Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids

• Published in: Philosophical transactions of the Royal Society of London. Series B. Biological sciences 2024-06, Vol.379 (1904), p.20230110-20230110
• Main Authors: Symes, Laurel B, Madhusudhana, Shyam, Martinson, Sharon J, Geipel, Inga, Ter Hofstede, Hannah M
• Format: Article
• Language: English
• Subjects: Animals; Forests; Light; Machine Learning; Vocalization, Animal
• ISSN: 0962-8436; 1471-2970
• DOI: 10.1098/rstb.2023.0110

Night-time light can have profound ecological effects, even when the source is natural moonlight. The impacts of light can, however, vary substantially by taxon, habitat and geographical region. We used a custom machine learning model built with the Python package to investigate the effects of moonlight on the calling activity of neotropical forest katydids over multiple years. We prioritised species with calls that were commonly detected in human annotated data, enabling us to evaluate model performance. We focused on eight species of katydids that the model identified with high precision (generally greater than 0.90) and moderate-to-high recall (minimum 0.35), ensuring that detections were generally correct and that many calls were detected. These results suggest that moonlight has modest effects on the amount of calling, with the magnitude and direction of effect varying by species: half of the species showed positive effects of light and half showed negative. These findings emphasize the importance of understanding natural history for anticipating how biological communities respond to moonlight. The methods applied in this project highlight the emerging opportunities for evaluating large quantities of data with machine learning models to address ecological questions over space and time. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.