Flow-Mediated Collective Olfactory Communication in Honey Bee Swarms

Nguyen, Dieu My Thanh

Graduate Thesis Or Dissertation

Flow-Mediated Collective Olfactory Communication in Honey Bee Swarms Public Deposited

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/0c483k76n

Abstract

Social insects are an example of biological integration of relatively simple individuals into a functional whole. Honey bees (Apis mellifera L.) exist in large groups and must employ effective strategies to communicate and coordinate group processes. A prevalent communication signal in bees is the volatile chemicals, pheromones, whose spatiotemporal decay limits the range and noise tolerance of information exchange. We draw from biology, physics, and computer science to study how honey bees localize the queen, the colony's reproductive machine and regulator, and aggregate around her--a collective behavior essential to reproductive swarming. We combine a behavioral assay with computer vision detection of animal location and behavior to study the search and aggregation dynamics, and connect the experiments to a computational agent-based model to explore the underlying physical parameters. We show that the bees solve the problem of short-lived pheromone signals by forming a scenting network of signal receivers and senders, who sense pheromones based on a concentration threshold and align at a characteristic distance to emit pheromones and fan their wings to direct the signals to other bees. As bees often navigate complex and unpredictable environments, we also introduce environmental stressors. Physical obstacles impose constraints on the behavioral parameter space and delay in the formation of the network, yet the bees are capable of using the collective scenting strategy to navigate to the queen and to search for the shortest route to her. We also show that as strong wind reduces the number of scenting bees and disrupts the directionality of pheromone signaling, the bees can adapt by aligning the scenting network along the wind's direction. Overall, we show how the bees self-organize from simple individuals to a collective that can overcome the limitations of short-lived signals, individuals' local range of interaction, and complex environments. This deeper understanding of animal collective behavior can be leveraged in bio-inspired system designs in various fields, such as dynamic construction materials, swarm robotics, and distributed communication.

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