Next Article in Journal
Managed Aquifer Recharge as a Strategic Storage and Urban Water Management Tool in Darwin, Northern Territory, Australia
Previous Article in Journal
Hydrodynamic-Based Numerical Assessment of Flood Risk of Tamuín City, Mexico, by Tampaón River: A Forecast Considering Climate Change
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 11
Issue 9
10.3390/w11091868
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions

by
Yuqing Lin
1,
Dayong Zhao
1,2,*,
Jin Zeng
3,
Xinyi Cao
1,3 and
Congcong Jiao
1,3
1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China
2
Experimental Teaching Center of Water Resources, Hohai University, Nanjing 210098, China
3
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(9), 1868; https://doi.org/10.3390/w11091868
Submission received: 6 July 2019 / Revised: 16 August 2019 / Accepted: 5 September 2019 / Published: 8 September 2019
(This article belongs to the Section Water Quality and Contamination)
Browse Figures
Versions Notes

Abstract

:
Bacterial communities play essential roles in multiple ecological processes, such as primary production and nutrient recycling in aquatic systems. However, although the composition, diversity and function of bacterial communities have been well studied, little is known about the interactions and co-occurrence characteristics of these communities, let alone their seasonal patterns. To investigate the seasonal variations of bacterial community interactions, we collected water samples from four seasons in Lake Taihu and applied network analysis to reveal bacterial community interactions. Bacterial community networks were non-random in structure, and interactions among bacterial taxa in the networks varied markedly in different seasons. The autumnal bacterial network was the largest and most complex among obtained networks, whereas the spring correlation network was the simplest, having no module hubs or connectors. The important species of the networks were the dominant bacterial phyla/classes (e.g., Alphaproteobacteria and Bacteroidetes), although their relative abundance varied among seasons. The relationships between species and measured environmental variables changed over seasons; fewer environmental factors were correlated with bacterial species in the spring bacterial network, while we observed a greater number of species-environment correlations in the winter network. Our study highlights the seasonal differences in bacterial community interactions and expands our understanding of freshwater microbial ecology in systems affected by aquaculture.

1. Introduction

China is the world’s largest producer of aquatic animals, its aquacultural output, which represents approximately 60% of global production [1]. Lake Taihu is a representative site of freshwater aquaculture within China. Aquaculture contributed 45% of the agricultural gross domestic product for the Taihu Lake watershed [2], and the majority of Eastern Lake Taihu has been covered with aquaculture nets to expand the area of aquaculture since 2006 [3]. Owing to the rapid development of aquaculture in Eastern Lake Taihu, large quantities of waste materials (e.g., feces, residual feed and antibiotics) have degraded water quality [4] and influenced the biogeochemical processes mediated by microorganism [5]. Meanwhile, antibiotic pollution generated further influences the distribution of antibiotic-resistant bacteria [6].
Bacterial communities in aquatic systems play essential roles in the ecological processes of water bodies, including in primary production and nutrient recycling [7,8]. Understanding interactions (e.g., competition, predation, mutualism and parasitism) between bacterial taxa [9] and isolating the relationships between bacterial communities and environmental forcing factors (e.g., seasonality [10,11,12] and nutrient concentrations [13,14]) in freshwater ecosystems is an essential theme in microbial ecology [15]. Numerous studies have been performed to evaluate the composition and diversity of bacterial communities, which have shown that microorganism assemblages are seasonally dependent [16,17,18,19]. However, most of these studies were centered on the variation of diversity or composition of bacterial communities, whereas the complex interactions between bacterial taxa and the seasonal co-occurrence/co-exclusion patterns of lacustrine bacterial communities remain largely unknown. Furthermore, co-occurrence relationships among bacterial taxa across seasons may provide valuable insights into the ecological mechanism underlying the response of microbes to environmental change [20].
The rapid development of high-throughput sequencing technologies has favored network analysis and the exploration of complex correlations of bacterial communities [21,22,23]. In an ecological network, each object (species or gene) is described as a node, and the pairwise relationship is described as an edge [24,25]. A series of network topological parameters can be used to characterize properties of the constructed network, such as modularity, clustering coefficient, network diameter, average path length, graph density and average degree. Moreover, although some co-occurring bacteria may not interact directly with each other, network analysis could provide effective insight into potential interactions among bacteria taxa owing to the available network analysis is correlation-based [9]. In the present study, 32 aquaculture-influenced water samples from Eastern Lake Taihu across four seasons (i.e., spring, summer, autumn and winter) to investigate the interactions between bacterial taxa and apply network analysis elucidate the relationships between bacterial interactions and environmental variables. The following scientific questions were addressed: (i) Do the interactions among bacterial taxa vary remarkably in different seasonal bacterial groups affected by aquaculture activities? (ii) Do individual Operational Taxonomic Units (OTUs) play different topological roles in the seasonal bacterial community networks? (iii) Do the relationships between bacterial species and environmental variables change over seasons?

2. Materials and Methods

2.1. Sample Collection and Measurement of Physicochemical Variables

We collected 32 water samples from a pair of zones in Eastern Lake Taihu, China (i.e., grass-crab zone (GC) and grass-crab-fish zone (GCF)) in four seasons (i.e., autumn, October 2015; winter, January 2016; spring, April 2016; summer, August 2016—eight water samples were collected during each season). Both zones have been subjected to long-term aquaculture. Details of the location and the distribution of the sampling sites were shown in Figure 1. We collected water samples at 0.5 m below the surface water. We measured physicochemical properties of water samples (including water temperature (T), pH and dissolved oxygen (DO)) in situ using a water quality sonde (YSI 6600, Yellow Springs, OH, USA). Other environmental variables such as chlorophyll-a (Chla), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3−-N) and nitrite nitrogen (NO2−-N), were measured in the laboratory following previous methods [14]. Dissolved organic carbon (DOC) and water transparency were examined as described in a previous study [26].

2.2. DNA Extraction, Polymerase Chain Reaction (PCR) Amplification and Illumina Sequencing

The methods of DNA extraction, amplification and Illumina MiSeq sequencing followed those of a recent study [26]. The raw sequencing data were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession number: SRP155498).

