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Very different mammals follow the same rules of behavior: Research hints at an underlying architecture
https://phys.org/news/2025-05-mammals-behavior-hints-underlying-architecture.html
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.
. . .
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 raccoon-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.
. . .
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.
. . .
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 raccoon-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.
. . .
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