Spatial Abundance and Diversity of Bacterioplankton in a Typical Stream-Forming Ecosystem, Huangqian Reservoir, China

Abstract

The specific freshwater environment of reservoirs formed by streams has not been well studied. In this paper, the bacterioplankton community in such a reservoir, the Huangqian Reservoir in eastern China, was described using culture-independent molecular methods. We found that the most dominant bacterioplankton were affiliated with Cyanobacteria, followed by Betaproteobacteria, Bacteroidetes, Gammaproteobacteria, and Actinobacteria. Both bacterial abundance and diversity increased along the direction of water flow, and the 16S rRNA gene copy number in the water outlet was nearly an order of magnitude higher than that in the water inlet. Pearson correlation analyses indicated that nitrate had a significantly negative correlation with the bacterial abundance (p < 0.05) and that ammonium was positively correlated with bacterial abundance (p < 0.05). Interestingly, owing to a remarkably negative correlation (p < 0.01), the ratio of nitrate and ammonium might serve as a good pre dictor of the relative abundance of bacterioplankton. According to redundancy analysis, nitrate and dissolved oxygen were the major factors influencing the bacterial communities. In addition, we attempted to determine the reasons why such a reservoir could maintain good ecological balance for a period of decades, and we found that the environmental factors and bacterial communities both played critical roles. This research will benefit our understanding of bacterial communities and their surrounding environments in freshwater ecosystems.

Introduction

Bacteria are crucial components of aquatic ecosystems and play vital roles in biogeochemical cycling [16, 38]. The bacterial community may predict the responses of ecosystem processes to environmental changes [2, 29]. Changes in the bacterial community with the development of some taxa, such as Cyanobacteria, may endanger water quality [36, 37]. Conversely, environmental factors, such as dissolved oxygen (DO), dissolved inorganic nitrogen (DIN), total phosphorus (TP), and temperature, also have significant effects on the bacterial community in freshwater aquatic habitats [13, 15, 28]. Thus, investigating the relationship between the microbial community and environmental factors is crucial for a better understanding of freshwater aquatic ecosystems.

With the advent of molecular techniques, microbial communities have been widely investigated using cultureindependent molecular fingerprinting methods, such as denaturing gradient gel electrophoresis (DGGE), terminal restriction fragment length polymorphism (TRFLP), clone library, high-throughput sequencing technology, and quantitative PCR (qPCR) [1, 17, 36]. Zhao et al. [41] reported the structure of the overall microbial community in a freshwater lake, as revealed by clone libraries and TRFLP. The temporal variability of cyanobacterial populations in Lake Taihu was determined by DGGE and qPCR methods [36]. Székely et al. [30] analyzed bacterial succession during mushroom compost production using DGGE and TRFLP methods. DGGE and subsequent sub-library analysis may provide information about the dominant species, but the separation of PCR products by DGGE is imperfect, especially for rare species [19], and the method is considered to be less sensitive than TRFLP [20, 30]. TRFLP analysis, as a semiquantitative and highly reproducible method, is adequate for the estimation of species diversity, but precise phylogenetic identification of TRFLP peaks with database information is difficult because of incomplete sequence databases [30]. Accordingly, a combination of DGGE and TRFLP methods have been widely used by microbial ecologists for monitoring bacterial communities [18, 20, 30]. The DGGE and TRFLP methods could not estimate the real bacterial abundance, but the 16S rRNA gene-based qPCR method was able to estimate this abundance [22].

The Huangqian Reservoir lies in the eastern foot of Mount Tai, China, and the stream water flowing from the mountain is the major water source for the reservoir. Huangqian Reservoir is a typical stream-fed ecosystem. Although its water quality has deteriorated to some extent owing to the surrounding agriculture, tourism, and other human activities, there have not been any severe eutrophication or blooms, and a relatively stable ecological balance has been maintained for decades in this reservoir. However, the specific freshwater environment of reservoirs formed by streams has not been well studied. Accordingly, we used a combination of DGGE, TRFLP, and qPCR methods to estimate the spatial abundance and diversity of bacterioplankton in the reservoir. We attempted to characterize the spatial distribution patterns of bacterial abundance and diversity as well as to elucidate the relationships between bacterial communities and environmental factors in this freshwater reservoir. This research will enhance our knowledge about sustaining the balance of freshwater ecosystems.

