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Peleg, O.

Publications and source records attributed to Peleg, O..

At least 19 recordsLinked to original sources
bioRxiv2026

3D Imaging of Honeybee Swarm Assembly and Disassembly

When honeybee colonies reproduce by fission, several thousand bees and their queen depart the parental nest and temporarily form a dense cluster on a tree branch or other surface while searching for a new nest site. Once the new nest site is selected, the swarm disassembles and flies toward it. How honeybees transition rapidly between dispersed flight and an aggregated cluster remains an open question. Here, we develop an experimental system and three-dimensional imaging pipeline to track individual flying bees together with the evolving morphology of the swarm during formation and dissolution. We report results from a representative swarming event. During assembly, swarms rapidly form low-density clusters before undergoing a slower contraction to a more dense steady state configuration. In contrast, disassembly occurs significantly faster than assembly and is characterized by strongly divergent flight, with bees departing the swarm in all directions. Overall, this method is able to demonstrate the coupled flight and morphological dynamics that underlie honeybee swarm assembly. Because the system is relatively low-cost and low-power, it is readily adaptable for three-dimensional imaging of other biological collectives in naturalistic environments.

animal behavior and cognition↗
bioRxiv2026

Excitation-inhibition interactions mediate firefly flash synchronization

Large populations of fireflies can synchronize their bioluminescent flashes with remarkable precision, producing collective rhythms that emerge from interactions among intrinsically variable individuals. In the North American firefly Photuris frontalis, this behavior usually manifests as a stable, population-level single-period beat whose mechanistic origins remain unresolved. To identify the local interaction rules giving rise to this emergent synchrony, we performed controlled perturbation experiments on isolated P. frontalis males using fixed-period light stimuli. By measuring changes in flash period as a function of stimulus timing, we reconstructed the phase-response curve (PRC) governing individual flash dynamics. The resulting PRC exhibits a biphasic structure, revealing phase-advancing (excitatory) and phase-delaying (inhibitory) responses. Using this PRC, we formalized an integrate-and-fire model that quantitatively reproduced the observed adaptable entrainment across tested stimuli. These results establish a direct mechanistic link between phase sensitivity and emergent collective synchronization, demonstrating how excitation-inhibition interactions influence large-scale rhythmic coherence in firefly populations.

animal behavior and cognition↗
bioRxiv2025

TAGINE: Fast Taxonomy-based Feature Engineering for Microbiome Analysis

SummaryTAGINE is a feature engineering algorithm that leverages the microbial taxonomic tree to optimize feature sets in microbiome data for predictive modeling. The algorithm starts with features at high taxonomic levels and iteratively splits them into lower-level clades in cases where it improves predictive accuracy, ultimately producing a feature set spanning multiple taxonomic levels. This approach aims to markedly reduce the number of features while preserving biological relevance and interpretability. We compare TAGINEs performances to other standard and taxonomy-based feature engineering methods on several different datasets, and show that TAGINE yields more compact feature sets and is orders of magnitude faster than other methods, while maintaining predictive accuracy. Availability and ImplementationTAGINE is freely available under the MIT license with source code available at https://github.com/borenstein-lab/tagine_fe.

bioinformatics↗
bioRxiv2024

Adaptive Cell Size, Merging, Tilting, and Layering in Honeybee Comb Construction

Honeybees are renowned for their skills in building intricate and adaptive hives that display notable variation in cell size. However, the extent of their adaptability in constructing honeycombs with varied cell sizes has not been in-vestigated thoroughly. We use 3D-printing and X-ray Microscopy to quantify honeybees capacity in adjusting the comb to different initial conditions. Using the average area of natural worker cells as a reference, our findings suggest three distinct construction modes when faced with foundations of varying cell sizes. For smaller cell size, bees occasionally merge cells to compensate for the reduced space. However, for larger cell sizes, the hive uses adaptive strategies like tilting for cells up to twice the reference size, and layering for cells that are three times larger than the reference cell. Our findings shed light on honey-bees adaptive comb construction strategies with potential to find applications in additive manufacturing, bio-inspired materials, and entomology.

