Spatiotemporal changes of cyanobacterial and heterotrophic bacterial communities during an algal bloom in a subtropical water source reservoir ecosystem of China

DOI: https://doi.org/10.21203/rs.3.rs-1549492/v1

Abstract

Massive nitrogen and phosphorus input into aquatic ecosystems makes algal bloom one of the most concerning problems in China. Algal blooms not only threaten the health and stability of aquatic ecosystems but also influence the microbial community within. However, less is known regarding how algal bloom affects the spatiotemporal variation of the aquatic microbial community, including cyanobacteria and other bacteria. In this study, we used high-throughput sequencing to investigate how the cyanobacterial and bacterial community diversity and composition spatiotemporally vary along with main algal bloom phases in upstream rivers of a eutrophicated water source reservoir. For both cyanobacteria and bacteria, their diversities demonstrated temporal significance amongst different phases in each river, indicating the apparent impact of algal bloom. Dominant cyanobacterial taxa included Cyanobacteriales and Synechococcales, and dominant bacterial taxa comprised Acinetobacter, CL500-29, hgcI clade, Limnohabitans, Flavobacterium, Rhodoluna, Porphyrobacter, Rhodobacter, Pseudomonas, and Rhizobiales, whose relative abundance varied along with the algal bloom, rendering distinct community composition for each river. Non-metric multidimensional scaling analysis additionally indicated significant differences amongst different phases, and the linear discriminant analysis (LDA) with LDA effect size analysis (LEfSe) helped to identify the dominant species (OTUs) in each river at different phases. Canonical correlation analysis (CCA) or redundancy analysis (RDA) revealed distinct correlation patterns of cyanobacterial and bacterial communities with the environmental parameters, which implies their distinct ecological functions. In general, these results demonstrated the significant influence of algal bloom on microbial communities in a eutrophicated water source reservoir basin. These observations also arouse universal demands for strategies conserving the aquatic microbial equilibrium and alleviating algal blooms in reservoirs.

Introduction

The frequency and intensity of algal blooms are increasing in many inland aquatic ecosystems, including rivers, lakes, and reservoirs, due to excessive nitrogen and phosphorus enrichment in surface waters over-enrichment[14]. These blooms are becoming one of the top concerns of environmental protection agencies, water regulation authorities, and society, and the biodiversity of aquatic ecosystems is decreasing. Furthermore, many bloom species can produce a variety of metabolites, which will bring dramatic health risks to humans and animals[5]. In China, to increase diversities of aquatic ecosystems, a series of water protection campaigns have been initiated in recent decades, which being further strengthened in the National Fourteenth Five Year Plan. The mechanisms of blooms outbreak, as well as corresponding counter-measures, have become one of the central research hotspots.

It has been universally acknowledged that the bloom species, such as cyanobacteria and other algal species, are also an integral part of the microbial community within the aquatic ecosystems[6], which not only rapidly respond to the changes of surrounding environmental factors and form blooms but also actively interact with their bacterial counterparts. Previous studies on bacteria-cyanobacteria interaction focused on the composition and diversity of heterotrophic bacteria living with cyanobacteria (forming micro-niche) as well as the differences between cyanobacteria-associated bacterial communities and the free-swimming communities. It should be noted that the cyanobacterial blooms exhibit an ebb-and-flow pattern when dynamically responding to the changes of environmental factors, therefore, the associated and the surrounding bacterial communities are presumed to be changing accordingly. However, less is known about the dynamic variations of bacterial communities in water bodies that served as drinking sources along the algal bloom course.

In a previous study, we evaluated the spatiotemporal changes and eutrophic characteristics of water quality of a life-dependent reservoir (Yankou Reservoir Basin) with its upstream rivers, which served as drinking water sources for Yiwu City, Zhejiang Province of China[1]. Monthly water qualities assessment from 2013 to 2018 demonstrated that over 90% of the months the upstream rivers were collectively under eutrophic conditions, which kept aggravating eutrophic conditions of the reservoir. Before and during then, massive algal blooms were observed in those upstream rivers and a large part of the reservoir infield, making it an immediate issue to be dealt with.

