Evidence map›Paper›PMID 34400696›Full record

ArticleScientific reports2021

Global potential distribution prediction of Xanthium italicum based on Maxent model.

Yang Zhang, Jieshi Tang, Gang Ren, Kaixin Zhao, Xianfang Wang

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

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3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

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  12. Distribution Patterns ofEcology and evolution · 2025
    Article
  13. Prediction of Potential Distribution and Response ofPlants (Basel, Switzerland) · 2025
    Article
  14. Global climate change andFrontiers in plant science · 2025
    Article
  15. Article
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  20. Evaluation ofJournal of fungi (Basel, Switzerland) · 2024
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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

5 authors.

Yang Zhang *College of Computer Science and Technology, Henan Institute of Technology, Henan, 453003, China. zhangyang@hait.edu.cn.
Jieshi Tang *College of Life Science, Sichuan University, Chengdu, 610000, China.
Gang RenCollege of Computer Science and Technology, Henan Institute of Technology, Henan, 453003, China.
Kaixin ZhaoCollege of Computer Science and Technology, Henan Institute of Technology, Henan, 453003, China.
Xianfang WangCollege of Computer Science and Technology, Henan Institute of Technology, Henan, 453003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum. The model consists of 366 occurrence records (10 repeats, 75% for calibration and 25% for verification) and 10 climate prediction variables. According to the model forecast, the distribution of X. italicum was expected to shrink in future climate scenarios with human intervention, which may be mainly caused by the rise in global average annual temperature. The ROC curve showed that the AUC values of the training set and the test set are 0.965 and 0.906, respectively, indicating that the prediction result of this model was excellent. The contribution rates of annual mean temperature, monthly mean diurnal temperature range, standard deviation of temperature seasonal change and annual average precipitation to the geographical distribution of X. italicum were 65.3%, 11.2%, 9.0%, and 7.7%, respectively, and the total contribution rate was 93.2%. These four variables are the dominant environmental factors affecting the potential distribution of X. italicum, and the influence of temperature is greater than that of precipitation. Through our study on the potential distribution prediction of X. italicum under the future climatic conditions, it has contribution for all countries to strengthen its monitoring, prevention and control, including early warning.

Identifiers

PMID34400696
PMCPMC8368065

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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.