Acoustic remote sensing with deep learning enables non-invasive estimation of seabird nest density

Abstract Passive Acoustic Monitoring (PAM) has advanced ecological research by enabling non-invasive recordings of wildlife vocalizations that provide insight into species presence, behavior, and reproductive activity. This remote-sensing approach is particularly valuable for species breeding in concealed habitats or remote areas where visual surveys are challenging. The Critically Endangered African Penguin, burrow-nesting seabird, exemplifies this challenge. Its highly vocal breeding behavior makes it an ideal case study for evaluating passive acoustic monitoring as a low-disturbance approach to estimating nest density. To evaluate this, we deployed Autonomous Recording Units at multiple sampling points across the Stony Point Penguin Colony, capturing soundscapes spanning different nest densities and environmental conditions. We then developed an automated detector for Ecstatic Display Songs (EDS), the species’ characteristic territorial song, using a Convolutional Neural Network trained on a multi-source dataset covering several breeding seasons, diverse acoustic environments, and both in situ and ex situ recordings. The model achieved high recall and precision and remained robust across diverse environmental conditions, supporting the use of heterogeneous training datasets for reliable bioacoustics detection. Using the automated EDS detections, we then investigated how vocal activity peaks relate to local nest density. A Generalized Additive Model revealed that EDS peaks strongly predicted nest density, with a nonlinear increase that plateaued at high calling rates. Importantly, models trained in one breeding season generalized well to the next. In conclusion, by integrating PAM with deep learning, this study provides a scalable, low-disturbance framework for estimating penguin nest density from soundscape data, supporting colony rangers in monitoring penguin colonies. Highlights Large-scale acoustic monitoring captured the soundscape of a critically endangered seabird species. Deep-learning-based acoustic detection reliably identified key breeding vocalizations in field recordings. Peak vocal activity strongly and nonlinearly predicted active nest density across sampling points. The vocal-nest relationship increases rapidly and plateaued at high levels of vocal activity. This approach enables scalable, low-disturbance monitoring of seabird nest density.