Evidence map›Paper›PMID 40879396›Full record

ArticlemBio2025

Nonlinear response of soil microfauna network complexity and stability to multilevel warming in an old-growth subtropical forest.

Debao Li, Yan Li, Haibian Xu, Jianping Wu

Abstract read
In one paragraph

Article in mBio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Debao LiState Key Laboratory for Vegetation Structure, Function and Construction, Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, Yunnan, China.
Yan LiState Key Laboratory for Vegetation Structure, Function and Construction, Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, Yunnan, China.
Haibian XuState Key Laboratory for Vegetation Structure, Function and Construction, Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, Yunnan, China.
Jianping WuState Key Laboratory for Vegetation Structure, Function and Construction, Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, Yunnan, China.ORCID 0000-0002-5784-834X

Funding

National Natural Science Foundation of China 31971497National Natural Science Foundation of China 32371733
6 · The paper itself

Abstract

The influence of climate warming on soil microbes and the mechanisms underlying these effects have become the subject of intense focus in microbial ecology and climate change research. However, it is largely unknown how warming affects soil microfauna network complexity and stability or how warming-induced changes may affect ecosystem functioning in old-growth forests. Here, we conducted a 3-year multilevel warming experiment in an old-growth subtropical forest using infrared heating with five treatments: ambient soil temperature and 0.8°C, 1.5°C, 3.0°C, and 4.2°C above ambient soil temperature. We found that soil microfauna network complexity and stability and multinutrient cycling were significantly higher under warming and showed similar hump-shaped trends across rising temperatures. The nonlinear responses of soil microfauna network complexity and stability were primarily linked to soil temperature, moisture, organic carbon, and microbial biomass. Importantly, we found that soil multinutrient cycling was positively influenced by microfauna network complexity and stability. Consequently, our findings provide insights into the key role of soil microfauna network structure in regulating soil multinutrient cycling, highlighting the need to consider soil organisms' potential interactions and that it is crucial to preserve soil microfauna "interactions" for ecosystem management in forests under global change.IMPORTANCEIt is largely unknown how warming affects soil microfauna network complexity and stability or how warming-induced changes may affect ecosystem functioning in old-growth forests. We conducted a 3-year multilevel warming experiment in an old-growth subtropical forest using infrared heating. We found that soil microfauna network complexity and stability were significantly higher under warming treatments and displayed nonlinear responses to different warming levels. Soil multinutrient cycling was positively and significantly influenced by microfauna network complexity and stability. Given that complex interconnections between soil microfauna are critical for sustaining ecosystem functioning, protecting microfauna "interactions" may be critical to mitigating the adverse impacts of warming-induced biodiversity reduction on ecosystem functioning.

Indexed as

ForestsGlobal WarmingSoilSoil MicrobiologyBiomassClimate ChangeEcosystemTemperatureTropical ClimateSoilfunctional groupsmultilevel warmingmultinutrient cyclingnetwork complexitynetwork stabilitynonlinear response

Identifiers

PMID40879396
PMCPMC12505885

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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.