Evidence map›Paper›PMID 38817815›Full record

ArticlemLife2023

Assessing mechanisms for microbial taxa and community dynamics using process models.

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

Abstract read
In one paragraph

Article in mLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. mLife · 2025
    Article
  3. Dynamic coexistence driven by physiological transitions in microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Linwei WuInstitute of Ecology, Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences Peking University Beijing China.ORCID 0000-0002-6649-5072
Yunfeng YangState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment Tsinghua University Beijing China.ORCID 0000-0001-8274-6196
Daliang NingInstitute for Environmental Genomics University of Oklahoma Norman OK USA.
Qun GaoState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment Tsinghua University Beijing China.
Huaqun YinSchool of Minerals Processing and Bioengineering Central South University Changsha China.
Naija XiaoInstitute for Environmental Genomics University of Oklahoma Norman OK USA.
Benjamin Y ZhouDepartment of Mathematics, Lunt Hall Northwestern University Evanston Illinois USA.
Si ChenDepartment of Civil and Environmental Engineering The University of Tennessee Knoxville Tennessee USA.
Qiang HeDepartment of Civil and Environmental Engineering The University of Tennessee Knoxville Tennessee USA.
Jizhong ZhouInstitute for Environmental Genomics University of Oklahoma Norman OK USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

community assembly mechanismsconsumer–resource modelneutral modelspecies dynamics

Identifiers

PMID38817815
PMCPMC10989933

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.