Evidence map›Paper›PMID 37454187›Full record

ArticleISME communications2023

The predictive power of phylogeny on growth rates in soil bacterial communities.

Jeth Walkup, Chansotheary Dang, Rebecca L Mau, Michaela Hayer, Egbert Schwartz, Bram W Stone, Kirsten S Hofmockel, Benjamin J Koch, Alicia M Purcell, Jennifer Pett-Ridge and 3 more

Abstract read
In one paragraph

Article in ISME communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

13 authors.

Jeth WalkupDivision of Plant and Soil Sciences, West Virginia University, Morgantown, WV, 26506, USA.ORCID http://orcid.org/0000-0002-7908-2963
Chansotheary DangDivision of Plant and Soil Sciences, West Virginia University, Morgantown, WV, 26506, USA.
Rebecca L MauCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.
Michaela HayerCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.ORCID http://orcid.org/0000-0002-7284-788X
Egbert SchwartzCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.
Bram W StoneEarth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
Kirsten S HofmockelEarth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0003-1586-2167
Benjamin J KochCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.ORCID http://orcid.org/0000-0002-1914-7681
Alicia M PurcellCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.
Jennifer Pett-RidgeLawrence Livermore National Laboratory, Physical and Life Science Directorate, Livermore, CA, USA.ORCID http://orcid.org/0000-0002-4439-2398
Chao WangCAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, LN, China.
Bruce A HungateCenter for Ecosystem Science and Society (Ecoss), Northern Arizona University, Flagstaff, AZ, 86011, USA.ORCID http://orcid.org/0000-0002-7337-1887
Ember M MorrisseyDivision of Plant and Soil Sciences, West Virginia University, Morgantown, WV, 26506, USA. ember.morrissey@mail.wvu.edu.ORCID http://orcid.org/0000-0002-5810-1096

Funding

National Science Foundation (NSF) 2114570U.S. Department of Energy (DOE) DE- SC0016207U.S. Department of Energy (DOE) DE-SC0020172U.S. Department of Energy (DOE) SCW1632U.S. Department of Energy (DOE) SCW1679
6 · The paper itself

Abstract

Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Identifiers

PMID37454187
PMCPMC10349831

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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.