Passive Acoustic Monitoring of Tasmanian Masked Owls and Swift Parrots: An Effective Tool for Conservation Actions

Abstract

The Tasmanian masked owl (Tyto novaehollandiae subsp. castanops) and swift parrot (Lathamus discolor) rely on old forest features that are declining across their ranges in Tasmania, Australia. Under the Australian Environment Protection and Biodiversity Conservation Act 1999, the swift parrot is listed as Critically Endangered, and the Tasmanian masked owl as Vulnerable. Their elusive behaviour and high mobility make monitoring difficult, hindering conservation actions. Passive acoustic monitoring can greatly increase spatial and temporal survey coverage, though the identification of the species' vocalisations within large audio datasets remains challenging. We deployed automated recording units at 101 sites in Tasmania's native forests to collect a large and representative acoustic dataset. We trained a ResNet convolutional neural network model to automate call detection for both the Tasmanian masked owl and the swift parrot. Our model demonstrated high performance, with a recall of 97.7% and precision of 97.8% for the Tasmanian masked owl, and a recall of 87.5% and precision of 88.4% for the swift parrot, outperforming both BirdNET and Kaleidoscope Lite. Through two real-world applications, we illustrated how our method provides detailed quantitative insights into habitat use patterns over extended spatial and temporal scales. Our deep learning model for automated call detection provides a framework to reliably and efficiently analyse acoustic datasets across large spatiotemporal scales, enhancing PAM's application to conservation planning.