Evidence map›Paper›PMID 41326384›Full record

ArticleNPJ biofilms and microbiomes2025

Coastal rest during the COVID-19 pandemic enhances microbial community stability and metabolic potential in a subterranean estuary.

Kaiqi Lu, Zongxiao Zhang, Xunchi Zhu, J S P Ibánhez, Ben Yang, Shan Jiang

Abstract read
In one paragraph

Article in NPJ biofilms and microbiomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Kaiqi LuState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China.
Zongxiao ZhangCollege of Geography and Tourism, Xinjiang Normal University, Urumqi, China.
Xunchi ZhuSchool of Marine Sciences, Hainan University, Haikou, China.
J S P IbánhezInstituto de Investigacións Mariñas, Consejo Superior de Investigaciones Científicas (IIM-CSIC), Vigo, Spain.
Ben YangDepartment of Environmental Science, University of Arizona, Tucson, AZ, USA.
Shan JiangState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China. sjiang@sklec.ecnu.edu.cn.

Funding

the National Natural Science Foundation of China 41706081
6 · The paper itself

Abstract

This study examines a subterranean estuary seepage face in China's Sanggou Bay by comparing environmental parameters and microbiome data before and after the COVID-19 lockdown, in order to reveal the regulatory mechanisms of coastal resting on microbial community stability and biogeochemical functions. The results revealed that reduced human activities significantly decreased sediment nutrient loading and shifted organic matter sources from terrestrial- to marine-dominated. This environmental restructuring drove profound microbial community reorganization: while α-diversity indices declined, the relative abundance of core species increased, with marked enhancements in community stability and metabolic efficiency, particularly in pathways related to amino acid metabolism, carbohydrate metabolism, and biogeochemical cycling. The study confirms that a coastal rest period can enhance ecosystem resilience by reducing anthropogenic disturbance, optimizing resource allocation, and activating microbial functional plasticity. These findings suggest that rest periods may represent a potential strategy for supporting ecosystem resilience and sustainability.

Indexed as

BacteriaCOVID-19EstuariesMicrobiotaChinaEcosystemGeologic SedimentsHumansPandemicsSARS-CoV-2Seawater

Identifiers

PMID41326384
PMCPMC12780172

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.