4.8 Article

Deterministic Assembly and Diversity Gradient Altered the Biofilm Community Performances of Bioreactors

期刊

ENVIRONMENTAL SCIENCE & TECHNOLOGY
卷 53, 期 3, 页码 1315-1324

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.est.8b06044

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资金

  1. Key Research Program of Frontier Sciences, CAS [QYZDB-SSW-DQC026]
  2. Key Research Program of the Chinese Academy of Sciences [KFZD-SW-219-3]
  3. CAS 100 talent program
  4. Open Project Program of State Key Laboratory of Applied Microbiology Southern China [SKLAM001-2015]
  5. GDAS's Special Project of Science and Technology Development [2017GDASCX-0401]

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Community assembly process (determinism vs stochasticity) determines the composition and diversity of a microbial community, and then shapes its functions. Understanding this complex process and its relationship to the community functions becomes a very important task for the applications of microbial biotechnology. In this study, we applied microbial electrolysis cells (MECs) with moderate species numbers and easily tractable functions as a model ecosystem, and constructed a series of biofilm communities with gradient biodiversity to examine the roles of community assembly in determining microbial community structure and functions. After stable biofilms formed, the best MEC reactor performances (e.g., gas productivity, total energy efficiency) were achieved in the group in which biofilms had the second highest a-diversity, and biofilms with even lower diversity showed declining performance. Null model analyses indicated that both deterministic and stochastic assembly played roles in the formation of biofilm communities. When deterministic assembly dominates this formation, the higher diversity of the biofilm community would generally show better reactor performance. However, when the stochasticity dominates the assembly process, the bioreactor performance would decline. This study provides novel evidence that the assembly mechanism could be one of the key processes to shift the functions, and proposes an important guidance for selecting the most efficient microorganisms for environmental biotechnologies.

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