Robust estimation of microbial diversity in theory and in practice
Robust estimation of microbial diversity in theory and in practice
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DOI：
10.1038/ismej.2013.10
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发表时间：
2013-06-01
期刊：
影响因子：
11
通讯作者：
Weitz, Joshua S.
中科院分区：
文献类型：
--
作者：
Haegeman, Bart;Hamelin, Jerome;Weitz, Joshua S.
对微生物群落结构、功能和进化的研究，多样性的量化是至关重要的。随着大规模宏基因组研究的出现，微生物多样性的估计再次受到关注。在这里，我们考虑在样本中观察到的多样性告诉我们被采样的群落的多样性。首先，我们认为，如果不对物种丰度分布做出不受支持的假设，就无法可靠地估计群落中存在的微生物物种的绝对数量和相对数量。其原因是样本数据不包含物种丰度分布尾部的稀有物种数量信息。我们通过将Chao的物种丰富度估计器应用于一组计算机群落来说明比较物种丰富度估计的困难：在存在大量稀有物种的情况下，它们的排名不正确。接下来，我们将分析扩展到多样性指标的一般家族（“希尔多样性”），并构建与样本数据一致的多样性值的下限和上限估计。该理论推广了Chao的估计，我们将其作为物种丰富度的较低估计。我们表明Shannon和Simpson多样性可以稳健地估计计算机社区。我们分析了来自广泛环境的九个宏基因组数据集，并表明我们的发现与经验抽样社区相关。因此，我们建议使用Shannon和Simpson多样性而不是物种丰富度来量化和比较微生物多样性。
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.