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Predicting the current and future distribution of Monochamus carolinensis (Coleoptera: Cerambycidae) based on the maximum entropy model.

Authors :
Zhao, Jiaqiang
Zou, Xvbing
Yuan, Fei
Luo, Youqing
Shi, Juan
Source :
Pest Management Science; Dec2023, Vol. 79 Issue 12, p5393-5404, 12p
Publication Year :
2023

Abstract

BACKGROUND: Monochamus carolinensis is an important vector of pinewood nematodes in North America that is under quarantine in several countries worldwide. The distribution of M. carolinensis was previously thought to be limited to North America; however, we discovered it during trapping in China in 2022. Using this discovery and information regarding the area of origin, we applied a machineā€learning algorithm based on the maximum entropy principle to predict the current and future (2050s, 2070s) potential distribution areas of M. carolinensis using bioclimatic variables. RESULTS: The biological suitability of M. carolinensis was mainly driven by precipitation factors (BIO18, BIO15, BIO19), with 87.18% of the potential distribution areas located in South America, Asia, North America and Africa. Future potential distribution areas of M. carolinensis are predicted to expand to high latitudes, with an average increase of 10 245 874.88 km2, and only 6.89% of the current suitable areas will become unsuitable. The potential distribution areas in 2070 are largest under the SSP585 scenario, with a 41.40% predicted increase (52 309 803.61 km2) above the current distribution, mainly reflecting an increase of the marginally and highly suitable areas. CONCLUSION: The determination of dominant climatic factors and potential distribution areas will help provide an early warning for an M. carolinensis invasion, as well as provide a scientific basis for the spread and outbreak, facilitating development of effective governmental prevention and control measures. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1526498X
Volume :
79
Issue :
12
Database :
Complementary Index
Journal :
Pest Management Science
Publication Type :
Academic Journal
Accession number :
173625342
Full Text :
https://doi.org/10.1002/ps.7753