This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Possums are among New Zealand's most destructive introduced mammals. They damage native forests, prey on wildlife and remain a major target of the country's Predator Free 2050 program.
But removing most possums from an area through predator control is only part of the challenge. Finding the last few survivors can be much harder. Even a handful of remaining animals can eventually rebuild a population, making it essential that conservation workers have effective tools to detect them.
One promising solution lies in microphones that record sounds overnight, combined with artificial intelligence (AI) software capable of scanning thousands of hours of audio for possum calls. Our new research shows this approach can work well—but only if the AI learns not to mistake other animals for possums. The study is published in the journal Acoustics Australia.
AI is becoming increasingly useful for analyzing wildlife recordings, allowing vast amounts of audio to be processed automatically rather than requiring volunteers or researchers to listen to every recording. This is particularly valuable during the final "mop-up" stage of eradication, when only a handful of animals remain and locating them can be challenging. Many AI models can also run directly on small, battery-powered recording devices in remote forests, avoiding the need to upload huge amounts of audio for processing.
AI models designed to run on low-power devices often produce more false alarms, wrongly attributing the calls of other animals to possums. For conservation teams, a false alarm can mean traveling to remote locations in search of an animal that isn't there. To tackle this problem, we developed a new training approach called "cross-model confusion mapping." Rather than asking the AI directly which sounds were possums, we first used BirdNET—a widely used AI system trained to identify more than 6,000 bird species.
Although BirdNET has never been trained to recognize possums, that proved to be an advantage. Because it could only classify sounds as birds, every possum call was forced into the bird species it most closely resembled. That revealed which bird species were most likely to be confused with possums.
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