Abstract
Many species are composed of two or more genetically distinct clades, indicating ongoing or past evolutionary divergence. Often however, there are no obvious morphological differences between clades, making it difficult to accurately assess specific aspects of biodiversity or to enact targeted conservation efforts. New advancements in artificial intelligence tools can be used to categorise individuals into their respective genetic clades and to highlight their distinguishing morphological characters that would otherwise be hidden from human observers. Here, we applied computer vision and explainable artificial intelligence techniques to four limpet species that display well-defined phylogeographic breaks along the Baja California and California coasts. A fine-tuned convolutional network, trained and evaluated over 100 resampling iterations, classified individuals into their genetic clades with median F1-scores of up to 0.96. F1-score performance was markedly higher for true clade groups than the controlled mixed-groups, confirming the presence of features specific to the clades. Saliency maps consistently emphasised structures such as the keyhole in Fissurella volcano and the ridge tips in Lottia conus as distinguishing features, and subsequent shape analyses confirmed significant divergence between clades. These results demonstrate the power of computer vision and explainable artificial intelligence to expose otherwise cryptic morphological diversity and provide a scalable, reproducible workflow that can broaden the biodiversity toolkit and refine eco-evolutionary research across taxa.
Data availability
Data for this project is available at:https://github.com/JackDanHollister/chapter_3-_genes_shells_and_AI_data.
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Acknowledgements
We would like to give special thanks to Shreya Banerjee for her help in the field and Doug Eernisse for helpful discussions about the project and limpet taxonomy. We are grateful for the help of Lindsey Groves at the LACM for facilitating access the collections and for many helpful conversations about the project.
Funding
This project was funded by NERC grant NE/X011518/1 to PBF and a Malacology Society of London grant to JDH. Nerc also funded the PhD programme for JDH.
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J.H. planned the original project, collected specimens in field, took photos, created dataset, created code, ran the code, created figures, wrote most of the paper. P.F. advised on project, helped collect specimens, advised on figures, advised on writing, helped edit final drafts of paper. D.P-G helped with locations for specimen collections. advised on and edited paper. R.B-L helped with locations for specimen collections. advised on and edited paper. T.H. and X.C. advised on and edited paper.
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The animal study was reviewed and approved by Animal Welfare and Ethical Review Body – ERGO II 63575. The permit to collect the field samples was provided by the Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA, Permiso de Pesca de Fomento No. PPF/DGOPA-291/17 and PPF/DGOPA-010/19).
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Hollister, J.D., Paz-García, D.A., Beas-Luna, R. et al. Genes, shells, and AI: using computer vision to detect cryptic morphological divergence between genetically distinct populations of limpets.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-30613-1
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DOI: https://doi.org/10.1038/s41598-025-30613-1
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