Social networks predict the life and death of honey bees

In many social systems, an individual9s role is reflected by its interactions with other members of the group. In honey bee colonies (Apis mellifera), workers generally perform different tasks as they age, yet there is high behavioral variation in same-aged bees. It is unknown how social interactions within the colony relate to an individual9s tasks throughout her life. We propose a new method to extract a single number from each individual9s interaction patterns in multimodal social networks that captures her current role in the colony. This "network age" is better than biological age at predicting task allocation (+99%), survival (+157%), and activity patterns (+44-108%) and even predicts task allocation up to one week (around a sixth of her typical lifespan) into the future. Network age identifies distinct developmental paths and task changes throughout a bee9s life: We show that individuals change tasks gradually and exhibit high repeatability in their allocated task, and that same aged bees form stable behavioral subgroups in which they predominantly interact with one another. While we derived interaction networks by automatically tracking a fully tagged colony, we show that tracking only 5% of the bees is sufficient to extract a meaningful representation of the individuals9 interaction patterns, demonstrating the feasibility of our method for detecting complex social structures with reduced experimental effort. Since network age more accurately predicts task allocation than biological age, it could be used in experimental manipulations to quantify shifts in the timing of task transitions as a response. We extend our method to extract interaction patterns relevant to other attributes of the individuals, such as their mortality, opening up a broad range of possible applications. Our approach is a scalable instrument to study individual behavior through the lens of social interactions over time in honey bees and other complex social systems.