'PhyTaxR', a set of phytoplankton taxonomic curation tools in R
Abstract
Taxonomic harmonisation is a persistent challenge for phytoplankton monitoring and biodiversity synthesis. Operational taxon fields often combine scientific names --some of which may be incomplete, misspelled, or otherwise non-standard-- with size classes, morphological descriptors, uncertainty qualifiers, vernacular labels, author citations, and historical nomenclature. Here we introduce PhyTaxR, an R package that formalizes the curation of phytoplankton taxonomic records as a reproducible, modular workflow. The package separates a standardized taxonomic core from ancillary information while preserving uncertainty and descriptive metadata. It then applies a conservative taxonomic-resolution workflow based primarily on searches of the World Register of Marine Species (WoRMS), supplemented where appropriate by stringent, cross-validated matches from Global Biodiversity Information Facility (GBIF). Matched records are reconciled to accepted taxonomic concepts and returned with persistent identifiers, resolution provenance, and a tidy taxonomic hierarchy. Labels that cannot be resolved automatically are directed to a semi-automatic review workflow, where users can evaluate candidate matches, supply manual identifications, flag records for expert review, or remove unsuitable entries. Illustrative applications show how 'PhyTaxR' can harmonize synonyms and spelling variants, retain contextual information associated with original labels, and make unresolved or uncertain records explicit rather than forcing unsupported assignments. By retaining intermediate decisions and supporting targeted expert intervention, 'PhyTaxR' reduces manual curation while keeping taxonomic uncertainty visible in reproducible R workflows.
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BibTeX
@misc{vina2026phytaxr,
title = {{'PhyTaxR', a set of phytoplankton taxonomic curation tools in R}},
author = {F. Rayon Vina and Z. V. Finkel and A. J. Irwin},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.23.761320},
url = {https://www.biorxiv.org/content/10.64898/2026.10.23.761320v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - 'PhyTaxR', a set of phytoplankton taxonomic curation tools in R
AU - F. Rayon Vina
AU - Z. V. Finkel
AU - A. J. Irwin
PY - 2026
DA - 2026/10/23
PB - bioRxiv
DO - 10.64898/2026.10.23.761320
UR - https://www.biorxiv.org/content/10.64898/2026.10.23.761320v1
AB - Taxonomic harmonisation is a persistent challenge for phytoplankton monitoring and biodiversity synthesis. Operational taxon fields often combine scientific names --some of which may be incomplete, misspelled, or otherwise non-standard-- with size classes, morphological descriptors, uncertainty qualifiers, vernacular labels, author citations, and historical nomenclature. Here we introduce PhyTaxR, an R package that formalizes the curation of phytoplankton taxonomic records as a reproducible, modular workflow. The package separates a standardized taxonomic core from ancillary information while preserving uncertainty and descriptive metadata. It then applies a conservative taxonomic-resolution workflow based primarily on searches of the World Register of Marine Species (WoRMS), supplemented where appropriate by stringent, cross-validated matches from Global Biodiversity Information Facility (GBIF). Matched records are reconciled to accepted taxonomic concepts and returned with persistent identifiers, resolution provenance, and a tidy taxonomic hierarchy. Labels that cannot be resolved automatically are directed to a semi-automatic review workflow, where users can evaluate candidate matches, supply manual identifications, flag records for expert review, or remove unsuitable entries. Illustrative applications show how 'PhyTaxR' can harmonize synonyms and spelling variants, retain contextual information associated with original labels, and make unresolved or uncertain records explicit rather than forcing unsupported assignments. By retaining intermediate decisions and supporting targeted expert intervention, 'PhyTaxR' reduces manual curation while keeping taxonomic uncertainty visible in reproducible R workflows.
ER -