Original Article

Subject Category: Microbial population and community ecology

The ISME Journal (2013) 7, 1092–1101; doi:10.1038/ismej.2013.10; published online 14 February 2013

Robust estimation of microbial diversity in theory and in practice

Bart Haegeman1, Jérôme Hamelin2, John Moriarty3, Peter Neal4, Jonathan Dushoff5 and Joshua S Weitz6

  1. 1Centre for Biodiversity Theory and Modelling, Experimental Ecology Station, Centre National de Recherche Scientifique, Moulis, France
  2. 2INRA, UR50, Laboratoire de Biotechnologie de l’Environnement, Narbonne, France
  3. 3School of Mathematics, University of Manchester, Manchester, UK
  4. 4Department of Mathematics and Statistics, University of Lancaster, Lancaster, UK
  5. 5Department of Biology and Institute of Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
  6. 6School of Biology and School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

Correspondence: B Haegeman, Centre for Biodiversity Theory and Modelling, CNRS, 2 route du CNRS, Centre National de Recherche Scientifique, Moulis 09200 France. E-mail: bart.haegeman@ecoex-moulis.cnrs.fr

Received 11 October 2012; Revised 7 December 2012; Accepted 23 December 2012
Advance online publication 14 February 2013

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Abstract

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao’s estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics (‘Hill diversities’), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao’s estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity.

Keywords:

Chao estimator; Hill diversities; metagenomics; Shannon diversity; Simpson diversity; species abundance distribution