2.3. Sequence Data Processing and Statistical Analysis

Raw reads of the bacterial 16SrRNA gene were processed using Quantitative Insights Into Microbial Ecology (QIIME) v.1.9 (http://qiime.org) [27]. We discarded any reads had a quality criterion with a quality score < 25 or a sequence length shorter than 100 bp for 16S amplicon reads using Trimmomatic [28]. Only those trimmed sequences that had overlapped lengths longer than 10 bp and a mismatch density lower than 0.25 were jointed using FLASH (http://ccb.jhu.edu/software/FLASH/) [29]. We identified chimeras and then used UCHIME in the de novo mode to remove them [30]. After the pre-processing of the obtained sequences, 979255 quality sequences were obtained for the 16S rRNA. Operating taxonomic units (OTUs) were clustered at 97% sequence similarity by using the UCLUST (pick_otus.py) [31] in QIIME, and we selected representative sequences by using VSEARCH (pick_rep_set.py) [32]. The ribosomal database project (RDP) classifier assigned taxonomic classifications and obtained taxonomic information based on 80% bootstrap cut-off threshold [33,34]. We aligned and filtered representative sequences using the Python Nearest Alignment Space Termination (PyNAST) algorithm [35] against a bacterial SILVA v.132 database of 16S rRNA genes [36], and the phylogenetic tree was constructed in FastTree [37]. The final OTU table was generated based on the clustering results and we excluded suspicious OTUs (sequence number < 2) that occurred in only one sample [38]. With the minimum numbers of sequences in one sample being 22,136 for the 16S rRNA gene, subsampled sequences were used for subsequently all OTU-based network analyses. We analyzed relative abundance of bacteria taxa and diversity of bacterial communities based on the final OTU table, following a recent study [27].

2.4. Seasonal Network Construction and Characterization

All water samples were divided into four groups (i.e., spring, summer, autumn and winter). Only OTUs that appeared in at least six samples in each group were selected to improve the network reliability [12,39]. The relative proportion of sequence numbers helped in the subsequent correlation analysis, as the sequence numbers of obtained individual OTUs obtained varied markedly among the different samples [40]. In each group, we constructed a pair of correlation matrices based on the relative abundance of the OTUs of each sample. We used the ‘Hmsic’ package in R statistical software (v.3.3.1) [41] to calculate both the correlation (R matrix) and significance matrices (P matrix) by computing all possible pairwise Spearman’s correlations among all OTUs in each group. We maintained only strong correlations (Spearman’s rank correlation coefficient, r > 0.9 (or r < −0.9)) and statistically significant (p ≤ 0.001) results to construct the networks [9,12,42]. The node connectivity (i.e., number of edges connected to a node) was plotted against the probability P(k) that a node would have that degree in the network. We used three methods (i.e., power law, exponential law and truncated law) to fit the degree distribution of the four seasonal networks [43].
The constructed correlation matrix was transformed into a Cytoscape dataset in R and we then generated topological networks. Network visualization was completed in Cytoscape v.3.6.1 (https://cytoscape.org) [44,45]. Other important information, including nodes (OTUs), edges (positive or negative interactions), modules and weights, was also imported into Cytoscape. We divided each seasonal network into modules by fast greedy modularity optimization [46]. The topological characteristics of seasonal networks were described by modularity, clustering coefficient, average path length, network diameter, average degree and graph density. We calculated these parameters using the ‘igraph’ package in R. We used these same parameters to compare our observed networks with random networks [47]. The network parameters of each random network were the average and standard deviation of 1000 random networks. Significant differences between the topological indices of the observed and random networks for each group were determined using a Z-test in R [12].

2.5. Topological Roles of Individual Nodes

We used a pair of parameters (i.e., within-module connectivity (Zi) and connectivity among modules (Pi)) to describe the topological roles of individual nodes (OTUs) [48]. The distribution of nodes in the networks was visualized in Sigmaplot (v.12.5). Nodes could be separated into four subcategories based on the values of Zi and Pi: (i) peripheral nodes (Zi ≤ 2.5, Pi ≤ 0.62); (ii) connectors (Zi ≤ 2.5, Pi > 0.62); (iii) module hubs (Zi > 2.5, Pi ≤ 0.62); (iv) network hubs (Zi > 2.5, Pi > 0.62) [41].

2.6. Relationships between Bacterial Networks and Environmental Variables

Environmental variables were integrated into the seasonal networks to explore the relationships between the distribution of nodes (species) and the network and environmental variables. To improve the reliability of species-environment networks, we considered only statistically significant (p < 0.01) and robust (r ≥ 0.6 or r ≤ −0.6) correlations. We visualized the species-environment networks using Cytoscape v.3.6.1 [45].

3. Results

3.1. Network Structure of Bacterial Communities in Different Seasons

Although we constructed four seasonal networks based on species-species correlations using identical thresholds, we found marked differences in the network size of the bacterial communities. Specifically, the largest network (905 nodes and 1696 edges) and the smallest network (243 nodes and 225 edges) occurred in autumn and spring, respectively, while the summer and winter networks were similar and moderate in size (Table 1 and Figure 2). Additionally, we observed remarkable differences of bacterial community alpha diversity, with the highest values in autumn and lowest in spring (Figure S1). Furthermore, significant difference of beta diversity of bacterial community was found in different seasons (Analysis of Variance (ANOVA): p < 0.001, r = 0.4933; Figure S2). Topological indices, modularity and the clustering coefficient for the 4 seasonal networks were greater (p < 0.001) than those of random networks based on Z-tests (Table 1). Moreover, the average path length and the network diameter of the spring and autumn networks were, respectively, lower and higher than those of the random networks; however, average path length and network diameter of the summer and winter networks were more similar to those of random networks (Table 1). The degrees of distribution of the 4 constructed networks had a best fit with the truncated power law, coefficients of 0.9955, 0.9984 and 0.9936 for the spring, summer and autumn groups, respectively; the winter network had a best fit by exponential law, with a coefficient of 0.331 (Figure S3).
The five phyla/classes including Actinobacteria, Alphaproteobacteria, Bacteroidetes, Betaproteobacteria and Gammaproteobacteria dominated (having a percentage of nodes >5%) in the four bacterial networks (Figure 3 and Table S1). These bacterial phyla/classes varied remarkably in different seasons (Figure 3). Other phyla (e.g., Aminicenantes, Cloacimonetes and Lentisphaerae) only appeared in a single season (Table S1). Among the five dominant phyla/classes, Bacteroidetes maintained a relatively high abundance between seasons. Additionally, Cyanobacteria were the most abundant in the summer (Figure 3). Moreover, we observed the relative abundance of most phyla/classes varied markedly across seasons (Figure S4).

