Evidence map›Paper›PMID 41006768›Full record

ArticleScientific reports2025

Climate and human-modified landscapes influence spread of invasive agricultural pest Popillia japonica Newman in American Midwest and Great Plains.

Nicole B Kucherov, Trevor Hefley, Tania N Kim

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. 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.

Nicole B KucherovDepartment of Entomology, Kansas State University, 123 W. Waters Hall 1603 Old Claflin Place, Manhattan, KS, 66506, USA. nkucherov@ksu.edu.
Trevor HefleyDepartment of Statistics, Kansas State University, 101 Dickens Hall 1116 Mid-Campus Drive N, Manhattan, KS, 66506, USA.
Tania N KimDepartment of Entomology, Kansas State University, 123 W. Waters Hall 1603 Old Claflin Place, Manhattan, KS, 66506, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Japanese beetle (Popillia japonica) is an invasive agricultural pest throughout the eastern United States and its distribution is expanding westward. Understanding how climate and land use intensification interact to affect Japanese beetle population dynamics and spread is essential to predict which areas are vulnerable to increased pest pressure. In this study, we combine data from the Global Biodiversity Information Facility (GBIF) with data from local state agricultural agencies and University extension offices to model how land cover and climate affect Japanese beetle occurrences at the leading edge of an invasion front in the central United States over 5 years. We found that beetle occurrences were positively correlated with proportion of developed areas, proportion of corn and soybean, minimum temperature, and precipitation within counties, and negatively correlated with the proportion of grassland and pasture, and maximum temperature. We modeled Japanese beetle occurrences in the near future and for future climate scenarios and found high probability of occurrence for Japanese beetles across the entire region. Our study highlights the importance of supplementing community science data in rural areas and including a time variable when assessing habitat associations for invasive species in novel areas.

Indexed as

ClimateColeopteraIntroduced SpeciesAgricultureAnimalsBiodiversityEcosystemHumansMidwestern United StatesPopulation DynamicsUnited StatesAgricultureClimate impactDeveloped areasGrasslandInvasion dynamicsInvasive pest

Identifiers

PMID41006768
PMCPMC12474974

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.