From Pest Traps to Management Maps: Predicting the Abundance and Phenology of Japanese Pine Bast Scale to Guide National Forest Adaptation and Timely Control

Abstract

Forest insect outbreaks are intensifying under climate change and global trade, yet their spatiotemporal dynamics remain poorly quantified. This lack of predictive understanding limits timely and effective management. The Japanese pine bast scale (Matsucoccus matsumurae), which has persisted in South Korea for more than 50 years despite ongoing control efforts, exemplifies this challenge. We developed the first national framework that converts raw monitoring data into spatially explicit forecasts of pest abundance and phenology. A nationwide pheromone-trap network (164 sites in 2022 and 65 in 2023) provided standardized observations of male flight activity. Deep learning automated the counting of captured insects, and statistical modeling of biweekly captures yielded the timing of first emergence and peak flight. These data, combined with a 30 m host-abundance map and 1 km environmental predictors, were used to train extreme gradient boosting (XGBoost) models to predict abundance and phenology across South Korea. The models explained 78% of abundance variance and up to 90% of phenological variance. They revealed strong spatial heterogeneity: captures ranged from 0 to >43,000 per trap, were concentrated along southern coasts, while emergence timing differed by up to 9 weeks nationwide. Warmer winters advanced emergence, and stable climates promoted higher abundance. Bootstrap ensembles quantified spatial prediction uncertainty, highlighting regions with low confidence that require intensified monitoring. Our results expose a highly dynamic and uneven infestation landscape that fixed, calendar-based strategies cannot address. By linking large-scale ecological observation with predictive modeling, this framework establishes a data-driven basis for adaptive, climate-resilient forest pest management.