Evidence map›Paper›PMID 40847328›Full record

ArticleBMC plant biology2025

Research on the synergistic prediction of the suitable distribution and chemical components of Panax Notoginseng under the background of climate warming.

Peiyuan Li, Zhitian Zuo, Yuanzhong Wang

Abstract read
In one paragraph

Article in BMC plant biology, 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

3 authors.

Peiyuan LiMedicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China.
Zhitian ZuoMedicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China. zzhitian0331@126.com.
Yuanzhong WangMedicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China. boletus@126.com.

Funding

Applied Basic Research Key Project of Yunnan Province 202501BD070001-017
6 · The paper itself

Abstract

Panax notoginseng is a well-known research species in China. The issue of continuous crop barriers has led to a reduction in suitable habitats in Wenshan. In the context of global warming, it is far from adequate to merely predict the suitability distribution of P. notoginseng under future climate, as it remains unclear whether the Highly suitable habitats in the results are also the high content areas. We used machine learning for the first time to predict the changes in chemical components under future climate scenarios and found that it was more scientific than the Biomod2 based on environmental variables. The results show that TreeBagger is an effective tool to predict the chemical composition of P. notoginseng under future climate change. Bio7 was an important environmental variable affecting the distribution of P. notoginseng, and Bio15 was the environmental variable that had the greatest impact on the quality of P. notoginseng. Comprehensive evaluation indicated that Pre11 might be a key factor affecting the distribution and quality of P. notoginseng. In the future, under climate change, most of the Highly suitable habitats for P. notoginseng in Wenshan area will be downgraded to moderately suitable and lowly suitable areas. Therefore, it is necessary to focus on protecting its ecosystem and plan the cultivation of P. notoginseng in Yuxi and its surrounding areas, which can effectively relieve the cultivation pressure in Wenshan area. This study can provide new ideas and methods for the future research on the species distribution of medicinal plants.

Indexed as

Climate ChangeGlobal WarmingPanax notoginsengChinaEcosystemMachine LearningEnvironment variablesMachine learningPanax NotoginsengSuitable habitat

Identifiers

PMID40847328
PMCPMC12372261

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.