Biology subjects

Peleg, O.

Publications and source records attributed to Peleg, O..

29 records · Page 2Linked to original sources

bioRxiv2021

Emergent collective locomotion in an active polymer model of entangled worm blobs

Numerous worm and arthropod species form physically-connected aggregations in which interactions among individuals give rise to emergent macroscale dynamics and functionalities that enhance collective survival. In particular, some aquatic worms such as the California blackworm (Lumbriculus variegatus) entangle their bodies into dense blobs to shield themselves against external stressors and preserve moisture in dry conditions. Motivated by recent experiments revealing emergent locomotion in blackworm blobs, we investigate the collective worm dynamics by modeling each worm as a self-propelled Brownian polymer. Though our model is two-dimensional, compared to real three-dimensional worm blobs, we demonstrate how a simulated blob can collectively traverse temperature gradients via the coupling between the active motion and the environment. By performing a systematic parameter sweep over the strength of attractive forces between worms, and the magnitude of their directed self-propulsion, we obtain a rich phase diagram which reveals that effective collective locomotion emerges as a result of finely balancing a tradeoff between these two parameters. Our model brings the physics of active filaments into a new meso- and macroscale context and invites further theoretical investigation into the collective behavior of long, slender, semi-flexible organisms.

biophysics↗

bioRxiv2021

Calibration-free 3D reconstruction of firefly trajectories from 360-degree cameras

Over the past few decades, progress in animal tracking techniques, from large migrating mammals to swarming insects, has facilitated significant advances in ecology, behavioural biology, and conservation science. Recently, we developed a technique to record and track flashing fireflies in their natural habitat using pairs of 360-degree cameras. The method, which has the potential to help identify and monitor firefly populations worldwide, was successfully implemented in various natural swarms. However, camera calibration remained tedious and time-consuming. Here, we propose and implement an algorithm that calibrates the cameras directly from the data, requiring minimal user input. We explain the principles of the calibration-free algorithm, and demonstrate the ease and efficiency of its implementation. This method is relatively inexpensive, versatile, and well-suited for automatic processing and the collection of a large dataset of firefly trajectories across species and populations. This calibration-free method paves the way to citizen science efforts for monitoring and conservation of firefly populations.

animal behavior and cognition↗

bioRxiv2021

Robustness of Collective Scenting in the Presence of Physical Obstacles

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.

animal behavior and cognition↗

bioRxiv2021

Self-organization in natural swarms of Photinus carolinus synchronous fireflies

Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior.

animal behavior and cognition↗

bioRxiv2020

Flow-Mediated Olfactory Communication in Honey Bee Swarms

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.

animal behavior and cognition↗

bioRxiv2020

Data-Driven Modeling of Resource Distribution in Honeybee Swarms

Trophallaxis is the mutual exchange and direct transfer of liquid food among eusocial insects such as ants, termites, wasps, and bees. This process allows efficient dissemination of nutrients and is crucial for the colonys survival. In this paper, we present a data-driven agent-based model and use it to explore how the interactions of individual bees, following simple, local rules, affect the global food distribution. We design the rules in our model using laboratory experiments on honeybees. We validate its results via comparisons with the movement patterns in real bees. Using this model, we demonstrate that the efficiency of food distribution is affected by the density of the individuals, as well as the rules that govern their behavior: e.g., how they move and whether or not they aggregate. Specifically, food is distributed more efficiently when donor bees do not always feed their immediate neighbors, but instead prioritize longer motions, sharing their food with more-distant bees. This non-local pattern of food exchange enhances the overall probability that all of the bees, regardless of their position in the colony, will be fed efficiently. We also find that short-range attraction improves the efficiency of the food distribution in the simulations. Importantly, this model makes testable predictions about the effects of different bee densities, which can be validated in experiments. These findings can potentially contribute to the design of local rules for resource sharing in swarm robotic systems.

animal behavior and cognition↗

bioRxiv2020

Spatiotemporal reconstruction of emergent flash synchronization in firefly swarms via stereoscopic 360-degree cameras

During mating season, males of synchronous firefly species flash in unison within swarms of thousands of individuals. These strongly-correlated collective displays have inspired numerous mathematical models to explain how global synchronous patterns emerge from local interactions. Yet, experimental data to validate these models remains sparse. To address this gap, we develop a method for three-dimensional tracking of firefly flashes, using a stereoscopic setup of 360-degree cameras. We apply this method to record flashing displays of the North American synchronous species Photinus carolinus in its natural habitat as well as within controlled environments, and obtain the 3D reconstruction of flash occurrences in the swarm. Our results show that even a small number of interacting males synchronize their flashes; however, periodic flash bursts only occur in groups larger than 15 males. Moreover, flash occurrences are correlated over several meters, indicating long-range interactions. While this suggests emergent collective behaviour and cooperation, we identify distinct individual trajectories that hint at additional competitive mechanisms. These reveal possible behavioural differentiation with early flashers being more mobile and flashing longer than late followers. Our experimental technique is inexpensive and easily implemented. It is extensible to tracking light communication in various firefly species and flight trajectories in other insect swarms.

