An eight-year investigation of bovine livestock fecal microbiota

Vet Microbiol. 2012 Dec 7;160(3-4):369-77. doi: 10.1016/j.vetmic.2012.06.003. Epub 2012 Jun 13.


Cattle represent a major source of fecal contamination worldwide. Understanding the natural variation of the bovine livestock fecal microbiota is therefore important. For this reason we addressed the yearly differences of the fecal microbiota for bovine livestock reared in the same geographical region from 1999 to 2007 - analyzing a total of 300 samples representing a range of experimental regimes. The aim of our work was to determine the effect of year visa experimental regime of the bovine livestock fecal microbiota. We used a newly developed high-throughput 16S rRNA sequencing approach (a single mixed Sanger sequence was generated per sample) in combination with deep pyrosequencing. We found that similar feeding and treatment regimes for different years showed major differences in the fecal microbiota, suggesting other factors important for shaping the fecal microbiota than those experimentally controlled. Ruminococcaeae, Peptostreptococcaceae, Acinetobacter, Escherichia/Shigella, Lachnospiraceae and Lactobacillales were the main taxa associated with the yearly fluctuations. Furthermore, we found that fecal samples with high levels of Lactobacillales, Ruminococcaea and Lachnospiraceae had the most even species distributions. The Peptostreptococcaceae and Acinetobacter dominated samples, on the other hand, showed a few highly dominant taxa. Testing of neutrality showed that the evenly distributed samples were explained by a neutral mode (that the assembly of the microbiota was random), while for the other samples there were overrepresentation of the dominant species (indicating bacterial-bacterial nice competition). We therefore propose that there are natural yearly fluctuations of the bovine livestock microbiota - both with respect to ecology and composition. This knowledge will have impact on our management of fecal bacteria in the environment, since it is very difficult to predict risk based on historical data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bacteria / classification*
  • Bacteria / genetics*
  • Bacteria / isolation & purification
  • Biodiversity*
  • Cattle
  • Cluster Analysis
  • Diet / veterinary
  • Feces / microbiology*
  • Female
  • Livestock*
  • Male
  • Microbiota / genetics
  • Microbiota / physiology*
  • Phylogeny
  • RNA, Ribosomal, 16S / genetics
  • Time Factors


  • RNA, Ribosomal, 16S