AI Insight
Researchers tested an automated deep-learning system (BirdNET) to identify five cicada species from audio recordings collected over 15 months at 18 sites in tropical north Queensland, Australia. The classifiers achieved high accuracy rates (0.753 to 0.982 AUPRC) and successfully detected calling patterns consistent with known cicada ecology and life history. By setting precision thresholds at 0.85 or higher, the system minimized false positives while maintaining useful detection rates for monitoring purposes.
Why it matters
This demonstrates that automated acoustic monitoring can effectively track insect populations without labor-intensive manual surveys, providing a scalable tool for monitoring insect biodiversity declines. The approach is particularly valuable for arboreal insects like cicadas that are difficult to survey using traditional methods but produce distinctive sounds.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.