Evidence map›Paper›PMID 39923116›Full record

ArticleEnvironmental microbiome2025

Soil organic matter composition affects ecosystem multifunctionality by mediating the composition of microbial communities in long-term restored meadows.

Wenyin Wang, Sisi Bi, Fei Li, A Allan Degen, Shanshan Li, Mei Huang, Binyu Luo, Tao Zhang, Shuai Qi, Tianyun Qi and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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

13 authors.

Wenyin Wang *State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Sisi Bi *State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Fei LiState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
A Allan DegenBlaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 8410500, Beer Sheva, Israel.
Shanshan LiNorthwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China.
Mei HuangState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Binyu LuoState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Tao ZhangState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Shuai QiState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Tianyun QiState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Yanfu BaiState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Peipei LiuState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
Zhanhuan ShangState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China. shangzhh@lzu.edu.cn.

Funding

the Gansu Province Science and Technology Program 24JRRA526the Natural Science Foundation of China 32301397the Natural Science Foundation of China U21A20183the Science-based Advisory Program of The Alliance of National and International Science Organizations for the Belt and Road Regions ANSO-SBA-2023-02
6 · The paper itself

Abstract

backgroundSoil organic matter composition and microbial communities are key factors affecting ecosystem multifunctionality (EMF) during ecosystem restoration. However, there is little information on their interacting mechanisms in degraded and restored meadows. To fill this knowledge gap, plant, root and soil samples from alpine swamp meadows, alpine Kobresia meadows, severely degraded alpine meadows, short-term restored meadows (< 5 years) and long-term restored meadows (6-14 years) were collected. We leveraged high-throughput sequencing, liquid chromatography and mass spectrometry to characterize soil microbial communities and soil organic matter composition, measured microbial carbon metabolism and determined EMF.

resultsIt emerged that the similarity of soil microorganisms in meadows decreased with increasing heterogeneity of soil properties. Dispersal limitation and ecological drift led to the homogenization of the bacterial community. Based on co-occurrence network analysis, an increase in microbial network complexity promoted EMF. Root total phosphorus and soil organic matter components were the key predictors of EMF, while organic acids and phenolic acids increased the stability of the microbial network in long-term restored meadows. Carbon metabolism did not increase in restored meadows, but the niche breadth of soil microorganisms and the utilization efficiency of small molecular carbon sources such as amino acids did increase.

conclusionsThese findings emphasize the importance of soil organic matter composition in ecological restoration and that the composition should be considered in management strategies aimed at enhancing EMF.

Indexed as

Ecological restorationEcosystem multifunctionalityMicrobial diversityMicrobial network complexitySoil microorganismSoil organic matter composition

Identifiers

PMID39923116
PMCPMC11807318

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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.