All or nothing: highly variable species-level performance of BirdNET’s bird classifier and a new Australian frog classifier

Effective conservation relies on accurate data on species distributions and their change over time. Recent advances in low-cost acoustic recorders and artificial intelligence (AI) now offer powerful new ways to collect these data, revitalising the use of ecoacoustics for monitoring fauna at scale. While open-source, multi-species AI models like BirdNET are now available for most bird species, similar tools for amphibians remain limited. Here, we present the first open-source, multi-species frog classifier for 16 native species from Victoria, Australia, and evaluate its performance alongside the bird classifier BirdNET using real-world data from freshwater wetlands. Both classifiers exhibited a characteristic bimodal distribution in precision, with most species detected either with very high (>90%) or very low (