Scaling laws predict global microbial diversity
- Published
- Accepted
- Subject Areas
- Biodiversity, Ecology, Microbiology
- Keywords
- biodiversity, macroecology, microbial ecology, scaling laws, dominance, richness
- Copyright
- © 2016 Locey et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Scaling laws predict global microbial diversity. PeerJ Preprints 4:e1451v3 https://doi.org/10.7287/peerj.preprints.1451v3
Abstract
Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ~35,000 sites and ~5.6·106 species, including the largest ever inventory of high-throughput molecular data and one of the largest compilations of plant and animal community data, we demonstrate similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upwards one trillion (1012) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.
Author Comment
We modified figure captions, revised a few sentences to clarify our methods and data choice, and revised figure 2.