On the presumption that the bacterial communities would dynamically change along the algal bloom course, the investigation into these variations will reveal which bacterial communities and how they will change, and the community information could eventually be used to develop potential predictive or mitigating tools against the algal bloom. For this purpose, surface water samples were collected from the four upstream rivers at different phases of full algal blooms, and the composition and diversity of cyanobacteria and bacterial communities of each river at each phase were investigated. The dynamic changes of cyanobacterial and bacterial communities along the algal bloom were revealed and correlated with the main environmental factors.

Materials And Methods

Sampling area and environmental factors determination

This study was carried out along the four upstream rivers, namely Huangshan River (HS), Jinfuzhai River (JFZ), Sihe River (SH), and Xihua River (XH), of Yankou Reservoir basin (29°17'25"~29°18'49" N, 119°54'11"~119°55'13" E) in Yiwu City, Zhejiang province, middle east of China (Fig. 1). Considering the dynamics of an algal bloom, the outbreak course was primarily divided into four phases, including before the algal bloom (Before AB), followed by early phase (Pre AB), outbreak phase (During AB), and late phase (Post AB). Additionally, each river has at least one tributary pool (HS has three pools) connecting to mainstream of the river; therefore, water samples would also be collected from the pools. A total number of 104 surface water samples (0.5 m depth) were collected, respectively in May, July, August, and September 2020, representing the four algal bloom phases. The surface water samples were collected within a 3-day period in all the phases. After immediate collection, the samples were kept in an ice box and transported back to the laboratory for subsequent treatment. Water samples (~ 3 000 mL) were first passed through a 200-µm pore-size sieve to remove any debris for subsequent determination of water quality parameters and microbial community analysis. For bacterial and cyanobacterial community analyses, water samples (~ 1 500 mL) were further filtered through 0.22-µm pore-size polycarbonate membranes (50 mm, Jinteng®, Tianjin, China), and those membranes were stored at -80oC until DNA extraction. The physicochemical parameters of the water samples were determined according to described previously[7]. Briefly, a Hydrolab HQ30D multiparameter water quality meter (HACH Company Co., Loveland, the USA) was used in situ to monitor the water temperature (TEMP), oxidation-reduction potential (ORP), pH value, and chlorophyll-a (Chl-a). Other water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), dissolved total nitrogen (DTN), ammonium (NH4+), nitrite (NO2), nitrate (NO3), total phosphorus (TP), dissolved total phosphorus (DTP) and orthophosphate (MPO4) were determined using the standard methods published by China’s State Environmental Protection Administration[8].

DNA extraction

The total DNA of bacterial and cyanobacterial communities was extracted directly from the membrane filters using a FastDNA™ Spin Kit (MP Biomedicals, Santa Ana, CA, the USA) according to the manufacturer’s instructions. The purified total DNA was processed and sequenced by Shanghai Majorbio Bio-Pharm Technology Co., Ltd, and the downstream alignment and analyses were performed using the integrated and free online platform of Majorbio Cloud Platform (www.majorbio.com).

Bioinformatic treatment

The sequenced paired-end reads were merged with FLASH (v.1.2.11), and the raw reads were processed and analyzed using QIIEM (v.1.9.1) to remove reads of low quality. After the quality control process, sequences were clustered into operation taxonomic units (OTUs) using UPARSE (v.7.0.1090) with a 97% sequence similarity threshold[10]. A representative sequence from each OTU was run against the databases of SILVA (Release 138). To avoid mistaken taxonomic alignments, the taxonomic classifications were double-checked against the reference prokaryotes. Those unclassified OTUs were discarded before subsequent analyses, and the sequence data were finally normalized to minimize sequencing biases and allow for appropriate comparison of community variations amongst those samples.

For comparisons of microbial taxa variations across the algal bloom phases, we defined those taxa with relative abundance less than 0.01% as rare taxa, more than 1% as abundant taxa, and between 0.01% and 1% as moderate taxa, respectively. Based upon the definition, those relevant taxa in subsequent analyses were categorized into groups as follows: always abundant taxa (AAT), always rare taxa (ART), always moderate taxa (AMT), and conditionally varied taxa (CVT), and the taxa with more than 0.01% relative abundance at certain phases in each sample were also referred as “dominant taxa” according to previous studies [11].

