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9 August 2024 Predicting habitat suitability for the soybean pod borer Leguminivora glycinivorella (Matsumura) using optimized MaxEnt models with multiple variables
Mingsheng Yang, Ying Wang, Weili Ding, Houhun Li, Aibing Zhang
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Abstract

The soybean pod borer Leguminivora glycinivorella (Matsumura) is one of the most important soybean pests and often causes serious damage to Glycine max (L.) Merr., a leading source of dietary protein and oil in animal feed. However, the potential distribution patterns of this economically important pest and its driving factors require further investigation. Here, we used the optimized MaxEnt model to predict the potential distribution of this pest with multiple variables associated with climate, land use, and host plant, at its recorded range and a globe scale. Based on 4 variable combinations, the results show that the current suitable habitats of L. glycinivorella are primarily distributed in most of China, the Korean Peninsula, and Japan. Whereas no suitable area is present in other continents. In future projections, the suitable region shows a slight northward expansion compared with the result predicted with current climatic conditions, and the suitable areas of almost all future projections were stable in size. Among the 9 bioclimatic factors, BIO03 (isothermality) consistently highly contributes to the predictions, indicating that temperature may be a key factor influencing the habitat distribution of L. glycinivorella. Comparative analyses of projections further show that non-climatic factors are informative in the modeling as routinely used bioclimate variables. The spatio-temporal distribution patterns of suitable habitats and the regulatory factors predicted in this study could provide important guidance for L. glycinivorella management.

Mingsheng Yang, Ying Wang, Weili Ding, Houhun Li, and Aibing Zhang "Predicting habitat suitability for the soybean pod borer Leguminivora glycinivorella (Matsumura) using optimized MaxEnt models with multiple variables," Journal of Economic Entomology 117(5), 1796-1808, (9 August 2024). https://doi.org/10.1093/jee/toae167
Received: 3 June 2024; Accepted: 17 July 2024; Published: 9 August 2024
JOURNAL ARTICLE
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KEYWORDS
Maxent algorithm
pest Management
soybean pests
species distribution modeling
Tortricidae
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