 

Materials and Methods

Sampling Site and Physicochemical Measurements

The Huangqian Reservoir (36°16’-36°28’N and 117°04’-117°22’E) is located in Huangqian town of Taian City in eastern China (Fig. 1). It plays an important role in urban drinking water. The reservoir was built in 1958, covers an area of 292 km2, and has a total reservoir capacity of 59.13 million m3. The mean depth is 7.50 m and the deepest part is 90 m. This area belongs to a temperate monsoon continental climate, and the annual average temperature is 18.5℃. The stream water flowing from Mount Tai is the major water source of Huangqian Reservoir.

Fig. 1.Map of the sampling sites in Huangqian Reservoir. A, B, C, D, and E represent the five sampling sites, and the italic annotations beside them indicate the short descriptions of sampling sites.

Surface water samples were collected using a water sampler (Wildlife Supply Company, USA) in September 2011, which is during the rainy season. They were taken from the water inlet (E), near the dam (D), the center of the open water (C), nearby submerged farmland (B), and the water outlet (A) of the reservoir (Fig. 1). Water samples from each site were a mixture of more than three subsamples. The samples were stored in a cooler and taken back to the laboratory immediately for subsequent analysis. From each sample, 500 ml of water was filtered using 0.22 μm sterile filters, and the filters were stored at -80℃ until DNA extraction. The in situ temperature, pH, salinity, and dissolved oxygen of water samples were measured by portable instruments on the scene (Shanghai Leici Instrument, China). Nitrate and ammonium concentrations were measured with a reduction in a Cd-Cu column followed by diazotization, as described in previous studies [9]. Total phosphorus was determined by acid digestion to phosphate using a molybdate reaction [9]. The physicochemical characteristics were measured at least three times for replication.

DNA Extraction and Quantitative PCR

The genomic DNA of each water sample was extracted using an E.N.Z.A. Water DNA Kit (Omega, USA) according to the manufacturer’s instructions. The abundance of bacteria was estimated by assessing the copy numbers of the 16S rRNA gene with qPCR using the primer pair Eub338 and Eub518 [22], followed by the amplification processes described in Table S1. Amplification reactions were carried out with the SYBR Premix Ex Taq (Takara, Japan) in a total volume of 20 μl. Each reaction mixture contained approximately 10 ng of template DNA, 10 μl of 2× SYBR Premix Ex Taq, and 0.1 μM of each primer. All samples were analyzed in triplicate. Standard curves were obtained using serial dilutions of a known number of plasmid copies containing a 16S rRNA gene fragment, and these were all linear from 1.34 × 105 to 1.34 × 109 gene copies/μl (R2 = 0.998).

