Evidence map›Paper›PMID 39105280›Full record

ArticleThe ISME journal2024

Microbial community composition predicts bacterial production across ocean ecosystems.

Elizabeth Connors, Avishek Dutta, Rebecca Trinh, Natalia Erazo, Srishti Dasarathy, Hugh Ducklow, J L Weissman, Yi-Chun Yeh, Oscar Schofield, Deborah Steinberg and 2 more

Abstract read
In one paragraph

Article in The ISME journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

12 authors.

Elizabeth ConnorsScripps Institution of Oceanography, UC San Diego, La Jolla, CA 92037, United States.ORCID 0000-0002-4992-4218
Avishek DuttaDepartment of Geology, University of Georgia, Athens, GA 30602, United States.
Rebecca TrinhLamont-Doherty Earth Observatory, Columbia University, New York, NY 10964, United States.
Natalia ErazoScripps Institution of Oceanography, UC San Diego, La Jolla, CA 92037, United States.
Srishti DasarathyScripps Institution of Oceanography, UC San Diego, La Jolla, CA 92037, United States.
Hugh DucklowLamont-Doherty Earth Observatory, Columbia University, New York, NY 10964, United States.
J L WeissmanDepartment of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States.
Yi-Chun YehDepartment of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States.
Oscar SchofieldCoastal Ocean Observation Laboratory, Institute of Marine and Coastal Sciences, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901-8520, United States.
Deborah SteinbergVirginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA 23062, United States.
Jed FuhrmanDepartment of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States.
Jeff S BowmanScripps Institution of Oceanography, UC San Diego, La Jolla, CA 92037, United States.

Funding

Gordon and Betty Moore Foundation 3779National Science Foundation NSF-OCE-1737409National Science Foundation Office of Polar Programs NSF OPP-1846837Simons Foundation 549943
6 · The paper itself

Abstract

Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites-Palmer LTER in Antarctica and Station SPOT in California-we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L-1 hr-1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L-1 hr-1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015 to 2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 × 10-4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.

Indexed as

BacteriaEcosystemMicrobiotaAntarctic RegionsCaliforniaOceans and SeasSeawaterbacterial productioncommunity structuremicrobial ecological functionrandom forest regression

Identifiers

PMID39105280
PMCPMC11385589

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.