Study Shows Smarter Search Strategies Can Help Save Wildlife Before It’s Too Late
New INFORMS Management Science Study Key Takeaways:
- Artificial intelligence (AI) can help find endangered animals, such as red wolves and Florida panthers, by predicting where they might be – even in places they’ve never been seen before.
- Smart strategies save time and money by focusing on the best areas to look and manage, instead of wasting resources in the wrong places.
- U.S. wildlife agencies have begun deploying this method on the front lines of wildlife management.
BALTIMORE, MD, May 8, 2025 – A new study published in the INFORMS journal Management Science introduces a data-driven strategy that is already beginning to change the way conservationists locate and protect endangered species in the U.S., which can be adopted worldwide.
From critically endangered red wolves in North Carolina to elusive Florida panthers, conservation efforts are often hampered by uncertainty about where species remain in the wild. This research provides a powerful, AI-driven approach to optimize search and protection efforts and ensure that every conservation dollar is used efficiently.
The study, “Optimal Learning and Management of Threatened Species,” was originally designed to help track the Hainan gibbon, the world’s rarest primate that has only 42 known individuals left. The researchers developed an optimization model to help conservationists determine where and how long to search, and how to shift resources to balance the search and protection – an approach that can be applied to any threatened species facing habitat loss and population decline.
“It is like playing Whack-a-Mole in the dark,” says lead author Jue Wang, associate professor at Queen’s University. “If you don’t manage the right spot at the right time, the species may vanish before you even know it was there.” Wang was a visiting scholar at Cornell University when the paper was accepted for publication.
“This research has tremendous benefits to wildlife agencies,” says Krysten Schuler, director of the Cornell Wildlife Health Lab, who was not involved in the study. “It could revolutionize how we collect data on wildlife.” Schuler and Wang are now collaborating to bring this method into use by wildlife agencies across the U.S. and Canada.
A Smarter Way to Find and Protect Endangered Species
The Hainan gibbon was once widespread across the tropical rainforest in Hainan Island, China, but habitat destruction and hunting have pushed it to the brink of extinction. Today, the entire known population lives in a single reserve covering only 6 square miles. However, there have been unverified sightings in other forested areas and finding any remaining gibbons is a top conservation priority.
“There is a huge area to search but we don’t have a lot of money, so every dollar must be used wisely,” says Xueze Song, a co-author from the University of Alabama. “If the species is just too hard to find, sometimes it makes more sense to skip the search and protect the area directly.”
Using advanced mathematical modeling, the research team found that areas in which gibbons have never been recorded before may actually be the best places to look, challenging traditional search methods. The model helps conservationists identify high-priority search areas, avoid wasted efforts in low-probability locations, and accelerate efforts to locate and protect species before they disappear.
Conservation in Action: Putting the Model to the Test
Many endangered species in North America – such as the black-footed ferret, Florida panther and red wolf – are difficult to locate, making conservation efforts slow and inefficient. Emerging infectious diseases in wildlife are also costly to detect. The Cornell Wildlife Health Lab is working with Jue Wang to implement the method for disease surveillance in free-ranging white-tailed deer in New York state.
“This new method can significantly enhance our ability to detect and control invasive species,” says Denys Yemshanov, senior research scientist at Natural Resources Canada, who is using the method to manage exotic species in Canadian forests.
The next step is to expand its use globally, helping conservationists beyond North America to apply smart search strategies to protect wildlife at risk.
The study’s approach can help:
- Locate previously undiscovered populations by predicting where species are most likely to be found.
- Optimize conservation budgets by directing resources to the most promising search or management areas.
- Speed up recovery efforts by ensuring that no time is wasted in areas unlikely to contain the species
A Critical Moment for Global Conservation
With biodiversity loss at an all-time high, conservationists need tools that can work faster, smarter and more efficiently.
“This research provides an innovative solution that can be immediately applied to species recovery efforts worldwide, giving critically endangered animals their best chance at survival,” says Roozbeh Yousefi of Queen’s University.
About INFORMS and Management Science
INFORMS is the world’s largest association for professionals and students in operations research, AI, analytics, data science and related disciplines, serving as a global authority in advancing cutting-edge practices and fostering an interdisciplinary community of innovation. Management Science, a leading journal published by INFORMS, publishes quantitative research on management practices across organizations. INFORMS empowers its community to improve organizational performance and drive data-driven decision-making through its journals, conferences and resources. Learn more at www.informs.org or @informs.
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Ashley Smith
443-757-3578
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Ashley Smith
Public Affairs Coordinator
INFORMS
Catonsville, MD
[email protected]
443-757-3578