3.2. Co-Occurrence/Co-Exclusion Patterns in the Different Seasonal Networks

The modularity values of the four seasonal networks (spring: 0.931, summer: 0.852, autumn: 0.739, winter: 0.851) were all > 0.4, indicating that all the networks had modular structures. Therefore, we divided the species-species association networks by module to explore the co-occurrence/co-exclusion patterns within the different seasonal bacterial communities. The species-species association networks differed clearly between seasons (Figure 2 and Table S2). All species-species networks contained more positive correlations (positive edges) than negative correlations (negative edges). The autumn network contained the most positive correlations and the highest average degree, followed by the summer and winter networks (Figure 2 and Table 1). We observed the highest modularity value in the spring network, although this network had no negative correlations.

3.3. Topological Roles of Individual Nodes in Different Seasonal Networks

The topological roles of the OTUs identified in the four seasonal networks are detailed in Figure 4. Most OTUs (spring: 100%, summer: 98.8%, autumn: 97.5%, winter: 98.4%) were peripherals, with the majority of their links inside their modules. Among these peripherals, most (99.2%, 91.1%, 79.8% and 90.4% for spring, summer, autumn and winter, respectively) had no links to other modules (i.e., Pi = 0). We found no module hubs and connectors in the spring network and all the seasonal networks lacked network hubs (Figure 4). For the summer network, we observed three module hubs and three connectors (Figure 4). As well, we found 21 module hubs and two connectors in the autumn network, as well as five module hubs and two connectors in the winter network (Figure 4). Most of these module hubs and connectors belonged to the five dominant phyla/classes (i.e., Actinobacteria, Alphaproteobacteria, Bacteroidetes, Betaproteobacteria and Gammaproteobacteria; Table 2); we observed some other phyla/classes occurring in relatively low proportion (i.e., Acidobacteria, Chloroflexi, Deltaproteobacteria, and Gemmatimonadetes) or some emerging phyla (i.e., Cloacimonetes and Lentisphaerae) associated to module hubs or connectors. Although Bacteroidetes maintained a relatively higher proportion in summer, module hubs in the summer network were mainly attached to Alphaproteobacteria (denovo3272 and denovo0386) (Table 2). In contrast, some module hubs of autumn and winter networks were affiliated with the phylum Bacteroidetes (autumn: denovo3266, denovo4788, denovo6589, denovo9608 and denovo10588; winter: denovo3884) (Table 2).

3.4. Relationships between Species and Environmental Variables in Different Seasons

We added the measured environmental variables to the four seasonal networks (solid blue and dotted pink edges represent positive and negative correlations, respectively) to explore the relationships between species interactions and environmental variables within the different seasons (Figure S5). Fewer environmental factors affected summer and spring bacterial groups, particularly in the summer species-environment network, while we observed more correlations between species interactions and environmental variables in the autumn and winter networks (Figure S5). Although many environmental factors appeared in the summer network, only a few environmental factors—DO, T and Chla—correlated strongly with species. Additionally, the summer network demonstrated a greater number of correlations between different environmental variables. The winter network contained the highest number of significant (p < 0.01) correlations (40 positive and 71 negative links) (Table S3). Moreover, the spring and autumn networks contained more positive links (38 and 29 for spring and autumn, respectively) than negative links (nine and 12 for spring and autumn, respectively).

4. Discussion

4.1. Interactions among Bacterial Taxa in the Correlation Networks Varied Remarkably between Seasons

The biodiversity of a bacterial community includes not only the number and abundance of taxa, but also the complex interactions among the various bacteria [41,49]. In the present study, the significant variations observed between the four seasonal networks indicated the markedly variable interactions among bacterial taxa depending on the season (Figure 2). We observed the fewest interactions (edges) in spring and highest number of interactions in the autumn (Table 1). These findings contrast to those of a previous study [12] that found the greatest number of associations in summer and fewest in winter. Seasonal changes in aquacultural activities may explain the unique interactions between bacterial taxa in Eastern Lake Taihu. Generally, juvenile aquacultural species polyculture (i.e., crab, fish and shrimp) only occurs in spring; all adult aquatic products are harvested in autumn. Thus, various forms of water treatment and disinfection are carried out during larviculture (spring). In autumn, however, aquacultural species received a greater amount of food, and there is also increased human activity happen in Eastern Lake Taihu as aquatic products are harvested at this time. Our results observations do match, however, those of by previous studies [50,51] that found that in sites subjected to intense aquacultural activities, fewer intensive bacterial interactions occurred in spring and that these networks became more complex in autumn.
Modularity, an important indicator of network complexity [49], can reflect the diversity of habitat and the clustering of closely related species [52]. We recorded the highest modularity values for the spring bacterial network and the lowest values in autumn (Table 1); therefore, the springtime bacterial community network has more complex correlations within modules, whereas the autumnal bacterial community network has more complex correlations among modules. As such, the autumnal bacterial groups may be more stable than bacterial groups in the other three seasons as a response to environmental change, as indicated by previous results [53]. In addition, the lower average path length and network diameter of the spring bacterial network, possibly due to the network being smallest in the spring, suggested that species were interconnected via very short paths. This pattern could favor the quick and more effective transfer of information (i.e., gives the impression of communication between bacteria) relative to other seasonal networks [54]. In addition, positive correlations dominated all species-species networks (Table 1 and Figure 2). Previous studies have suggested that species tend to have more positive interactions under eutrophic conditions because of reduced competition for nutrients among species sharing similar ecological niches [12,43,55]. Thus, given the current eutrophication of Lake Taihu [56,57], the favorable nutrient status for bacterial communities and decreased nutrients could explain the dominance of positive interactions in our study [58]. However, these correlations changed markedly between seasons, and therefore, may also reflect both variable nutrients abundance and changes in the dominant aquaculture activities of the different seasons (Table 3).

