Rarefaction, Alpha Diversity, and Statistics

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 d...

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Bibliographic Details
Published in:Frontiers in microbiology 2019-10, Vol.10, p.2407-2407
Main Author: Willis, Amy D.
Format: Article
Language:English
Subjects:
bioinformatics
computational biology
ecological data analysis
latent variable model
measurement error
Microbiology
reproducibility
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Summary: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 utilized 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.
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2019.02407