Acta Entomologica Sinica ›› 2022, Vol. 65 ›› Issue (5): 630-637.doi: 10.16380/j.kcxb.2022.05.011

• RESEARCH PAPERS • Previous Articles     Next Articles

Potential geographical distribution of the soybean aphid, Aphis glycines (Hemiptera: Aphididae), in the world based on MaxEnt model

MA Shi-Yan, YU Hong-Chun, ZHAO Kui-Jun, XIE Tong-Yin*   

  1. (College of Agriculture, Northeast Agricultural University, Harbin 150030, China)
  • Online:2022-05-20 Published:2022-05-08

Abstract: 【Aim】 The objective of this study is to predict and analyze the potential geographical distribution of the soybean aphid, Aphis glycines, in the world, and to study the relationship between its potential geographical distribution and environmental variables. 【Methods】 Maximum entropy niche-based modeling (MaxEnt) was combined with the geographic information system ArcGIS to predict the potential geographical distribution of A. glycines in the world and to infer the influence of environmental variables on its 
distribution based on the distribution records of A. glycines and a set of environmental variables. 【Results】 The results showed that the suitable distribution areas of A. glycines are mainly in the low altitude areas, and the high suitable distribution areas are China, Japan, South Korea, North Korea, Canada, the United States, Italy, and Georgia at 25°-50°N. The dominant environmental variables affecting the potential geographical distribution of A. glycines are the precipitation of warmest quarter, mean temperature of warmest quarter, mean temperature of wettest quarter, precipitation of driest month, mean diurnal range [mean of monthly (max temp-min temp)], and temperature seasonality (stardard deviation×100). 【Conclusion】 The potential geographical distribution of A. glycines is wide. To prevent A. glycines from further spreading, inspection and quarantine should be done properly in the global trade of soybean agricultural products.

Key words: Aphis glycines, MaxEnt model, potential geographical distribution, environmental variables, ecological niche modeling