacteria and several heterotrophic bacteria had similar fluctuations in the diurnal changes of the studied site.
Cyanobacteria, which can photosynthesize, is common in the aquatic systems (Bryant, 1995). Since the livelihood of Cyanobacteria is directly dependent upon light, a comprehensive understanding of the metabolism of these organisms is required to account for the effects of day-night transitions and circadian regulation. In the present study, the circadian rhythms of Synechococcus and Prochlorococcus were not too apparent. In the surface layer, the relative abundance of Synechococcus had a steady increase from 4:00 to 13:00, but a drop at 16:00. Dolan and Šimek (1999) found a similar phenomenon: the Synechococcus cell count peaked in the late afternoon and early evening hours in the bay of Villefranche (northwestern Mediterranean). Nevertheless, in the middle water layer of our study, a general increase was observed from 1:00 to 10:00, and a slight decrease was observed from 10:00 to 13:00. This phenomenon was also found in several previous studies, like the open Mediterranean Sea (Llabrés et al., 2011) and the northern South China Sea (Liu et al., 2016). Jacquet et al. (1998) suggested that the Synechococcus cell cycle was carried out in stages, with the daily photoperiod possibly being genetically controlled by a “clock”. Owing to different environmental conditions and nutrient in different layers, the daily change of Synechococcus was not entirely consistent. In the surface layer, Synechococcus is possibly affected by light, wind, ultraviolet ray and other uncontrolled physical factors (Gao et al., 2019), while in the middle layer, nutrients and grazing may be the domain factors (Suyama et al., 2002; García et al., 2018).
Little Prochlorococcus was found at the studied site by 16S rRNA gene Illumina HiSeq technology. There are several reasons that may explain why little Prochlorococcus presented in the samples. The distribution of Prochlorococcus could be sensitive to multiple factors (Jiao et al., 2002), including temperature, which is a crucial environmental factor (Olson et al., 1990; Partensky et al., 1999). Prochlorococcus was proposed to be limited to the minimum temperature (26°C) in summer in Chinese seas (Jiao et al., 2002), which may explain the lack of Prochlorococcus in our samples where the temperature was lower than 26.4°C. Additionally, physical conditions such as mixing and stratification may also work (Olson et al., 1990; Partensky et al., 1996).
Tight coupling between phytoplankton and bacteria could cause some bacteria to follow similar circadian cycles (Kirchman, 2008). Here, the families Rhodobacteraceae and Flavobacteriaceae displayed similar trends to Synechococcus in the diel cycle. More Rhodobacteraceae was found in the oxic samples, and positively related to DO concentration. Studies have shown that Rhodobacteraceae comprises aerobic phototrophs, which can utilize light to live (Pujalte et al., 2014) and some Rhodobacteraceae have been found in algae-associated biofilms (Elifantz et al., 2013). A previous study found that Rhodobacteraceae is the dominant family associated with diatom Leptocylindrus (Ajani et al., 2018). Ye et al. (2016) found that diatoms can sink to the deep layer and then release organic materials. Here, Rhodobacteraceae also existed in the hypoxic layer, especially during the night. A slight increase of relative abundance occurred at night, which may be caused by the sinking dead diatoms. Several researchers have also found similar results that some Rhodobacteraceae are likely to thrive under hypoxic conditions (Drews, 1981). The relative abundance of the family Flavobacteriaceae is also associated with that of Synechococcus. Zheng et al. (2017) suggested that Flavobacteriaceae tends to aggregate or attach to the Synechococcus cells, and could degrade complex organic matter by directly attaching to and attacking algal cells using exoenzymes (Kirchman, 2002; Gómez-Pereira et al., 2012; Teeling and Amann, 2012). A previous study found the abundance of Cytophaga-Flavobacterium had diurnal fluctuations in Monterey Bay (USA), and reached the highest during the high tide over a tide cycle (Olapade, 2012). In this study, there was a similar rhythm between the tide variation and the relative abundance change of Flavobacteriaceae in the hypoxic layer. Cottrell and Kirchman (2000) purposed that their high abundance might be associated with DOM which directly inflowed during the high wind mixing.
