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Record W3136676845 · doi:10.7554/elife.62813

Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning

2021· article· en· W3136676845 on OpenAlexaff
Cameron Wagg, Yann Hautier, Sarah Pellkofer, Samiran Banerjee, Bernhard Schmid, Marcel G. A. van der Heijden

Bibliographic record

VenueeLife · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsAgriculture and Agri-Food Canada
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche Forschungsgemeinschaft
KeywordsEcosystemBiodiversityEcologyAsynchrony (computer programming)Soil biodiversitySpecies richnessSpecies evennessBiogeochemical cycleEnvironmental scienceSoil waterEcosystem servicesSoil biologySoil microbiologyBiologyEcosystem engineerSoil ecologySoil organic matter

Abstract

fetched live from OpenAlex

Theoretical and empirical advances have revealed the importance of biodiversity for stabilizing ecosystem functions through time. Despite the global degradation of soils, whether the loss of soil microbial diversity can destabilize ecosystem functioning is poorly understood. Here, we experimentally quantified the contribution of soil fungal and bacterial communities to the temporal stability of four key ecosystem functions related to biogeochemical cycling. Microbial diversity enhanced the temporal stability of all ecosystem functions and this pattern was particularly strong in plant-soil mesocosms with reduced microbial richness where over 50% of microbial taxa were lost. The stabilizing effect of soil biodiversity was linked to asynchrony among microbial taxa whereby different soil fungi and bacteria promoted different ecosystem functions at different times. Our results emphasize the need to conserve soil biodiversity for the provisioning of multiple ecosystem functions that soils provide to the society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.189
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations258
Published2021
Admission routes1
Has abstractyes

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