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

Abstract

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

Keywords

• global warming
• species distribution model
• maximum entropy
• random forest
• alien species
• invasive species