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Toward Understanding the Dynamics of Microbial Communities in an Estuarine System

Overview of attention for article published in PLOS ONE, April 2014
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Title
Toward Understanding the Dynamics of Microbial Communities in an Estuarine System
Published in
PLOS ONE, April 2014
DOI 10.1371/journal.pone.0094449
Pubmed ID
Authors

Weipeng Zhang, Salim Bougouffa, Yong Wang, On On Lee, Jiangke Yang, Colin Chan, Xingyu Song, Pei-Yuan Qian

Abstract

Community assembly theories such as species sorting theory provide a framework for understanding the structures and dynamics of local communities. The effect of theoretical mechanisms can vary with the scales of observation and effects of specific environmental factors. Based on 16S rRNA gene tag pyrosequencing, different structures and temporal succession patterns were discovered between the surface sediments and bottom water microbial communities in the Pearl River Estuary (PRE). The microbial communities in the surface sediment samples were more diverse than those in the bottom water samples, and several genera were specific for the water or sediment communities. Moreover, water temperature was identified as the main variable driving community dynamics and the microbial communities in the sediment showed a greater temporal change. We speculate that nutrient-based species sorting and bacterial plasticity to the temperature contribute to the variations observed between sediment and water communities in the PRE. This study provides a more comprehensive understanding of the microbial community structures in a highly dynamic estuarine system and sheds light on the applicability of ecological theoretical mechanisms.

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Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 24%
Student > Ph. D. Student 9 18%
Researcher 8 16%
Student > Doctoral Student 4 8%
Student > Bachelor 3 6%
Other 8 16%
Unknown 6 12%
Readers by discipline Count As %
Environmental Science 15 30%
Agricultural and Biological Sciences 15 30%
Biochemistry, Genetics and Molecular Biology 8 16%
Computer Science 1 2%
Immunology and Microbiology 1 2%
Other 2 4%
Unknown 8 16%