animal behavior and cognition↗
bioRxiv2024

Interpolation of Microbiome Composition in Longitudinal Datasets

The human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, are still lacking. This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome datasets that follow individuals over a long period of time, and a leave-one-out approach. Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across datasets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point. Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high.

bioinformatics↗
bioRxiv2023

Collecting-Gathering Biophysics of the Blackworm L. variegatus

Many organisms exhibit collecting and gathering behaviors as a foraging and survival method. Certain benthic macroinvertebrates are classified as collector-gatherers due to their collection of particulate matter as a food source, such as the aquatic oligochaete Lumbriculus variegatus (California blackworms). Blackworms demonstrate the ability to ingest organic and inorganic materials, including microplastics, but previous work has only qualitatively described their possible collecting behaviors for such materials. The mechanism through which blackworms consolidate discrete particles into a larger clumps remains unexplored quantitatively. By analyzing a group of blackworms in a large arena with an aqueous algae solution, we discover that their relative collecting efficiency is proportional to population size. Examining individual blackworms under a microscope reveals that both algae and microplastics physically adhere to the worms body due to external mucus secretions, which cause the materials to clump around the worm. We observe that this clumping reduces the worms exploration of its environment, potentially due to thigmotaxis. To validate the observed biophysical mechanisms, we create an active polymer model of a worm moving in a field of particulate debris with a short-range attractive force on its body to simulate its adhesive nature. We find that the attractive force increases gathering efficiency. This study offers insights into the mechanisms of collecting-gathering behavior, informing the design of robotic systems, as well as advancing our understanding the ecological impacts of microplastics on benthic invertebrates.

ecology↗
bioRxiv2023

Embracing firefly flash pattern variability with data-driven species classification

Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with a precision and recall of approximately seventy percent. In addition to scaling population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.

ecology↗
bioRxiv2022

Honey Bees Find the Shortest Path: A CollectiveFlow-Mediated Approach

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 of 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 queens 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 the honey bees can effectively use the collective scenting behavior to overcome local and volatile pheromone communication and find the shortest path to the queen.

animal behavior and cognition↗
bioRxiv2022

Noise-mediated self-organization in mutually shading sunflowers

Circumnutations are widespread in plants and typically associated with exploratory movements, however a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering three orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a Langevin-type parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution represents a sharp transition in the force-noise ratio, conferring advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements, and establishes a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. Since plants move by growing, space and time are coupled, and dynamics of self-organization lead to emergent 3D patterns. As such this system provides conceptual insight for other interacting growth-driven systems such fungal hyphae, neurons and self-growing robots, as well as active matter systems where agents interact with past trajectories of their counterparts, such as stigmergy in social insects. This foundational insight has implications in statistical physics, ecological dynamics, agriculture, and even swarm robotics. One sentence summary of paperThe study highlights noisy circumnutations as a strategy plants use for optimizing growth in crowded conditions.

plant biology↗
bioRxiv2022

Firefly-inspired vocabulary generator for communication in multi-agent systems

Fireflies dazzling light displays are courtship rituals: flying males announce their presence as suitable mates to the females on the ground. Their light signal is composed of a species-specific on/off light sequence repeated periodically. However, thousands of fireflies flashing in a swarm can create immense visual clutter that hinders the detection of potential mates. A partial solution to this visual clutter problem is to flash according to sequences that are more distinct and detectable than those of other individuals. Here, we investigate how distinguishable flash sequences can co-evolve by developing a method for simulating sequences that minimize their mutual similarity with each other while minimizing their energetic cost and predation risk. This simple set of rules produces flash sequences that are remarkably similar to those of real fireflies. In particular, we observe an emergent periodicity in the resulting sequences, despite the lack of any periodicity requirements on the sequences. In addition, we demonstrate a method of reconstructing the evolutionary pressures acting on sets of firefly species. We do so by carrying out simulations that follow known phylogenetic relationships of extant species alongside their characteristic flash patterns.

animal behavior and cognition↗
bioRxiv2022

Physical Obstacles Constrain Behavioral Parameter Space of Successful Localization in Honey Bee Swarms

