Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method

摘要

The spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losses caused by codling moths are immeasurable. It is essential to understand the potential distribution of codling moths to reduce the risks of codling moth establishment. In this study, we adopted the Maxent (Maximum Entropy Model), a machine learning method to predict the potential global distribution of codling moths with global accessibility data, apple yield data, elevation data and 19 bioclimatic variables, considering the ecological characteristics and the spread channels that cover the processes from growth and survival to the dispersion of the codling moth. The results show that the areas that are suitable for codling moth are mainly distributed in Europe, Asia and North America, and these results strongly conformed with the currently known occurrence regions. In addition, global accessibility, mean temperature of the coldest quarter, precipitation of the driest month, annual mean temperature and apple yield were the most important environmental predictors associated with the global distribution of codling moths.