Allen-Ankins, Slade, Roe, Paul, and Schwarzkopf, Lin (2026) Needle in a haystack: Searching for threatened species using an acoustic observatory. Ecological Informatics, 96. 103850.

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Abstract

Monitoring threatened species is crucial for conservation, providing the necessary information on species distribution and abundance required to detect declines and evaluate the impact of conservation actions. Passive acoustic monitoring networks, such as the Australian Acoustic Observatory (A2O), have significant potential to aid the monitoring of vocal threatened species by collecting continuous data across many locations in a cost-effective manner. We analysed over 2 million hours of acoustic recordings collected as part of the A2O from 63 sites between 2019 and 2022 to determine the site presence of 74 threatened species. Using the existing deep-learning model BirdNET to detect species belonging to classes within the model, and embeddings search for species outside the model, we successfully detected 41 out of the 74 threatened target species at a minimum of one A2O location, with some broadly distributed threatened species detected at more than ten sites. Threatened species belonging to all three threatened categories (i.e., Vulnerable, Endangered, Critically Endangered), and all three broad taxonomic groupings (i.e., birds, frogs, mammals) were found within A2O recordings, and the majority of sites searched (83%) detected at least one threatened species. We provide information on the distribution of detections for all target species, an evaluation of classification performance for all species within the existing BirdNET model, as well as easy-to-use linear classifiers trained on BirdNET embeddings for all threatened species detected, which have improved performance over the standard BirdNET model. We conclude that acoustic monitoring networks such as the A2O, powered by deep-learning models, can serve as an important tool for monitoring threatened species.

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