Predicting microbial species richness

doi:10.1073/pnas.0507245102

Predicting microbial species richness

^*Department of Biology, Northeastern University, Boston, MA 02115; ^†Department of Environmental Science, Kangwon National University, Kangwon-Do 200701, Korea; ^‡Department of Statistical Science, Cornell University, Ithaca, NY 14853; and ^§Marine Science Center, Northeastern University, Nahant, MA 01908

Edited by Rita R. Colwell, University of Maryland, College Park, MD, and approved November 4, 2005 (received for review August 19, 2005)

Abstract

Microorganisms are spectacularly diverse phylogenetically, but available estimates of their species richness are vague and problematic. For example, for comparable environments, the estimated numbers of species range from a few dozen or hundreds to tens of thousands and even half a million. Such estimates provide no baseline information on either local or global microbial species richness. We argue that this uncertainty is due in large part to the way statistical tools are used, if not indeed misused, in biodiversity research. Here we develop a powerful synthetic statistical approach to quantify biodiversity. It provides statistically sound estimates of microbial richness at any level of taxonomic hierarchy. We apply this approach to a large original 16S rRNA dataset on marine bacterial diversity and show that the number of bacterial species in a sample from marine sediments is (2.4 ± 0.5 SE) × 10³. We argue that our methodology provides estimates of microbial richness that are reliable and general, have biologically meaningful SEs, and meet other fundamental statistical standards. This approach can be an essential tool in biodiversity research, and the estimates of microbial richness presented here can serve as a baseline in microbial diversity studies.

Footnotes

↵ ¶ To whom correspondence should be addressed at: 360 Huntington Avenue, 134 Mugar Hall, Department of Biology, Northeastern University, Boston, MA 02115. E-mail: s.epstein{at}neu.edu.
Author contributions: S.-H.H., J.B., and S.S.E. designed research; S.-H.H., J.B., S.-O.J., and S.S.E. performed research; S.-S.H., J.B., S.-O.J., and S.S.E. analyzed data; J.B. and S.S.E. contributed new reagents/analytic tools; and J.B. and S.S.E. wrote the paper.
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: ML, maximum likelihood; ACE, abundance-based coverage estimator; OTU, operational taxonomic unit.