{
  "abstract": "Taxonomic ambiguity, arising from mixed levels of taxonomic identification within ecological datasets, can bias biodiversity estimates and ecological inference. Existing harmonisation approaches typically apply a single decision rule across all taxa, often sacrificing either taxonomic precision or the retention of abundance or occurrence data. Here, we present TaxHarmonizeR, an open-source R tool that implements a reproducible, data-driven workflow for taxonomic harmonisation. The method quantifies ambiguity within each taxonomic lineage as the ratio of occurrences or abundance between a focal taxon and all lower-levels of identification within the same lineage (focal-offspring ratio) and applies a user-defined Taxonomic Ambiguity Threshold (TAT) to determine whether taxa should be aggregated, retained, or deleted. Unlike conventional approaches, TaxHarmonizeR allows harmonisation decisions to vary among taxonomic lineages according to their observed level of ambiguity, thereby maximising taxonomic precision while ensuring that each lineage is represented only once in the harmonised dataset. We illustrate the workflow using a freshwater macroinvertebrate dataset from the Rhône River and evaluate the influence of different TAT values on taxonomic richness, occurrence / abundance retention, and α-, β-, and γ-diversity. Harmonisation had marked effects on biodiversity metrics, demonstrating that taxonomic ambiguity can substantially influence ecological interpretations and highlighting the importance of selecting an appropriate harmonisation strategy. By coupling lineage-specific harmonisation with sensitivity analyses across alternative ambiguity thresholds, TaxHarmonizeR provides a flexible and transparent framework that can be adapted to diverse ecological datasets and research objectives, while allowing users to assess the sensitivity of downstream results to the choice of TAT.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Kurt Villsen"
    },
    {
      "name": "Maxence Forcellini"
    },
    {
      "name": "Johan Pansu"
    },
    {
      "name": "David Eme"
    },
    {
      "name": "Maxime Logez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8wvnjgvxtaqxa.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "8wvnjgvxtaqxa",
  "issns": [],
  "landing_url": "https://hal.inrae.fr/hal-05766542v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sde"
    ],
    [
      "hal",
      "1.sde.be"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.inrae.fr/hal-05766542/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8wvnjgvxtaqxa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "hal-05766542",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05766542v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sde.be",
      "subfield": "ecology"
    },
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "TaxHarmonizeR: A data-driven taxonomic harmonisation tool for ecological datasets",
  "title_text": "TaxHarmonizeR: A data-driven taxonomic harmonisation tool for ecological datasets",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/8wvnjgvxtaqxa",
  "venue": "HAL",
  "versions": []
}