Passive Acoustic Monitoring and Deep Learning Reveal a Lag From Rainfall to Gibbon Song Across a Mosaic Forest Landscape

ABSTRACT Understanding the fundamental ecology of endangered species is essential for effective conservation, yet this remains challenging for elusive species inhabiting tropical forests. For the endangered Bornean white‐bearded gibbon ( Hylobates albibarbis ), much of the available ecological information derives from peat swamp forests, while comparatively little is known from other forest types that make up a large part of its range. Passive acoustic monitoring provides an opportunity to address this gap, enabling the study of species' vocal behaviour over larger spatial and temporal scales than previously possible. We deployed eight autonomous recording units across three forest types in Central Kalimantan, Indonesia, collecting 23,244 h of acoustic data over 18 months. A pretrained deep learning detector was applied to identify great calls, performed by female gibbons as part of morning duets and used as a key indicator for comparing population density. We identified 83,956 great calls and examined how daily calling activity varied across habitats and in response to seasonal rainfall. Daily calling activity differed significantly among forest types, consistent with expected differences in gibbon population density. Significant temporal variation in calling behaviour was observed consistently across habitats. We documented negative short‐term and positive long‐term effects of rainfall on calling activity. Daily calling activity peaked 51–52 days following rainfall events, with effect sizes increasing with rainfall dose, suggesting that calling activity reflects a lagged phenological fruiting response to seasonal rainfall. Our findings highlight the importance of accounting for variable vocalisation rates in acoustic monitoring, particularly when evaluating the additive effects of anthropogenic disturbance and climate change on species' behaviour and ecology. We emphasise the value of incorporating spatial data to strengthen ecological inferences from acoustic datasets, and demonstrate the power of deep learning for long‐term monitoring of species' vocal behaviour, providing deeper ecological understanding across increasingly broad spatiotemporal scales.