Microbial Ecology

, Volume 66, Issue 3, pp 500–511 | Cite as

Microbial Community Assembly and Succession on Lake Sturgeon Egg Surfaces as a Function of Simulated Spawning Stream Flow Rate

  • Masanori Fujimoto
  • James A. Crossman
  • Kim T. Scribner
  • Terence L. Marsh
Microbiology of Aquatic Systems

Abstract

We investigated microbial succession on lake sturgeon (Acipenser fulvescens) egg surfaces over the course of their incubation period as a function of simulated stream flow rate. The primary objective was to characterize the microbial community assembly during succession and to examine how simulated stream flow rate affect the successional process. Sturgeon eggs were reared under three flow regimes; high (0.55 m/s), low (0.18 m/s), and variable (0.35 and 0.11 m/s alternating 12 h intervals). Eggs were collected from each flow regime at different egg developmental stages. Microbial community DNA was extracted from egg surface and the communities were examined using 16S rRNA gene-based terminal restriction fragment length polymorphism and 454 pyrosequencing. Analysis of these datasets using principal component analysis revealed that microbial communities were clustered by egg developmental stages (early, middle, and late) regardless of flow regimes. 454 pyrosequencing data suggested that 90–98 % of the microbial communities were composed of the phyla Proteobacteria and Bacteroidetes throughout succession. β-Protebacteria was more dominant in the early stage, Bacteroidetes became more dominant in the middle stage, and α-Proteobacteria became dominant in the late stage. A total of 360 genera and 5,826 OTUs at 97 % similarity cutoff were associated with the eggs. Midway through egg development, the egg-associated communities of the low flow regime had a higher diversity than those communities developed under high or variable flow regimes. Results show that microbial community turnover occurred during embryogenesis, and stream flow rate influenced the microbial succession processes on the sturgeon egg surfaces.

Supplementary material

248_2013_256_MOESM1_ESM.pdf (135 kb)
Online Resource 1Bray–Curtis dissimilarity table that summarized time and flow effect on the egg surface microbial communities (PDF 135 kb)
248_2013_256_MOESM2_ESM.pdf (170 kb)
Online Resource 2Examples of the association of certain microbial phylotypes with certain egg developmental stages. The dotted line (linear coefficient = −1.89, R2 = 0.44), solid line (quadratic coefficient = −2.83, R2 = 0.23), and breaking line (linear coefficient = 1.61, R2 = 0.55) represent regression lines for PT3, PT7, and PT16, respectively. HhaI data were used for the analysis (PDF 169 kb)
248_2013_256_MOESM3_ESM.pdf (114 kb)
Online Resource 3Principle component loading plots displaying temporal distributions of 25 major microbial phylotypes (PT) associated with the egg surface. PC1 and PC2 account for 27.9 % and 13.6 % of the data variation, respectively. HhaI-digested TRFLP data were used for the analysis (PDF 114 kb)
248_2013_256_MOESM4_ESM.pdf (91 kb)
Online Resource 4Jaccard index tree constructed using OTUs at 97 % similarity cutoff (PDF 90 kb)
248_2013_256_MOESM5_ESM.pdf (469 kb)
Online Resource 5Complete listings of detected genera (PDF 469 kb)
248_2013_256_MOESM6_ESM.pdf (99 kb)
Online Resource 6Rarefaction analysis for pyrosequencing data (PDF 99 kb)
248_2013_256_MOESM7_ESM.pdf (21 kb)
Online Resource 7Water microbial community collected in a different year from the same stream (PDF 20.6 kb)
248_2013_256_MOESM8_ESM.pdf (10 kb)
Online Resource 8Ambient water temperature throughout the egg incubation periods. Solid line is a linear regression line for daily mean temperature (PDF 10 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Masanori Fujimoto
    • 1
    • 2
  • James A. Crossman
    • 3
    • 4
  • Kim T. Scribner
    • 3
    • 5
  • Terence L. Marsh
    • 1
    • 2
  1. 1.Department of Microbiology and Molecular GeneticsMichigan State UniversityEast LansingUSA
  2. 2.The Center for Microbial EcologyMichigan State UniversityEast LansingUSA
  3. 3.Department of Fisheries and WildlifeMichigan State UniversityEast LansingUSA
  4. 4.B.C. HydroCastlegarCanada
  5. 5.Department of ZoologyMichigan State UniversityEast LansingUSA

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