4.2. Different Topological Roles of Individual OTUs Observed in Bacterial Community Networks

Individual species (OTUs) have various topological roles in the network [48]. In accordance with previous studies [40,43,59], individual OTUs in our study could be separated into four types of topological nodes in a network. However, most OTUs were peripheral OTUs, having the majority of their links inside their associated modules [41] (Figure 4). Moreover, both connectors and module hubs played vital topological roles in the network and were associated with the modular nature of the network [49]. Our results further revealed that not only topological roles of individual OTUs varied over the seasons, but also modular nature significantly changed across different seasons. These findings were fully supported by previous studies [12,60]. In addition, we observed an increase in the number of connectors and module hubs in the autumn network. This pattern indicated that species were more tightly linked in the autumn, thereby maintaining a more stable bacterial network [53]. On the contrary, no module hubs or connectors were found in the spring network. This absence suggests that the spring network could fragment more easily and be more vulnerable to the collapse of the entire network [49]. Meanwhile, the observation of only positive correlations in the spring bacterial communities may also reflect this instability, as previous work has shown that more negative correlations within ecological communities enhance the stability of networks under disturbed conditions [43,49].
Most module hubs and connectors were assigned to the five dominant phyla/classes (i.e., Actinobacteria, Alphaproteobacteria, Bacteroidetes, Betaproteobacteria and Gammaproteobacteria) (Table 2). These bacteria adapt easily to environmental change, as shown by their high relative proportions (Figure 3) [40]. It is worth noting that Alphaproteobacteria are ubiquitous in freshwater lakes and competitive at utilization of nutrients, as well as capable of degrading complex organic compounds [61,62]. Therefore, OTUs of this phylum occupied important topological roles in the summer, autumn and winter bacterial networks, possibly due to the eutrophication of the eastern portion of Lake Taihu. Additionally, literatures have recorded that Bacteroidetes showed strong correlations with high concentrations of dissolved organic carbon (DOC) [61,63], which indicated that Bacteroidetes possessed higher activity under high DOC concentrations. Therefore, OTUs assigned to Bacteroidetes were more likely to be positioned as module hubs or connectors in the autumn and winter networks due to the relatively higher DOC concentrations in both of these seasons (Table 3); subsequently, no Bacteroidetes OTUs acted as module hubs or connectors in the summer bacterial network when the lowest DOC concentrations were recorded (Table 3).

4.3. Relationships between Species and Environmental Variables Varied between Seasons

The effects of environmental variables on bacterial communities are well known [64,65,66,67]; however, relationships between species interactions and environmental variables remain unclear. In Lake Taihu, relationships between species networks and environmental variables dramatically changed between seasons (Figure S5), an observation consistent with previous studies [8,12,68,69]. In addition, we observed that fewer environmental variables correlated strongly with bacterial species in the spring than in the other seasons. One of the possible reasons may be attributed to the simpler network in spring.
Our present study was carried out in a shallow and eutrophic lake [4]. This makes it relatively easy to have strong hydrological mixing, especially in the summer when rainfall events are more frequent [70]. Hence, although similar networks were found in summer and winter, the relationships between species interactions and environmental variables varied remarkably in summer and winter. This difference may reflect the more homogeneous environmental conditions in summer related to these more frequent and strong rainfall-related hydrological disturbance in the summer [71]. Moreover, higher concentrations of TN and TP in the summer also weaken environmental filtering [58], thereby decreasing the environmental dependence of species. This lower dependence leads to fewer significant relationships between species interactions and environmental variables in summer. On the contrary, we observed more negative correlations in the species-environment association network in winter, especially for those associations that involved temperature. Lower temperatures in winter reduce the growth, maintenance and survival rates of microbes [72]; in turn, the environmental dependence of species increases. Only the seasonal scale was considered in this study, thereby ignoring the effects of aquaculture on interactions among bacterial taxa. Additionally, given that this study was carried out in only one year, a multi-year assessment is valuable to explore the effects of aquaculture activities on bacterial species interactions.

5. Conclusions

Here, we demonstrated that network structures and co-occurrence patterns varied remarkably between seasons in a freshwater system subjected to a strong aquacultural influence (Eastern Lake Taihu, China). We observed stronger and more complex interactions in the autumn network and a simpler bacterial network in the spring. The topological roles of individual OTUs changed over the seasons, although the dominant phyla/classes maintained key roles in different seasons. We also revealed that the relationships between species and environmental variables changed markedly over the seasons. Fewer environmental factors were correlated with bacterial species in spring, while we observed fewer correlations in the summer species-environment association network and a greater number of relationships between species and environmental factors in winter. Our findings highlight the effects of seasonality on bacterial community interactions and provide a basis for a more comprehensive understanding of the effects of aquacultural activities on freshwater ecosystems.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4441/11/9/1868/s1, Figure S1: Diversity (A) and richness (B) of the bacterial communities across seasons. Different letters above columns show the remarkable differences based on one-way ANOVA with Duncan’s comparisons (p < 0.05). Figure S2: Non-metric multidimensional scaling analysis (NMDS) of bacterial community composition for each season (A) and each zone (B) based on the weighted UniFrac distance. Difference was tested based on 999 permutations using ‘anosim’ functions in R. Figure S3: The OTU connectivity distributions of bacterial community networks in different seasons as fitted by power-law, exponential-law and truncated power-law models (A, spring; B, summer; C, autumn; D, winter). The x-axis is the node connectivity. The y-axis is the number of nodes under a given connectivity. The values on both axes are log-transformed. Rsqr values represent the degree of the fitting models (undetermined fitting value found are shown by ‘Error’). Figure S4: The relative abundance of dominant bacterial phyla/classes for each season. Phyla/classes with a relative abundance of >0.5% are presented, whereas those with relative abundance <0.5% were included in the ‘others’ group. The height of each bar denotes the average relative abundance of each phylum. Asterisks above bars represent significant differences from the Kruskal-Wallis H-test. * p < 0.05; ** p < 0.01; *** p < 0.001. Figure S5: Species-environment network of bacterial communities in spring (A), summer (B), autumn (C) and winter (D). Only correlations between species that were statistically significant (p < 0.001) and strong (r > 0.9 or r < −0.9) are shown by solid blue lines (positive correlations) and dotted pink lines (negative correlations), respectively. Only correlations between environmental factors and species interactions that were significant (p < 0.01) are shown. Correlations between pairs of OTUs and OTUs uncorrelated to any environmental variables are not shown. Different bacterial phyla/classes are represented by different colors, and the number on each node represents the ID number of each OTU. T, water temperature; DO, dissolved oxygen; SD, Secchi depth; Chla, chlorophyll a; TN, total nitrogen; TP, total phosphorus; DOC, dissolved organic carbon. Table S1: The percentage (%) of OTUs/nodes in the bacterial networks assigned to each phylum/subphylum for each season. Table S2: The top 10 correlations of the species-species association network in the present study. Table S3: The number of positive/negative links between environmental variables and species interactions in species-environment association network of the different seasons.

Author Contributions

Conceptualization, D.Z. and Y.L.; Methodology, X.C. and C.J.; Software, X.C. and Y.L.; Validation, Y.L., D.Z. and J.Z.; Formal Analysis, C.J. and Y.L.; Investigation, C.J. and Y.L.; Writing—Original Draft Preparation, Y.L.; Writing—Review and Editing, D.Z. and J.Z.; Supervision, D.Z.; Funding Acquisition, D.Z. and J.Z.

