The Temporal Scaling of Bacterioplankton Composition: High Turnover and Predictability during Shrimp Cultivation
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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.
KeywordsMicrobial Community Chemical Oxygen Demand Bacterial Community Total Organic Carbon Canonical Correspondence Analysis
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.
- 2.APHA (1976) Standard methods for the examination of water and wastewater 14ed. APHA American Public Health AssociationGoogle Scholar
- 18.Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, Wallenstein MD, Brodie EL (2011) Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol 2:1–10Google Scholar
- 20.Hammer Ø, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4:1–9Google Scholar
- 26.Legendre P, Legendre L (1998) Numerical ecology, 2nd English edn. Developments in environmental modeling. Dev Environ Model 20:1–853Google Scholar
- 33.R Development Team (2012) R: A language and environment for statistical computing. http://cran.r-project.org
- 36.Sørensen TA (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol Skr 5:1–34Google Scholar
- 45.Xiong J, Wu L, Tu S, Van Nostrand JD, He Z, Zhou J, Wang G (2010) Microbial communities and functional genes associated with soil arsenic contamination and rhizosphere of the arsenic hyper-accumulating plant Pteris vittata L. Appl Environ Microbiol 76:7277–7284PubMedCentralCrossRefPubMedGoogle Scholar