Predicting Honey Bee Foraging: Recent Dance Floor Visit Outweighs Age and Temporal Cues

Understanding the factors that drive foraging decisions in worker honey bees (Apis mellifera L.) is a first step toward understanding bees’ collective behavior. In this study, we tested the hypothesis that a honey bee's decision to leave the hive to forage is best predicted by its age, followed in decreasing importance by a recent visit to the hive's "dance floor" where returned foragers communicate their findings, the time of day, and the time of year. Using spatial tracking data collected from an observation hive at the University of Konstanz, Germany, a data cleaning pipeline was developed and the best machine learning algorithm to predict individual foraging events based on these factors was identified. The Random Forest model achieved the highest predictive performance after addressing dataset imbalance, and provided insight into the relative importances of each factor. Our results disproved the central role of age and confirmed the significance of recent social contact in predicting foraging behavior, with time of day and seasonality contributing secondary effects. These findings deepen our understanding of honey bee decision-making and highlight the power of computational approaches in studying collective behavior.