Funding

This research was funded by the National Key R&D Program of China (2016YFC0402710), the National Natural Science Foundation of China (31730013, 41621002, 41571108, 41671078 and 41871096), the National Key Technology R&D Program (2015BAD13B01), the Key Research Program of Frontier Science, CAS (QYZDJ-SSW-DQC030), the Natural Science Foundation of Jiangsu Province (BK20181311), the Fundamental Research Funds for the Central Universities (2018B43414) and the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2018490211).

Acknowledgments

We thank Huiming Xu and Xiaowei He for sample collection and water filtration.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO. The State of World Fisheries and Aquaculture 2018—Meeting the Sustainable Development Goals. 2018. Available online: http://www.fao.org/3/i9540en/I9540EN.pdf (accessed on 12 July 2018).
  2. Cai, C.; Gu, X.; Ye, Y.; Yang, C.; Dai, X.; Chen, D.; Yang, C. Assessment of pollutant loads discharged from aquaculture ponds around Taihu Lake, China. Aquac. Res. 2013, 44, 795–806. [Google Scholar] [CrossRef]
  3. Fang, Y.; Li, H.; He, H. Dynamic Monitoring of Lake Reclamation in the Taihu Lake and Lake Enclosure Culture of the East Taihu Lake in Recent 30 Years. Resour. Environ. Yangtze Basin 2012, 21, 121–126. [Google Scholar]
  4. Qin, B.; Xu, P.; Wu, Q.; Luo, L.; Zhang, Y. Environmental issues of lake Taihu, China. In Eutrophication of Shallow Lakes with Special Reference to Lake Taihu, China; Springer: Berlin/Heidelberg, Germany, 2007; pp. 3–14. [Google Scholar]
  5. Wu, Y.; Xiang, Y.; Wang, J.; Zhong, J.; He, J.-Z.; Wu, Q.L. Heterogeneity of archaeal and bacterial ammonia-oxidizing communities in Lake Taihu, China. Environ. Microbiol. Rep. 2010, 2, 569–576. [Google Scholar] [CrossRef] [PubMed]
  6. Yin, Q.; Yue, D.; Peng, Y.; Liu, Y.; Xiao, L. Occurrence and Distribution of Antibiotic-resistant Bacteria and Transfer of Resistance Genes in Lake Taihu. Microbes Environ. 2013, 28, 479–486. [Google Scholar] [CrossRef] [Green Version]
  7. Grossart, H.-P.; Ploug, H. Microbial degradation of organic carbon and nitrogen on diatom aggregates. Limnol. Oceanogr. 2001, 46, 267–277. [Google Scholar] [CrossRef]
  8. Jiao, N.; Herndl, G.J.; Hansell, D.A.; Benner, R.; Kattner, G.; Wilhelm, S.W.; Kirchman, D.L.; Weinbauer, M.G.; Luo, T.; Chen, F.; et al. Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean. Nat. Rev. Genet. 2010, 8, 593–599. [Google Scholar] [CrossRef] [PubMed]
  9. Weiss, S.; Van Treuren, W.; Lozupone, C.; Faust, K.; Friedman, J.; Deng, Y.; Xia, L.C.; Xu, Z.Z.; Ursell, L.; Alm, E.J.; et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016, 10, 1669–1681. [Google Scholar] [CrossRef]
  10. Gilbert, J.A.; Field, D.; Swift, P.; Newbold, L.; Oliver, A.; Smyth, T.; Somerfield, P.J.; Huse, S.; Joint, I. The seasonal structure of microbial communities in the Western English Channel. Environ. Microbiol. 2009, 11, 3132–3139. [Google Scholar] [CrossRef]
  11. Gilbert, J.A.; Steele, J.A.; Caporaso, J.G.; Steinbrück, L.; Reeder, J.; Temperton, B.; Huse, S.; McHardy, A.C.; Knight, R.; Joint, I. Defining seasonal marine microbial community dynamics. ISME J. 2012, 6, 298–308. [Google Scholar] [CrossRef]
  12. Zhao, D.; Shen, F.; Zeng, J.; Huang, R.; Yu, Z.; Wu, Q.L. Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton. Sci. Total Environ. 2016, 573, 817–825. [Google Scholar] [CrossRef]
  13. Nelson, C.E. Phenology of high-elevation pelagic bacteria: The roles of meteorologic variability, catchment inputs and thermal stratification in structuring communities. ISME J. 2009, 3, 13–30. [Google Scholar] [CrossRef] [PubMed]
  14. Zhao, D.; Cao, X.; Huang, R.; Zeng, J.; Xu, H.; Wang, S.; He, X.; Yu, Z.; Shen, F. The heterogeneity of composition and assembly processes of the microbial community between different nutrient loading lake zones in Taihu Lake. Appl. Microbiol. Biotechnol. 2017, 101, 5913–5923. [Google Scholar] [CrossRef] [PubMed]
  15. Jones, A.C.; Hambright, K.D.; Caron, D.A. Ecological patterns among bacteria and microbial eukaryotes derived from network analyses in a low-salinity lake. Microbial Ecol. 2018, 75, 917–929. [Google Scholar] [CrossRef] [PubMed]
  16. Kandel, P.P.; Pasternak, Z.; Nahum, O.; Van Rijn, J.; Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 2014, 89, 149–161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Wang, L.; Li, T. Effects of seasonal temperature variation on nitrification, anammox process, and bacteria involved in a pilot-scale constructed wetland. Environ. Sci. Pollut. R. 2015, 22, 3774–3783. [Google Scholar] [CrossRef] [PubMed]
  18. Denef, V.J.; Fujimoto, M.; Berry, M.A.; Schmidt, M.L. Seasonal Succession Leads to Habitat-Dependent Differentiation in Ribosomal RNA:DNA Ratios among Freshwater Lake Bacteria. Front. Microbiol. 2016, 7, 32. [Google Scholar] [CrossRef]
  19. Giovannoni, S.J.; Vergin, K.L. Seasonality in Ocean Microbial Communities. Science 2012, 335, 671–676. [Google Scholar] [CrossRef] [Green Version]
  20. Williams, R.J.; Howe, A.; Hofmockel, K.S. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Front. Microbiol. 2014, 5, 358. [Google Scholar] [CrossRef]
  21. Ings, T.C.; Montoya, J.M.; Bascompte, J.; Blüthgen, N.; Brown, L.; Dormann, C.F.; Edwards, F.; Figueroa, D.; Jacob, U.; Jones, J.I. Ecological networks–beyond food webs. J. Anim. Ecol. 2009, 78, 253–269. [Google Scholar] [CrossRef]
  22. Poulin, R. Network analysis shining light on parasite ecology and diversity. Trends Parasitol. 2010, 26, 492–498. [Google Scholar] [CrossRef]
