Session D20: Physics of Social Interactions

Collective Behavior in Growth-Driven Systems

A variety of biological systems are not motile, but sessile in nature, relying on growth as the main driver of their movement. Groups of such growing organisms can form complex structures, such as the functional architecture of growing axons, or the adaptive structure of plant root systems. These processes are not yet understood, however the decentralized growth dynamics bear similarities to the collective behavior observed in groups of motile organisms, such as flocks of birds or schools of fish. Equivalent growth mechanisms make these systems amenable to a theoretical framework inspired by tropic responses of plants, where growth is considered implicitly as the driver of the observed bending towards a stimulus. Here we set the stage for the study of emergent growth-driven structures by developing a model for interacting growth-driven organs. Particularly, we analytically and numerically investigate the 2D dynamics of pairs of organs interacting via allotropism, i.e. each organ senses signals emitted at the tip of their neighbor and responds accordingly. In the case of local sensing we find a rich state space.   

Collective Aggregation via Directed Pheromone Signaling in Honeybee Swarms

While pheromones are a prevalent volatile communication signal in nature, the range and noise tolerance of information exchange is limited by the spatiotemporal decay of these signals. Using honeybees as a model organism, we study the communication network of honeybee swarms that locate their queen by tracking her pheromones. Specifically, how can honeybees that are far away from the queen locate her? Our results suggest that bees who locate her pheromones, stop at a certain distance from her, raise their abdomens and fan their wings, driving airflow across the Nasonov gland, which disperses pheromones to the rest of the swarm. We show that bees arrange in a specific spatial distribution with a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signal. This dynamic structure recruits new broadcasting bees over time as the pheromones traveled a distance which is orders of magnitude the size of an individual. We connect our experimental results to an agent based model of volatile communication network, and characterize the advantage of this directional communication strategy vs. an axisymmetric one.   

Social context alters behavioral interactions in bumblebees

Bumblebees are eusocial insects that rely on successful cooperation to carry out collective tasks which keep the colony running smoothly. Individual bumblebees must be able to correctly interpret and respond to social cues within a busy hive in order to properly fulfill their role. In this study, we investigate how social context alters behavioral interactions among bumblebee (Bombus impatiens) workers by probing the behavior of differently aged bees either alone or paired with another bee. We track the posture of bees over time using a deep learning algorithm and quantify variation in social behavior by clustering the dynamics of individual body parts in addition to relative positioning, bee-to-bee antennation, and locomotion. We find a number of interesting differences between bees of different ages and castes, including variation in the responsiveness of individual bees to novel social partners.   

Brownian motion of fire ants hinders raft formation

When flooded, fire ants aggregate and form rafts to avoid drowning. Finding neighbors and attaching to them is a critical first step for raft formation. In this study, we observe individual fire ants perform random walk on the water surface. We characterize this exploratory behavior by measuring the orientational and translational diffusivity. Brownian motion of fire ants can be considered as thermal fluctuations which in turn inhibit the assembly of small rafts. Our results suggest that this repulsive mechanism is balanced by the attractive surface tension effect in the formations of larger rafts.   

Behavioral plasticity in jackdaw flocks

Bird flocks are a classic example of collective behavior, where the cohesive motion of the flock as a whole is presumed to arise purely from local interactions. Flocking models tend to assume that every individual is an identical agent that plays by the same rules, and these rules are usually assumed to be immutable. In reality, however, interactions may be influenced by many factors, such as external stimuli and social relationships. I will present evidence from field studies of jackdaws, a highly social corvid species, that indeed flocks of this single species display different interaction rules in different ecological contexts. During the roosting season, large flocks spontaneously form in the evening as the birds return to their roosts. In these transit flocks, individual jackdaws interact topologically with a fixed number of their neighbors. Jackdaws also gather together in mobbing flocks to drive away predators, and such flocks can be induced experimentally using a model predator and playbacks of scolding and recruitment calls. In these mobbing flocks, jackdaws interact metrically over a fixed physical distance. This change in interaction type leads to a clear ordering phase transition as a function of group density in mobbing flocks that is absent in transit flocks.   

Shared behavioral mechanisms underlie C. elegans aggregation and swarming

In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescence multi-worm tracking, we quantify aggregation in terms of individual dynamics and population-level statistics. Then we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules for aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. We further expand our work by examining swarming at very high densities.   