Denaturing Gradient Gel Electrophoresis Analysis of the Bacterial Community

The V3-V5 regions of the bacterial 16S rRNA gene fragments were amplified using the primer pair 341F-GC and 907R [27] and following the PCR condition in Table S1. The PCR in 25 μl volume contained approximately 10 ng of genomic DNA, 2.5 μl of 10× buffer (containing 1.5 mM MgCl2), 0.25 mM of deoxyribonucleoside triphosphate mixture (dNTPs), 0.4 μM of each primer, and 1.25 U of rTaq DNA polymerase (TaKaRa, Japan). The PCR products with three replicates were pooled together after examination by 1% (w/v) agarose gel electrophoresis and were analyzed through DGGE with a 30-70% denaturing gradient (100% denaturing gradient is 7 M urea and 40% deionized formamide) using a 7.5% acrylamide solution (acrylamide-bis-acrylamide: 37.5:1) on a Bio-Rad DCode system (Bio-Rad Laboratories, USA). Forty-five microliters of PCR products was loaded in each sample hole, and electrophoresis was performed in 1× TAE buffer (20 mM Tris, 10 mM acetate, 0.5 mM EDTA) at 100 V and 60℃ for 12 h. After electrophoresis, the gel was stained for 15 min in 1× TAE buffer containing diluted ethidium bromide. A gel image was taken under UV light using the Bio-Rad GelDoc 2000 system and analyzed with Quantity One software (Bio-Rad Laboratories, USA) to determine the peak density of each band as described previously [34]. For the DGGE sequence analysis, dominant bands with high optical densities in the DGGE gel were excised and soaked in Milli-Q water overnight at 4℃. The DNA was reamplified as described above, except that no GC clamp was incorporated into the forward primer to construct a subclone library. The positive clones were screened by PCR and sequenced using an ABI PRISM 3730 automated sequencer (Applied Biosystems, USA).

Terminal Restriction Fragment Length Polymorphism Analysis

The bacterial communities in different sampling sites were also detected by TRFLP method. Amplification of the nearly fulllength 16S rRNA gene was performed with the universal primers 27F-FAM and 1492R [14] following the conditions described in Table S1. The PCR mixtures were the same as described for the DGGE analysis above. After 1% agarose gel electrophoresis, the PCR products with three replicates were assembled and purified with an Agarose Gel DNA Retrieval Kit (Solarbio, China). The purified PCR products were digested with 10 U of the restriction enzyme MspI (TaKaRa, Japan) in a 20 μl reaction mixture at 37℃ for 3 h [39]. The digested products were analyzed with a 3730 XL (Applied Biosystems, USA) automated sequencer. Peaks of ˂30 bp and longer than 600 bp or representing ˂1% of the total peak area were not considered.

Statistical Analysis

The nucleic acid sequences were matched to similar sequences in GenBank using BLAST on the NCBI, and the chimera sequences were identified by the Chimera Check software program in the Ribosomal Database Project (RDP, freely available at http://rdp.cme.msu.edu/). The phylogenetic tree was constructed by the MEGA 5.1 software [31] using the neighbor-joining method based on distance estimations calculated with the method of Jukes and Cantor. The bootstrap was 1,000 replicates to estimate the reliability of phylogenetic reconstructions. Cluster analysis of the DGGE profiles was performed with the Quantity-One 4.6.2 software package (Bio-Rad, USA) using the unweighted pair-group method. The CANOCO for Windows software (ver. 4.5) was used to identify the relationships between microbial communities and environmental variables by redundancy analysis (RDA) [4]. Based on TRFLP and DGGE data, the diversity indices and nonmetric multidimensional scaling (NMDS) plots were calculated by Paleontological Statistics (PAST) software (http://folk.uio.no/ohammer/past). SPSS 19 statistics software (IBM, USA) was used to perform Pearson correlations analyses between bacterial abundance and environmental factors as well as analysis of variances (ANOVAs), including significance tests in physicochemical and qPCR data.

Nucleotide Sequence Accession Numbers

Sequences are available in the GenBank database of NCBI under accession numbers KF615871-KF615885.

 

