Session A14: Physics of Social Interactions I

Live

From cells in tissue to human crowds, living systems display a stunning variety of group behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a method for the markerless tracking of nearly all individuals in a colony of honey bees Apis mellifera. We leverage advances in machine vision to solve two interrelated problems; (1) detection of highly similar objects in dense configurations and (2) matching of these detections into trajectories based on visual features which are largely invisible to the human eye. We apply the detection method to demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of cell-bees. Our tracking method recovers ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide new opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.   

Live

Unlike bird flocks, fish schools, or migratory herds, insect swarms do not possess either positional or orientational order. However, this does not imply that the structure of a swarm is completely random. Here, we analyze the structural properties of laboratory midge swarms not by considering the locations of the midges themselves but rather the properties of the negative space between them. By constructing Voronoi tessellations of swarms, we demonstrate that we can divide them into distinct concentric layers. We use this decomposition to study differences in the statistical properties of individuals in the bulk of the swarm versus those near its edges.   

Live

Biological systems not only have the remarkable capacity to build and maintain complex spatio-temporal structures in noisy environments, they can also rapidly break up and rebuild such structures. How such systems can simultaneously achieve both robust specialisation and plasticity is poorly understood. Here we use primitive societies of Polistes wasps as a model system where we experimentally perturb the social structure by removing the queen and follow the re-establishment of the social steady state over time. We combine a unique experimental strategy correlating time-resolved measurements across vastly different scales of biological organisation with a theoretical approach, here we show that Polistes integrates antagonistic processes on multiple scales of biological organisation to distinguish between intrinsic perturbations of molecular states and extrinsic cues affecting the society as a whole, and thereby achieves both robust specialisation and rapid plasticity. Furthermore, we show that the long-term stability of the social structure relies on dynamic DNA methylation which controls transcriptional noise. Such dynamics provide a general principle of how both specialization and plasticity can be achieved in biological systems.   

Live

In air-fluidized beds containing inert granular media, particles remain immobile for increasing airflow (fluidization) until the pressure drop through the medium balances the weight per area; at this point, the medium expands with increasing flow. Decreasing flow from a fluidized state (defluidization) results in the evolution of the bed height along a different trajectory. In a fluidized bed (9.5 cm in diameter) of active granular media consisting of 300 and 600 g of black soldier fly larvae, we observe no hysteresis in a fluidization/defluidization cycle, while dead (freshly frozen) larvae behave like inert granular media. During fluidization, the active larval bed expands smoothly below the pressure-based fluidization condition, while during defluidization, active larvae pack more densely than their dead counterparts. 2D simulations coupling CFD and agent-based algorithms recapitulate these effects; in particular, beds with Gaussian random forcing show no hysteresis, indicating that the larvae are not actively responding to flow effects, but randomly crawling through the bed (and squeezing past each other) with dynamics independent of external forcing.   

Live

Many organisms aggregate for the purposes of survival, forming collectives in which interactions between individuals give rise to emergent macroscale dynamics. Aquatic worms, for example, aggregate into an entangled blob to shield themselves against external stressors and preserve moisture in dry conditions. Motivated by recent experiments, we investigate the blob dynamics by modeling each worm as a self-propelled Brownian polymer. These simulations allow us to track the behavior of individual worms in order to uncover the mechanisms driving phase separation and emergent locomotion. We demonstrate how a blob is able to collectively traverse temperature gradients via the coupling between the active motion and the environment.   

Live

Living systems such as neural networks or animal groups process information about their environment via the dynamics of many interacting units and can transition between distinct macroscopic behaviors. While many studies focus on the idea that being close to such a transition optimally manages a trade-off between desired functions of the macroscopic behaviors, or yields optimal computation, little attention has been given to the fact that this ’optimality’ will depend on environmental context. Here, we combine experimental data and computer simulations to show that for escape waves in schooling fish the distance to a critical point is changed according to the environment’s perceived risk via a modulation of density. We find that even though dynamical range and sensitivity are maximized at the critical point, the fish schools remain subcritical, which we attribute to a trade-off between false and true positives.   

Live

Bumblebees are eusocial insects that carry out a variety of collective tasks which keep the colony functioning. In order to successfully cooperate, individual bees must be able to interpret and respond to a range of social cues. Bumblebees go through an acclimation period of about nine days after they eclose from their pupa during which they settle into the role they will have within the hive. In this study we investigate whether this period is also an important part of social development by quantifying the effects of social isolation on adult behavior. We track the posture of bees over time using SLEAP and quantify bee behavior alone and in pairs. We cluster the dynamics of bee body parts to identify stereotyped behaviors, as well as investigating relative positioning, bee-to-bee antennation, and locomotion. Among our results, we find that isolated and hive-reared bees respond to a range of social contexts in distinct ways, differing in edge preference, locomotion profiles, and inter-thorax distance when paired with another bee.   

Live

Super-organisms such as social insects solve complex physiological problems collectively, sans plan or planner, on scales much larger than the individual. Motivated by observations in the field and in the lab, I will describe our attempts to understand how insects build and use active architectures to regulate their micro-environment in such contexts as termite mound morphogenesis and physiology, and active ventilation, mechanical stabilization and thermoregulation in bee clusters. These everyday examples of functional collective sensing and action link physics, physiology and behavior on multiple scales and might be of relevance beyond the world of social insects, which we will demonstrate with some preliminary experiments using robotic ants.   

Live

We tracked longhorn crazy ant collective as they collectively haul large items through a semi-natural, randomized environment. To set a scale on the navigational efficiency of the ants ,we mapped their motion onto the 'Ant-in-a-Labyrinth' framework which studies physical transport through disordered media. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the 'Ant-in-a-Labyrinth' problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Our results provide an algorithmic perspective to the ant-in-a-labyrinth problem while illustrating the potential advantages of group living and collective cognition for increasing transport efficiency.   

Live

To become a coherent swarm, honey bees (Apis mellifera L.) locate their queen by tracking her pheromones. How can distant individuals exploit these chemical signals, which decay rapidly in space and time? We combine a novel behavioral assay with computer vision for bee detection and scenting recognition to track the swarming dynamics. We find that the bees propagate the signals by creating a communication network, where there is a characteristic distance between individuals and directional signaling away from the queen. We also connect our experimental results to an agent-based model where bee agents with simple, local behavioral rules exist in a flow environment with a stationary queen. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (axi-symmetrical) communication, such as small bee clusters. 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.   

Live

The western honey bee (Apis mellifera) is a domesticated pollinator famous for living in highly social colonies. In the spring, thousands of worker bees and a queen fly from their hive in search of a new home. They hang from a tree branch for several days, clinging on to one another and protecting the queen. We use x-ray computed tomography to investigate how honey bees structure their arrangement within the swarm such that the load on each bee is bearable, the bees do not fall down, and the swarm can adapt to the changing environment. We reconstruct the 3D structure of honey bee swarms ranging in size from 2,000 to 10,000 bees hanging from a flat board in the laboratory. The density of the bees is highest near the board and decreases toward the tip of the swarm, as the bees attached to the board have to support the most weight. We track individual bees in the swarm over time to find evidence of a mechanical division of labor. Most bees remain stationary, and a selected few move around within less dense areas of the swarm. Despite not having a clear leader, honey bees are able to organize into a swarm that protects the queen and remains stable until scout bees locate a new hive.