Statistical analyses

In this study, the algal bloom was mainly attributed to the cyanobacterial overproduction, therefore, the cyanobacterial community would be further extracted from the normalized data for each sample, and alpha- and beta diversities were analyzed both on cyanobacterial and the rest of bacterial communities (will be referred as “bacterial communities” below). Additionally, a comprehensive analysis on the changes of bacterial communities at different phases in the same river as well as the variance of communities at the same phase amongst different rivers was performed.

The alpha-diversity indices including OTU numbers (Sobs), abundance-based coverage estimator (ACE), Shannon, and Chao 1, as well as Venn diagrams compared to different samples were calculated using the vegan package in R language (v.4.0.0). Rarefaction curves and Good’s coverage were performed with MOTHUR (v.1.30.2). Significance amongst alpha-diversity indices were calculated using one-way ANOVA and Student’s t-test with the significant level at 0.05. For beta-diversity analysis, the non-metric multidimensional scaling analysis (NMDS) with weighted unifrac similarity coefficient and permutational multivariate analysis of variance test (PERMANOVA) was used to evaluate both the discrepancy of bacterial communities at different phases and the variance of communities at the same phase in different rivers.

To identify the cyanobacterial and bacterial OTUs contributing to the difference amongst samples described above, a significance test was first performed using the Kruskal-Wallis test coupled with Tukey-Kramer post-hoc examination, based upon OTUs’ relative abundances. Subsequently, the linear discriminant analysis (LDA) coupled with the LDA effect size (LEfSe) technique, with a discriminant analysis score of 2.0, was performed to identify the significantly different dominant OTUs amongst the samples. Additionally, cyanobacterial and bacterial OTUs with LDA scores over 3.0 and 4.0, respectively, were screened out to double-check with the significance tests. The significance tests and LEfSe analyses were all examined at a significant level of 0.05.

Finally, a variance inflation factor analysis (VIF) was first performed to determine which physiochemical parameters would be selected for subsequent correlation analyses between microbial communities and these parameters. Canonical correlation analysis (CCA) or redundancy analysis (RDA) was conditionally chosen based upon results of detrended correspondence analysis (DCA) to reveal the correlations.

Data Availability

The original sequencing data was updated to the NCBI database under the accession number from SAMN23614736 to SAMN23614843.

Results

Environmental parameters of each river at different phases

Water samples were collected from corresponding water bodies, important environmental parameters including water temperature, pH, Chl-a, total nitrogen, and total phosphorus were summarized in Table 1, and complete environmental parameters were summarized in Table S1. In general, the Chl-a in the pools was higher than that in the corresponding rivers, and the maximum concentration reached 535.68 µg/L in JFZ’s pool. Further statistical analyses on the complete parameters of each river at different algal bloom phases indicated that for each river, there was no significant difference amongst the parameters at different phases (indicating as N.S.), except ORP and Chl-a (P < 0.05, Table S1). For these two parameters, detailed significances between relevant phases were included.

Table 1 Summary of important environmental parameters determined in this study. Complete environmental variables determined in this study were summarized in Table S1

General statistics of sequencing data and alpha-diversity comparison

General statistics of sequencing data

In this study, a total number of 5,115,068 sequences was pooled for the total 104 samples, and for each sample, the sequencing data was normalized to 18,633 sequences, which were categorized into 8,689 OTUs and 2,998 bacterial species. The cyanobacterial community was collectively extracted from each sample, stratified into 515 OTUs and 191 species, leaving the bacterial communities constituting 8,174 OTUs and 2,807 species.

Rarefaction curves for each sample showed that most samples tended to approach saturation (Fig. S1), and the Good’s coverage ranged from 94.91–99.42% amongst the samples. The two indices indicated that the majority of the bacterial taxa had been extracted from the studied communities. Considering that the tributary pools were one of the main sources of cyanobacterial communities in rivers, alpha-diversity indices, including Shannon and Chao 1, were analyzed on cyanobacterial and the rest of bacterial communities in pools and rivers.

