Prediction of the spatial distribution of vine weevil under climate change using multiple variable selection methods

Abstract

The vine weevil (Otiorhynchus sulcatus) is a polyphagous pest that affects various economically important crops, but its potential distribution has not been studied. This research developed multiple species distribution models (SDMs) using different variable selection methods, including correlation, biological considerations, and principal component analysis, and integrated them into an ensemble model to predict the pest’s distribution under climate change. The MaxEnt algorithm was used to develop the models, showing robust performance with raw bioclimatic variables (TSS 0.34–0.37, F1 score 0.60–0.67), while lower performance and different distribution patterns were observed with reconstructed variables (TSS 0.13, F1 score 0.48). The vine weevil was predicted to be primarily distributed in North America and Europe, with the highest invasion risk in Far East Asia and northern India. Climate change could shift its habitat northward, particularly in areas where it currently occurs, and human activities may help spread the pest to new regions. This study offers a potential distribution map to aid in monitoring and controlling the vine weevil, emphasizing the importance of variable selection methods in predictive modeling.