Evidence map›Paper›PMID 34402638›Full record

ArticlemSystems2021

Guided by Microbes: Applying Community Coalescence Principles for Predictive Microbiome Engineering.

Jennifer D Rocca, Mario E Muscarella, Ariane L Peralta, Dandan Izabel-Shen, Marie Simonin

Abstract read
In one paragraph

Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  14. Top-down and bottom-up cohesiveness in microbial community coalescence.Proceedings of the National Academy of Sciences of the United States of America · 2022
    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

5 authors.

Jennifer D RoccaDepartment of Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, USA.
Mario E MuscarellaInstitute of Arctic Biology, Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, Alaska, USA.
Ariane L PeraltaDepartment of Biology, East Carolina Universitygrid.255364.3, Greenville, North Carolina, USA.
Dandan Izabel-ShenDepartment of Ecology, Environment, and Plant Sciences, Stockholm University, Stockholm, Sweden.
Marie SimoninUniversity of Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, Angers, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Every seed germinating in soils, wastewater treatment, and stream confluence exemplify microbial community coalescence-the blending of previously isolated communities. Here, we present theoretical and experimental knowledge on how separated microbial communities mix, with particular focus on managed ecosystems. We adopt the community coalescence framework, which integrates metacommunity theory and meta-ecosystem dynamics, and highlight the prevalence of these coalescence events within microbial systems. Specifically, we (i) describe fundamental types of community coalescences using naturally occurring and managed examples, (ii) offer ways forward to leverage community coalescence in managed systems, and (iii) emphasize the importance of microbial ecological theory to achieving desired coalescence outcomes. Further, considering the massive dispersal events of microbiomes and their coalescences is pivotal to better predict microbial community dynamics and responses to disturbances. We conclude our piece by highlighting some challenges and unanswered question yet to be tackled.

Indexed as

biostimulantscommunity coalescencecommunity inoculantsmanaged ecosystemsmicrobiome engineering

Identifiers

PMID34402638
PMCPMC8407356

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.