Evidence map›Paper›PMID 41809599›Full record

ArticleFrontiers in microbiology2026

Topological vulnerability explains fungal and bacterial stability differences in restoration of alpine grasslands.

Junguang Zhao, Qianqian Lu, Xiaoyu Zhang, Shikui Dong

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2026. 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
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1 · What the graph read from it

What it found

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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

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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.

Junguang Zhao *School of Science, Beijing Forestry University, Beijing, China.
Qianqian Lu *School of Grassland Science, Beijing Forestry University, Beijing, China.
Xiaoyu ZhangSchool of Science, Beijing Forestry University, Beijing, China.
Shikui DongSchool of Grassland Science, Beijing Forestry University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The alpine grasslands of the Qinghai-Tibetan Plateau (QTP) are critical ecosystems for regional climate regulation and biodiversity but are threatened by severe degradation. The success of ecological restoration in these fragile environments is intrinsically linked to the reassembly of belowground soil microbial communities. However, the specific successional trajectories and network-level mechanisms driving their reassembly during long-term restoration remain unclear. Objective: This study aimed to characterize the distinct successional trajectories of bacterial and fungal communities and to elucidate the topological mechanisms driving their transition along a long-term restoration chronosequence. Methods: Utilizing a space-for-time substitution approach on "Black Beach" degraded plots, we integrated consensus clustering with topological null model analyses to quantify community assembly processes. We evaluated the transition from stochastic to deterministic assembly by calculating Modularity Z-scores and analyzing structural changes in co-occurrence networks. Results: Microbial reassembly follows a predictable, non-linear trajectory comprising an early "Chaos Stage" (structural fragmentation) and a later "Recovery Stage" (high cohesion and deterministic interactions). Bacteria and fungi exhibited decoupled temporal dynamics: bacteria displayed an oscillatory "fast-in, fast-out" pattern, whereas fungi followed a "lagged but persistent" trajectory. Network analysis revealed a shift from random associations to competitive dominance. While the Recovery Stage exhibited increased complexity, it revealed a hidden topological fragility where stability became heavily dependent on specific keystone taxa, particularly within fungal networks. Conclusions: Microbial succession is driven by a regime shift from stochastic to deterministic network assembly. The identified topological fragility constitutes a core mechanism underlying the differential stability of fungi relative to bacteria. Monitoring network vulnerability and kingdom-specific decoupling provides a more accurate indicator for evaluating long-term ecosystem restoration success than species diversity alone.

Indexed as

community assemblyco-occurrence networkecological restorationkeystone taxamicrobial succession

Identifiers

PMID41809599
PMCPMC12968003

What Socratic holds

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LicenceCC BY
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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 Socratic graph.