The conservation and breeding of the western honey bee (Apis mellifera) is central dependent on accurate subspecies assignment, but the most commonly used methods are labor-intensive classical morphometrics and costly molecular assays. We developed an XGBoost-based classification framework using a compact set of routinely measurable characters. A curated dataset of labeled workers was measured under harmonized protocols; features were screened according to embedded importance, and model performance was assessed using five-fold cross-validation, outperforming standard machine learning baselines. The resulting model using only the top 10 characters—primarily forewing venation angles and abdominal plate metrics—achieved high performance (accuracy = 0.98; F1 = 0.99) and an area under the receiver operating characteristic curve (AUC) of 0.99 (95% CI = 0.995–0.999). SHAP analyses confirmed the discriminatory contributions of these features, while error inspection suggested that misclassifications were concentrated in morphologically overlapping lineages. The model’s performance supports its use as a rapid triage tool alongside genetic testing, providing a scalable and interpretable tool for researchers to create and deploy custom morphometric models, demonstrated here for A. mellifera but portable to other insect taxa.

Safe overwintering is a challenging issue in rearing management that is inevitably faced by beekeepers in high-latitude regions. Under the combined influence of multiple factors, the overwintering loss rate of Western honey bees has risen continuously, and investigating the molecular mechanisms related to safe overwintering has become key. The Hunchun bee, an Apis mellifera ecotype in Jilin Province, China, exhibits strong overwintering ability during an overwintering period of more than five months. To investigate the molecular mechanisms of its cold resistance, we conducted a comparative transcriptomic analysis between the summer breeding period (July) and different overwintering intervals (November, December, January, and February), and then systematically identified key genes and signaling pathways related to cold resistance. The results showed that the highest number of differentially expressed genes (DEGs) was found between December and July. Compared with July, the upregulated genes in Hunchun bee in December were significantly enriched in several pathways, such as ion transport and neuroactive ligand–receptor interactions, and the downregulated genes were significantly enriched in pathways related to fatty acid metabolism, glutathione metabolism, and the peroxisome. Notably, a total of 378 shared DEGs were obtained from the four comparison groups, and several candidate cold-resistant gene families, such as AFPs, HSPs, C₂H₂-ZFPs, STKs, and LRRCs, were identified among the shared DEGs of the winter season. Additionally, 749 shared DEGs related to protein modification and metabolic process regulation were identified between the four successive overwintering intervals. Four shared genes, including sensory neuron membrane protein 1 (SNMP1), were revealed by pairwise comparison of the four intervals. The above results collectively indicate that the Hunchun bee attenuates winter-induced stress responses during the overwintering process by maintaining osmotic pressure balance, reducing fatty acid metabolism, increasing antioxidant capacity, and synthesizing cold-resistant macromolecular proteins. It was also found that chemical signal perception may serve a role in maintaining the stability of the overwintering bee colony. The key genes and pathways related to cold resistance identified in this study not only provide a basis for explaining the overwintering molecular mechanism for Apis mellifera of Hunchun bee but also offer key data to improve overwintering management strategies for Western honey bees.

The collection of honey and propolis is crucial for the nutrition and health of honey bee colonies. In this study, Apis mellifera with high productivity for both honey and propolis (MJ) was developed through long-term selective breeding using Apis mellifera caucasica and Apis mellifera carnica as breeding materials, resulting in substantial improvements in both honey yield and propolis production. To elucidate the genetic basis and molecular mechanisms underlying efficient foraging behavior for nectar and resins in honey bees, whole-genome resequencing was performed on MJ, the C-lineage, and the O-lineage of Apis mellifera. In addition, transcriptome sequencing was conducted using MJ and Apis mellifera ligustrica, which exhibited the highest honey and propolis production traits. MJ had a composite genetic background dominated by O-lineage ancestry. Compared with C-lineage honey bees, MJ exhibited 234 candidate genes under selection, mainly related to chitin and amino sugar metabolism. Transcriptome analysis identified 504 and 2676 differentially expressed genes between MJ and Italian honey bees in the nectar-forager and resin-forager comparisons, respectively, involving chemosensory gene families. Genes upregulated in MJ nectar and resin foragers were mainly associated with redox regulation and energy metabolism, respectively. Integrated analyses identified 7 candidate genes associated with nectar foraging and 41 candidate genes associated with resin foraging; these genes were involved in chemosensory responsiveness, flight-related structural traits, energy metabolism, and redox regulation in MJ. These findings provide genetic and transcriptomic resources for further studies on the division of labor in foraging and molecular breeding in honey bees.