Random physics helps model individual ant motion
Researchers develop a model that reproduces and predicts individual ant movement, opening new avenues for understanding ant behavior in urban environments.
Okinawa Institute of Science and Technology (OIST) Graduate University
image:
A long-legged ant climbing over a plant on a concrete structure in Okinawa, Japan.
view moreCredit: Jack Featherstone
Ants make up one of the most diverse groups of animals on the planet, with many species boasting unrivaled physical and social behavior, and an overall species richness that far exceeds that of mammals. Various types of ants can carry up to one hundred times their weight or forage hundreds of meters from their nest, the equivalent of a human carrying thousands of kilograms and walking hundreds of kilometers. These feats have inspired researchers to study not only their anatomy that makes this possible, but how they make use of these capabilities to forage and explore. By recording how individual ants move around a laboratory environment, researchers at the Okinawa Institute of Science and Technology (OIST) have come up with a physics-based model for how these ants navigate, now published in the journal PLOS Computational Biology.
Animal motion has been a topic of fascination for scientists for a long time, though physicists have only really gotten involved in the past few decades. OIST PhD student Jack Featherstone, part of the Nonlinear and Non-equilibrium Physics Unit, explains: “Life is one of the most interesting mysteries out there, rivaling the universal extremes of quantum mechanics or astrophysics in complexity. Physicists have now begun applying many of the quantitative tools developed to study the non-living world to explore why animals move and behave the way they do.”
Trekking out to the wilds — or rather, the parking lot — to gather specimens
The foundation of most animal behavior studies begins with observing living specimens and gathering quantitative data about how they behave. The OIST researchers were interested to study one type of ant, known as the long-legged or yellow crazy ant (A. gracilipes), due to its reputation as a particularly aggressive invasive species. As a result, these ants are increasingly colonizing urban habitats as they spread across the globe, making it all the more important to understand how they navigate and explore in artificial environments.
In a clear sign of their widespread presence in Okinawa, the researchers had little difficulty finding specimens. After gathering more than 100 ants from locations like parking lots, walkways, and sitting areas around campus, Featherstone and colleagues placed them alone in arenas in the laboratory to record how they explore the new environment. The researchers emphasize that understanding how these ants move when they are alone is very important, though sometimes overlooked.
“Ants are very famous for their collective interactions, mediated by pheromones or other sophisticated communication systems, but the individual motion of an ant is the building block that makes this collective behavior possible,” says Featherstone.
Following the experiments, the researchers trained a neural network to analyze video recordings of the ants. The network tracked the position of different parts of each ant’s body as it explored, generating spatial trajectories that the researchers could then quantitatively analyze.
How random physics can help us understand animal behavior
The researchers found that the key ingredient to better understand and model the ant behavioral data lay in stochastic, or random, modelling. Famously important to understanding diffusion and the motion of small particles, this approach assumes that the overall, non-random behavior of a system can be constructed from individual, random contributions.
“The behavior of any animal depends on a massive number of variables, from the states of individual neurons in their brains, to the weather around them, to what they had for breakfast. Trying to measure every factor that might be relevant here is impossible; instead, the stochastic approach allows us to explore macroscopic behavior without getting lost in the microscopic details,” says Featherstone.
Their proposed model, which includes a combination of several techniques often used in studying bacterial motion or diffusion processes, can reproduce many of the features of the experimental trajectory data. It also allows them to computationally or mathematically derive predictions about how the ants might behave in new environments, which they hope to use to study how these ants interact with other species, either as predators or as prey.
As one of the most rigorous descriptions of ant exploration behavior — including the ability to simulate ant-like motion — this study may help other researchers understand how locomotion fits into the rest of an ant’s life; external stimuli, in the form of food, water, predators, or obstacles will have the ants adjust their behavior from the baseline that this work provides. In the future, it could even be used to better understand and contain invasive ants. And even beyond ants, this modeling approach could be easily adapted to explore how navigation, foraging, and exploration vary throughout the rest of the animal kingdom.
Journal
PLOS Computational Biology
Method of Research
Computational simulation/modeling
Subject of Research
Animals
Article Title
Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments
Article Publication Date
6-Aug-2026
Laboratory tracking experiments [VIDEO]
Researchers place individual long-legged ants in a laboratory arena and record their exploration behavior and movement patterns using a camera.