Results and Discussion

Physicochemical Characteristics

The physicochemical variables of each sampling site were measured three times, and their mean values are shown in Table 1. The temperatures varied from 20.9℃ to 22.8℃, and salinity ranged from 0.40‰ to 1.33‰. Interestingly, high pH values were detected in the reservoir, ranging from 8.75 to 9.26. Because the water source of Huangqian Reservoir is stream water from Mount Tai, alkaline substances from montane rocks (e.g., calcium oxide, etc.) might be dissolved in the water and produced the high pH values. The large amount of water from the local rainy season increased the mobility of surface water, which allowed more oxygen to be dissolved in the surface water and resulted in high dissolved oxygen values (10.13 to 16.97 mg/l; Table 1) being detected in the reservoir. Because of the increased fluidity of water, the dissolved oxygen at the water inlet (site E) and outlet (site A) showed higher values than those in the other sites. Along the direction of water flow (from site E to C to A, Fig. 1), nitrate concentrations decreased while concentrations of ammonium increased. Owing to the influence of nearby farmlands, the highest total phosphorus and ammonium concentrations were observed in site B. The sampling sites showed high DIN/TP ratios (the ratio of dissolved inorganic nitrogen and the total phosphorus), which were more than 56.90 and even up to 148.30 in site D. These physicochemical characteristics indicated that Huangqian Reservoir is a typical nitrogen-rich and phosphorus-poor alkalescent freshwater ecosystem.

Table 1.Sal: salinity; TP: total phosphorus; DO: dissolved oxygen; NH4+: ammonium; NO3-: nitrate; NO3-/NH4+: the ratio of nitrate and ammonium. DIN/TP: the ratio of dissolved inorganic nitrogen (DIN = NH4++NO3-) and total phosphorus. One-way analysis of variance according to Duncan’s multiple range test was performed using SPSS ver. 19.0 software, and different letters (a to e) above the data indicate significant differences (p ˂ 0.05 ).

Bacterioplankton Abundance, Community Composition, and Diversity in Huangqian Reservoir

The abundance of bacterioplankton was estimated by bacterial 16S rRNA gene copy numbers using qPCR, and the results are shown in Fig. 2. The bacterial abundance in the water inlet (site E) was significantly lower than that from other samples (p ˂ 0.05). Along the direction of water flow (Fig. 1), the bacterial abundance increased significantly from 4.51 × 106 to nearly 4 × 107 copies/ml water. The most abundant bacterial 16S rRNA gene was detected in the submerged farmland (site B), and it was an order of magnitude higher than that in the water inlet. These results indicate that the abundance of bacterioplankton was sensitive to the direction of water flow and ambient nutrient input, such as those from the farmland ecosystems.

Fig. 2.The abundance of the bacterial 16S rRNA gene in Huangqian Reservoir. A, B, C, D, and E represent the five sampling sites. The data are presented as the mean ± SE of three independent experiments. Different lowercase letters above the columns indicate significant differences (p ˂ 0.05) according to Duncan’s multiple range test.

To accurately assess the bacterioplankton community in Huangqian Reservoir, DGGE and TRFLP analyses were performed. A total of 126 DGGE bands were detected, corresponding to 48 different band migration points. Fifteen dominant bands, which accounted for more than 80% of the total band intensities, were cloned and sequenced (Fig. 3A). Bands HQ-1, -2, -5, -6, -8, -9, -13, and -14 were detected at all five sampling sites, suggesting that these bacteria might be well adapted to the surroundings. Bands HQ-3, -4, -7, -12, and -15 were found in all sampling sites, except at the water inlet (site E); this indicated that these bacteria were not from the upstream but came from the ambient environments. Of the 15 dominants, HQ-10 was only found in site E, indicating that some bacteria, such as HQ-10, came from upstream but were replaced by others because of their weak ecological flexibility with respect to the surrounding environments. Sequences from these bands affiliated with five different phyla, namely Cyanobacteria (HQ-1, -6, -11, -12, and -13), Bacteroidetes (HQ-2, -3, and -7), Betaproteobacteria (HQ-4, -5, -8, -9, and -10), Gamaproteobacteria (HQ-14), and Actinobacteria (HQ-15) (Fig. 3B). In the reservoir, the most dominant bacterial group was Cyanobacteria, consisting of 54.91% to 66.57% of the total band intensity in different sites, followed by Bacteroidetes and Betaproteobacteria, accounting for 7.53% to 17.63% and 6.66% to 12.71%, respectively. Cyanobacteria played an important part in influencing the spatial community composition and abundance. Compared with the water inlet (site E) and other sites, the dominant Cyanobacteria HQ-1 in the water inlet (site E) was displaced by other Cyanobacteria, such as HQ-11, -12, and -13, whereas the Gamaproteobacteria had a higher ratio in the water inlet than in other sites (Figs. 3A and 3B).

