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Perspective ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Microbiol. | doi: 10.3389/fmicb.2019.02407

Rarefaction, alpha diversity, and statistics

  • 1Department of Biostatistics, School of Public Health, University of Washington, United States

Understanding the drivers of diversity is a fundamental question in ecology. Extensive literature discusses different methods for describing diversity and documenting its effects on ecosystem health and function. However, it is widely believed that diversity depends on the intensity of sampling. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I describe the state of the statistical literature for addressing these problems, focusing on the analysis of microbial diversity. I argue that latent variable models can address issues with variance, but bias corrections need to be utilised as well. I encourage ecologists to use estimates of diversity that account for unobserved species, and to use measurement error models to compare diversity across ecosystems.

Keywords: Bioinformatics & Computational Biology, ecological data analysis, latent variable model, reproducibility, measurement error

Received: 19 Aug 2019; Accepted: 07 Oct 2019.

Copyright: © 2019 Willis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Amy D. Willis, Department of Biostatistics, School of Public Health, University of Washington, Washington, WA 98195, Maine, United States, adwillis@uw.edu