Microorganisms are the most abundant, diverse and functionally important organisms on Earth. Over the past decade, microbial ecologists have produced the largest ever community datasets. However, these data are rarely used to uncover law-like patterns of commonness and rarity, test theories of biodiversity, or explore unifying explanations for the structure of microbial communities. Using a global scale compilation of >20,000 samples from environmental, engineered and host-related ecosystems, we test the power of competing theories to predict distributions of microbial abundance and diversity–abundance scaling laws. We show that these patterns are best explained by the synergistic interaction of stochastic processes that are captured by lognormal dynamics. We demonstrate that lognormal dynamics have predictive power across scales of abundance, a criterion that is essential to biodiversity theory. By understanding the multiplicative and stochastic nature of ecological processes, scientists can better understand the structure and dynamics of Earth’s largest and most diverse ecological systems.
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We thank J. Gilbert and S. Gibbons for providing EMP data and guidance on using it. We also thank the researchers who collected, sequenced and provided metagenomic data on MG-RAST, as well as the individuals who maintain and provide the MG-RAST service. We also acknowledge the researchers who provided the open-source code for conducting some of our analyses. Finally, we thank the HMP Consortium for providing their data on the NIH’s publically accessible DACC server. This work was supported by a National Science Foundation Dimensions of Biodiversity Grant (no. 1442246 to J.T.L. and K.J.L.) and the US Army Research Office (W911NF-14-1-0411 to J.T.L.).
The authors declare no competing financial interests.
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Shoemaker, W., Locey, K. & Lennon, J. A macroecological theory of microbial biodiversity. Nat Ecol Evol 1, 0107 (2017). https://doi.org/10.1038/s41559-017-0107
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