Fig. 3.DGGE profiling of the bacterioplankton. (A) DGGE band profiles of 16S rRNA gene fragments in Huangqian Reservoir. (B) Bacterial community composition at the phylum (or class) level based on DGGE band profiles. (C) UPGMA dendrogram analysis from the DGGE profiles. The capital letters A to E refers to the five sampling sites and the numbers HQ-1 to HQ-15 indicate the 15 dominant DGGE bands that were sequenced.

A total of 51 distinct TRFs (terminal restriction fragments) were identified in the TRFLP profiles. Among the dominant TRFs (relative ratio ˃5% in each group), the 518 bp TRF was the most dominant in all samples, except site E, and it was replaced by TRFs of 501 bp in site E. A TRF of 486 bp was detected in all of the sampling sites and represented ratios of 10.93%, 9.07%, 10.96%, 13.23%, and 11.11%, respectively. In addition, TRFs of 471, 482, 485, and 494 bp were only detected in site E, which represented 34.32% of the total TRFs population. To further confirm the affiliating phyla of dominant TRFs, the PAT+ process of MiCA [26] was combined with the RDP Classifier process and the NCBI GenBank for analysis. It was found that the dominant TRFs were mainly affiliated to phyla Cyanobacteria, Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, Verrucomicrobia, and Chloroflexi, which is basically consistent with the DGGE-classified results.

To visually present the similarity of bacterial community composition among different sites, the UPGMA dendrogram based on DGGE band patterns and the NMDS plot depending on TRFLP data were drawn, respectively (Figs. 3C and 4). The UPGMA dendrogram showed that all samples were divided into three clusters if according to a 90% similarity. The bacterial communities in sites A, C, and D were more similar (˃90%) than those in the other clusters (sites B and E). Notably, the bacterial communities in the water center (site C) and around the dam (site D) were most similar (˃92%). The NMDS plot depending on TRFLP data was strikingly consistent with the UPGMA dendrogram of the DGGE results, and the bacterial communities were more similar, excluding the submerged farmland (site B) and the water inlet (site E) (Fig. 4). These results indicated that the bacterial community structure changed largely after the water flowed into the reservoir, and influences from the surrounding farmland made site B’s composition distinct from the other sites.

Fig. 4.A nonmetric multidimensional scaling (NMDS) plot analysis based on the TRFLP results. A, B, C, D, and E represent the five sampling sites.

The diversity indexes were calculated based on the DGGE and TRFLP data, respectively (Table 2). The lowest species richness was at the water inlet (site E) in both methods. The Shannon diversity values were similar according to either of the two methods. Specifically, the TRFLP-based Shannon diversity suggested that the water outlet (site A) had the highest diversity and the submerged farmland (site B) had the lowest. The DGGE-based Shannon diversity showed that the sampling site in the dam (site D) had the highest diversity and the water inlet (site E) had the lowest. Interestingly, the Shannon indexes of both methods indicated that the bacterial diversity increased along the direction of water flow. The dominance index showed that there was greater species dominance in the water inlet (site E) and in the submerged farmland (site B) than in other sites. This result resulted from the fact that some dominant bacteria (such as HQ-1) might play important roles in the water inlet before it enters the reservoir, and this dominance decreased after the water flowed in. In this relatively closed habitat, some bacteria in site B became more dominant owing to the influence of submerged farmland, indicating that environmental factors vitally influenced the diversity and community structure of the reservoir ecosystems.

Table 2.The diversity indexes are calculated based on the TRFLP data and the DGGE pattern, respectively.

