Applied Microbiology and Biotechnology

, Volume 66, Issue 4, pp 457–463 | Cite as

Quantifying bacterial population dynamics in compost using 16S rRNA gene probes

  • Patrick D. Schloss
  • Anthony G. Hay
  • David B. Wilson
  • James M. Gossett
  • Larry P. Walker
Environmental Biotechnology


Composting provides a dynamic setting for studying ecological topics such as succession, competition, and community stability in a relatively short period of time. This study used hierarchical small sub-unit-based rRNA gene probes to quantify the change in the relative abundance of phylogenetic groups common to compost in laboratory scale reactors. Bacterial 16S rRNA gene targets accounted for only 37% of all small subunit (SSU) rRNA genes initially, but increased to a maximum of 83% of the total at 84 h. The sum of rRNA genes detected using probes specific to Pseudomonas and low-G+C Gram-positive rRNA genes represented between 16% and 87% of the total. The lack of hybridization to the taxon-specific probes was most pronounced between 36 h and 60 h, when the pH was between 4.6 and 4.8. During this period the relative abundance of taxon-specific gene targets accounted for only 17–33% of the total bacterial rRNA gene targets. Pseudomonas-type 16S rRNA genes were the most abundant of the groups measured until 72 h. Those genes had their highest relative abundance at 12 h (78% of bacterial rRNA genes; 30% of all rRNA genes), after which time their relative abundance began to decline as the temperature increased. Prior to 72 h, 16S rRNA genes from low-G+C Gram-positive bacteria (LGC-GPB) represented less than 7% of the bacterial rRNA genes. However, by 84 h the relative abundance of LGC-GPB and Bacillus rRNA genes had increased to 60% and 18% of the bacterial rRNA gene targets, respectively (50% and 15% of all rRNA genes, respectively).


Relative Abundance Lactic Acid Bacterium Acidic Phase rRNA Molecule Laboratory Scale Reactor 
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.



The authors thank Ann Hansgate for her assistance in processing samples. This project was supported in part by the United States Department of Education under agreement number P200A8045 and the Spencer Family Fund.


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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Patrick D. Schloss
    • 1
  • Anthony G. Hay
    • 2
  • David B. Wilson
    • 3
  • James M. Gossett
    • 4
  • Larry P. Walker
    • 1
  1. 1.Department of Biological and Environmental Engineering, Riley-Robb HallCornell UniversityIthacaUSA
  2. 2.Department of MicrobiologyCornell UniversityIthacaUSA
  3. 3.Department of Molecular Biology & GeneticsCornell UniversityIthacaUSA
  4. 4.School of Civil and Environmental EngineeringCornell UniversityIthacaUSA

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