{
  "abstract": "Cryptic species complexes pose fundamental challenges to biologists, as species exhibit minimal morphological differences that require integrating morphology, genetics, and biogeography for identification. Here, we present a deep learning approach to support species identification in the freshwater snail genus Radomaniola (Hydrobiidae), a morphologically cryptic group from the Balkans. Our approach mirrors the integrative workflow of expert taxonomists by combining shell images, morphometric measurements, and collection‑site metadata, with optional phylogenetic information. Despite being trained on fewer than 700 specimens across 20 visually similar species with strongly imbalanced class sizes, the system achieved high identification performance. Careful control of spurious correlations, such as those arising from site‑specific imaging conditions or overly precise geographic metadata, was essential to ensure that the network learned biologically meaningful features. Across all experiments, integrating multiple data types and jointly optimizing meaningful embeddings and classification consistently improved performance over image‑only and classification‑only baselines. On specimens from collection sites seen during training we achieved a macro-averaged F1 score of 0.93. Even though this dropped as low as 0.14 when evaluating on specimens from previously unsampled localities, it could be rapidly recovered by retraining with 2–3 newly labeled specimens. Additionally, model top-3 accuracy stayed consistently above 80% in all settings. These results show that relatively lightweight deep learning models can provide practical decision support in real taxonomic workflows. Author summary: Taxonomy, the identification and classification of species, is a time‑consuming task that relies heavily on scarce expert knowledge. In this study, we use Siamese neural networks to support the classification of freshwater snails of the genus Radomaniola, a group in which species are notoriously difficult to distinguish based on shell morphology alone. Unlike many machine learning applications that depend on thousands of labeled examples, our system learns effectively from fewer than 700 specimens distributed across 20 visually similar species with very uneven sample sizes. This is achieved by combining high‑quality shell photographs with morphometric measurements and collection‑site information, and by explicitly reducing confounding factors such as site‑specific backgrounds and imaging conditions. We further test the system in realistic scenarios, including cases where specimens originate from previously unsampled sites and where expert labels become available only gradually. These experiments show that the method can provide useful candidate species suggestions and quickly improve as new labeled specimens are added. Overall, our work illustrates how deep learning can be integrated into taxonomic workflows as a practical aid, while leaving final decisions and species delimitation to human experts.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Goethe University Frankfurt, Frankfurt am Main, Germany",
    "The Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany",
    "Museo Nacional de Ciencias Naturales CSIC, Madrid, Spain",
    "SNSB – Bavarian State Collection for Palaeontology and Geology, Munich, Germany",
    "Department of Earth and Environmental Sciences, Palaeontology & Geobiology, Ludwig-Maximilians-Universität München, Munich, Germany",
    "Department of Animal Ecology & Systematics, Justus Liebig University, Giessen, Germany"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dennis Vetter"
    },
    {
      "affs": [
        1
      ],
      "name": "Muhammad Ahsan"
    },
    {
      "affs": [
        2
      ],
      "name": "Diana Delicado"
    },
    {
      "affs": [
        3
      ],
      "name": "Thomas A. Neubauer"
    },
    {
      "affs": [
        4
      ],
      "name": "Thomas Wilke"
    },
    {
      "affs": [
        5
      ],
      "name": "Gemma Roig"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fmentv6jxn4kv.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014733",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014733",
  "id": "fmentv6jxn4kv",
  "issns": [
    "1553-7358"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1109|Mollusks and Parasites Studies"
    ],
    [
      "plos",
      "/Biology and life sciences/Evolutionary biology/Evolutionary processes/Speciation/Cryptic speciation"
    ],
    [
      "plos",
      "/Biology and life sciences/Evolutionary biology/Evolutionary processes/Speciation/Species delimitation"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics"
    ],
    [
      "plos",
      "/Biology and life sciences/Taxonomy"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Metadata"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Taxonomy"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Morphometry"
    ]
  ],
  "openalex": "W7206218334",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014733&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/fmentv6jxn4kv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pcbi.1014733",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014733"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:evolutionary biology",
      "subfield": "evolutionary-biology"
    },
    {
      "field": "biology",
      "origin": "plos:genetics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Speeding up taxonomy in the digital age: A deep learning approach for identifying cryptic freshwater snails",
  "title_text": "Speeding up taxonomy in the digital age: A deep learning approach for identifying cryptic freshwater snails",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/fmentv6jxn4kv",
  "venue": "PLOS Computational Biology",
  "versions": []
}