In this study, bacterioplankton communities were monitored with qPCR, DGGE, and TRFLP. In agreement with previous studies comparing DGGE and TRFLP methods [18, 20, 30], DGGE revealed less diversity (Shannon indexes from 2.339 to 2.662 per sample) than TRFLP (Shannon indexes from 2.654 to 2.906 per sample) (Table 2), but the evaluation of the similarity between bacterial communities showed that both molecular fingerprinting techniques gave very consistent clustering of the samples (Figs. 3C and 4). Moreover, sequencing of the bands excised from the DGGE gel gave a good classification of the dominant taxa, whereas the PAT+ process of MiCA [26] based TRFLP methods could only give a rough classification at the phylum level. Neither methods reflected the real bacterial abundance, but qPCR as a sensitive, quantitative, and highly reproducible method was able to estimate the real bacterial abundance through the 16S rRNA gene copy numbers (Fig. 2). Therefore, these three molecular methods could rapidly and accurately detect the dominant bacterial community abundance, structure, and diversity in environmental samples.

Phylogenetic Analysis of the Bacterial Communities

Based on the 15 dominant sequences, a neighbor-joining phylogenetic tree was constructed (Fig. 5). In the tree, the five dominant cyanobacterial OTUs (operational taxonomic units) were assigned to two clades, with HQ-1 and HQ-6 in one and HQ-11, HQ-12, and HQ-13 in another. Most of their close relatives (99% similarity) in NCBI GenBank mainly derived from freshwater ecosystems, such as Dongping Lake, Lake Taihu, and Bagre Reservoir (Fig. 5). HQ-11, HQ-12, and HQ-13 are attributed to the genus of Prochlorothrix. This genus was originally obtained during a routine study of cyanobacterial blooms in the Loosdrecht lake system near Amsterdam [5], and the members of this genus are unique in eutrophic lakes occupied mainly by Cyanobacteria [21]. The genome sequence of Prochlorothrix hollandica PCC 9006 reveals that this bacterial strain has the characteristic of efficient phosphorus and nitrogen uptake [21]. Therefore, we deduced that the predominant OTUs (HQ-11, HQ-12, and HQ-13) of Huangqian Reservoir may have played a critical part in controlling the phosphorus and nitrogen levels, especially in maintaining the high value of DIN/TP.

Fig. 5.Phylogenetic tree of dominant 16S rRNA gene sequences in Huangqian Reservoir. The neighbor-joining method was used and bootstrap analysis was performed with 1,000 replications. Bootstrap values above 50 are indicated at branch points. The Archaea sequence from an environmental sample (KC248369) was used as an outgroup. HQ-1 to HQ-15 (bold and italic) refer to the 15 dominant sequenced OTUs.

Betaproteobacterial OTUs were dominant in Huangqian Reservoir and clustered into four clades in the tree, HQ-9 and -10, HQ-4, HQ-5, and HQ-8, respectively. All of the clades are assigned to families of the order Burkholderiales. HQ-8, affiliated with Polynucleobacter, had the closest sequences (100% similarity) from freshwater lakes and reservoirs, and it has been detected as a euryoecious adaptation of free-living planktonic freshwater bacteria in a broad range of habitats, which are closely related with dissolved organic matter [10, 33, 35]. HQ-9 and HQ-10 are affiliated with genus Polaromonas and Acidovorax, respectively. The most similar sequences (99% similarity) of HQ-9 were obtained from glacier and soil samples, and HQ-10 had close relatives (100% similarity) in drinking water treatment sand, denitrifying activated sludges and mangrove (Fig. 5). Furthermore, HQ-4 and HQ-5 were closely related (100% similarity) to clones from freshwater ecosystems, such as glaciers, lakes, and reservoirs (Fig. 5).