Honey bees (Apis mellifera L.) localize the queen and aggregate into a swarm by forming a collective scenting network to directionally propagate volatile pheromone signals. Previous experiments show the robustness of this communication strategy in the presence of physical obstacles that partially block pheromone flow and the path to the queen. Specifically, there is a delay in the formation of the scenting network and aggregation compared to a simple environment without perturbations. To better understand the effect of obstacles beyond temporal dynamics, we use the experimental results as inspiration to explore how the behavioral parameter space of collective scenting responds to obstacle. We extend an agent-based model previously developed for a simple environment to account for the presence of physical obstacles. We study how individual agents with simple behavioral rules for scenting and following concentration gradients can give rise to collective localization and swarming. We show that the bees are capable of navigating the more complex environment with a physical obstacle to localize the queen and aggregate around her, but their range of behavioral parameters is more limited and less flexible as a result of the spatial density heterogeneity in the bees imposed by the obstacle.

animal behavior and cognition↗
bioRxiv2022

Chimera States among Synchronous Fireflies

Systems of oscillators, whether animate or inanimate, often converge to a state of collective synchrony, when sufficiently interconnected. Twenty years ago, the mathematical study of models of coupled oscillators revealed the possibility for complex phases which exhibit the coexistence of synchronous and asynchronous clusters, since then referred to as "chimera states". Beyond their recurrence in theoretical models, chimera states have been observed in specifically-designed, non-biological experimental conditions, yet their emergence in nature has remained elusive. Here, we report robust evidence for the occurrence of chimera states in a celebrated realization of natural synchrony: fireflies. In video recordings of collective displays of Photuris frontalis fireflies, we observe, within a single swarm, the spontaneous emergence of different groups flashing with the same periodicity but with a constant time delay between them. From the three-dimensional reconstruction of the swarm, we demonstrate that these states are stable over time and spatially intertwined, but find no evidence of enhanced correlations in their spatial dynamics. We discuss the implications of these findings on the synergy between mathematical models and firefly collective behavior.

biophysics↗
bioRxiv2022

A versatile, chemically-controlled DNA binding switch enables temporal modulation of Cas9-based effectors

CRISPR-Cas9s RNA-guided genome targeting ability has been leveraged to develop a plethora of effectors including targeted transcriptional activators, DNA base editors, and DNA prime editors. Although systems for inducibly modulating Cas9 activity have been developed, current approaches for conferring temporal control require extensive screening of functional protein components. A simpler and general strategy for conferring temporal control over diverse Cas9-based effector activities is needed. Here we describe a versatile chemically-controlled and rapidly-activated DNA binding Cas9 module (ciCas9) that is able to confer temporal control over a variety of Cas9-based effectors. Using the ciCas9 module, we engineer temporally-controlled cytidine and adenine DNA base editors. We employ the ciCas9 base editors to reveal that in vivo bystander editing kinetics occurs via a dependent process where editing at a preferred nucleotide position increases the frequency of edits at a second nucleotide within a target site. Finally, we demonstrate the versatility of the ciCas9 module by creating a temporally-controlled transcriptional activator, a dual cytidine and adenine base editor, and a prime editor.

molecular biology↗
bioRxiv2022

Honeycomb crystallography: Experiment-theory approach for bee comb formation under geometric frustrations

1As honeybees build their nests in pre-existing tree cavities, they must deal with dealing with the presence of geometric constraints, resulting in non-regular hexagons and topological defects in the comb. In this work, we study how bees adapt to their environment in order to regulate the comb structure. Specifically, we identify the irregularities in honeycomb structure in the presence of various geometric frustrations. We 3D-print experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of reoccurring patterns built by bees in response to specific geometric frustrations on these starter frames. Furthermore, using an experimental-modeling framework, we demonstrate that these patterns can be successfully modeled and replicated through a simulated annealing process, in which the minimized potential is a variation of the Lennard-Jones potential that only considers first-neighbor interactions according to a Delaunay triangulation. Our simulation results not only confirm the connection between honeycomb structures and other crystal systems such as graphene, but also show that irregularities in the honeycomb structure can be explained as the result of local interactions between honeybees and their immediate surroundings, leading to emergent global order. Additionally, our computational model can be used to describe specific strategies that bees use to effectively solve each geometric mismatch problem while minimizing cost of comb building.

