4.6 Article

Genetic diversity patterns of microbial communities in a subtropical riverine ecosystem (Jiulong River, southeast China)

期刊

HYDROBIOLOGIA
卷 678, 期 1, 页码 113-125

出版社

SPRINGER
DOI: 10.1007/s10750-011-0834-x

关键词

Plankton; Microbial community composition; DGGE; Redundancy analysis; Biomonitoring; Subtropical river

资金

  1. Chinese Academy of Sciences [KZCX2-YW-QN401, KZCX2-YW-Q02-04]
  2. Xiamen Project of Science and Technology for Distinguished Young Scholars [3502Z20116006]
  3. Key Science and Technology Project of Fujian Province, China [2009Y0044]
  4. National Natural Science Foundation of China [41006087]
  5. China International Science and Technology Cooperation Program [2009DFB90120]

向作者/读者索取更多资源

Prokaryotic and eukaryotic microbes are key organisms in aquatic ecosystems and play pivotal roles in the biogeochemical cycles, but little is known about genetic diversity of these communities in subtropical rivers. In this study, microbial planktonic communities were determined by using denaturing gradient gel electrophoresis (DGGE) analysis from the Jiulong River, southeast China, and their relationships with local environmental factors were studied. The Betaproteobacteria (26%) and Dinophyceae (26%) were the most dominant taxa in prokaryotic and eukaryotic clones derived from DGGE bands, respectively. Further, both cluster and ordination analyses of prokaryotic and eukaryotic DGGE fingerprinting resulted in three identical groups from the 15 sites, which were closely related with the environmental factors. Partial redundancy analysis (partial RDA) revealed that agricultural pollution (phosphorus and nitrogen) and saltwater intrusion (conductivity and salinity) were the main factors impacting microbial community composition, by explaining more than two-thirds of the total variation in both prokaryotic (67.0%) and eukaryotic (70.5%) communities. Moreover, the robust and quantifiable relationship between DGGE results and environmental variables indicated that the community-level molecular fingerprinting techniques could support the physicochemical assessment of riverine water quality and ecosystem health.

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