Evidence map›Paper›PMID 42568814›Full record

ArticleiScience2026

Stochasticity dominated adaptive assembly of benthic microbiota across oasis-to-desert sections of an arid inland river.

Yongcheng Chen, Yingchao Sun, Rongzheng Huang, Shuangming Li, Yuhang Liu, Xingke Yuan, Xinyi Chen, Junfeng Li, Lihe Yin, Chunhui Ma and 1 more

Abstract read
In one paragraph

Article in iScience, 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
–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

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

11 authors.

Yongcheng ChenShihezi University, Shihezi 832003, China.
Yingchao SunShihezi University, Shihezi 832003, China.
Rongzheng HuangShihezi University, Shihezi 832003, China.
Shuangming LiShihezi University, Shihezi 832003, China.
Yuhang LiuShihezi University, Shihezi 832003, China.
Xingke YuanShihezi University, Shihezi 832003, China.
Xinyi ChenShihezi University, Shihezi 832003, China.
Junfeng LiShihezi University, Shihezi 832003, China.
Lihe YinXi'an Geological Survey Center, China Geological Survey, Key Laboratory of Groundwater and Ecology in Arid and Semi Arid Regions, China Geological Survey, Xi'an, China.
Chunhui MaShihezi University, Shihezi 832003, China.
Fanfan ZhangShihezi University, Shihezi 832003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sediment microbial communities in arid inland rivers are important for biogeochemical cycling, but the assembly mechanisms and network responses across the transition from irrigated oases to desert margins remain unclear. We investigated bacterial and fungal communities in Yarkand River sediments along salinity and nutrient gradients using 16S rRNA and ITS sequencing, sediment chemistry, iCAMP null-model analysis, and co-occurrence networks. Downstream increases in electrical conductivity, total nitrogen, and pH were associated with reduced bacterial α-diversity and turnover from nutrient-responsive Pseudomonadota in the oasis midstream to stress-tolerant Chloroflexota and Actinomycetota in the desert downstream. Fungal diversity showed high inter-site variability without a linear decline. Despite strong physicochemical gradients, ecological drift dominated assembly in both kingdoms, while desert networks showed higher connectivity. These findings support a hierarchical assembly model in which salinity filters community composition, but stochastic processes and network densification shape benthic microbiota in arid river sediments.

Indexed as

arid inland riverco-occurrence networkecological driftsalinizationsediment microbiome

Identifiers

PMID42568814
PMCPMC13448187

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.