Daniel M. Abrams

During fieldwork in Thailand, we observed nearly identical tempos of co-located flashing fireflies and chirping crickets. Motivated by this, we survey published data showing that an abundance of evolutionarily distinct species communicate isochronously at ~0.5–4 Hz, suggesting that this might be a tempo “hotspot.” We hypothesize that this timescale may have a universal basis in the biophysics of the receiver’s neurons. We test this by demonstrating that small receiver circuits constructed from elements representing typical neurons will be most responsive in the observed tempo range.

Synchronization has been studied across vastly different spatiotemporal scales in physical, biological, and social systems. Research into the mechanisms that give rise to temporal order, however, has been, by and large, mostly theoretical. Here, we seek to advance the fiddler crab as a model organism for collective synchronization. To attract mates, males of many fiddler crab species wave their claws in sync. These crabs are found in many places around the world, so they are generally accessible and observable. Translating observation (e.g., from recorded video) into actionable data, however, remains a challenge. We provide an easy‐to‐use and open source tracking algorithm that detects claw wave activity in video recordings and preserves IDs over time. We demonstrate the robustness of the algorithm by running it on videos displaying different species from different locations. We discuss possible future directions (both in the lab and the field) and call for renewed interest in these unique animals.

Southeast Asia hosts a firefly species, Pteroptyx malaccae , that holds incredible value both scientifically and culturally. These insects exhibit synchronous swarm flashing behavior for long periods while remaining immobile. This offers a unique opportunity for scientific study: it is possible to film the fireflies and label each individual with a unique virtual ID that is preserved over the course of a video. From early March 2023 until the end of February 2024, we gathered data in the form of short one- to two-minute videos on 16 distinct dates, recording up to sixty videos in one night for a total of 281 videos. To our knowledge, such precise long-term monitoring of insect wildlife is unprecedented. We use this data and custom computer vision techniques to gain insight into fluctuations in the firefly population over the course of that year. The data gathered can also be readily analyzed to obtain further insight: we present typical flashing frequencies and how that varies throughout the year—measures that are hard to acquire with the non-automated methods that have been used previously. By comparing the observed trends with various local weather and environmental conditions, we suggest possible explanations for their relative abundance over time. This work may allow for future automated monitoring of other firefly species, as well as help in long-term (i.e., multi-year) monitoring to accurately assess the severity of population decline.

Group synchrony in the animal kingdom is usually associated with mating. Being in sync is likely advantageous, as it may help in luring the opposite sex. Yet there are also disadvantages—such as the homogenization of the group—which make it harder for individuals to stand‐out. Here we address this trade‐off, bringing together the Kuramoto model with concepts from evolutionary game theory. We focus on the existence of self‐interested cheaters, which have been extensively studied in a variety of species. In our scenario, cheating individuals take part in the synchronous group display but position themselves (in terms of phase) slightly ahead of or behind the pack. This allows them to enjoy both the group benefit of advertisement and the individual benefit of being unique. But a group can only tolerate a limited number of such individuals while still achieving synchrony. We therefore incorporate a form of policing into our model: If an individual strays too far form the group's synchronous phase, they reveal themselves as dishonest and are punished. Our model offers testable predictions regarding natural population compositions, and will hopefully spur further investigation into not only how, but also why, natural systems synchronize.

Group synchrony in the animal kingdom is usually associated with mating. Being in sync is likely advantageous, as it may help in luring the opposite sex. Yet there are also disadvantages-such as the homogenization of the group-which make it harder for individuals to stand-out. Here we address this trade-off, bringing together the Kuramoto model with concepts from evolutionary game theory. We focus on the existence of self-interested cheaters, which have been extensively studied in a variety of species. In our scenario, cheating individuals take part in the synchronous group display but position themselves (in terms of phase) slightly ahead of or behind the pack. This allows them to enjoy both the group benefit of advertisement and the individual benefit of being unique. But a group can only tolerate a limited number of such individuals while still achieving synchrony. We therefore incorporate a form of policing into our model: If an individual strays too far form the group's synchronous phase, they reveal themselves as dishonest and are punished. Our model offers testable predictions regarding natural population compositions, and will hopefully spur further investigation into not only how, but also why, natural systems synchronize.

Researchers across different fields, including but not limited to ecology, biology, and healthcare, often face the challenge of sparse data. Such sparsity can lead to uncertainties, estimation difficulties, and potential biases in modeling. Here we introduce a novel data augmentation method that combines multiple sparse time series datasets when they share similar statistical properties, thereby improving parameter estimation and model selection reliability. We demonstrate the effectiveness of this approach through validation studies comparing Hawkes and Poisson processes, followed by application to subjective pain dynamics in patients with sickle cell disease (SCD), a condition affecting millions worldwide, particularly those of African, Mediterranean, Middle Eastern, and Indian descent.

Information criteria such as Akaike's (AIC) and Bayes' (BIC) are widely used for model selection in physics and beyond, quantifying the tradeoff between model complexity and goodness-of-fit to enforce parsimony. However, their derivation assumes uncorrelated samples, an assumption systematically violated by dynamical systems data. Here, through analysis of simple but representative dynamical models -- exponential decay, harmonic oscillation, and chaos -- we demonstrate that model selection depends sensitively on sampling rate and system dimensionality. We derive explicit formulas predicting when standard information criteria fail that should be adaptable to many real-world scenarios, enabling experimentalists to design sampling protocols that avoid pathological regimes.

The dynamics and spontaneous organization of coupled particles is a classic problem in modeling and applied mathematics. Here, we examine the behavior of particles coupled by a Mexican hat type potential—one exhibiting finite local repulsion transitioning to distal attraction, leading to an energy-minimizing “preferred distance.” When confined by a background potential well, these particles exhibit intricate self-organization into “stacks” with varying sizes and positions. We examine bifurcations of these high-dimensional arrangements, yielding tantalizing glimpses into a rich dynamical zoo of behavior.

During fieldwork in Thailand we observed nearly identical frequencies of co-located flashing fireflies and chirping crickets. Motivated by this, we perform a meta-analysis and show an abundance of evolutionarily distinct species that communicate isochronously at ~0.5-4 Hz, suggesting that this might be a frequency "hotspot." We hypothesize that this timescale may have a universal basis in the biophysics of the receiver's neurons. We test this by demonstrating that small receiver circuits constructed from elements representing typical neurons will be most responsive in the observed frequency range.

Group synchrony in the animal kingdom is usually associated with mating. Being in sync is likely advantageous, as it may help in luring the opposite sex. Yet there are also disadvantages -- such as the homogenization of the group -- which make it harder for individuals to stand-out. Here we address this tradeoff, bringing together the Kuramoto model with concepts from evolutionary game theory. We focus on the existence of self-interested ''cheaters,'' which have been extensively studied in a variety of species. In our scenario, cheating individuals take part in the synchronous group display but position themselves (in terms of phase) slightly ahead or behind the pack. This allows them to enjoy both the group benefit of advertisement and the individual benefit of being unique. But a group can only tolerate a limited number of such individuals while still achieving synchrony. We therefore incorporate a from of ''policing'' into our model: if an individual strays too far from the group's synchronous phase, they reveal themselves as dishonest and are punished. Our model offers testable predictions regarding natural population compositions, and will hopefully spur further investigation into not only how, but also why, natural systems synchronize.