Evidence map›Paper›PMID 32948774›Full record

ArticleNature communications2020

A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming.

Daliang Ning, Mengting Yuan, Linwei Wu, Ya Zhang, Xue Guo, Xishu Zhou, Yunfeng Yang, Adam P Arkin, Mary K Firestone, Jizhong Zhou

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 412 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
412citing papers in PubMed, 1 pooled it
–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

412 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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352 more citing papers are in PubMed but not listed here.

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.

Daliang NingInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.ORCID http://orcid.org/0000-0002-3368-5988
Mengting YuanInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.ORCID http://orcid.org/0000-0003-0017-3908
Linwei WuInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.ORCID http://orcid.org/0000-0002-6649-5072
Ya ZhangInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.
Xue GuoInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.ORCID http://orcid.org/0000-0002-4309-6140
Xishu ZhouInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.
Yunfeng YangState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing, China.ORCID http://orcid.org/0000-0001-8274-6196
Adam P ArkinEnvironmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94710, USA.
Mary K FirestoneDepartment of Environmental Science Policy and Management, University of California, Berkeley, CA, 94720, USA.
Jizhong ZhouInstitute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.ORCID http://orcid.org/0000-0003-2014-0564

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93-0.99), precision (0.80-0.94), sensitivity (0.82-0.94), and specificity (0.95-0.98) on simulated communities, which are 10-160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and 'drift' (59%). Interestingly, warming decreases 'drift' over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology.

Indexed as

EcologyGlobal WarmingGrasslandMicrobiotaAnimalsBacteriaBiodiversityDroughtsModels, BiologicalPhylogenySensitivity and Specificity

Identifiers

PMID32948774
PMCPMC7501310

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.