Robust estimation of microbial diversity in theory and in practice

TL;DR

This paper examines the challenges in accurately estimating microbial diversity from samples, highlighting the limitations of species richness estimators and proposing robust methods for diversity metrics like Shannon and Simpson indices.

Contribution

The authors develop a theoretical framework for estimating microbial diversity, extending existing estimators, and demonstrate the robustness of Shannon and Simpson indices in empirical data analysis.

Findings

Species richness estimates are unreliable without assumptions about species abundance.

Chao's estimator can rank communities incorrectly with many rare species.

Shannon and Simpson diversities can be robustly estimated from sample data.

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.