  23. Proulx, S.; Promislow, D.; Phillips, P. Network thinking in ecology and evolution. Trends Ecol. Evol. 2005, 20, 345–353. [Google Scholar] [CrossRef] [PubMed]
  24. Bascompte, J. Networks in ecology. Basic Appl. Ecol. 2007, 8, 485–490. [Google Scholar] [CrossRef]
  25. Montoya, J.M.; Pimm, S.L.; Solé, R.V. Ecological networks and their fragility. Nature 2006, 442, 259–264. [Google Scholar] [CrossRef] [PubMed]
  26. Zeng, J.; Lin, Y.; Zhao, D.; Huang, R.; Xu, H.; Jiao, C. Seasonality overwhelms aquacultural activity in determining the composition and assembly of the bacterial community in Lake Taihu, China. Sci. Total Environ. 2019, 683, 427–435. [Google Scholar] [CrossRef] [PubMed]
  27. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  29. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef]
  30. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef] [Green Version]
  31. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [Google Scholar] [CrossRef] [Green Version]
  32. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef]
  33. Zhao, D.; Xu, H.; Zeng, J.; Cao, X.; Huang, R.; Shen, F.; Yu, Z. Community composition and assembly processes of the free-living and particle-attached bacteria in Taihu Lake. FEMS Microbiol. Ecol. 2017, 93, fix062. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [PubMed]
  35. Caporaso, J.G.; Bittinger, K.; Bushman, F.D.; DeSantis, T.Z.; Andersen, G.L.; Knight, R. PyNAST: A flexible tool for aligning sequences to a template alignment. Bioinformatics 2010, 26, 266–267. [Google Scholar] [CrossRef] [PubMed]
  36. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2012, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
  37. Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2—Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef] [PubMed]
  38. Bokulich, N.A.; Subramanian, S.; Faith, J.J.; Gevers, D.; Gordon, J.I.; Knight, R.; Mills, D.A.; Caporaso, J.G. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 2013, 10, 57–59. [Google Scholar] [CrossRef] [PubMed]
  39. Xu, H.; Zhao, D.; Huang, R.; Cao, X.; Zeng, J.; Yu, Z.; Hooker, K.V.; Hambright, K.D.; Wu, Q.L. Contrasting Network Features between Free-Living and Particle-Attached Bacterial Communities in Taihu Lake. Microb. Ecol. 2018, 76, 303–313. [Google Scholar] [CrossRef]
  40. Zhou, J.; Deng, Y.; Luo, F.; He, Z.; Yang, Y. Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2. mBio 2011, 2, e00122-11. [Google Scholar] [CrossRef]
  41. Harrell, F.E., Jr.; Dupont, C. Hmisc: Harrell Miscellaneous. R Package Version. 2008. Available online: http://ftp.auckland.ac.nz/software/CRAN/contrib/main/Descriptions/Hmisc.html (accessed on 7 September 2019).
  42. Cao, X.; Zhao, D.; Xu, H.; Huang, R.; Zeng, J.; Yu, Z. Heterogeneity of interactions of microbial communities in regions of Taihu Lake with different nutrient loadings: A network analysis. Sci. Rep. 2018, 8, 8890. [Google Scholar] [CrossRef]
  43. Junker, B.H.; Schreiber, F. Analysis of Biological Networks; Wiley-Interscience: Hoboken, NJ, USA, 2008; Volume 2, pp. 31–59. [Google Scholar]
  44. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  45. Su, G.; Morris, J.H.; Demchak, B.; Bader, G.D. Biological Network Exploration with Cytoscape 3. Curr. Protoc. Bioinform. 2014, 47, 8.13.1–8.13.24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Clauset, A.; Newman, M.E.J.; Moore, C. Finding community structure in very large networks. Phys. Rev. E 2004, 70, 066111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Csardi, G.; Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 2006, 1695, 1–9. [Google Scholar]
  48. Guimerà, R.; Amaral, L.A.N. Functional cartography of complex metabolic networks. Nature 2005, 433, 895–900. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Olesen, J.M.; Bascompte, J.; Dupont, Y.L.; Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. USA 2007, 104, 19891–19896. [Google Scholar] [CrossRef] [Green Version]
  50. Olafsen, J.A. Interactions between fish larvae and bacteria in marine aquaculture. Aquaculture 2001, 200, 223–247. [Google Scholar] [CrossRef]
  51. Blancheton, J.; Attramadal, K.; Michaud, L.; D’Orbcastel, E.R.; Vadstein, O. Insight into bacterial population in aquaculture systems and its implication. Aquac. Eng. 2013, 53, 30–39. [Google Scholar] [CrossRef]
  52. Lewinsohn, T.M.; Prado, P.I.; Jordano, P.; Bascompte, J.; Olesen, J.M. Structure in plant-animal interaction assemblages. Oikos 2006, 113, 174–184. [Google Scholar] [CrossRef] [Green Version]
  53. Mougi, A.; Kondoh, M.; Hurtgen, M.T. Diversity of Interaction Types and Ecological Community Stability. Science 2012, 337, 349–351. [Google Scholar] [CrossRef]
  54. Zou, L.; Pei, W.; Li, T.; He, Z.; Cheung, Y. Topological fractal networks introduced by mixed degree distribution. Phys. A Stat. Mech. Its Appl. 2007, 380, 592–600. [Google Scholar] [CrossRef] [Green Version]
  55. May, R.; McLean, A.R. Theoretical Ecology: Principles and Applications; Oxford University Press on Demand: Oxford, UK, 2007. [Google Scholar]
  56. Tang, X.; Chao, J.; Gong, Y.; Wang, Y.; Wilhelm, S.W.; Gao, G. Spatiotemporal dynamics of bacterial community composition in large shallow eutrophic Lake Taihu: High overlap between free-living and particle-attached assemblages. Limnol. Oceanogr. 2017, 62, 1366–1382. [Google Scholar] [CrossRef]
  57. Paerl, H.W.; Xu, H.; Hall, N.S.; Rossignol, K.L.; Joyner, A.R.; Zhu, G.; Qin, B. Nutrient limitation dynamics examined on a multi-annual scale in Lake Taihu, China: Implications for controlling eutrophication and harmful algal blooms. J. Freshw. Ecol. 2015, 30, 5–24. [Google Scholar] [CrossRef]
