Ensemble species distribution modeling predicts the spread of an invasive ant species in South Korea, indicating high-risk areas for management.
Linepithema humile is a well‐known invasive alien species that was first detected in 2019 at the cargo handling area of Busan Station in South Korea and has since established a population near the discovery site. Despite sufficient time for potential spread, no additional occurrence records have been reported outside the initial detection site. To predict its potential distribution across South Korea, we constructed species distribution models (SDMs) using occurrence data from its native range in South America and invasive range in North America. Modeling was conducted with the BIOMOD2 platform using five algorithms (ANN, GBM, MARS, MAXENT, and RF) and six environmental variables (Bio03, Bio04, Bio13, Bio16, Bio18, and the mean annual cumulative soil temperature degree‐days above 15.9°C). Model performance was evaluated with Kappa, ROC, and TSS, and only models meeting thresholds (Kappa ≥ 0.7, ROC ≥ 0.7, and TSS ≥ 0.5) were used for ensemble modeling via EMmean, EMwmean, and EMca methods. Projection accuracy was assessed using chi‐square tests based on occurrence data not used in model training. The predicted potential distribution included southern and southwestern coastal areas, which was consistent with the record in Busan. This study demonstrates the utility of SDMs trained on occurrence data from outside Korea in predicting the potential distribution of L. humile with limited domestic records and highlights high‐risk areas beyond Busan. Such approaches may support early detection and management strategies in the initial stages of biological invasion.
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Namgung et al. (2026) studied this question.
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