Rhinehart, T. A. | bioRxiv Science

bioRxiv2026

Overcoming software bottlenecks for scalable passive acoustic monitoring: insights from a global expert assessment

1.Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

ecology↗

bioRxiv2025

Automated identification of individual birds by song enables multi-year recapture from passive acoustic monitoring data

Autonomous sensors and machine learning are transforming ecology by enabling large-scale observation of organisms and ecosystems. However, sensor data collected by camera traps, acoustic recorders, and satellites are typically used only to produce detections of unidentified individuals. Tracking individuals over time to study movement, survival, and behavior continues to require invasive and high-effort capture and marking techniques. Here, we introduce an automated, general approach that identifies individual animals from passive acoustic recordings based on individually distinctive vocalizations. Unlike previous approaches, ours can identify individuals in passive acoustic recordings without previously labeled examples of their vocalizations. We apply our approach to a model songbird species (Ovenbird, Seiurus aurocapilla), estimating abundance and annual survival across 126 locations and four years. Our approach identifies individuals with 96% accuracy. We find high Ovenbird apparent annual survival (0.70) and acoustic recapture probability (0.89) across 405 individuals. Our approach can readily be applied to other species with individually distinctive vocalizations using open-source Python implementations. Automated individual identification will broadly unlock the ability to passively recapture individual animals at the massive scale of autonomous sensing, supporting the study of population trajectories and informing proactive ecosystem management to prevent biodiversity loss.

ecology↗