Alpha-diversity comparisons and statistics

For cyanobacterial communities in pools of HS, JFZ, SH, and XH across the algal bloom phases, the Shannon index maximized at the “During AB” phase for JFZ and XH. Although it maximized at the “Post AB” and “Before AB” phase for HS and SH, respectively, the diversities at the “During AB” phase were also relatively high for both pools (Fig. S2 A). Whilst Chao 1 index of these cyanobacterial communities showed a concurrent pattern that community diversities minimized at “During AB” and maximized at “Post AB” phase. For bacterial communities, both Shannon and Chao 1 indicated that community diversity decreased at “During AB” and increased afterward. On the contrary to pools, cyanobacterial communities in all rivers minimized at “During AB” phases according to Shannon and Chao 1 index (Fig. S2 B), except for Shannon of HS and JFZ. Similar to pools, bacterial communities in all rivers were minimized at “During AB” phases and increased afterward based upon both indices estimates.

Differences of microbial communities on alpha-diversity were further determined amongst different rivers at the same phase as well as individual rivers at the consecutive phases (Fig. 2). For the cyanobacterial communities at each phase, there was, in general, no significant difference amongst the studied rivers (P > 0.05), except for the comparisons between HS and JFZ at “Before AB”, as well as JFZ and SH at “Post AB” phases (P < 0.05) (Fig. 2A). Whereas, community diversities in individual rivers exhibited apparent variances across the consecutive phases (Fig. 2B). Cyanobacterial communities in HS showed no significant difference across the phases, whilst communities in JFZ, SH, and XH demonstrated significant differences amongst these phases, and especially, communities at the “During AB” phase were significantly lower than the rest three phases according to Chao 1 index for JFZ, SH, and XH.

Similarly, for the bacterial counterparts at the same phase, there was, in general, no significant difference amongst the rivers (P > 0.05), except for the comparison between JFZ and XH according to Shannon index (Fig. 2C). Bacterial communities in individual rivers also showed similar variance with the cyanobacteria across the phases (Fig. 2D). HS communities had no significant difference amongst the phases (P > 0.05), whilst JFZ, SH, and XH communities showed significant differences, and communities at the “During AB” phase were also significantly lower than some of the rest phases in JFZ, SH, and XH, respectively.


OTU comparisons of both cyanobacteria and bacteria in tributary pools and their rivers at corresponding phases were performed (Fig. S3). In general, for both cyanobacterial and bacterial OTUs, the comparisons either amongst pools and rivers at certain phases (Fig. S3) or in each pool and river at different phases (Fig. S3), only a small portion of OTUs was shared in common amongst the four compared counterparts. It should be noted that only a small number of cyanobacterial OTUs was classified in pools of JFZ, SH, and XH during the algal bloom (Fig. S3), which supported the alpha-diversity analyses that the cyanobacteria diversities during the algal bloom were significantly lower than the rest phases for JFZ, SH, and XH (Fig. 2 ).

Comparisons of cyanobacterial and bacterial community composition

In general, the most diverse cyanobacterial OTUs were assigned to phylogenetic orders of Cyanobacteriales and Synechococcales in both rivers and their tributary pools. However, the cyanobacterial community composition exhibited spatial variance amongst these ecosystems. In each pool, the 2 taxa took turns as the most dominant community, for example, in the tributary pool of HS, the most dominant community was Synechococcales and Cyanobacteriales across each of the phases; whilst JFZ had Cyanobacteriales as the most dominant community. And Synechococcales was the most dominant in the rest of the three rivers along with the algal bloom (Fig. 3A).

For bacterial communities, the abundant phylogenetic genus to which most diverse OTUs were assigned were sorted out in each river. Based upon the relative abundance of each taxon, the phylogenetic genera were further categorized into groups of AAT, ART, AMT, and CVT, respectively (Fig. 3B-E). Although these rivers had some genera in common in each group (AAT, ART, AMT, and CVT), such as Acinetobacter and Flavobacteriumthe bacterial communities composition also exhibited spatial variance amongst the studied rivers. For example, a community of hgcI clade was always abundant in HS, JFZ, and SH, whilst was conditionally varied in XH, and its relative abundance was greatly reduced during the algal bloom, especially in XH. Communities of Pseudarcicella and Limnohabitans in HS were always abundant, and their relative abundance increased during the algal bloom, however, their relative abundances were conditionally varied and noticeably reduced in the rest three rivers during the same phase. Similarly, Comamonadaceae was always abundant in each river, but its relative abundance clearly reduced during the algal bloom, and Allobaculum was always rare in HS, SH, and XH, but conditionally varied in JFZ that its relative abundance greatly increased during the algal bloom.


