1. Introduction
Qingcaosha Reservoir is the largest salt-avoidance freshwater reservoir in the world. It is an important drinking water source in Shanghai, representing around 50% of the total raw water consumed in this city. However, Qingcaosha Reservoir is located in the Yangtze River Delta, a densely populated area that is currently undergoing rapid economic development. Pollutants from urban development, industrial production, and domestic sewage flow into the Yangtze River estuary and adjacent sea areas [
1,
2]. The reservoir is prone to eutrophication [
3], which increases risks that compromise the safety of the drinking water for residents [
4]. It is therefore crucial to ensure good ecological quality and safety of the water for resident health. In recent years, the reservoir water quality has gradually been improved through hydrodynamic regulation, restoration of submerged plants, and biological manipulation through stocking silver carp and bighead carp. Much research has been conducted on the water quality [
5], hydrodynamics [
6], and zooplankton and phytoplankton [
7] in the reservoir area. However, there has been little research on the bacterioplankton in the reservoir.
Water bacteria play an essential role in maintaining ecosystem function and health [
8]. Bacteria are the main drivers of biogeochemical cycles [
9], acting in concert as producers and decomposers in aquatic ecosystems [
10,
11], through participation in the circulation of essential elements [
12], and facilitating the exchange of matter and energy between the water, plants, animals, and sediments [
13]. Bacteria in the water are abundant and widely distributed, displaying a high level of metabolic activity, and their abundance is strongly related to environmental factors [
14].
In recent years, the degree of freshwater eutrophication has been increasing. Harmful algae not only endanger aquatic biological safety [
15], but also pose a threat to human health. Bacteria play an essential ecological role in the process of eutrophication [
16,
17]. Bacteria are the primary providers of inorganic nitrogen and phosphorus to phytoplankton, producing the necessary biological compounds that stimulate algal growth [
18], and promote the aggregation and degradation of specific algal blooms [
19]. At the same time, some bacteria have algae-killing characteristics [
20]. Bacteria can regulate interactions between bacteria and algae through quorum sensing [
21], and algal blooms can indirectly influence bacterial communities by influencing water quality through inter-species interactions [
22].
The community structure of bacterioplankton results from the coupling effects of different factors at different spatial and temporal scales [
23]. With the severe evolution of the water environment and the continuous implementation of ecological restoration measures, the bacterial abundance and community composition have gradually become a research hotspot. At present, the bacterioplankton in water that are the main subjects of research are mainly concentrated in the sea, large rivers and lakes, sediments, and riverside soil [
24]. There are few studies on the coupling effect of algae, the bacterial community, and physical and chemical factors of the water, especially on the bacterioplankton in drinking water source reservoirs.
For the above reasons, in this research, we monitored the water quality, phytoplankton, and bacterioplankton of Qingcaosha Reservoir in during non-salt tide period. The interactions among water quality, phytoplankton, and bacterioplankton were analyzed through correlation analysis, with the aim of revealing the succession characteristics of phytoplankton and bacterioplankton, and the potential correlations between them. The primary purposes of the study were (1) to explore the temporal and spatial variations in reservoir water quality and phytoplankton; (2) to study the temporal and spatial succession rules of bacterioplankton by means of high-throughput sequencing technology; (3) to analyze the potential coupling of water quality, phytoplankton, and bacterioplankton using correlation analysis and multivariate statistical methods; and (4) to analyze bacterial metabolic changes in the reservoir area by bacterial function prediction. The results of this study will provide an interpretation of the structure and function of the reservoir biogeochemical system at the bacterial level, and lay a foundation for water source protection and ecological management.
2. Materials and Methods
2.1. Sampling Site
Qingcaosha Reservoir is located on the northwest side of Changxing Island, Chongming District, Shanghai, China. The total area of the reservoir is 66.15 km
2, the total storage capacity is 527 million m
3, and the designed daily water supply is 7.19 million m
3. The reservoir is a light-storage and salt-avoidance reservoir, with water diversion and drainage of the reservoir area controlled by the gates on the northwest (upstream) and northeast (downstream) sides. In this study, eight stations (ST1–ST8) were set up in Qingcaosha Reservoir (
Figure 1). Sampling was conducted mid-monthly in the period from April to December 2019.
2.2. Sample Collection and Determination of Physical and Chemical Indexes of Water Quality
A columnar water collector was used to collect 2 L water from the depth of 0–0.5 m, and each sample was taken three times in parallel. Dissolved oxygen (DO), electrical conductivity (Cond), and pH were measured using a multi-parameter water quality analyzer (YSI, EXO, Yellow Springs, Ohio, USA), and a SAN++ continuous flow analyzer (Skalar, SAN++, Breda, North Brabant, NL,) was used to determine the mass concentrations of total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH4+-N), and nitrate nitrogen (NO3−-N). Chemical oxygen demand (COD) was determined by the potassium dichromate reflux method (SHIMADZU, UV 1900, Nakagyo-ku, JPN). Chlorophyll a (Chl-a) concentration was determined through spectrophotometry (SHIMADZU, UV 1900, Nakagyo-ku, JPN). The water transparency (SD) was measured using a Secchi disc.
