Evidence map›Paper›PMID 39371265›Full record

ArticleEcology and evolution2024

Changes in Soil Bacterial and Fungal Community Composition and Functional Groups During the Artificial Restoration of Degraded Grassland of "Black-Soil Mountain".

Lele Xie, Yushou Ma, Yanlong Wang, Yuan Ma, Xiaoli Wang

Abstract read
In one paragraph

Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  4. Response of Patch Characteristics ofPlants (Basel, Switzerland) · 2025
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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

5 authors.

Lele XieQinghai Academy of Animal and Veterinary Science Qinghai University Xining Qinghai China.ORCID https://orcid.org/0009-0007-7616-6903
Yushou MaQinghai Academy of Animal and Veterinary Science Qinghai University Xining Qinghai China.ORCID https://orcid.org/0000-0003-2672-9222
Yanlong WangQinghai Academy of Animal and Veterinary Science Qinghai University Xining Qinghai China.
Yuan MaQinghai Academy of Animal and Veterinary Science Qinghai University Xining Qinghai China.
Xiaoli WangQinghai Academy of Animal and Veterinary Science Qinghai University Xining Qinghai China.ORCID https://orcid.org/0009-0004-0599-6828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

About 35% of grassland in Sanjiangyuan area of China has degenerated into black-soil mountain. Artificial grassland is considered to be an effective measure to alleviate the severely degraded grassland in the alpine region of the three rivers and has been widely used. However, the pattern, potential function, and changes of carbon and nitrogen contents of soil microorganisms in degraded grassland in Heimushan by planting artificial grassland are still unclear. In this study, mixed-sown artificial alpine grassland (AG) was the focus of our study, whereas degraded black-soil mountain grassland (BG) and natural alpine grassland (NG) served as controls. Illumina 16S and ITS gene sequence analyses were used to analyze the community structure of the soil bacteria and fungi. The functional groups of NG, AG, and BG were predicted using the FAPROTAX and FUNGuild databases. In addition, the levels of soil carbon, nitrogen, and soil enzyme activities were evaluated. The results indicated a significant increase in the aboveground biomass of BG due to the planting artificial grassland. Moreover, the contents of total carbon (TC), total nitrogen (TN), ammonium nitrogen (

Indexed as

artificial plantingblack‐soil mountaincarbon and nitrogenfunction groupsoil microbial community

Identifiers

PMID39371265
PMCPMC11450185

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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.