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), both endangered species, rely on old forest features that are declining across their ranges in Tasmania, Australia. Their elusive behavior 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 108 sites in Tasmanias native forests to collect a large and representative acoustic dataset. We trained a neural network model to automate call detection for both the Tasmanian masked owl and the swift parrot. Our model demonstrated high performance, with a sensitivity of 97.7% and specificity of 96.5% for the Tasmanian masked owl, and a sensitivity of 87.5% and specificity of 83.3% for the swift parrot. Through two real-world applications, we illustrated how our method provides detailed quantitative insights into habitat use patterns over extended spatial and temporal scales. This innovative approach enhances both site-specific and population monitoring, enabling more effective and targeted conservation actions for these endangered species.