Bacteroidetes, which exist abundantly at mesotrophic and eutrophic water bodies [11, 32], were another dominant group in this study and formed two clades in the tree. HQ-2 and HQ-3 were clustered together as members of the family Chitinophagaceae. Some bacterial strains of this family have been isolated from bulking sludge, polluted farmland, and freshwater springs, and these bacteria may be effective for removing pollutants [12, 40]. HQ-7, as the other clade, belongs to the family Cyclobacteriaceae. HQ-2, HQ-3, and HQ-7 were most similar to bacteria (99% similarity) in eutrophic lakes (like Lake Taihu) and other mesotrophic freshwater environments around the world [7, 38]. Furthermore, HQ-14 falls in the genus Pseudomonas, and the closest sequences (100% similarity) to it were usually retrieved from agricultural soil and rhizosphere soil. The distribution of HQ-14 in Huangqian Reservoir proved that there were relative high ratios in the submerged farmland (site B) and water inlet, suggesting that HQ-14 might thrive in the submerged farmland soil and the rhizosphere soil of upstream plants. Finally, HQ-15 was the only band that was identified in Huangqian Reservoir belonging to genus Corynebacterium of Actinobacteria. It had the closest phylogenetic relationship with bacteria (100% similarity) from human skin and foot (Fig. 5), indicating that HQ-15 may originate from human activities near Huangqian Reservoir.

Correlations Between the Bacterial Community and Environmental Factors

To reveal the correlation between the bacterial abundance and environmental factors, the Kolmogorov–Smirnov test was first performed using SPSS software to determine the distribution model of variables. The test indicated that the abundance and environmental data were consistent with a Normal distribution, so Pearson correlation analysis was used to estimate the correlation between the bacterial abundance and environmental factors (Table S2). The bacterial abundance was significantly and positively related with the concentration of ammonium (r = 0.925, p ˂ 0.05) and had a significant negative correlation with nitrate (r = -0.895, p ˂ 0.05). Interestingly, the ratio of nitrate and ammonium had remarkably negative correlation with the bacterial abundance (r = -0.991, p ˂ 0.01). These results indicate that the value of NO3-/NH4+ greatly influenced the abundance of bacterioplankton in Huangqian Reservoir, and the ratio might be a predictor of relative abundances of bacterioplankton in freshwater ecosystems. Moreover, the correlations between other environmental factors and the bacterial abundance were not significant (p ˃ 0.05, Table S2).

To determine the influence between environmental factors and bacterial communities in Huangqian Reservoir, DGGE- and TRFLP-based RDA analyses were employed (Figs. 6A and 6B). In the DGGE-based RDA analysis (Fig. 6A), the first two axes can explain 96.3% (91.9% of Axis 1 and 4.4% of Axis 2) of the species-environment relationships. In the Axis 1 direction, the dissolved oxygen and the NO3- had the longest projections, followed by the salinity, pH, and NO3-/NH4+. Forward selection and Monte Carlo testing indicated that DO (in Axis 1, r = 0.917, p ˂ 0.05) and NO3- (r = 0.891, p ˂ 0.05) significantly influenced the bacterial community composition (Fig. 6A). Most of the predominant bacteria, especially the Cyanobacteria (HQ-1, HQ-11, HQ-12, and HQ-13) and the Proteobacteria (HQ-9, HQ-10, and HQ-14), had close correlations with DO and NO3-. There were consistent results in the TRFLP-based RDA analysis (Fig. 6B). The first two axes can explain 80.9% (56.0% of Axis 1 and 24.9% of Axis 2) of the speciesenvironment relationships. DO (in Axis 1, r = 0.973, p ˂ 0.05) and NO3- (r = 0.949, p ˂ 0.05), as the major factors, significantly influenced the bacterial communities (Fig. 6B). Previous studies also indicated that the differences in the concentration of NO3- and DO significantly influenced the bacterial communities in water bodies all over the world [8, 16, 25]. These results demonstrated that the changes in bacterioplankton communities were mainly due to the changing of dominant groups, driven by the NO3- and DO in Huangqian Reservoir.

