Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Listeria monocytogenes is an intracellular food-borne pathogen that has evolved to enter mammalian host cells, survive within them, spread from cell to cell, and disseminate throughout the body. A series of secreted virulence proteins from Listeria are responsible for manipulation of host-cell defense mechanisms and adaptation to the intracellular lifestyle. Identifying when and where these virulence proteins are located in live cells over the course of Listeria infection can provide valuable information on the roles these proteins play in defining the host-pathogen interface. These dynamics and protein levels may vary from cell to cell, as bacterial infection is a heterogeneous process both temporally and spatially. No assay to visualize virulence proteins over time in infection with Listeria or other Gram-positive bacteria has been developed. Therefore, we adapted a live, long-term tagging system to visualize a model Listeria protein by fluorescence microscopy on a single-cell level in infection. This system leverages split-fluorescent proteins, in which the last strand of a fluorescent protein (a 16-amino-acid peptide) is genetically fused to the virulence protein of interest. The remainder of the fluorescent protein is produced in the mammalian host cell. Both individual components are nonfluorescent and will bind together and reconstitute fluorescence upon virulence-protein secretion into the host cell. We demonstrate accumulation and distribution within the host cell of the model virulence protein InlC in infection over time. A modular expression platform for InlC visualization was developed. We visualized InlC by tagging it with red and green split-fluorescent proteins and compared usage of a strong constitutive promoter versus the endogenous promoter for InlC production. This split-fluorescent protein approach is versatile and may be used to investigate other Listeria virulence proteins for unique mechanistic insights in infection progression.

Honey bee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones, but how can distant individuals exploit these chemical signals which decay rapidly in space and time? Here, we combine a novel behavioral assay with the machine vision detection of organism location and scenting behavior to track the search and aggregation dynamics of the honey bee Apis mellifera L. We find that bees collectively create a communication network to propagate pheromone signals, by arranging in a specific spatial distribution where there is a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signals. To better understand such a flow–mediated directional communication strategy, we connect our experimental results to an agent–based model where virtual bees with simple, local behavioral rules, exist in a flow environment. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic communication, such as small bee clusters that persist throughout the simulation. Our results highlight a novel example of extended classical stigmergy: rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Honey bees (Apis mellifera L.) aggregate around the queen by collectively organizing a communication network to propagate volatile pheromone signals. Our previous study shows that individual bees "scent" to emit pheromones and fan their wings to direct the signal flow, creating an efficient search and aggregation process. In this work, we introduce environmental stressors in the form of physical obstacles that partially block pheromone signals and prevent a wide open path to the queen. We employ machine learning methods to extract data from the experimental recordings, and show that in the presence of an obstacle that blocks most of the path to the queen, the bees need more time but can still effectively employ the collective scenting strategy to overcome the obstacle and aggregate around the queen. Further, we increase the complexity of the environment by presenting the bees with a maze to navigate to the queen. The bees require more time and exploration to form a more populated communication network. Overall, we show that given volatile pheromone signals and only local communication, the bees can collectively solve the swarming process in a complex unstructured environment with physical obstacles.

Honey bees (Apis mellifera L.) are social insects that makes frequent use of volatile pheromone signals to collectively navigate unpredictable and unknown environments. Ants have been shown to effectively use pheromone trails to find the shortest path between two points, the nest and the food source. The ant pheromone trails are accomplished by depositing pheromones which are then diffused passively, creating isotropic (i.e., non-directional and axi-symmetric) signals. In this study, we report the first instance of the honey bees’ ability to solve the shortest path problem to localize the queen and aggregate around her by using a collective flow-mediated scenting strategy. In this strategy, individual bees not only emit pheromones but also fan their wings to actively direct the flow of the signals, providing colony members with directional messages to the queen’s location. We use computer vision and deep learning approaches to perform automatic and accurate image analysis. As a result, we quantify the number of bees in the short and long paths, and show that the short path is frequented by significantly more bees over time. We also reconstruct attractive surfaces using the positions and directions of scenting bees, and show that this surface is more “attractive” along the short path and around the queen as scenting bees send out directional messages and the swarm makes their way to the queen. Overall, we show that honey bees can effectively use the collective scenting behavior to overcome local and volatile pheromone communication and find the shortest path to the queen.