Collective Sensing and Information Transfer in Animal Groups

Abstract

This dissertation addresses several topics in collective motion in animal groups. Coordination among social animals often requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the interaction network used for communication. In two experimental systems and in one simulation study, we study the nature of interactions and interaction networks, and how these interactions scale up to global order in the system. The resulting collective properties allow animal groups access to "collective computation" such that they can process and respond to stimuli in ways in which a single individual cannot.

First, we study collective evasion maneuvers, manifested through rapid cascades of behavioral change (a ubiquitous behavior among taxa), in schooling fish (Notemigonus crysoleucas). We determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals employ simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates are complex; being weighted and directed. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion, and establish that individuals with relatively few, but strongly-connected, neighbors are both most socially influential, and most susceptible to influence.

Next, we study the relationship between emergent periodic synchronization in ant colonies, and interactions between ants in different behavioral states. We investigate the factors driving fluctuations in the overall level of synchronization observed in the colony, and find that flexible behavioral responses to interactions can explain these fluctuations, in both real data and in a simulated ant colony.

Finally, we model collective movement, motivated by experimental data, demonstrating that simple behavioral rules can allow groups to maximize performance in dynamical search tasks. Additionally, the behaviors that optimize performance place the population near a transitional regime. Individuals locate and track dynamic resources by splitting and fusing to form groups that match the length scale of these resources. This occurs even when individuals cannot evaluate resource sizes or determine the sizes of groups to which they belong. Our model demonstrates that fission-fusion dynamics can allow social animals to balance the exploration-exploitation tradeoff.