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, Martin Hofmann Data-intensive Systems and Visualization Group (dAI.SY), Technical University Ilmenau, Ilmenau , 98693, Germany Corresponding author: Mail: Martin.Hofmann@tu-ilmenau.de, orcid: 0000-0002-4440-3317 Search for other works by this author on: Oxford Academic Steffen Kiel Department of Palaeobiology, Swedish Museum of Natural History, Stockholm , 104 05, Sweden Search for other works by this author on: Oxford Academic Lara M Kösters Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena , 07745, Germany Search for other works by this author on: Oxford Academic Jana Wäldchen Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena , 07745, Germany German Centre for Integrative Biodiversity Research (iDiv) , Halle-Jena-Leipzig, Germany Search for other works by this author on: Oxford Academic Patrick Mäder Data-intensive Systems and Visualization Group (dAI.SY), Technical University Ilmenau, Ilmenau , 98693, Germany German Centre for Integrative Biodiversity Research (iDiv) , Halle-Jena-Leipzig, Germany Faculty of Biological Sciences, Friedrich Schiller University, Jena , 07745, Germany Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0002-4440-3317 Systematic Biology, syae042, https://doi.org/10.1093/sysbio/syae042
Published:
24 July 2024
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Received:
25 September 2023
Published:
24 July 2024
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Martin Hofmann, Steffen Kiel, Lara M Kösters, Jana Wäldchen, Patrick Mäder, Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: a Case Study with a Bivalve dataset, Systematic Biology, 2024;, syae042, https://doi.org/10.1093/sysbio/syae042
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Abstract
Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of two deep learning methods – supervised classification approaches and unsupervised similarity learning – to infer organism relationships from specimen images. As a basis, we assembled an image dataset covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this dataset for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our dataset. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.
Deep Learning, Phylogenetics, Similarity Learning, Bivalves, Morphology Inference
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