Evidence map›Paper›PMID 42436367›Full record

ArticleBMC microbiology2026

From chaos to order: cross-kingdom coordination buffers microbial communities against typhoon impacts.

Hui Huang, Junze Wu, Zhangxi Hu, Huaming Wu, Yulei Zhang

Abstract read
In one paragraph

Article in BMC 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.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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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.

Hui HuangCollege of Fisheries, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China.
Junze WuCollege of Fisheries, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China.
Zhangxi HuCollege of Fisheries, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China.
Huaming WuCollege of Fisheries, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China.
Yulei ZhangCollege of Fisheries, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China. zhangyl@gdou.edu.cn.

Funding

National Natural Science Foundation of China 42506209Program for Scientific Research Start-up Funds of Guangdong Ocean University 060302022102Undergraduate Innovation Team of Guangdong Ocean University CXTD2023002
6 · The paper itself

Abstract

backgroundTyphoon disturbances profoundly reshape coastal microbial ecosystems; however, the coupled responses and co-successional dynamics of bacterial and phytoplankton communities remain poorly understood. Identifying their interactions and key drivers is essential for understanding ecosystem resilience and predicting post-disturbance algal dynamics.

methodsUsing Zhanjiang Bay as a model system, we analyzed microbial succession across two typhoon events with contrasting intensities. High-throughput sequencing and environmental data were integrated with multiple machine learning models to identify the influence patterns of bacterial communities on phytoplankton diversity. SHAP analysis was applied to quantify feature contributions and detect threshold effects, and network analysis was used to characterize cross-kingdom interactions across successional stages.

resultsMicrobial communities exhibited consistent directional succession under both typhoons, although Prapiroon induced stronger displacement (2.38-fold in bacteria and 2.53-fold in phytoplankton). Random Forest achieved the best predictive performance (R

conclusionOur findings reveal a predictable, stage-dependent framework of microbial succession under typhoon disturbance, driven by environmental filtering and biotic interactions. The identified thresholds and microbial indicators provide a basis for recognizing critical temporal windows of elevated bloom risk, highlighting the potential of integrating microbial data with machine learning for adaptive coastal management.

Indexed as

BacteriaCyclonic StormsMicrobiotaPhytoplanktonBaysBiodiversityChinaEcosystemHigh-Throughput Nucleotide SequencingSeawaterTemperatureCross-kingdom interactionsEcosystem resilienceMicrobial community dynamicsTyphoon disturbance

Identifiers

PMID42436367
PMCPMC13602602

What Socratic holds

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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.