Microbial Ecology

, Volume 52, Issue 4, pp 655–661

Analysis of Bacterial Communities in Seagrass Bed Sediments by Double-Gradient Denaturing Gradient Gel Electrophoresis of PCR-Amplified 16S rRNA Genes

Article

DOI: 10.1007/s00248-006-9075-3

Cite this article as:
James, J.B., Sherman, T.D. & Devereux, R. Microb Ecol (2006) 52: 655. doi:10.1007/s00248-006-9075-3

Abstract

Bacterial communities associated with seagrass bed sediments are not well studied. The work presented here investigated several factors and their impact on bacterial community diversity, including the presence or absence of vegetation, depth into sediment, and season. Double-gradient denaturing gradient gel electrophoresis (DG-DGGE) was used to generate banding patterns from the amplification products of 16S rRNA genes in 1-cm sediment depth fractions. Bioinformatics software and other statistical analyses were used to generate similarity scores between sections. Jackknife analyses of these similarity coefficients were used to group banding patterns by depth into sediment, presence or absence of vegetation, and by season. The effects of season and vegetation were strong and consistent, leading to correct grouping of banding patterns. The effects of depth were not consistent enough to correctly group banding patterns using this technique. While it is not argued that bacterial communities in sediment are not influenced by depth in sediment, this study suggests that the differences are too fine and inconsistent to be resolved using 1-cm depth fractions and DG-DGGE. The effects of vegetation and season on bacterial communities in sediment were more consistent than the effects of depth in sediment, suggesting they exert stronger controls on microbial community structure.

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Office of Research and Development, US Environmental Protection Agency, NHEERL–Gulf Ecology DivisionGulf BreezeUSA
  2. 2.Department of BiologyUniversity of South AlabamaMobileUSA

Personalised recommendations