Very different mammals follow the same rules of behavior

Research hints at an underlying architecture that orders the movements of animals

To the Point

• Surprising similarities: scientists have uncovered striking similarities in how wild animals behave.
• Novel data: Researchers used high-resolution accelerometer data to track behavioral sequences in three wild mammals: hyenas, meerkats, coatis.
• Unexpected patterns: One of the most surprising patterns discovered was that the longer an animal engaged in a behavior, the less likely it was to stop.

In the natural world—where predators pounce, prey flee, and group members feed and sleep in solidarity—animal behavior is glorious in its variety. Now, new research suggests there may be an underlying architecture that orders the movements of animals as they go about their very different lives. And it’s more widespread than previously imagined.

In a study spanning meerkats in the Kalahari desert, coatis in Panama’s rainforest, and spotted hyenas in Kenya’s savanna, researchers have discovered that the daily actions of these animals show surprisingly similar patterns. Whether a meerkat scratches in the sand for scorpions or a coati rests in the canopy, a shared ordering of the behaviors persists across different landscapes, species, individuals, and types of behaviors. To the international team of fourteen authors, led by researchers at the Max Planck Institute of Animal Behavior, the findings are unexpected and—possibly—profound.

“We assumed there would be differences,” said Pranav Minasandra, a postdoctoral researcher at MPI-AB and lead author of the study in PNAS. After all, differences are apparent when comparing meerkats, coatis, and hyenas, which occupy dissimilar environments and ecological roles. “But we found common patterns in how animals switch between behaviors, regardless of what species and which individual. It's as if their behavior was built on the same hidden algorithm.”

Uncovering underlying patterns

The hidden algorithm came to light in data that were collected from wild animals tagged with accelerometers—the same small sensors in phones and watches that track our activity. The species studied are all social mammals, but they differ in their ecology and behavior. Spotted hyenas are large carnivores, meerkats are small burrowing animals, and coatis are racoon-sized tree-dwellers. Accelerometers measure posture changes many times each second and the recordings can continue for several days. These high-resolution motion traces collected from animals were then classified using machine learning into behavioral states like lying, foraging, and walking. For instance, a meerkat might lie down for 10 minutes then briefly stand up to look around for 20 seconds before moving around to search for food for another few minutes.

“This approach allowed us to capture detailed behavioral sequences over days and even weeks from multiple individuals across three distinct species,” says Ariana Strandburg-Peshkin, group leader at MPI-AB and senior author on the study.

Across behaviors, individuals, and species, one common principle emerged: the longer an animal stays in one behavioral state, the less likely it is to change it in the next moment. “This was unexpected,” adds Minasandra.

Imagine a hyena walking continuously for 10 minutes. Most people would probably guess that the hyena would be more likely to stop over time, and the authors did too. “We originally thought the probability of switching behaviors would increase over time, as we assumed it would not be optimal to lock-in to any behavior.” Remarkably, this kind of lock-in, also called a decreasing hazard function, was consistent across all studied animals and species.

The authors further examined how current behavior predicts future actions—a concept they call "predictivity decay." Predictivity decay reflects the increasing difficulty in predicting behavior the further we look into the future, primarily due to random, unpredictable variations. The shape of the decay graph conveys how decision-making systems across different timescales interact to generate animals' behavioral sequences.  “We found that the pattern of predictivity decay was remarkably consistent across all animals studied, implying a shared architecture beneath the surface.”

Why these patterns?

The study raises a big question: Why do such patterns occur? The authors propose two broad explanations.

First is positive feedback: the longer an animal remains in a state—say, lying down—the more likely that staying put is rewarded, whether because it’s warm, safe, or socially reinforced. Behavior becomes self-reinforcing.

The second possibility is multi-timescale decision-making. Instead of a single internal clock governing when to switch behaviors, animals may integrate cues from many processes—internal hunger, external threats, social context—each with its own tempo. The interplay of these overlapping signals could generate the observed patterns.

Future studies may explore whether these patterns hold in other animals beyond the three mammals in the study: non-social species, across developmental stages, or under different ecological pressures. There’s also the question of whether these long-time behaviors offer advantages—perhaps by optimizing attention, conserving energy, or enhancing group coordination.

Says co-author Meg Crofoot, Director of the Department for the Ecology of Animal Societies: “What this study suggests is that real animals, be they hunting, hiding, or resting, are guided by hidden structures that seem to echo across life’s branches.”