Variations of cyanobacterial and bacterial communities were further illustrated through NMDS with PERMANOVA test. Cyanobacterial communities at the “Before AB” phase exhibited significant variation amongst the studied rivers (PERMANOVA, df = 3, F model = 3.220, R2 = 0.305, P = 0.001), however not at the rest three phases (Fig. 4A. Across the algal bloom phases, cyanobacterial communities in SH and XH showed significant variations (PERMANOVA, df = 3, F model = 2.252 and 2.298, R2 = 0.252 and 0.256, P = 0.011 and 0.02, respectively), whilst those in HS and JFZ did not (Fig. 4B). For bacterial communities, they only showed significant difference at the “Post AB” phase amongst the studied rivers (df = 3, F model = 1.729, R2 = 0.191, P = 0.039) (Fig. 4C). However, except for HS, bacterial communities in JFZ, SH, and XH collectively revealed significant discrepancy across the algal bloom phases (df = 3, F model = 1.884, 3.731, and 4.525, R2 = 0.220, 0.359, and 0.404, P = 0.006, 0.001, and 0.001, respectively) (Fig. 4D1-D4).


The significant difference in cyanobacterial and bacterial dominant species (OTUs)

The above results indicated that the discrepancies of both cyanobacterial and bacterial communities could be more attributed to the difference of algal bloom phases instead of the spatial heterogeneity of rivers. Therefore, the significantly different OTUs of cyanobacterial and bacterial communities were analyzed amongst samples at different phases in each river (containing corresponding tributary pools) (Table S2). The significance test was subsequently used for LEfSe analysis (Table S3) to identify those cyanobacterial and bacterial OTUs at different phases contributing to the significance.

For cyanobacteria, more OTUs were identified in JFZ, SH, and XH, and these OTUs have mainly identified at the rest three phases except the “During AB” phase (Fig. 5). Cyanobacterial OTUs at different phases were mainly classified into Cyanobacteriales (OTU1843), and Leptolyngbyales (OTU3191) in SH (Fig. 5C), and were pooled into Cyanobacteriales (OTU6199) in XH (Fig. 5D), respectively. Additionally, several OTUs, including OTU2648, OTU7367, OTU8417, and OTU867, were found to be widely distributed amongst these three rivers, which indicated that they might share a core cyanobacterial community.


Similarly for the bacterial community, more OTUs were identified in JFZ, SH, and XH, however, were universally identified across the algal bloom phases (Fig. 6). Members of Rhodobacteraceae (OTU 853 and OTU 5851), Limnohabitans (OTU4177, OTU216, and OTU3910), Porphyrobacter (OTU6265), and Sporichthyaceae (OTU2594) were mainly screened out in JFZ (Fig. 6B), whilst Pseudarcicella (OTU3296), Comamonadaceae (OTU3244), Sporichthyaceae (OTU2594), Limnohabitans (OTU3910), Acinetobacter (OTU8318, OTU2460, OTU2685, OTU5230, OTU2703, OTU5164, OTU483, and OTU2184) were broadly identified in common in SH and XH (Fig. 6C and D). Notably, members of Rhodobacteraceae (OTU853 and OTU5851), Porphyrobacter (OTU6265), and Acinetobacter (OTU2460, OTU2685, OTU2184, and OTU5230) in JFZ, SH, and XH exhibited significantly higher (P < 0.05) relative abundances at “During AB” phase than the rest three phases (Fig. 6B-D), which may indicate their relationships with the cyanobacterial communities causing algal bloom.


Correlations between the dominant cyanobacterial taxa and the above-identified the dominant species were subsequently analyzed (Fig. 7). In general, the results indicated that some bacterial taxa in different aquatic ecosystems showed different correlations with the same cyanobacterial taxa. For example, OTU853 in JFZ was negatively correlated with Cyanobacteriales along with the whole bloom phases (Fig. 7A), however, it showed contrasting correlation patterns with Cyanobacteriales in XH (Fig. 7C). OTU6265 in JFZ was positively correlated with Cyanobacteriales and Synechococcales for the first three phases, but negatively correlated in the last phase (Fig. 7A), however, it was mostly negatively correlated with these cyanobacterial taxa in SH (Fig. 7B). These discrepancies in correlation analyses indicated that the bacteria-cyanobacteria interaction may additionally be affected by environmental factors.


