If you look at a photograph of leopards, would you be able to tell which two were related based on their spots?
Unless you’re a leopard expert, the answer is most likely not, says Tanya Berger-Wolf, director of the Translational Data Analytics Institute (TDAI) at Ohio State University. But, she says, computers can.
Berger-Wolf and her team are pioneering a new field of study called imageomics. As the name suggests, imageomics uses machine learning to extract biological data from photos and videos of living organisms. Berger-Wolf and her team have recently begun collaborating with studying leopards in India to compare spot patterns of moms and children using algorithms.
“Images have become the most abundant source of information now, and we have the technology, too. We have computer vision machine learning,” says Berger-Wolf. She compares this technology to the invention of the microscope, offering scientists a completely different way to look at wildlife.
Building on TDAI’s open-source platform called researchers Wildbook, which helps wildlife gather and analyze photos, the team is now focusing on generative AI approaches. These programs use existing content to generate meaningful data. In this case, they are attempting to analyze crowdsourced images to make biological traits that humans may naturally miss computable, like the curvature of a fish’s fin — or a leopard’s spots. The algorithms scan images of leopards publicly available online, from social media to digitized museum collections.
In simple terms, the algorithms “quantify the similarity,” she says. The aim is to help wildlife researchers overcome a data deficiency problem and, ultimately, better protect animals at risk of extinction.
Ecologists and other wildlife researchers are currently facing a data crunch — it’s tedious, expensive, and time-consuming for people to spend time in the field monitoring animals. Due to these challenges, 20,054 species on the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species are labeled as “data deficient,” meaning there’s not enough information to make a proper assessment of its risk of extinction. As Berger-Wolf sums it up, “biologists are making decisions without having good data on what we’re losing and how fast.”
The platform started with supervised learning — Berger-Wolf says the computer uses algorithms “simpler than Siri” to count how many animals are in the image, as well as where it was taken and when, which could contribute to metrics like population counts. Not only can AI do this at a much lower cost than hiring people but also at a faster rate. In August 2021, the platform analyzed 17 million images automatically.
There are also barriers that only a computer can seem to overcome. “Humans are not the best ones at figuring out what’s the informative aspect,” she says, noting how humans are biased in how we see nature, focusing mostly on facial features. Instead, AI can scan for features humans would likely miss, like the color range of the wings on a tiger moth. A March 2022 study found that the human eye couldn’t tell male polymorphic wood tiger moth genotypes apart — but moth vision models with ultraviolet light sensitivity could.
“That’s where all the true innovation in all of this is,” Berger-Wolf says. The team is implementing algorithms that create pixel values of patterned animals, like leopards, zebras, and whale sharks, and analyze those hot spots where the pixel values change most — it’s like comparing fingerprints. Having these fingerprints means researchers can track animals non-invasively and without GPS collars, count them to estimate population sizes, understand migration patterns, and more.
As Berger-Wolf points out, population size is the most basic metric of a species’ well-being. The platform scanned 11,000 images of whale sharks to create hot spots and help researchers identify individual whale sharks and track their movement, which led to updated information about their population size. This new data pushed the IUCN to change the conservation status of the whale shark from “vulnerable” to “endangered” in 2016.
There are also algorithms using facial recognition for primates and cats, shown to be about 90 percent accurate, compared to humans being about 42 percent accurate.
Generative AI is still a burgeoning field when it comes to wildlife conservation, but Berger-Wolf is hopeful. For now, the team is cleaning the preliminary data of the leopard hot spots to ensure the results are not data artifacts — or flawed — and are true biologically meaningful information. If meaningful, the data could teach researchers how species are responding to changing habitats and climates and show us where humans can step in to help.