title: Random sampling process leads to overestimation of β-diversity of microbial communities.
abstract: The site-to-site variability in species composition, known as β-diversity, is crucial to understanding spatiotemporal patterns of species diversity and the mechanisms controlling community composition and structure. However, quantifying β-diversity in microbial ecology using sequencing-based technologies is a great challenge because of a high number of sequencing errors, bias, and poor reproducibility and quantification. Herein, based on general sampling theory, a mathematical framework is first developed for simulating the effects of random sampling processes on quantifying β-diversity when the community size is known or unknown. Also, using an analogous ball example under Poisson sampling with limited sampling efforts, the developed mathematical framework can exactly predict the low reproducibility among technically replicate samples from the same community of a certain species abundance distribution, which provides explicit evidences of random sampling processes as the main factor causing high percentages of technical variations. In addition, the predicted values under Poisson random sampling were highly consistent with the observed low percentages of operational taxonomic unit (OTU) overlap (<30% and <20% for two and three tags, respectively, based on both Jaccard and Bray-Curtis dissimilarity indexes), further supporting the hypothesis that the poor reproducibility among technical replicates is due to the artifacts associated with random sampling processes. Finally, a mathematical framework was developed for predicting sampling efforts to achieve a desired overlap among replicate samples. Our modeling simulations predict that several orders of magnitude more sequencing efforts are needed to achieve desired high technical reproducibility. These results suggest that great caution needs to be taken in quantifying and interpreting β-diversity for microbial community analysis using next-generation sequencing technologies. IMPORTANCE Due to the vast diversity and uncultivated status of the majority of microorganisms, microbial detection, characterization, and quantitation are of great challenge. Although large-scale metagenome sequencing technology such as PCR-based amplicon sequencing has revolutionized the studies of microbial communities, it suffers from several inherent drawbacks, such as a high number of sequencing errors, biases, poor quantitation, and very high percentages of technical variations, which could greatly overestimate microbial biodiversity. Based on general sampling theory, this study provided the first explicit evidence to demonstrate the importance of random sampling processes in estimating microbial β-diversity, which has not been adequately recognized and addressed in microbial ecology. Since most ecological studies are involved in random sampling, the conclusions learned from this study should also be applicable to other ecological studies in general. In summary, the results presented in this study should have important implications for examining microbial biodiversity to address both basic theoretical and applied management questions.
authors.affiliation: State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China. jzhou@ou.edu
authors.forename: Jizhong
authors.initials: J
authors.lastname: Zhou
authors.forename: Yi-Huei
authors.initials: YH
authors.lastname: Jiang
authors.forename: Ye
authors.initials: Y
authors.lastname: Deng
authors.forename: Zhou
authors.initials: Z
authors.lastname: Shi
authors.forename: Benjamin Yamin
authors.initials: BY
authors.lastname: Zhou
authors.forename: Kai
authors.initials: K
authors.lastname: Xue
authors.forename: Liyou
authors.initials: L
authors.lastname: Wu
authors.forename: Zhili
authors.initials: Z
authors.lastname: He
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Yang
mesh_terms: D058448:Biota; D004783:Environmental Microbiology; D056186:Metagenomics / Q000379:methods*; D008962:Models, Theoretical; D008967:Molecular Biology / Q000379:methods*; D015203:Reproducibility of Results; D015983:Selection Bias