From Frog Calls to Global Conservation: Scaling Citizen Science with AI

Summary

Image credit: Mantis Design / Adobe Stock The Growing Challenge of Biodiversity Data Citizen science projects are generating more data than ever before. Global biodiversity databases now contain millions of species observations , creating an incredible resource for ecological research. While collecting the data is not a problem, researchers still need to analyze the sounds hidden within thousands or even millions of recordings. Many contain background noise. Others capture multiple species calling at the same time. Still others need a trained ear to hear the difference between two similar-sound species. Sorting through all of that information manually can take a huge amount of time. This is a challenge Julia Tan is trying to tackle. In a previous collaboration with ESP, Julia evaluated our flagship model, NatureLM-audio, for use in processing and analyzing hundreds of thousands of frog recordings, and found strong performance and potential time-savings, even without fine-tuning. In a paper presented at the AI for Non-Human Communication workshop at NeurIPS 2025, Julia shows how an AI pipeline can help researchers extract useful information from noisy citizen science recordings, supporting biodiversity monitoring at scale. What motivated you to develop this pipeline for FrogID, and what problem were you trying to solve? The core motivation behind developing this pipeline for FrogID was the growing bottleneck created by manual validation. Citizen science initiatives like FrogID produce a large volume of valuable acoustic data, but expert review remains time-intensive, which limits the speed and effectiveness of species monitoring and delays timely management action. At the same time, while there is now more ecological data available than ever before, much of it is noisy, weakly labelled, or only coarsely annotated, making it difficult to use in conventional machine learning pipelines. We were also motivated by the rapid progress taking place in other areas of AI, particularly in fields such as human speech and bird call recognition. However, many other taxa, including frogs, have not been represented to the same extent in these advances. This raised an important question for us: how can we take the scale of citizen science data and combine it with progress in adjacent AI domains to support a more specialised ecological application? More broadly, we wanted to…