Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

Abstract

AbstractPassive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data.To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these recordings both with BirdNET and by manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min).Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the current recall of the algorithm is markedly low when the minimum confidence threshold is adjusted to ensure high levels of precision. Thus, we found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied. We therefore estimate that acoustic datasets of extended duration are currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. Our results also suggest that BirdNET performance is not significantly influenced by the type of recorder used or the habitat recorded but is modulated by the volume of species-specific acoustic data available online.We conclude that a judicious use of AI-based IDs provided by BirdNET can represent a novel and powerful method to assist in the assessment of bird community composition through the automated analysis of large-scale ecoacoustic data. Finally, we provide best use recommendations to ensure optimal results from the algorithm for the ecological study of bird communities.