Correlations of cyanobacterial and bacterial communities with the physicochemical parameters

The VIF analysis (Table S4) on the physiochemical parameters against the cyanobacterial and bacterial communities concluded that all parameters expect DTN, DTP, and COD were selected for cyanobacterial CCA/RDA, whilst all the parameters were used for bacterial CCA/RDA, and analyses were separately performed on microbial communities of each river at different phases (Fig. 8).


In general, cyanobacterial communities in these rivers at different phases were mainly correlated with ORP, pH, TEMP, MPO4, NO3 and Chl-a, and communities of each river had a specific correlation with additional parameters (Fig. 8A). For example, cyanobacteria in JFZ also had a close correlation with TP, TN, and NH4+, and the ones in SH had a close correlation with TN, NH4+, and NO2. Additionally, the cyanobacterial communities of these rivers also exhibited variance in correlations with the main parameters, which indicated the discrepancy of the cyanobacterial communities amongst these rivers. For instance, most of the samples of HS across algal bloom were positively correlated with ORP, TN, NH4+, NO3, MPO4, TP, TN, and were either not or negatively correlated with the rest main parameters, including pH, TEMP, NO2, and Chl-a, whilst most samples in JFZ but the ones of “Before AB” showed a positive correlation with TEMP, ORP, NH4+, and MPO4, and negative correlation with TN, NO3, TP, pH, and Chl-a. Cyanobacterial communities in SH were universally negatively correlated with the main parameters, whilst the ones in XH largely illustrated a positive correlation with TEMP, pH, NO3, and MPO4. The correlation between XH communities and the main parameters was reflected through RDA according to the DCA analysis.

For bacterial communities in all rivers at different phases, they were mainly correlated with COD, TN, DTN, NO3, ORP, TEMP, and pH, and communities of each river also showed specific correlation with additional parameters (Fig. 8B). Bacterial communities in HS additionally exhibited a close correlation with TP, NO2 and Chl-a, the counterparts in JFZ were with NH4+ and Chl-a, the ones in SH and XH were both with TP, MPO4 and NH4+, whilst XH showed additional correlation with DTP, and NO2. Interestingly, different from cyanobacteria, the bacterial communities of these rivers demonstrated relatively consistent correlation patterns with the main parameters. For example, communities of “During AB” in these rivers universally showed a positive correlation with pH, TEMP, and TP, and negatively with TN, DTN, NO3, and ORP, except HS, the rest three rivers also collectively showed negative correlations with Chl-a. Furthermore, communities of “During AB” in these rivers consistently exhibited contrasting correlation patterns compared with their counterparts either in “Post AB” or “Before AB”.

Discussion

Algal bloom is a major factor influencing diversities of microbial community

Although the diversity indices indicated that both the cyanobacterial and bacterial communities varied not only across the algal bloom phases in each individual river of HS, JFZ, SH, and XH, but also amongst rivers at each of the same phases (Fig. S2), the significance tests demonstrated that comparing with other factors (Table S1) the algal bloom was a major factor influencing the diversities of both cyanobacterial and bacterial communities (Fig. 2). Only several cases of significant differences of diversities were determined between rivers at the same phases, such as HS and JFZ at the “Before AB” phases well as JFZ and SH at the “Post AB” phase (Fig. 2A) for cyanobacteria, and JFZ and XH at “Post AB” phase for bacteria, more universal significant difference cases were shown in rivers of JFZ, SH, and XH across the algal bloom, which all indicated that both cyanobacterial and bacterial communities were significantly lower during algal bloom. It has been universally revealed that the main harmful algal blooms could significantly reduce the diversities and functions of other microbial communities, including planktonic communities of coast and freshwater [12, 13], microbial communities associated with algae [14, 15], as well as those grew with macrophytes [16], and sedimentary dwellers [17]. During the algal bloom phase, cyanobacterial community diversities in JFZ, SH, and XH also significantly reduced, which inferred that the algal bloom was due to the overproduction of a narrower range of cyanobacterial taxa. Most studies concerning algal blooms focused on Microcystis and related species, which were reported ubiquitous in life-depending rivers and lakes of China, including Yangtze River, Taihu Lake, and Chaohu Lake [1821], it is of importance to identify the dominant cyanobacterial taxa, their dynamic changes, and how the bacterial community compositions were correspondingly affected along the course of algal bloom in rivers of this study.

