Deep learning-based fine-scale bioacoustic monitoring reveals nocturnal calling activity patterns of translocated Pelophylax nigromaculatus and native Dryophytes leopardus in a rice paddy

Passive Acoustic Monitoring (PAM) offers a powerful approach for detecting and assessing the presence of invasive species, thereby supporting the conservation of native ecosystems. In this study, we developed a species-specific classification model using convolutional neural networks (CNNs) to analyse the nocturnal calling activity patterns of Pelophylax nigromaculatus and Dryophytes leopardus in rice paddies, a microhabitat where interactions between translocated and native species are of ecological concern. Despite environmental noise from bird calls and wind, the mel-spectrogram-based model classified anuran vocalisations with high accuracy (88.45%). Misclassifications at dawn were mitigated by limiting the ecological analysis to specific nocturnal periods. The results revealed clearly distinct peak times of nocturnal calling activity between the two species at the study site. These findings demonstrate the usefulness of deep learning for describing fine-scale activity patterns in field-recorded amphibian soundscapes. This study provides methodological insights into the acoustic monitoring of domestically translocated and native species and highlights the potential of bioacoustics approaches for ecological assessment in paddy environments. Future research should focus on refining species classification models and integrating sound-source separation for more accurate species-specific assessments of calling activity patterns.