TLDR: A new study published in the INFORMS journal Management Science details an AI-driven approach that is transforming how conservationists locate and protect endangered species. This data-driven strategy optimizes search efforts, ensuring efficient use of resources and has already begun deployment by U.S. wildlife agencies. The model, initially developed for the critically endangered Hainan gibbon, can be applied globally to various threatened species.
A groundbreaking study featured in the INFORMS journal Management Science is set to revolutionize global wildlife conservation by introducing an AI-driven model for optimizing the search and protection of endangered species. Published on May 8, 2025, this research provides a powerful, data-driven strategy that is already being adopted by U.S. wildlife agencies to enhance their efforts in locating and safeguarding vulnerable animal populations.
The study highlights how artificial intelligence can predict the whereabouts of endangered animals, such as red wolves and Florida panthers, even in areas where they have not been previously observed. This predictive capability allows conservationists to implement smarter strategies, saving both time and financial resources by focusing on the most promising areas for management and intervention, rather than expending efforts in less critical locations.
Lead author Jue Wang, an associate professor at Queen’s University, likened traditional conservation efforts to ‘playing Whack-a-Mole in the dark.’ He emphasized the critical need for precise, timely interventions, stating, ‘If you don’t manage the right spot at the right time, the species may vanish before you even know it was there.’ The optimization model developed by the researchers helps conservationists determine the optimal duration and location for searches, as well as how to strategically allocate resources to balance search and protection activities.
Initially, the model was designed to track the Hainan gibbon, the world’s rarest primate with only 42 known individuals remaining. However, its adaptable framework means it can be applied to any threatened species facing habitat loss and population decline worldwide. Krysten Schuler, director of Cornell University’s Wildlife Health Lab, praised the research, noting its ‘tremendous benefits to wildlife agencies.’
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Roozbeh Yousefi, also from Queen’s University, underscored the urgency of such innovations, stating, ‘With biodiversity loss at an all-time high, conservationists need tools that can work faster, smarter and more efficiently.’ He added that this research offers an ‘innovative solution that can be immediately applied to species recovery efforts worldwide, giving critically endangered animals their best chance at survival.’ This AI-powered approach marks a significant step forward in ensuring that every conservation dollar is utilized with maximum efficiency and impact.
