Abstract
For more than a century, microbiologists have sought to determine the species richness of bacteria in soil, but the extreme complexity and unknown structure of soil microbial communities have obscured the answer. We developed a statistical model that makes the problem of estimating richness statistically accessible by evaluating the characteristics of samples drawn from simulated communities with parametric community distributions. We identified simulated communities with rank-abundance distributions that followed a truncated lognormal distribution whose samples resembled the structure of 16S rRNA gene sequence collections made using Alaskan and Minnesotan soils. The simulated communities constructed based on the distribution of 16S rRNA gene sequences sampled from the Alaskan and Minnesotan soils had a richness of 5,000 and 2,000 operational taxonomic units (OTUs), respectively, where an OTU represents a collection of sequences not more than 3% distant from each other. To sample each of these OTUs in the Alaskan 16S rRNA gene library at least twice, 480,000 sequences would be required; however, to estimate the richness of the simulated communities using nonparametric richness estimators would require only 18,000 sequences. Quantifying the richness of complex environments such as soil is an important step in building an ecological framework. We have shown that generating sufficient sequence data to do so requires less sequencing effort than completely sequencing a bacterial genome.
Highlights
Enumerating the human population of a country or region through a census is an ancient problem that is complicated by the challenges inherent in accurately representing a large and often inaccessible population
The aim of this work was to estimate the taxonomic richness in an Alaskan soil sample through a library of 16S rRNA gene sequences derived from the sample
We assigned more than 92% of the 1,033 Alaskan 16S rRNA gene sequences to seven phyla, including the Proteobacteria (48.6%), Acidobacteria (15.3%), Bacteroidetes (9.3%), Actinobacteria (5.8%), Gemmimonas (5.7%), Planctomycetes (4.0%), and Verrucomicrobia (4.0%); the remaining sequences
Summary
Enumerating the human population of a country or region through a census is an ancient problem that is complicated by the challenges inherent in accurately representing a large and often inaccessible population. The same issues manifest in censuses of microbial communities, but are intensified by greater complexity and methodological challenges. A complete census of a country is theoretically possible, it is currently impractical to survey all 109 bacterial cells in a gram of soil [1], making a sample-based census the best option for estimating richness—the number of bacterial taxa in soil. To do so accurately requires a reliable means to access the bacteria, a reasonable definition of ‘‘species,’’ and a robust description of the frequency distribution of the species. Our inability to estimate richness impedes investigation of the effects of soil chemistry, pollution, and land use on the soil microbial community
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