Journal
FRONTIERS IN MICROBIOLOGY
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2019.02407
Keywords
bioinformatics; computational biology; ecological data analysis; latent variable model; reproducibility; measurement error
Categories
Funding
- Department of Biostatistics at the University of Washington
- National Institutes of Health [R35GM133420]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available