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Abstract

Bats are ecologically important mammals whose monitoring increasingly relies on acoustic data. However, many tools for bat call identification remain subscription-based, closed-source, region-specific or limited in scalability, creating barriers to global data integration and method development. We present BSG-BATS, an open-access annotation portal and convolutional neural network (CNN)-based classifier for bat calls. The portal enables researchers and practitioners to annotate calls at species or phonic group level and contribute directly to the iterative improvement of the classifier. As a proof of concept, we trained the first BSG-BATS model using over 4000 annotated recordings of 21 European species. It gave promising results and outperformed many commonly used commercial and academic tools by providing higher AUC (area under the receiver operating characteristic curve) scores for test data. All annotations are transparently annotated in the portal (https://bsg.laji.fi/bats/identification/instructions) and the trained model, code and documentation are available at www.zenodo.org/records/15495676. BSG-BATS offers a foundation for community-driven development of bat sound identification tools. By integrating annotation and model retraining on an open-access platform, it enables collective improvement and adaptation to new regions, species and sound types. We invite all bat researchers and experts to join this collaborative effort, whether by contributing data (bsg-bat@helsinki.fi), annotating sounds (https://bsg.laji.fi/bats/identification/instructions) or testing the model in their own work (https://www.zenodo.org/records/15495676).

Keywords

bat sound recognition; bioacoustics; Chiroptera; collaborative research; community science; convolutional neural networks; deep learning; echolocation

Published in

Methods in Ecology and Evolution
2025

SLU Authors

UKÄ Subject classification

Other Computer and Information Science
Ecology

Publication identifier

  • DOI: https://doi.org/10.1111/2041-210x.70220

Permanent link to this page (URI)

https://res.slu.se/id/publ/145267