Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors

Honey bees play a crucial role in agricultural productivity and ecological stability, yet their interactions with environmental stressors, particularly volatile organic compounds (VOCs) and pollutants, pose significant challenges to their cognitive functions and behavior. This study investigates the effects of VOCs on the acoustic communication within honeybee colonies and foraging behavior, specifically focusing on how these compounds influence distance-related cues conveyed through colony sounds. Using OpenL3 embeddings and machine learning models, the study achieved accurate classification of food source distances based on acoustic features, with the K-Nearest Neighbors (KNN) model demonstrating superior performance. The introduction of ethyl acetate and acetone caused minor reductions in classification accuracy but had divergent impacts on foraging dynamics: ethyl acetate enhanced landing efficiency, whereas acetone disrupted foraging activity. These findings highlight the utility of acoustic analysis for studying honey bee behavior and underscore the importance of mitigating environmental stressors to sustain pollinator populations.