Automated acoustic classifiers provide insights into calling patterns of cicadas in tropical Australia

Abstract

Insects are a key group in ecosystems and economics, but are increasingly under threat, making ongoing monitoring essential to preserving them. Traditional monitoring approaches are time consuming, among other limitations. Automated acoustic recognition can be a good solution for monitoring arboreal, soniferous insects, such as cicadas, and the acoustic biology of cicadas in Australia is understudied, especially using automated methods. To evaluate the applicability of automated approaches for detecting cicadas, we used the deep-learning acoustic model BirdNET (v2.4) to perform embedding searches for Cystosoma schmeltzi (lesser bladder), Illyria burkei (eastern rattler), Macrotristria intersecta (corroboree cicada), Thopha sessiliba (northern double-drummer), and Pauropsalta opaca (fairy dust squawker), using audio recorded by Passive Acoustic Monitoring over 15-months at 18 sites in north Queensland. All classifiers achieved high area under the precision-recall curve (AUPRC) ranging from 0.753 to 0.982. We used a precision-prioritised threshold ([≥] 0.85) to constrain the false positive rate while retaining adequate outputs, which were consistent with the documented species ecology and life history. This approach was readily applicable to the Cicadidae, offering a scalable solution for biodiversity monitoring in complex acoustic environments.