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Microbial Ecology

, Volume 67, Issue 2, pp 256–264 | Cite as

The Temporal Scaling of Bacterioplankton Composition: High Turnover and Predictability during Shrimp Cultivation

  • Jinbo Xiong
  • Jianlin Zhu
  • Kai Wang
  • Xin Wang
  • Xiansen Ye
  • Lian Liu
  • Qunfen Zhao
  • Manhua Hou
  • Linglin Qiuqian
  • Demin ZhangEmail author
Microbiology of Aquatic Systems

Abstract

The spatial distribution of microbial communities has recently been reliably documented in the form of a distance–similarity decay relationship. In contrast, temporal scaling, the pattern defined by the microbial similarity–time relationships (STRs), has received far less attention. As a result, it is unclear whether the spatial and temporal variations of microbial communities share a similar power law. In this study, we applied the 454 pyrosequencing technique to investigate temporal scaling in patterns of bacterioplankton community dynamics during the process of shrimp culture. Our results showed that the similarities decreased significantly (P = 0.002) with time during the period over which the bacterioplankton community was monitored, with a scaling exponent of w = 0.400. However, the diversities did not change dramatically. The community dynamics followed a gradual process of succession relative to the parent communities, with greater similarities between samples from consecutive sampling points. In particular, the variations of the bacterial communities from different ponds shared similar successional trajectories, suggesting that bacterial temporal dynamics are predictable to a certain extent. Changes in bacterial community structure were significantly correlated with the combination of Chl a, TN, PO4 3-, and the C/N ratio. In this study, we identified predictable patterns in the temporal dynamics of bacterioplankton community structure, demonstrating that the STR of the bacterial community mirrors the spatial distance–similarity decay model.

Keywords

Microbial Community Chemical Oxygen Demand Bacterial Community Total Organic Carbon Canonical Correspondence Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was financially supported by the National High Technology Research and Development Program of China (863 Program, 2012AA092000), the Science and Technology Project of the Ministry of Education (Grant No. 208053), the Natural Science Foundation of Ningbo City (2013A610169), the Research Fund from 2011 Center of Modern Marine Aquaculture of East China Sea, and the KC Wong Magna Fund of Ningbo University.

Supplementary material

248_2013_336_MOESM1_ESM.doc (72 kb)
Table S1 (DOC 72 kb)
248_2013_336_MOESM2_ESM.doc (48 kb)
Table S2 (DOC 47 kb)
248_2013_336_MOESM3_ESM.doc (135 kb)
Fig. S1 (DOC 135 kb)
248_2013_336_MOESM4_ESM.doc (151 kb)
Fig. S2 (DOC 151 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jinbo Xiong
    • 1
    • 4
  • Jianlin Zhu
    • 2
  • Kai Wang
    • 1
    • 4
  • Xin Wang
    • 3
  • Xiansen Ye
    • 5
  • Lian Liu
    • 5
  • Qunfen Zhao
    • 1
  • Manhua Hou
    • 1
  • Linglin Qiuqian
    • 1
  • Demin Zhang
    • 1
    • 4
    Email author
  1. 1.Faculty of Marine SciencesNingbo UniversityNingboChina
  2. 2.Faculty of Architectural, Civil Engineering and EnvironmentNingbo UniversityNingboChina
  3. 3.Medical SchoolNingbo UniversityNingboChina
  4. 4.2011 Center of Modern Marine Aquaculture of East China SeaNingboChina
  5. 5.Marine Environmental Monitoring Center of Ningbo, SOANingboChina

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