Evidence map›Paper›PMID 41044145›Full record

ArticleScientific reports2025

Prediction of potential suitable habitats for Liriomyza sativae blanchard in China under climate change scenarios.

Qinwen Hou, Gaopeng Bian, Shan Jin, Huifang Song, Haiyan Bai

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

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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

5 authors.

Qinwen HouDepartment of Life Sciences, Changzhi University, Changzhi, 046011, China. hqinwen@163.com.
Gaopeng BianDepartment of Life Sciences, Changzhi University, Changzhi, 046011, China.
Shan JinDepartment of Life Sciences, Changzhi University, Changzhi, 046011, China.
Huifang SongDepartment of Life Sciences, Changzhi University, Changzhi, 046011, China.
Haiyan BaiDepartment of Life Sciences, Changzhi University, Changzhi, 046011, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liriomyza sativae is a significant agricultural pest globally, inflicting damage on a wide array of crops and spreading at alarming rates. Our study aims to thoroughly investigate the potential habitat of this pest in China under varying climate change scenarios. Here, the MaxEnt model was employed to predict current and future potential suitable habitat for L. sativae in China, based on 102 distribution points and 7 key environmental variables. The test set's average AUC was 0.895 with a standard deviation of 0.014, indicating accurate and reliable simulation results. Jackknife analysis revealed that annual mean temperature (Bio01), precipitation seasonality (Bio15), and precipitation of wettest quarter (Bio16) have the most significant impacts on the potential geographical habitat of L. sativae. Under current climate conditions, this pest posed a potential threat to all provinces, autonomous regions, and municipalities in China, with a total area of potential suitable habitats of 540.8×10

Indexed as

Liriomyza sativaeMaxEnt modelMonitoring and early warningPotential suitable habitats

Identifiers

PMID41044145
PMCPMC12494766

What OpenQuestion holds

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Registered trials

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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.