Palmieri_2026_Mol.Ecol_35_e70532

Diapause polymorphisms shape insect life cycles, affecting their responses to seasonal environmental fluctuations. In the spruce bark beetle Ips typographus, diapause phenotypes, classified as facultative or obligate, significantly impact voltinism and thus outbreak potential, yet the genetic mechanisms underlying this trait remain incompletely understood. Here, we combined reduced-representation genome-wide association study (rGWAS) and machine learning (ML) to identify genetic polymorphisms associated with diapause phenotypes. Using ddRADseq, we analysed genomic data from diapause-phenotyped individuals originating from distinct geographic locations in Central and Northern Europe. rGWAS identified SNPs associated with diapause phenotypes, with a locus encoding juvenile hormone esterase (JHE) emerging as the strongest and only association robust to empirical null calibration. ML classification independently recovered JHE, detected additional loci involved in protein regulation, signal transduction and stress-response pathways, and, following permutation and cross-population validation, achieved high predictive accuracy. Our combined rGWAS and ML approach outperformed either method alone, effectively identifying a minimal set of SNPs capable of robustly distinguishing facultative from obligate diapause phenotypes. Applying our model to wild, non-phenotyped I. typographus populations revealed a high proportion (> 50%) of facultative diapause phenotypes (potentially multivoltine) in Central European regions and obligate diapausing individuals (~97%; univoltine) prevailing at northern latitudes, consistent with adaptive responses to local environmental conditions and demonstrating the feasibility of inferring voltinism potential from genomic data in natural populations. Our study provides significant advances for ecological genomics research and presents new tools for predicting and managing outbreak risks in pest species under climate change.