ecology↗
bioRxiv2022

Strength-mass scaling law governs mass distribution inside honey bee swarms

To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How can this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability and thermoregulation. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000-10000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the {approx} 1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.

animal behavior and cognition↗
bioRxiv2022

Emergent periodicity in the collective synchronous flashing of fireflies

In isolation from their peers, Photinus carolinus fireflies flash with no intrinsic period between successive bursts. Yet, when congregating into large mating swarms, these fireflies transition into predictability, synchronizing with their neighbors with a rhythmic periodicity. Here we propose a mechanism for emergence of synchrony and periodicity, and formulate the principle in a mathematical framework. Remarkably, with no fitting parameters, analytic predictions from this simple principle and framework agree strikingly well with data. Next, we add further sophistication to the framework using a computational approach featuring groups of random oscillators via integrate-and-fire interactions controlled by a tunable parameter. This agent-based framework of P. carolinus fireflies interacting in swarms of increasing density also shows quantitatively similar phenomenology and reduces to the analytic framework in the appropriate limit of the tunable coupling strength. We discuss our findings and note that the resulting dynamics follow the style of a decentralized follow-the-leader synchronization, where any of the randomly flashing individuals may take the role of the leader of any subsequent synchronized flash burst.

animal behavior and cognition↗
bioRxiv2022

Statistical analysis reveals the onset of synchrony in sparse swarms of Photinus knulli fireflies

Flash synchrony within firefly swarms is an elegant but elusive manifestation of collective animal behaviour. It has been observed, and sometimes demonstrated, in a few populations across the world, but exactly which species are capable of large-scale synchronization remains unclear, especially in low-density swarms. The underlying question which we address here is: how does one qualify a collective flashing display as synchronous, given that the only information available is the time and location of flashes? We propose different statistical approaches and apply them to high-resolution stereoscopic video recordings of the collective flashing of Photinus knulli fireflies, hence establishing the occurrence of synchrony in this species. These results substantiate detailed visual observations published in the early 1980s and made at the same experimental site: Pena Blanca Canyon, Coronado National Forest, Arizona, USA. We also remark that P. knullis collective flashing patterns mirror that observed in Photinus carolinus fireflies in the Eastern United States, consisting of synchronous flashes in periodic bursts with rapid accretion and quick decay.

animal behavior and cognition↗
bioRxiv2021

Thermoregulatory morphodynamics of honeybee clusters

During reproductive swarming, honeybees form clusters of more than 10,000 bees that hang from structures in the environment (e.g., tree branches) and are exposed to diurnal variations in ambient temperature for up to one week during the search for a new nesting site. Swarm clusters collectively modulate their morphology in response to these variations (i.e., expanding/contracting in response to heating/cooling) to maintain their internal temperature within a tolerable range and to avoid exhausting their honey stores prematurely. To understand the spatiotemporal aspects of thermoregulatory morphing, we measured the change in size and shape of swarm clusters over time and the internal temperature profiles in response to dynamic temperature ramp perturbations. We found that swarm clusters can achieve a twofold increase/decrease their volume/density when heated from 15{degrees}C to 30{degrees}C, but they do not reach an equilibrium size or shape when held at 30{degrees}C for 5 hours, long after the core temperature of the cluster has stabilized. Furthermore, the changes in cluster shape and size are hysteretic, contracting in response to cooling faster than expanding in response to heating. Although the contact diameter of the cluster increased continuously when the swarm is heated, the change in length of the swarm (base to tip) over time is non-monotonic. Consequently, the aspect ratio of the swarm fluctuated continuously even when held at a constant temperature. Taken together, our results quantify the hysteretic and anisotropic morphological responses of swarm clusters to ambient temperature variations while suggesting that both mechanical constraints and heat transfer govern the thermoregulatory morphing dynamics of swarm clusters.

animal behavior and cognition↗