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Phyllotreta striolata (Fabricius, 1803) (Coleoptera: Chrysomelidae) is a major pest of cruciferous vegetables, causing substantial economic losses across Asia, Europe, North America, and other regions. Building on previous bioclimatic studies, we combined global occurrence records with environmental predictors to compare optimized MaxEnt models and BIOMOD2 ensemble models and project the species’ potential distribution under current, paleoclimatic, and future conditions. Across the two optimized MaxEnt approaches, mean AUC, TSS, and Kappa values ranged from 0.920–0.930, 0.845–0.851, and 0.815–0.825, respectively. Under current climatic conditions, areas of moderate and high suitability were concentrated in three regions: Europe, especially central and western Europe and parts of northern Europe; East Asia, including central, eastern, and southern China, the Korean Peninsula, and Japan; and North America, particularly the central and eastern United States and southern and western Canada. Paleoclimatic projections from the individual MaxEnt models indicated that, during the Last Glacial Maximum (LGM), moderately and highly suitable habitats were concentrated in central and eastern China and adjacent areas of East Asia, parts of Europe, and eastern North America. During the Mid-Holocene (MH), these habitats expanded, occurring primarily in central and western Europe, the central and eastern United States, central and eastern China, the Korean Peninsula, and parts of Japan. Future projections indicated that suitable habitats would remain concentrated in Europe, North America, and East Asia, with the total suitable area exceeding the current estimate across all future periods and scenarios examined. Centroid analysis of the final MaxEnt projections further indicated that late-century centroids were located farther north than the current centroid under all three scenarios, although their trajectories differed among scenarios. These findings characterize potential changes in climatic suitability for P. striolata and may inform surveillance planning under climate change.
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Liu et al. (2026) studied this question.
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