Fig. 6.(A) The redundancy analysis (RDA) between dominant bacterial DGGE OTUs and environmental factors and (B) the RDA analysis between bacterial TRFs and environmental factors. The circles represent the sampling sites: A, B, C, D, and E. The arrows show the environmental factors. Tem: temperature; Sal: salinity; TP: total phosphorus; NH4+: ammonium; NO3-: nitrate; DO: dissolved oxygen; NO3-/NH4+: the ratio of nitrate and ammonium; DIN/TP: the ratio of dissolved inorganic nitrogen (DIN = NH4++NO3-) and total phosphorus. The hollow upper triangles represent the species. HQ-1 to HQ-15 (left) refer to the 15 dominant DGGE OTUs. The numbers (right) represent the length of TRFs (bp). TRFs1 indicates the 168, 435, and 502 bp TRFs share a common triangle; TRFs2 shows that the 38, 47, 84, and 492 bp TRFs have a common triangle; TRFs3 refers that the 32, 40, 82, 136, 436, 471, 482, 485, 494, 496, 528, and 536 bp TRFs share a common triangle; TRFs4 indicates the 31, 86, 88, and 188 bp TRFs have a common triangle.

Over the past few decades, Huangqian Reservoir has never broken out in severe blooms and has had a relatively stable ecological balance. High concentrations of nitrogen alone may not lead to eutrophication [3], and phosphorus has often been identified as the foremost limiting nutrient in freshwater environments [6, 23, 24]. Combining previous studies and their study in river-dominated coastal waters, Justić et al. [13] proposed that if the value of DIN/TP ˃ 22, the phosphorus would limit eutrophication. The values of DIN/TP in Huangqian Reservoir are extremely high, far more than 22 (Table 1). Therefore, according to the hypothesis, phosphorus should be one of the important limiting nutrients in preventing eutrophication in Huangqian Reservoir. Furthermore, the RDA result indicated that DIN/TP, NO3-/NH4+, DO, pH, and the NO3- were negatively correlated with most of the bacteria (HQ-3, -4, -5, -7, -8, -11, -12, and -13), especially Cyanobacteria (HQ-11, -12, and -13) (Fig. 6A), which may be related to eutrophication. The Pearson analysis also showed that these factors had negative correlation (Pearson’s r ˂ 0) with the bacterial abundance (Table S2). These results prove that a high value of DIN/TP, NO3-/NH4+, DO, pH, and NO3- might control the eutrophication together, along with a low level of eutrophication-related bacterial species and bacterial abundance. Another dominant bacterium, HQ-6 (Cyanobacteria), was highly positively correlated with total phosphorus (p ˂ 0.001, Fig. 6A), suggesting that HQ-6 might be an efficient phosphorus uptake bacteria and might play an important part in maintaining a low level of phosphorus in the reservoir. These results indicated that the ecological balance of Huangqian Reservoir could be maintained, principally benefited from its special environmental factors and bacterial community structures.

In conclusion, the bacterioplankton community of a typical reservoir formed by stream water, Huangqian Reservoir, was investigated using DGGE, TRFLP, and qPCR methods. The combination of these three methods rapidly and accurately reflected the bacterial abundance, diversity, and community compositions. TRFLP, as a supplemental method, agreed closely with the DGGE results. The most dominant bacterial group in the reservoir affiliated with Cyanobacteria, followed by Betaproteobacteria, Bacteroidetes, Gammaproteobacteria, and Actinobacteria. Most of their close relatives are distributed in similar freshwater ecosystems around the world. Both bacterial abundance and diversity increased along the direction of water flow. The spatial distributions of bacterioplankton communities showed greater differences in the sampling sites of the water inlet and submerged farmland than that in the other sites of the reservoir. In this reservoir, NH4+ and NO3- were strongly correlated with bacterial abundance, and NO3- and dissolved oxygen were important factors for the bacterial community structure. The special bacterial communities and environmental factors should be the reasons why Huangqian Reservoir has maintained ecological balance for decades.