Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Zhao, Dayong; Shen, Feng; Zeng, Jin; Huang, Rui; Yu, Zhigang; Wu, Qinglong L.

doi:10.1016/j.scitotenv.2016.08.150

Cited by 112 publications

(75 citation statements)

References 45 publications

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“…Bacteroidetes, and most of the connections were negative. As previous studies have shown, species with similar ecological niches may compete when resources are scarce (Cao et al, 2018;Zhao et al, 2016). Notably, positive connections dominated the interactions in the OTUs-based networks, suggesting more cooperation rather competition in the microbial commu- (Appendix Figure A3) (Deng et al, 2012;Olesen, Bascompte, Dupont, & Jordano, 2007).…”

Section: Correlation Network Analysis Of Microbial Communitiesmentioning

confidence: 58%

“…However, there was very little connection between Proteobacteria and Bacteroidetes , and most of the connections were negative. As previous studies have shown, species with similar ecological niches may compete when resources are scarce (Cao et al., ; Zhao et al., ). Notably, positive connections dominated the interactions in the OTUs‐based networks, suggesting more cooperation rather competition in the microbial community structure (Chow, Kim, Sachdeva, Caron, & Fuhrman, ; Yang et al., ; Zhang, Zhao, Dai, Jiao, & Herndl, ).…”

Section: Resultsmentioning

confidence: 89%

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Microbial community composition in alpine lake sediments from the Hengduan Mountains

et al. 2019

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Microbial communities in sediments play an important role in alpine lake ecosystems. However, the microbial diversity and community composition of alpine lake sediments from the Hengduan Mountains remain largely unknown. Therefore, based on the Illumina MiSeq platform, high‐throughput sequencing analysis of the 16S rRNA gene was performed on 15 alpine lake sediments collected at different locations in the Hengduan Mountains. The abundance‐based coverage estimate (ACE), Chao1, and Shannon indices indicated that the microbial abundance and diversity of these sediments were high. There are some differences in the composition of microbial communities among sediments. However, in general, Proteobacteria accounted for the largest proportion of all sediments (22.3%–67.6%) and was the dominant phylum. Followed by Bacteroidetes, Acidobacteria, Chloroflexi, and Planctomycetes. In addition, the operational taxonomic unit (OTU) interactions network had modular structures and suggested more cooperation than competition in the microbial community. Besides, we also found that temperature has a significant contribution to the sample–environment relationship. This study revealed the diversity and composition of microbial communities in alpine lake sediments from the Hengduan Mountains, and describe the correlation between microbial community structure and different environmental variables.

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Section: Correlation Network Analysis Of Microbial Communitiesmentioning

confidence: 58%

Section: Resultsmentioning

confidence: 89%

Microbial community composition in alpine lake sediments from the Hengduan Mountains

et al. 2019

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“…Thus, phytoplankton communities and densities were undergoing a directional change responding to environmental fluctuations during the mixing period in Jinpen reservoir. A co-occurrence network that was developed andhasbeenwidely applied to reveal associations among different microbial functional groups or populations can visually show the interaction between microbial communities [13,25]. As shown in Table 2 and Figure 4, the resulting networks consisted of 17 nodes linked by 15 edges in October, 18 nodes linked by 15 edges in November, and 16 nodes linked by 14 edges in December.…”

Section: Phytoplankton Dynamics and Community Structurementioning

confidence: 99%

Community Compositions of Phytoplankton and Eukaryotes during the Mixing Periods of a Drinking Water Reservoir: Dynamics and Interactions

Yan

Chen

Huang

et al. 2020

IJERPH

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In deep drinking water reservoir ecosystems, the dynamics and interactions of community compositions of phytoplankton and eukaryotes during the mixing periods are still unclear. Here, morphological characteristics combined with high-throughput DNA sequencing (HTS) were used to investigate the variations of phytoplankton and the eukaryotic community in a large canyon-shaped, stratified reservoir located at the Heihe River in Shaanxi Province for three months. The results showed that Bacillariophyta and Chlorophyta were the dominant taxa of the phytoplankton community, accounting for more than 97% of total phytoplankton abundance, which mainly consisted of Melosira sp., Cyclotella sp., and Chlorella sp., respectively. Illumina Miseq sequencing suggested that the biodiversity of eukaryotes increased over time and that species distribution was more even. Arthropoda (6.63% to 79.19%), Ochrophyta (5.60% to 35.16%), Ciliophora (1.81% to 10.93%) and Cryptomonadales (0.25% to 11.48%) were the keystone taxa in common, contributing over 50% of the total eukaryotic community. Cryptomycota as a unique fungus was observed to possess significant synchronization with algal density, reaching a maximum of 10.70% in December (when the algal density distinctly decreased) and suggesting that it might affect the growth of algae through parasitism. Co-occurrence network patterns revealed the complicated and diverse interactions between eukaryotes and phytoplankton, suggesting that eukaryotes respond to variations in dynamic structure of the phytoplankton community, although there might be antagonistic or mutualistic interactions between them. Redundancy analysis (RDA) results showed that environmental variables collectively explained a 96.7% variance of phytoplankton and 96.3% variance of eukaryotic microorganisms, indicating that the temporal variations of phytoplankton and eukaryotic microorganisms were significantly affected by environmental conditions. This study shows that potential interactions exist between phytoplankton and eukaryotic microorganism communities, andcould improve our understanding of the ecological roles of phytoplankton and eukaryotic microorganisms in changing aquatic ecosystems. However, long-term investigations are necessary in order to obtain comprehensive understandings of their complicated associations.

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“…In a study carried out by Zhao et al, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China [54]. The results showed that temperature, pH and NO 3− -N were the most important factors influencing the composition of the bacterioplankton community.…”

Section: Microbial Community Structuresmentioning

confidence: 99%

Current Trend of Metagenomic Data Analytics for Cyanobacteria Blooms

Huang¹,

Zheng²,

Wang³

2017

GEP

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Cyanobacterial harmful algal blooms are a major threat to freshwater ecosystems globally. To deal with this threat, researches into the cyanobacteria bloom in fresh water lakes and rivers have been carried out all over the world. This review presents an overlook of studies on cyanobacteria blooms. Conventional studies mainly focus on investigating the environmental factors influencing the blooms, with their limitation in lack of viewing the microbial community structures. Metagenomics study provides insight into the internal community structure of the cyanobacteria at the blooming, and there are researchers reported that sequence data was a better predictor than environmental factors. This further manifests the significance of the metagenomic study. However, large number of the latter appears to be confined only to present snapshoot of the microbial community diversity and structure. This type of investigation has been valuable and important, whilst an effort to integrate and coordinate the conventional approaches that largely focus on the environmental factors control, and the Metagenomics approaches that reveals the microbial community structure and diversity, implemented through machine learning techniques, for a holistic and more comprehensive insight into the cause and control of Cyanobacteria blooms, appear to be a trend and challenge of the study of this field.

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Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Cited by 112 publications

References 45 publications

Microbial community composition in alpine lake sediments from the Hengduan Mountains

Microbial community composition in alpine lake sediments from the Hengduan Mountains

Community Compositions of Phytoplankton and Eukaryotes during the Mixing Periods of a Drinking Water Reservoir: Dynamics and Interactions

Current Trend of Metagenomic Data Analytics for Cyanobacteria Blooms

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