Evidence map›Paper›PMID 27242680›Full record

ArticleFrontiers in microbiology2016

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Xavier Le Roux, Nicholas J Bouskill, Audrey Niboyet, Laure Barthes, Paul Dijkstra, Chris B Field, Bruce A Hungate, Catherine Lerondelle, Thomas Pommier, Jinyun Tang and 3 more

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Impacts of Elevated COPlants (Basel, Switzerland) · 2024
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  2. Elevated COPlants (Basel, Switzerland) · 2024
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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.

Xavier Le RouxUMR INRA 1418, UMR CNRS 5557, Microbial Ecology Centre, INRA, CNRS, Université Lyon 1, Université de Lyon Villeurbanne, France.
Nicholas J BouskillEarth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, USA.
Audrey NiboyetUMR 8079, AgroParisTech, Ecology Systematics and Evolution Laboratory, CNRS, Université Paris-Sud 11 Orsay, France.
Laure BarthesUMR 8079, AgroParisTech, Ecology Systematics and Evolution Laboratory, CNRS, Université Paris-Sud 11 Orsay, France.
Paul DijkstraEcosystem Science and Society Center, Department of Biological Sciences, Northern Arizona University, Flagstaff AZ, USA.
Chris B FieldDepartment of Global Ecology, Carnegie Institution, Stanford University, Stanford CA, USA.
Bruce A HungateEcosystem Science and Society Center, Department of Biological Sciences, Northern Arizona University, Flagstaff AZ, USA.
Catherine LerondelleUMR INRA 1418, UMR CNRS 5557, Microbial Ecology Centre, INRA, CNRS, Université Lyon 1, Université de Lyon Villeurbanne, France.
Thomas PommierUMR INRA 1418, UMR CNRS 5557, Microbial Ecology Centre, INRA, CNRS, Université Lyon 1, Université de Lyon Villeurbanne, France.
Jinyun TangEarth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, USA.
Akihiko TeradaDepartment of Environmental Engineering, Technical University of Denmark Kongens Lyngby, Denmark.
Maria TournaUMR INRA 1418, UMR CNRS 5557, Microbial Ecology Centre, INRA, CNRS, Université Lyon 1, Université de Lyon Villeurbanne, France.
Franck PolyUMR INRA 1418, UMR CNRS 5557, Microbial Ecology Centre, INRA, CNRS, Université Lyon 1, Université de Lyon Villeurbanne, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO2+Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.

Indexed as

bacterial functional traitselevated CO2nitrifiersnitrogen fertilisationtrait-based modeling

Identifiers

PMID27242680
PMCPMC4868854

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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.