Evidence map›Paper›PMID 42345837›Full record

ArticleBiology2026

Spatial Heterogeneity of Phytoplankton Taxa and Functional Groups Under Multidimensional Environmental Factors in Karst Urban Rivers.

Ting Wu, Qiuhua Li, Heng Wang, Yan Chen, Lan Chen, Qian Chen, Yongxia Liu

Abstract read
In one paragraph

Article in Biology, 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

7 authors.

Ting WuKey Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guiyang 550001, China.
Qiuhua LiGuizhou Key Laboratory of Advanced Computing, Guizhou Normal University, Guiyang 550001, China.
Heng WangGuizhou Key Laboratory of Advanced Computing, Guizhou Normal University, Guiyang 550001, China.
Yan ChenGuizhou Key Laboratory of Advanced Computing, Guizhou Normal University, Guiyang 550001, China.
Lan ChenCollege of Foreign Languages, Guizhou Normal University, Guiyang 550001, China.
Qian ChenKey Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guiyang 550001, China.
Yongxia LiuGuizhou Provincial Center for Ecological and Environmental Monitoring, Guiyang 550081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid urbanization and industrialization have profoundly affected aquatic ecosystems in urban rivers, with phytoplankton taxa and functional group composition being particularly sensitive to environmental changes. Field surveys were conducted in the Nanming River, Guiyang, in October 2018 and July 2019, with 33 sampling sites evenly distributed across the upstream, midstream, and downstream reaches. The results revealed that: (1) The phytoplankton community comprised 6 phyla, 53 genera, and 61 species, dominated by Bacillariophyta, Chlorophyta, and Cyanobacteria. The community was classified into 20 functional groups, among which B, D, MP, P, and S1 were dominant and exhibited clear spatial heterogeneity along the longitudinal gradient. (2) Analysis of variance indicated that physicochemical parameters were the dominant factors explaining the variation in phytoplankton taxonomic and functional groups, with their independent contribution significantly higher than that of anthropogenic disturbance indicators and geographical factors. Redundancy analysis further identified NH

Indexed as

functional groupsNanming Riverphytoplanktonspatial heterogeneity

Identifiers

PMID42345837
PMCPMC13296165

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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.