  58. Zhou, J.; Deng, Y.; Zhang, P.; Xue, K.; Liang, Y.; Van Nostrand, J.D.; Yang, Y.; He, Z.; Wu, L.; Stahl, D.A.; et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc. Natl. Acad. Sci. USA 2014, 111, E836–E845. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Wu, L.; Yang, Y.; Chen, S.; Zhao, M.; Zhu, Z.; Yang, S.; Qu, Y.; Ma, Q.; He, Z.; Zhou, J.; et al. Long-term successional dynamics of microbial association networks in anaerobic digestion processes. Water Res. 2016, 104, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Eiler, A.; Heinrich, F.; Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 2012, 6, 330–342. [Google Scholar] [CrossRef]
  61. Newton, R.J.; Jones, S.E.; Eiler, A.; McMahon, K.D.; Bertilsson, S. A Guide to the Natural History of Freshwater Lake Bacteria. Microbiol. Mol. Boil. Rev. 2011, 75, 14–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Williams, K.P.; Sobral, B.W.; Dickerman, A.W. A Robust Species Tree for the Alphaproteobacteria. J. Bacteriol. 2007, 189, 4578–4586. [Google Scholar] [CrossRef]
  63. Eiler, A.; Bertilsson, S. Flavobacteria Blooms in Four Eutrophic Lakes: Linking Population Dynamics of Freshwater Bacterioplankton to Resource Availability. Appl. Environ. Microbiol. 2007, 73, 3511–3518. [Google Scholar] [CrossRef]
  64. Yannarell, A.C.; Triplett, E.W. Geographic and Environmental Sources of Variation in Lake Bacterial Community Composition. Appl. Environ. Microbiol. 2005, 71, 227–239. [Google Scholar] [CrossRef]
  65. Lindström, E.S.; Langenheder, S. Local and regional factors influencing bacterial community assembly. Environ. Microbiol. Rep. 2012, 4, 1–9. [Google Scholar] [CrossRef]
  66. Beisner, B.E.; Peres-Neto, P.R.; Lindström, E.S.; Barnett, A.; Longhi, M.L. The Role of Environmental and Spatial Processes in Structuring Lake Communities from Bacteria to Fish. Ecology 2006, 87, 2985–2991. [Google Scholar] [CrossRef]
  67. Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 2005, 8, 1175–1182. [Google Scholar] [CrossRef] [PubMed]
  68. Kara, E.L.; Hanson, P.C.; Hu, Y.H.; Winslow, L.; McMahon, K.D. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 2013, 7, 680–684. [Google Scholar] [CrossRef] [PubMed]
  69. Bowers, R.M.; McCubbin, I.B.; Hallar, A.G.; Fierer, N. Seasonal variability in airborne bacterial communities at a high-elevation site. Atmos. Environ. 2012, 50, 41–49. [Google Scholar] [CrossRef]
  70. Zhu, W.; Tan, Y.; Wang, R.; Feng, G.; Chen, H.; Liu, Y.; Li, M. The trend of water quality variation and analysis in typical area of Lake Taihu, 2010–2017. J. Lake Sci. 2018, 30, 296–305. [Google Scholar]
  71. Zhu, C.; Zhang, J.; Nawaz, M.Z.; Mahboob, S.; Al-Ghanim, K.A.; Khan, I.A.; Lu, Z.; Chen, T. Seasonal succession and spatial distribution of bacterial community structure in a eutrophic freshwater Lake, Lake Taihu. Sci. Total Environ. 2019, 669, 29–40. [Google Scholar] [CrossRef] [PubMed]
  72. Price, P.B.; Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Natl. Acad. Sci. USA 2004, 101, 4631–4636. [Google Scholar] [CrossRef] [Green Version]
Water 11 01868 g001 550
Figure 1. Location map of sampling sites in Eastern Lake Taihu. GC, grass-crab zone; GCF, grass-crab-fish zone.
Figure 1. Location map of sampling sites in Eastern Lake Taihu. GC, grass-crab zone; GCF, grass-crab-fish zone.
Water 11 01868 g001
Water 11 01868 g002 550
Figure 2. Species-species association network divided by modules for the spring (A), summer (B), autumn (C) and winter (D) bacterial communities. Only correlations between species that were statistically significant (p < 0.001) and strong (r > 0.9 or r < −0.9) are shown by the solid blue lines (positive correlations) and dotted pink lines (negative correlations), respectively. Correlations solely between pairs of operating taxonomic units (OTUs) are not shown. Different bacterial phyla/classes are represented by different colors, and the number on each node refers to the ID number of each OTU.
Figure 2. Species-species association network divided by modules for the spring (A), summer (B), autumn (C) and winter (D) bacterial communities. Only correlations between species that were statistically significant (p < 0.001) and strong (r > 0.9 or r < −0.9) are shown by the solid blue lines (positive correlations) and dotted pink lines (negative correlations), respectively. Correlations solely between pairs of operating taxonomic units (OTUs) are not shown. Different bacterial phyla/classes are represented by different colors, and the number on each node refers to the ID number of each OTU.
Water 11 01868 g002
Water 11 01868 g003 550
Figure 3. The percentage (%) of OTUs/nodes in each phylum/subphylum in the four seasonal networks of bacterial communities. Only phyla/classes that appeared in four seasonal species-species association networks are presented.
Figure 3. The percentage (%) of OTUs/nodes in each phylum/subphylum in the four seasonal networks of bacterial communities. Only phyla/classes that appeared in four seasonal species-species association networks are presented.
Water 11 01868 g003
Water 11 01868 g004 550
Figure 4. Zi-Pi plot showing the distribution of OTUs based on their topological roles. Each symbol represents an OTU in the spring (A), summer (B), autumn (C) or winter (D) bacterial group. The topological role of each OTU was determined according to the scatterplot of within-module connectivity (Zi) and among-module connectivity (Pi).
Figure 4. Zi-Pi plot showing the distribution of OTUs based on their topological roles. Each symbol represents an OTU in the spring (A), summer (B), autumn (C) or winter (D) bacterial group. The topological role of each OTU was determined according to the scatterplot of within-module connectivity (Zi) and among-module connectivity (Pi).
