Occurrence Prediction of Western Conifer Seed Bug (Leptoglossus occidentalis: Coreidae) and Evaluation of the Effects of Climate Change on Its Distribution in South Korea Using Machine Learning Methods

The western conifer seed bug (WCSB; Leptoglossus occidentalis) causes huge ecological and economic problems as an alien invasive species in forests. In this study, a distribution model (SDM) was developed to evaluate the potential occurrence of WCSBs effects climate on WCSB South Korea. Based environmental data, including geographical meteorological variables, SDMs were with maximum entropy (MaxEnt) random forest (RF) algorithms, which are machine learning methods, they showed good performance predicting occurrence. On map by ensemble integrated MaxEnt RF models, areas mostly located at low altitudes, near roads, urban areas. Additionally, factors associated anthropogenic activities, such roads night lights, strongly influenced dispersal WCSBs. Metropolitan cities their vicinities Korea high probability Furthermore, is predicted intensify future owing change.