Neural network tracking approach [VIDEO]
Researchers use a neural network to track the position of individual ants as they explore an experimental arena.
Credit
Jack Featherstone
Ready, set, march: Researchers use math to explain sudden activity bursts in ants
NYU Tandon School of Engineering
Scientists have long known that ant colonies sometimes seem to move as one. A nest that appears quiet can suddenly erupt into activity, with workers throughout the colony springing into motion almost simultaneously before settling back into stillness.
These synchronized bursts, first documented more than three decades ago, have intrigued biologists because they resemble collective phenomena seen in systems as diverse as neurons, fireflies and even chemical reactions.
Now, new research from engineers and biologists at New York University and the New Jersey Institute of Technology offers a mathematical explanation for how these rhythmic waves of activity emerge.
The study, published in Physical Review X Life, suggests that synchronized bursts arise when a colony balances two competing forces: the ability of a single active ant to rapidly excite its nestmates and the colony’s capacity to fully return to rest before the next wave begins.
Using a computational model grounded in empirical observations of ant behavior, the researchers found that colonies undergo a kind of phase transition — a sudden shift from unsynchronized movement to coordinated collective activity.
“Activity bursts emerge as a balance between the responsiveness of the colony to the first ant that activates and the ability of the colony to completely deactivate before the onset of the next burst,” said lead author Michael Napoli, a doctoral researcher in the Department of Mechanical and Aerospace Engineering at NYU.
The team combined decades of observations of ant movement with established theories of social activation. In the model, ants can occupy one of three states: active, inactive or refractory — a temporary resting period during which they cannot immediately become active again. Active ants move through a virtual nest and interact with others, sometimes triggering them to become active as well.
What emerged from the simulations was a surprisingly powerful role for individual workers. Rather than requiring many ants to coordinate simultaneously, a single ant often acted as the spark that ignited a colony-wide cascade of activity.
The researchers call this worker the “first mover.” Once activated, that ant can stimulate others, which in turn activate additional nestmates, creating a rapid chain reaction that sweeps through the colony. The process resembles a line of falling dominoes or the spread of information through a social network.
“Our results indicate that activity bursts in ant colonies are the result of a first mover that excites the colony in a synchronized regime, thereby favoring the rapid communication of new behaviors throughout the group,” the authors write.
The study also revealed that speed matters. Ants appear to operate in what the researchers describe as a “high-speed interaction regime,” where information spreads through the nest far more quickly than the duration of an activity burst itself. Under these conditions, workers constantly form and break social connections as they move, allowing information to travel efficiently across the colony.
According to senior author Maurizio Porfiri, the findings suggest that synchronized behavior depends not simply on how many interactions occur, but on how rapidly information can propagate through the network of moving individuals.
“The timescale of the motion of individuals through the nest is faster than that of the burst, suggesting that ants operate in a high-speed interaction regime where new behaviors are near-instantaneously transferred through the nest,” Porfiri said.
Although the study focuses on ants, its implications extend beyond insect societies. Simon Garnier, Professor of Biological Sciences at NJIT and coauthor on the paper, suggests similar leader-driven cascades appear in many complex systems, from grazing sheep that suddenly cluster together to neurons firing in coordinated patterns. By identifying the conditions that promote synchronization, the researchers hope to uncover general principles that govern collective behavior across biology.
The work could even inspire new approaches to engineering. Swarms of robots, for example, often rely on local interactions rather than centralized control. Understanding how a single agent can trigger coordinated action across a large population could help designers create more efficient systems for tasks such as warehouse logistics, environmental monitoring or disaster response.
The authors caution that their model simplifies many aspects of real ant colonies, including differences among workers and the complex spatial organization of nests. Future experiments will test whether real colonies operate near the synchronization threshold predicted by the model and whether manipulating density or movement patterns can alter the emergence of activity bursts.
For now, the research offers a compelling explanation for one of social insects’ most mysterious behaviors. What appears to be a colony acting with a single mind may actually begin with one ant taking the first step — and thousands of others rapidly following its lead.
This research was supported by a grant from the National Science Foundation.
Journal
PRX Life
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Nest-Level Phase Transition Drives Synchronized Activity Bursts in Ant Colonies
Article Publication Date
5-Aug-2026
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