Assessing mechanisms for microbial taxa and community dynamics using process models

Wu, Linwei; Yang, Yunfeng; Ning, Daliang; Gao, Qun; Yin, Huaqun; Xiao, Naija; Zhou, Benjamin Y.; Chen, Si; He, Qiang; Zhou, Jizhong

doi:10.1002/mlf2.12076

Title: Assessing mechanisms for microbial taxa and community dynamics using process models

Journal Article · 11 September 2023 · mLife

DOI: https://doi.org/10.1002/mlf2.12076 · OSTI ID:2205081

^[1];

^[2]; Ning, Daliang ^[3]; Gao, Qun ^[2]; Yin, Huaqun ^[4]; Xiao, Naija ^[3]; Zhou, Benjamin Y. ^[5]; Chen, Si ^[6]; He, Qiang ^[6]; Zhou, Jizhong ^[7]

Peking University, Beijing (China); University of Oklahoma, Norman, OK (United States)
Tsinghua University, Beijing (China)
University of Oklahoma, Norman, OK (United States)
Central South University, Changsha (China)
Northwestern University, Evanston, IL (United States)
University of Tennessee, Knoxville, TN (United States)
University of Oklahoma, Norman, OK (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); U.S. Environmental Protection Agency

Grant/Contract Number:: AC02-05CH11231; SC0014079; SC0016247; SC0020163

OSTI ID:: 2205081

Journal Information:: mLife, Journal Name: mLife Journal Issue: 3 Vol. 2; ISSN 2097-1699

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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