The OM60 clade is the marine gammaproteobacterial branch of aerobic anoxygenic phototrophic bacteria and has been reported to coincide with high chlorophyll concentrations (Fuchs et al., 2007; Yan et al., 2009). Here, the relative abundance of OM60 was steady and high in the daytime in the oxic layer. On the contrary, in the hypoxic layer, the relative abundance of OM60 was low and fluctuated. Furthermore, the OM60 clade was positively correlated with DO, indicating that the clade may prefer habitats with sufficient oxygen concentrations. From night to early morning, the percentage of OM60 decreased, then rose. This phenomenon may due to the tide changes or the lack of light at night, meaning that individuals of the clade could not grow well. Nevertheless, the respiration of the phytoplankton may have produced sufficient dissolved oxygen during the night that OM60 could thrive (Fuchs et al., 2007; Yan et al., 2009).
In addition to the above-mentioned families, there are other families associated with Synechococcus such as HTCC2188, C111, wb1_P06, Nitrospinaceae and A714017. However, little is known about the ecological functions about these families. More efforts are needed to focus on these families to explain the correlation of different communities in marine environments. Together, the results suggest that some photosynthetic bacteria and several heterotrophic bacteria may have similar circadian rhythms to Synechococcus. Considering close associations within microbial loop (Porter, 1996), different preferences for organic substrates or phytoplankton species (Pinhassi et al., 2004; Alonso-Sáez and Gasol, 2007), some bacteria may have diurnal circadian rhythms associated with phytoplankton. The trend was not distinct in the upper layer, which may due to the influence of various uncontrolled physical factors.
5 Conclusions
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In this study, we detected the bacterial communities in the oxic and hypoxic layers in the CRE during July 2016. Distinct community structure was observed between oxic and hypoxic layers. In addition, we found that some photosynthetic autotrophic bacteria and several heterotrophic bacteria have similar diurnal variations, hinting that there are some potential ecological associations between them. Our results may contribute to further understanding of the factors that affect the microbial communities in the estuary. More efforts are needed to verify the above-mentioned findings in other coastal areas, and more efforts are needed to explore the diurnal changes in bacterial, archaeal or eukaryotic plankton in oceanic hypoxic zones and potential influences on microbial loop.
Acknowledgements
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We are grateful to all the staff who assisted in the field sampling. Particularly, we thank Xiaoyu Zhu from the Ocean University of China, Wu Qu from the Zhejiang Ocean University and Xiaomin Xia from the South China Sea Institute of Oceanology for their valuable comments and suggestions to improve the manuscript, and Elixigen (www.elixigen.com) for its linguistic assistance of this manuscript.
Funding
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  • The National Key R&D Program of China under contract No. 2019YFD0901305; the Science and Technology Program of Zhoushan under contract No. 2019C21011; the National Natural Science Foundation of China under contract Nos 31270160 and J1310037; the Natural Science Foundation of Zhejiang Province, China under contract No. LY12C03003; the Zhejiang Public Welfare Technology Application Research Project under contract No. 2016C33084; the Research Project of Ecological Environment Protection and Restoration of Yangtze River in Zhoushan under contract No. SZGXZS2020068.
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doi: 10.1007/s13131-021-1778-2
  • Receive Date：2019-11-22
  • Online Date：2026-02-28
  • Published：2021-04-25
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  • Received：2019-11-22
  • Accepted：2020-06-03
Funding
The National Key R&D Program of China under contract No. 2019YFD0901305; the Science and Technology Program of Zhoushan under contract No. 2019C21011; the National Natural Science Foundation of China under contract Nos 31270160 and J1310037; the Natural Science Foundation of Zhejiang Province, China under contract No. LY12C03003; the Zhejiang Public Welfare Technology Application Research Project under contract No. 2016C33084; the Research Project of Ecological Environment Protection and Restoration of Yangtze River in Zhoushan under contract No. SZGXZS2020068.
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    1 Marine Microorganism Ecological & Application Laboratory, Zhejiang Ocean University, Zhoushan 316022, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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