Predicting residential segregation using statistical physics approaches

We introduce a statistical physics based method to predict racial residential segregation in human populations. Such predictions are increasingly important for informing policy decisions as human populations become more diverse and mobile. Here, we demonstrate how to make such predictions by extending a novel statistical physics approach called Density-Functional Fluctuation Theory (DFFT) to multi-component time-dependent systems. This technique uses observations of fluctuations in the local density of neighborhood racial composition to extract functions that separately quantify social and spatial preferences/constraints to predict demographic changes. As a demonstration, we simulate a population distribution using a Schelling-type segregation model, and use DFFT to predict both steady-state probability distributions and migration events after changes in the environment, social interactions, or number of individuals. Should these results extend to actual human populations, DFFT could be applied to demographic data to quantify segregation between different groups of people and predict how such populations will respond to proposed demographic changes.   

Leader cells in collective chemotaxis: optimality and trade-offs

Clusters of cells can work together in order to follow a signal gradient, chemotaxing even when single cells do not. This behavior is robust over many cell types and many signals. Cells in different regions of migrating streams show different gene expression, suggesting cells specialize to leader and follower roles in collective chemotaxis. We use a simple mathematical model to find when specialization would be advantageous. In our model, leader cells sense the gradient with an accuracy that depends on the kinetics of ligand-receptor binding while follower cells attempt to follow the cluster's direction with a finite error. Intuitively, specialization into leaders and followers should be optimal when a few cells have much more information than the rest of the cluster, such as in the presence of a sharp transition from one chemical concentration to another. We do find this - but also find that high levels of specialization can be optimal in the opposite limit of a very shallow gradient. There is also an important tradeoff: clusters have to choose between speed in following a gradient and ability to reorient quickly.   

One fish, two fish, win fish, lose fish: Imaging and analyzing the fighting behavior of zebrafish in 3D

Social interactions represent some of the most intriguing aspects of animal behavior, yet principled methods for quantifying the joint actions of two individuals are lacking. We detail a novel effort to measure and model social behavior in the 3D swimming dynamics of the adult zebrafish, Danio rerio. We describe a custom tracking apparatus consisting of multiple fast cameras, a large imaging volume, and a transparent interior cage to avoid reflection artifacts. We leverage advances in convolutional neural networks to develop 3D markerless bodypoint tracking of interacting fish, while maintaining organism identity. We focus on small groups, below any obvious collective limit, yet with a rich repertoire of interacting behaviors. Specifically, we examine stereotyped male-male fighting behaviors and analyze the dynamics using short segments of bodypoint configurations to identify ethological motifs directly from tracked data. We quantify longer-time dynamics as transitions between motifs, and we repeat our analysis in mutant fish with known social deficits.   

A Foraging Approach to Analyzing Infant and Caregiver Vocal Behavior

Previous research on infant vocal development suggests that human infants and adult caregivers search for sounds that have social value. We hypothesized that this could be a foraging process in a high-dimensional acoustic space where the resources are adapting to the forager’s behavior. We studied day-long recordings of vocalizations in a naturalistic setting over the infants’ first year. We examined inter-vocalization time intervals and distance steps in an acoustic space defined by mean pitch and mean amplitude. Infant inter-vocalization intervals were shorter immediately following a vocal response from an adult. Adult intervals were shorter following an infant response and adult inter-vocalization pitch differences were smaller following the receipt of a vocal response from the infant. These findings are consistent with the hypothesis that infants forage vocally for social input. Increasing infant age was associated with changes in adult and infant inter-vocalization step sizes. The study represents a novel application of foraging theory to characterize infant-caregiver vocal interactions by assessing vocal exploration in terms of patterns of movement in acoustic space, which will allow this domain of behavior to be compared to other foraging behaviors.   

The Superorganism’s Circulatory System: Collective control of development through a socially exchanged fluid

How can a distributed system like a social insect colony collectively decide how to allocate resources and mature over the long-term? Many but not all species of social insects engage in the social fluid exchange of trophallaxis. In species that perform ample trophallaxis, each individual within the colony is connected through the trophallactic network, including larvae. In carpenter ants, we’ve shown that components of trophallactic fluid can influence larval development, regulating the number of new adults produced. Furthermore, we find that some trophallactic fluid proteins have been co-opted from typical insect developmental pathways: as these proteins have become abundant in this social fluid, they show increasing signatures of adaptation such as repeated duplications and positive selection. Recent advances using long-term fluorescence imaging reveal how the content and timing of larval feeding through trophallaxis controls growth and developmental timing. Thus, in species that engage in this behavior, trophallaxis and trophallactic fluid present a means by which adults can regulate larval development according to the needs of the colony.