2.3. Collection and Identification of Phytoplankton
A 100 mL water sample was fixed with Lugol’s solution and preserved for phytoplankton counting. The species of phytoplankton were identified and counted using an inverted microscope. After the concentrated sample was shaken evenly, 0.1 mL was taken, and added to a 0.1 mL counting box. A 22 mm × 22 mm cover glass was used. The number of algal cells was determined through observation in 100 fields of vision. For each species, the cell size, i.e., volume, was estimated based on the cell morphology and direct measurement of the main cell dimensions in more than 25 randomly selected individuals. When size differences were observed within a species, the individuals of that species were divided into several cell sizes to determine the cell volume. The biomass of each species was calculated assuming a wet weight density of 1 g·cm
−3 (abundance × cell volume) [
25], and the total biomass of phytoplankton was calculated as the total biomass of all present species.
2.4. DNA Extraction and High-Throughput Sequencing
The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined using a NanoDrop 2000 UV–Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The hypervariable V3–V4 region of the bacterial 16S rRNA gene were amplified with primer pairs 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’), using an ABI GeneAmp® 9700 PCR thermocycler (ABI, Vernon, CA, USA). PCR reactions were performed in triplicate. Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA), according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).
2.5. Data Analysis
2.5.1. Bioinformatics Analysis
The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fastp (version 0.20.0) [
26], and merged by FLASH (version 1.2.7) [
27], according to the default criteria. Operational taxonomic units (OTUs) with 97% [
28] similarity cutoff were clustered using UPARSE (version 7.1) [
29], and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed using RDP Classifier (version 2.2) [
30] against the 16S rRNA database (Silva v138), with a confidence threshold of 0.7.
2.5.2. Biodiversity Analysis
The richness and diversity of microbial communities were reflected by alpha diversity analysis of a single sample. The Shannon index was chosen as the index of community diversity. The similarity or difference of community composition of samples from different groups was studied by beta diversity analysis of multiple samples, and was described using principal co-ordinates analysis (PCoA). A statistical analysis and mapping were carried out using R language (version 3.3.1). The dominant species of phytoplankton was determined based on the McNaughton dominance index (Y) [
31], where Y > 0.02 indicated the species was dominant.
2.5.3. Statistical Analysis
The Student–Newman–Keuls (SNK-q) method was used to describe the differences in the physical and chemical factors of the assessed water. The Wilcoxon signed-rank test method was used for the non-parametric test of two groups of samples to evaluate the significance level of differences in species abundance between groups. The relationship between environmental factors and sample grouping was described using distance-based redundancy analysis (db-RDA). Correlation heatmap analysis was used to describe the relationship between environmental factors and specific species (genus level). The correlation analysis was based on the R (version 3.3.1) stats package, vegan package, and heatmap package. The species correlation network was constructed using Network software for two-factor network analysis, to describe the interspecies correlation.
2.5.4. Functional Predictive Analysis
PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) software was used to predict the function of bacterioplankton. The function and abundance information of bacteria in the samples were obtained by comparing the EggNOG database (Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups) and KEGG database (Kyoto Encyclopedia of Genes and Genomes). By comparison with the FAPROTAX database, the specific metabolic or ecological functions of samples were described, especially regarding the sulfur, nitrogen, hydrogen, and carbon cycling functions in marine and lake biogeochemical processes.
5. Conclusions
The dominant classes of bacterioplankton in the reservoir were Gammaproteobacteria, Alphaproteobacteria, Actinobacteria, Acidimicrobiia, and Cyanobacteria, which are also typical freshwater ecosystem bacterioplankton classes. However, the abundance of β-proteobacteria was low, which was speculated to be related to the recharge of the salt tide in the reservoir. Bacterial community diversity was significantly different at the time, but not on the spatial scale. The changes of bacterial community structure in the non-salt tide period in the reservoir area were significantly affected by phytoplankton density and biomass, DO, TP, Cond, and NO3−-N, and the effects of phytoplankton density and biomass, DO, and Cond, were the most significant. The presence of Pseudomonas and Legionella was positively correlated with that of Pseudanabaena sp., and Sphingomonas and Paragonimus with Melosira granulata. On the contrary, Planctomycetes presence was negatively correlated with Melosira granulate. as was Deinococcus-Thermus with Cyclotella sp. The main functions of the community were aerobic chemical energy decomposition, photosynthetic nutrition-related functions, aromatic compound and carbohydrate degradation functions, and nitrification and denitrification of nitrogen. The relative abundance of nitrogen-removal functional bacteria decreased from April to December, while the abundance of nitrogen-fixing bacteria increased.