Scientists Can Now Identify Elusive Animals Using Just Their Footprints

Researchers have developed a new way to identify hard-to-distinguish wildlife species using nothing more than footprints, a breakthrough that could transform biodiversity monitoring and reduce the need for invasive trapping methods in the field.

The method, known as Footprint Identification Technology, or FIT, allows scientists to accurately identify animal species by analyzing subtle differences in footprint shape and geometry. In a recent study published in the journal Frontiers in Ecology and Evolution, January 27. 2026 the technique proved capable of distinguishing between two nearly identical small mammal species with accuracy rates reaching 96 percent.

The findings suggest that footprints — long used by Indigenous trackers and field naturalists — can now be combined with modern statistical analysis to create a powerful, ethical tool for wildlife research.

Small mammals are among the most difficult animals to monitor in the wild. Many species are nocturnal, fast-moving and visually similar, often differing only in tiny anatomical details that are impossible to observe without close handling or genetic testing.

The study focused on sengis, also known as elephant shrews, a group of small insect-eating mammals native to Africa. Despite their name, sengis are not rodents, and several species are so alike in appearance that even experienced researchers struggle to tell them apart in the field.

“These animals are ecologically important, but they’re often missing from biodiversity surveys because they’re so difficult to identify accurately,” said Zoë Jewell, a co-author of the study and a researcher at Duke University’s Nicholas School of the Environment. “We wanted a method that was accurate, repeatable and didn’t involve harming or stressing the animals.”

The research team tested FIT on two species, the Eastern Rock sengi and the Bushveld sengi, which inhabit overlapping regions in southern Africa. Traditional range maps suggest these species occupy distinct territories, but field observations have long hinted at more complex patterns.

Tracking the Unseen

Instead of relying on physical characteristics or DNA samples, the researchers turned to footprints. Animals were briefly guided across specially prepared track plates that recorded clean impressions of their feet. The animals were not injured, sedated or restrained for long periods, and were released shortly after.

High-resolution images of the footprints were then analyzed using morphometric techniques that measure distances, angles and surface areas within each print. These measurements captured differences invisible to the naked eye, such as the relative spacing of toes or the curvature of foot pads.

The team applied statistical models to determine which combinations of measurements best distinguished one species from the other. According to the study, a single footprint image was often sufficient to correctly identify the species.

“The level of accuracy surprised us,” said Nico Avenant, a mammalogist at South Africa’s National Museum and a co-author of the study. “It shows that even very small animals leave consistent, measurable signatures behind.”

Beyond proving the method’s accuracy, the study revealed unexpected ecological insights. In one protected area, the two sengi species were found living within roughly 120 meters of each other, far closer than previously documented.

“This kind of overlap would have been almost impossible to detect using conventional survey methods,” Avenant said. “It suggests that our understanding of species distributions may be less precise than we thought.”

Such findings have implications for conservation planning, particularly in regions where land-use change and climate pressures are rapidly altering habitats. Small mammals often respond quickly to environmental shifts, making them valuable indicators of ecosystem health.

“If we can track these species more easily, we gain an early warning system for broader ecological changes,” Jewell said.

A Tool for Conservation and Communities

Researchers say FIT could significantly lower the cost and complexity of wildlife monitoring. Traditional methods, such as live trapping and genetic analysis, require specialized equipment, permits and trained personnel. Camera traps, while useful, often fail to capture small or fast-moving animals.

Footprint analysis, by contrast, relies on relatively simple materials and photography, making it accessible to conservation groups with limited resources. With proper training, park rangers and citizen scientists could contribute valuable data.

“This opens the door for much wider participation in biodiversity monitoring,” said Sky Alibhai, another co-author and director of the conservation organization WildTrack, which helped develop the FIT framework. “It allows local communities to play a role in documenting and protecting the wildlife around them.”

The researchers emphasize that FIT is not intended to replace other methods, but to complement them. Combining footprint data with camera traps, acoustic monitoring and environmental DNA could provide a more complete picture of wildlife populations.

How Footprint Identification Technology Works

Footprint Identification Technology blends traditional tracking knowledge with modern data analysis. The process begins by collecting clear footprints from animals as they walk naturally across prepared surfaces. These footprints are photographed under standardized conditions to ensure consistency in scale and orientation.

Each footprint image is then analyzed using software that records precise geometric measurements, such as toe spacing, angles between pads and overall footprint shape. Rather than relying on subjective judgment, FIT converts these features into numerical data.

Statistical techniques are used to identify patterns within the data that consistently separate one species from another. In the sengi study, researchers used linear discriminant analysis to determine which measurements carried the most identifying power.

Once a reference database is established, new footprints can be compared against it, allowing researchers to identify species quickly and non-invasively. With further development, the approach could be expanded to distinguish individuals, sexes or age classes within a species.

The method draws inspiration from ancient human tracking traditions, but applies scientific rigor and repeatability. Researchers say future versions may incorporate machine learning, enabling automated identification from field images.

While the current study focused on two small mammal species, the researchers believe FIT can be adapted to a wide range of animals, including carnivores, primates and other elusive species.

“As biodiversity declines globally, we need tools that are both effective and ethical,” Jewell said. “Footprints are everywhere. We’re just learning how to read them properly.”

The researchers are now working to expand footprint databases and test the method in different environments. If successful, Footprint Identification Technology could become a cornerstone of wildlife monitoring in an era when understanding what remains may be as urgent as protecting it. (Sulung Prasetyo)