The distinct spatial and temporal variance of cyanobacterial community composition along the algal bloom course

Instead of Microcystis, the dominant cyanobacterial taxa were Cyanobacteriales and Synechococcales both in the concerning rivers and their tributary pools (Fig. 3A). Even though these water bodies are close in the locality and have similar environmental conditions, they revealed distinct cyanobacterial community compositions and patterns of dynamic changes in relative abundances along with the algal bloom phases. Additional beta-diversity analyses on OTU level compared both different rivers at the same phase and each river along the algal bloom course, the results further illustrated that cyanobacterial community significances were shown in rivers at different algal phases (Fig. 4A-B), especially for SH, and XH. Beside the algal bloom influence, the differences of community composition amongst rivers at each corresponding phase were also shown. The spatial heterogeneity might be due to human activities and riverine characteristics as suggested in other studies [22, 23]. Because of the relatively slow flowrate and large surface area, the tributary pools may therefore possess distinct cyanobacterial communities relative to the mainstream rivers, rendering the pools relatively independent ecosystems to some extent.

The cyanobacterial dominant species (OTUs) were further screened out through significance tests and LEfSe analyses on each river at different algal bloom phases (Fig. 5). The identified the dominant species belonged to cyanobacterial taxa of Mychonastes, Trachydiscus, Oscillatoriales, Chamaesiphon, Nannochloropsis, Monoraphidium, and Cyanobium, whose ecological functions included feeds for livestock [24, 25], constitutive members of biofilms [26, 27], biodiesel derivation [28, 29], and cyanobacterial early-stage bloom [30, 31]. No the dominant species were identified from the “During AB” phase of the rivers, which may imply that relevant cyanobacterial OTUs in each river were relatively similar in abundance and composition during the algal bloom.

These results together may shed light on the ongoing treatment campaigns on eutrophication and algal blooms in China, that corresponding strategies should be taken into consideration when dealing with rivers and pools (ponds, reservoirs, and lakes). In general, the algal bloom is the major factor that leads to the dynamic variation of community composition, which not only reduces the microbial diversities, assimilating cyanobacterial communities but also would affect the functions of relevant bacterial communities.

Dynamic changes of dominant bacterial community and implications concerning their ecological functions

The dominant bacterial genera along the algal bloom course in each river were identified and categorized into groups of AAT, CVT, AMT, and ART based upon dynamic changes of their relative abundance along the course (Fig. 3B-E). Comparisons of bacterial genera composition in each river at different phases and amongst rivers at the same phases collectively revealed the spatial and temporal heterogeneity of bacterial communities. Similar to cyanobacteria, the beta-diversity analyses illustrated that bacterial community significances were more apparent in rivers along the algal bloom course (Fig. 4C-D), which additionally proved the major influence imposed by algal bloom. Furthermore, the identified bacterial dominant species in each river at each phase (Fig. 6) further demonstrated the responses of exact members of the dominant bacterial taxa along the algal bloom course, indicating their close interactions with the algal bloom.

The dominant bacterial taxa identified along the algal bloom course included Acinetobacter, CL500-29, hgcI clade, Limnohabitans, Flavobacterium, Rhodoluna, Porphyrobacter, Rhodobacter, Pseudomonas, and Rhizobiales (Fig. 3B-E), which could also be traced in other relevant studies. In a study concerning Tianmuhu Lake, CL500-29 and hgcI clade were found as dominant genera, which also closely associated with cyanobacteria, whilst in the lake’s river system, Flavobacterium, Limnohabitans, and Rhodoluna were identified as the dominant genera [32]. Porphyrobacter, Rhodobacter, Pseudomonas, and Rhizobiales were also found dominant in other studies concerning either cyanobacterial bloom or Microcystis bloom in different aquatic ecosystems [3337]. Additionally, it was reported that species of Acinetobacter exhibited algicidal activity against Microcystic aeruginosa [38], Flavobacterium and Limnohabitans also showed cyanobacterial and growth-inhibiting activities [33, 34], and Rhizobiales could degrade the cyanobacteria-derived particulate organic matters [37], all of which could be used as biological measures to alleviate the ecological influences of algal bloom. The relative abundances of Limnohabitans were significantly reduced at the “During AB” phase in JFZ, SH, and XH laterally proved its cyanobacteria-inhibiting activity, and noteworthily, Pseudarcicella, Sporichthyaceae, and other taxa identified in this study may also serve a similar function.

