Evidence map›Paper›PMID 40993491›Full record

ArticleMicrobial ecology2025

Temporal Dynamics and Adaptive Mechanisms of Microbial Communities: Divergent Responses and Network Interactions.

Shengjie Sun, Zhiyi Qiao, Denis V Tikhonenkov, Yingchun Gong, Hua Li, Renhui Li, Kexin Sun, Da Huo

Abstract read
In one paragraph

Article in Microbial ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Shengjie Sun *Tianjin Key Laboratory of Aqua-Ecology and Aquaculture, College of Fisheries, Tianjin Agricultural University, Tianjin, 300384, China.
Zhiyi Qiao *Tianjin Key Laboratory of Aqua-Ecology and Aquaculture, College of Fisheries, Tianjin Agricultural University, Tianjin, 300384, China.
Denis V TikhonenkovPapanin Institute for Biology of Inland Waters, Russian Academy of Sciences, Borok, Russia.
Yingchun GongCAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
Hua LiCAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
Renhui LiZhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, College of Life and Environmental Sciences, Wenzhou University, Wenzhou, 325035, Zhejiang, China.
Kexin SunFrasergen Bioinformatics Co, Ltd, Wuhan, China.
Da HuoCAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China. huoda@ihb.ac.cn.

Funding

China Postdoctoral Science Foundation 2021M703430National Natural Science Foundation of China 32361133561Russian Science Foundation 24-44-00093
6 · The paper itself

Abstract

Microbial communities are vital to aquatic ecosystems, driving biogeochemical cycles, nutrient recycling, and overall ecosystem functioning. However, their instant feedback, particularly in response to environmental fluctuations, remain insufficiently understood. In this study, we investigated the interaction of prokaryotic and eukaryotic microbial communities in riverine ecosystems under temporal dynamics using high-throughput sequencing and co-occurrence network analysis. We observed distinct patterns, with eukaryotic communities showing a consistent increase in alpha diversity, while prokaryotic communities exhibited more variable and directional shifts over time. Two key phases were identified: a dynamic phase characterized by rapid changes in both alpha and beta diversity and a stabilization phase where community composition became more stable, with increased evenness. Co-occurrence network analysis revealed a transition from a modular structure in the dynamic phase to a more centralized and highly connected network in the stabilization phase. While modularity can enhance stability by localizing perturbations within distinct subnetworks, increased centralization and connectivity may weaken this stabilizing effect, potentially making the network less resilient to environmental fluctuations. Our findings provide new insights into the adaptive mechanisms that sustain microbial community stability and resilience in dynamic aquatic ecosystems, emphasizing the importance of diversity and network structure in maintaining ecological stability.

Indexed as

BacteriaEukaryotaMicrobiotaRiversBiodiversityEcosystemHigh-Throughput Nucleotide SequencingWater MicrobiologyBiodiversityCo-occurrence networkMicrobial communityTemporal dynamics

Identifiers

PMID40993491
PMCPMC12460500

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.