Water 11 01868 g004
Table
Table 1. Topological properties of the empirical networks of bacterial communities and their associated random networks in different seasons.
Table 1. Topological properties of the empirical networks of bacterial communities and their associated random networks in different seasons.
Empirical NetworkRandom Network
GroupsNumber of nodesNumber of edgesModularityClustering coefficientAverage path lengthNetwork diameterAverage degreeGraph densityModularity (SD)Clustering coefficient (SD)Average path length (SD)Network diameter (SD)
PositiveNegative
Spring24322500.931 a0.193 a2.492 a8 a1.8520.0080.773 (0.016)0.007 (0.009)7.488 (0.573)18.536 (2.400)
Summer482521640.852 a0.251 a6.445 b17 b2.4270.005 0.699 (0.009)0.005 (0.004)6.604 (0.166)15.627 (1.365)
Autumn90515941020.739 a0.177 a6.994 a22 a3.7480.0040.540 (0.004)0.004 (0.002)5.240 (0.033)11.322 (0.792)
Winter435440680.851 a0.196 a6.723 b17 b2.3200.0050.714 (0.010)0.005 (0.004)6.759 (0.193)16.230 (1.530)
SD, standard deviation. a Significant difference (p < 0.001) between the empirical network and the random network. b Significant difference (p > 0.05) between the empirical network and the random network.
Table
Table 2. Module hubs and connectors in the species-species association networks. k, p, c, o, f and g represent kingdom, phylum, class, order, family and genus, respectively.
Table 2. Module hubs and connectors in the species-species association networks. k, p, c, o, f and g represent kingdom, phylum, class, order, family and genus, respectively.
SeasonType of PointNumber of NodeOTU ID Number of ModuleMean Abundance (%)Phylum/ClassLowest Taxonomic Rank
SummerModule hubs1denovo3272B80.006Alphaproteobacteriag__Sphingorhabdus
2denovo4293B10.013Unclassifiedk__Bacteria
3denovo10386B60.003Alphaproteobacteriaf__Sphingomonadaceae
Connectors4denovo830B10.014Chloroflexig__Oscillochloris
5denovo7514B20.095Deltaproteobacteriag__Labilithrix
6denovo12061B30.030Alphaproteobacteriaf__Acetobacteraceae
AutumnModule hubs7denovo113C90.002Proteobacteria_unclassifiedp__Proteobacteria
8denovo1308C240.035Gammaproteobacteriao__Chromatiales
9denovo1615C100.002Lentisphaeraeg__Oligosphaera
10denovo3226C60.009Bacteroidetesg__Fluviicola
11denovo3806C330.010Unclassifiedk__Bacteria
12denovo4788C140.002Bacteroidetesp__Bacteroidetes
13denovo6589C70.010Bacteroidetesg__Terrimonas
14denovo7064C230.009Betaproteobacteriac__Betaproteobacteria
15denovo8549C290.089Acidobacteriao__Gp6
16denovo8853C80.014Unclassifiedk__Bacteria
17denovo9608C140.002Bacteroidetesp__Bacteroidetes
18denovo9867C90.005Cloacimonetesc__Candidatus_Cloacamonas
19denovo9976C130.003Chloroflexif__Caldilineaceae
20denovo10529C80.005Unclassifiedk__Bacteria
21denovo10588C80.017Bacteroidetesp__Bacteroidetes
22denovo10876C120.005Gemmatimonadetesg__Gemmatimonas
23denovo10885C340.005Unclassifiedk__Bacteria
24denovo11435C80.003Alphaproteobacteriaf__Caulobacteraceae
25denovo11533C90.002Unclassifiedk__Bacteria
26denovo11626C110.006Chloroflexif__Anaerolineaceae
27denovo11710C230.003Gammaproteobacteriac__Gammaproteobacteria
Connectors28denovo715C100.009Unclassifiedk__Bacteria
29denovo5057C30.003Bacteroideteso__Cytophagales
WinterModule hubs30denovo95D30.018Gammaproteobacteriac__Gammaproteobacteria
31denovo2361D40.315Betaproteobacteriaf__Methylophilaceae
32denovo2444D160.004Unclassifiedk__Bacteria
33denovo3884D150.003Bacteroidetesf__Chitinophagaceae
34denovo13572D30.087Actinobacteriao__Actinomycetales
Connectors35denovo3821D240.623Alphaproteobacteriaf__Rhodobacteraceae
36denovo9640D40.004Alphaproteobacteriag__Caulobacter
Table
Table 3. Environmental characteristics of water samples collected from Eastern Lake Taihu under aquaculture conditions for each season.
Table 3. Environmental characteristics of water samples collected from Eastern Lake Taihu under aquaculture conditions for each season.
SeasonT (°C)pHDO (mg/L)SD (m)Chla (μg/L)TN (mg/L)TP (mg/L)DOC (mg/L)NH4+-N (mg/L)NO3−-N (mg/L)NO2−-N (mg/L)
Spring17.19 ± 0.118.84 ± 0.607.57 ± 0.961.26 ± 0.232.30 ± 2.380.80 ± 0.150.072 ± 0.0457.35 ± 0.860.074 ± 0.0380.165 ± 0.1380.010 ± 0.004
Summer31.60 ± 1.028.23 ± 0.404.02 ± 1.240.38 ± 0.1159.05 ± 26.721.25 ± 0.540.127 ± 0.0355.52 ± 1.190.021 ± 0.0080.279 ± 0.0780.003 ± 0.002
Autumn19.61 ± 0.138.25 ± 0.257.43 ± 0.590.42 ± 0.107.98 ± 3.250.64 ± 0.070.031 ± 0.0059.86 ± 2.280.058 ± 0.0190.067 ± 0.0290.004 ± 0.002
Winter3.36 ± 0.367.96 ± 0.117.19 ± 0.790.61 ± 0.346.47 ± 2.011.16 ± 0.220.025 ± 0.00611.32 ± 2.840.099 ± 0.0260.456 ± 0.1420.006 ± 0.004
T, water temperature; DO, dissolved oxygen; SD, Secchi depth; Chla, chlorophyll a; TP, total phosphorus; DOC, dissolved organic carbon; TN, total nitrogen; NO3−-N, nitrate nitrogen; NH4+-N, ammonia nitrogen; NO2−-N, nitrite nitrogen.

Share and Cite

MDPI and ACS Style

Lin, Y.; Zhao, D.; Zeng, J.; Cao, X.; Jiao, C. Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions. Water 2019, 11, 1868. https://doi.org/10.3390/w11091868

AMA Style

Lin Y, Zhao D, Zeng J, Cao X, Jiao C. Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions. Water. 2019; 11(9):1868. https://doi.org/10.3390/w11091868

Chicago/Turabian Style

Lin, Yuqing, Dayong Zhao, Jin Zeng, Xinyi Cao, and Congcong Jiao. 2019. "Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions" Water 11, no. 9: 1868. https://doi.org/10.3390/w11091868

APA Style

Lin, Y., Zhao, D., Zeng, J., Cao, X., & Jiao, C. (2019). Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions. Water, 11(9), 1868. https://doi.org/10.3390/w11091868

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop