Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton.
Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, 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. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address: dyzhao@hhu.edu.cn.
Dayong
D
Zhao
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Feng
F
Shen
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Jin
J
Zeng
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Rui
R
Huang
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address: zyu@hhu.edu.cn.
Zhongbo
Z
Yu
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Qinglong L
QL
Wu
D001419:Bacteria / Q000145:classification / Q000235:genetics / Q000302:isolation & purification; D002681:China; D002947:Cities; D000458:Cyanobacteria / Q000145:classification* / Q000235:genetics / Q000302:isolation & purification; D060106:Lakes / Q000382:microbiology*; D064307:Microbiota; D008954:Models, Biological; D010839:Phytoplankton / Q000145:classification* / Q000235:genetics / Q000302:isolation & purification; D012329:RNA, Bacterial / Q000235:genetics; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D012621:Seasons; D017423:Sequence Analysis, RNA
D012329:RNA, Bacterial; D012336:RNA, Ribosomal, 16S
Bacterioplankton; Cyanobacteria; Ecological network analysis; Freshwater lakes; Module eigengene