Evidence map›Paper›PMID 42774626›Full record

ArticleEcology and evolution2026

Spatiotemporal Dynamics and Climate Change Impacts on Massive Invasive Plants in Henan Province, China.

Hang Wang, Gaoxiang Cheng, Lvyuan Niu, Yizhen Shao, Fengqin Liu, Yun Chen, Xiangyu Tian, Zhiliang Yuan

Abstract read
In one paragraph

Article in Ecology and evolution, 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

The trial behind it

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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

8 authors.

Hang WangCollege of Life Sciences Henan Agricultural University Zhengzhou China.ORCID https://orcid.org/0009-0004-1246-8310
Gaoxiang ChengCollege of Life Sciences Henan Agricultural University Zhengzhou China.
Lvyuan NiuCollege of Life Sciences Henan Agricultural University Zhengzhou China.
Yizhen ShaoCollege of Life Sciences Henan Agricultural University Zhengzhou China.
Fengqin LiuCollege of Life Sciences Henan Agricultural University Zhengzhou China.
Yun ChenCollege of Life Sciences Henan Agricultural University Zhengzhou China.ORCID https://orcid.org/0000-0002-8092-9981
Xiangyu TianCollege of Life Sciences Henan Agricultural University Zhengzhou China.
Zhiliang YuanCollege of Life Sciences Henan Agricultural University Zhengzhou China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Invasive alien plant species have attracted significant global attention due to their profound ecological disruptions and substantial economic losses. As a critical ecological barrier in the middle and lower reaches of the Yellow River, Henan Province has experienced increasing pressure from biological invasions that jeopardize local biodiversity and ecosystem integrity. In this study, we utilized the species distribution modeling framework to predict the potential geographic distributions of 17 massive invasive plant species under current and future climatic conditions. Our findings indicate that suitable habitats for these massive invasive plants are mainly concentrated in the western and southern regions of Henan. Currently, the high-risk areas, which are highly suitable for these 17 massive invasive plants, encompass 18.42% of the province's total land area. Under future climate scenarios for 2050, this area is projected to decrease to between 17.27% and 15.99%, with a maximum relative reduction of 13.17% occurring under the highest greenhouse gas emission scenario (SSP585). Furthermore, the results suggest an elevational shift from low to high altitudes in the future among these invasive species, accompanied by a southwestward migration. This research provides a scientific basis for risk assessment and the management of invasive species and offers valuable guidance for biodiversity conservation and ecosystem resilience in the context of global environmental change.

Indexed as

climate changehabitat suitabilityHenan provinceMaxEnt modelplant invasions

Identifiers

PMID42774626
PMCPMC13595032

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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.