animal behavior and cognition↗

bioRxiv2019

Attraction, Dynamics, and Phase Transitions in Fire Ant Tower-Building

Many insect species, and even some vertebrates, assemble their bodies to form multi-functional materials that combine sensing, computation, and actuation. The tower-building behavior of red imported fire ants, Solenopsis invicta, presents a key example of this phenomenon of collective construction. While biological studies of collective construction focus on behavioral assays to measure the dynamics of formation and studies of swarm robotics focus on developing hardware that can assemble and interact, algorithms for designing such collective aggregations have been mostly overlooked. We address this gap by formulating an agent-based model for collective tower-building with a set of behavioral rules that incorporate local sensing of neighboring agents. We find that an attractive force makes tower building possible. Next, we explore the trade-offs between attraction and random motion to characterize the dynamics and phase transition of the tower building process. Lastly, we provide an optimization tool that may be used to design towers of specific shapes, mechanical loads, and dynamical properties such as mechanical stability and mobility of the center of mass.

systems biology↗

bioRxiv2019

Social inhibition maintains adaptivity and consensus of foraging honeybee swarms in dynamic environments

To effectively forage in natural environments, organisms must adapt to changes in the quality and yield of food sources across multiple timescales. Individuals foraging in groups act based on both their private observations and the opinions of their neighbors. How do these information sources interact in changing environments? We address this problem in the context of honeybee swarms, showing inhibitory social interactions help maintain adaptivity and consensus needed for effective foraging. Individual and social interactions of a mathematical swarm model shape the nutrition yield of a group foraging from feeders with temporally switching food quality. Social interactions improve foraging from a single feeder if temporal switching is fast or feeder quality is low. When the swarm chooses from multiple feeders, the most effective form of social interaction is direct switching, whereby bees flip the opinion of nestmates foraging at lower yielding feeders. Model linearization shows that effective social interactions increase the fraction of the swarm at the correct feeder (consensus) and the rate at which bees reach that feeder (adaptivity). Our mathematical framework allows us to compare a suite of social inhibition mechanisms, suggesting experimental protocols for revealing effective swarm foraging strategies in dynamic environments.

ecology↗

bioRxiv2017

Fluid mediated self-organization of ventilation in honeybee nests

European honey bees (Apis mellifera) live in large congested nest cavities with a single opening that limits passive ventilation. These nests are actively ventilated by individual bees which fan their wings at the nest entrance when the local air temperature exceeds a threshold. Here we show that colonies with relatively large nest entrances use an emergent ventilation strategy where fanning bees self-organize to form fanning groups, separating regions of continuous inflow and outflow. The observed spatio-temporal patterns correlate the air velocity and air temperature along the entrances to the distribution of fanning bees. A mathematical model that couples these variables to known fanning behavior of individuals recapitulates their collective dynamics. Additionally, the model makes predictions about the temporal stability of the fanning group as a function of the temperature difference between the environment and the nest. Consistent with these predictions, we observe that the fanning groups drift, cling to the entrance boundaries, break-up and reform as the ambient temperature varies over a period of days. Overall, our study shows how honeybees use flow-mediated communication to self-organize into a steady-state in fluctuating environments.

animal behavior and cognition↗

bioRxiv2017

Collective mechanical adaptation of honeybee swarms

Honeybee Apis mellifera swarms form clusters made solely of bees attached to each other, forming pendant structures on tree branches (1). These clusters can be hundreds of times the size of a single organism. How these structures are stably maintained under the influence of static gravity and dynamic stimuli (e.g. wind) is unknown. To address this, we created pendant conical clusters attached to a board that was shaken with varying amplitude, frequency and total duration. Our observations show that horizontally shaken clusters spread out to form wider, flatter cones, i.e. the cluster adapts to the dynamic loading conditions, but in a reversible manner - when the loading is removed, the cluster recovers its original shape, slowly. Measuring the response of a cluster to a sharp pendular excitation before and after it adapted shows that the flattened cones deform less and relax faster than the elongated ones, i.e. they are more stable mechanically. We use particle-based simulations of a passive assemblage to suggest a behavioral hypothesis that individual bees respond to local variations in strain. This behavioral response improves the collective stability of the cluster as a whole at the expense of increasing the average mechanical burden experienced by the individual. Simulations using this rule explain our observations of adaptation to horizontal shaking. The simulations also suggest that vertical shaking will not lead to significant differential strains and thus no adaptation. To test this, we shake the cluster vertically and find that indeed there is no response to this stimulus. Altogether, our results show how an active, functional super-organism structure can respond adaptively to dynamic mechanical loading by changing its morphology to achieve better load sharing.

animal behavior and cognition↗