This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2025. The fellowship supports research and training of the fellow that will contribute to biology in innovative ways. Effective information processing is crucial for species to respond appropriately to their environment, particularly in social species like humans that integrate a wide range of individual experiences into collective responses. Theoretical models that are grounded in real world observations are needed to explain and predict patterns of social communication across species. This research addresses this need by using the common eastern bumblebee (Bombus impatiens) to trace the flow of nutritional information throughout the colony. Bumblebee colonies are an optimal model to test key hypotheses in social communication due to their complex social behaviors and ease with which they can be manipulated for study. This research will leverage this model, integrating methods in behavioral ecology and deep-learning, to study the role of colony size, communication, and individual variability on the social network formed following a successful foraging expedition. This research will be accompanied by educational activities focused on increasing STEM engagement among incarcerated college students, who historically have not had opportunities to critically engage with scientific thinking. This research seeks to answer three questions: 1) What are the individual dynamics which underlie bumblebee communication? 2) How does the communication network change with colony size? 3) How does individual variation impact communication? To answer these questions, the fellow will utilize a deep-learning pipeline to track the location of all bees in a colony, as well as their body parts. The fellow will generate a multimodal social network consisting of different modalities of interaction between bumblebees. An experiment will be designed to monitor network behavior before and after introducing a highly n