Discrepancies of correlations between microbial communities and environmental parameters

In general, the correlation patterns of cyanobacterial communities with the environmental parameters additionally indicate the discrepancies of community composition amongst samples at different algal bloom phases in each river (Fig. 8). The environmental parameters, including temperature, nitrogen, and phosphorus, were universally considered to be the crucial factors affecting algal growth and bloom [2, 3, 39, 40]. This study additionally showed that environmental parameters even with a minor difference (Table S1) could shape distinct cyanobacterial community composition, which could be categorized as nitrogen-limited and phosphorus-limited growth types [24, 41].

The algal bloom influenced the correlation patterns of bacterial communities of each river with the environmental parameters (Fig. 8B), that bacterial communities during the algal bloom phase generally had contrasting correlation patterns compared with the rest phases. The difference may infer that functional bacterial community on certain environmental parameters were additionally impaired due to the algal bloom, and which metabolic pathways were impaired will be included in future studies.

The interaction between cyanobacteria and bacteria

This study reveals the dynamic changes of cyanobacteria and bacteria along with the algal bloom, and it is believed that the algal bloom-forming cyanobacteria have profound interaction with the bacterial communities, which has long been the research of interest [6, 4245]. Cyanobacterial species can act as bactericidal or growth-inhibiting factors, influencing the bacterial communities [4649], similar as various microbial agents act in other natural environments and artificial constructions [50, 51]. On the other hand, various bacterial species, including those included in this study, have also been reported to be cyanobacterial or growth-inhibiting factors [38, 5254], which are further studied to develop microbial reagents to alleviate or control the effects of algal bloom. Additionally, many cyanobacteria species are integral members of biofilms in various environments [26, 27, 55]. These facts jointly reveal that cyanobacteria as a whole are the indispensable part of the microbial community. Based upon this cognition, to alleviate or control the algal bloom is to restore microbial equilibrium in ecosystems rather than to eliminate cyanobacteria, and understand about the dynamic changes of cyanobacterial and bacterial communities, as well as those microbial taxa and dominant species relevant to each algal bloom phase is taking one key step towards the comprehension of cyanobacteria-bacteria interactions and the behind mechanisms.

Conclusions

This study investigated the dynamic changes of cyanobacterial and bacterial communities in four upstream rivers, namely Huangshan River (HS), Jinfuzhai River (JFZ), Sihe river (SH), and Xihua River (XH), of a eutrophicated water source reservoir in the subtropical area of China. The major conclusions were drawn as follows.

The dynamic shifts of cyanobacterial and bacterial communities may imply the interactions between cyanobacteria and bacteria, to fully understand and verify the interaction, it is suggested that future studies should expand to network analysis and field experiments investigating the effects of bacterial species identified in this study on the cyanobacterial blooms.

Declarations

This study was financially supported by the Natural Science Foundation of China (No. 42177099 and No.91951108), the Key R&D plan of Ningxia Hui Autonomous Region (2019BFG02032), and the CAS International Partnership Program (No.121311KYSB20200017). We have also received research support from Shanghai Majorbio Bio-Pharm Technology Co.,Ltd.

Competing Interest

The authors collectively declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this study.

Acknowledgements

This study was financially supported by the Natural Science Foundation of China (No. 42177099 and No.91951108), the Key R&D plan of Ningxia Hui Autonomous Region (2019BFG02032), and the CAS International Partnership Program (No.121311KYSB20200017).

Author Contributions. Shengjun Xu and Xuliang Zhuang were involved in planning and supervised the work. Zhenhua Huang performed the experiments and drafted the manuscript. Ping Lv, Cong Wang, and Xiaoxu Zheng performed the preliminary experiments to determine effective parameters on physicochemical property. Cancan Jiang and Dongsheng Wang revised the first draft of the article. All authors read and approved the final manuscript.

Ethics approval and consent to participate: Not applicable

Consent to publish: Not applicable

Competing interests: The authors declare no competing interests.

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