Ten rules for robust taxonomy in ecological datasets.

Accurate taxonomy, including species identification and naming, underpins ecological inference, yet taxonomic information in ecological datasets is often inconsistently recorded or weakly standardized. With the increasing integration of ecological datasets across taxa and regions, datasets with poor taxonomic practices can hinder data exchange and propagate errors. We propose 10 rules to improve the storage and management of taxonomic information in ecological datasets. These rules draw on our experiences in plant ecology and taxonomy, but the conclusions may be generalized to other organisms and disciplines. They emphasize aspects from species identification to the permanent storage of verifiable records (e.g. voucher specimens) and include taxonomic standardization, nomenclature, validation, and updating. We also address the treatment of morphotypes and incomplete identifications, the use of automated name-matching tools, and (meta)data standards. Closer collaboration between ecologists and taxonomists is key to ensuring more robust datasets. The application of these rules consumes more time and resources, but they enhance data quality and interoperability, saving time during data integration and improving the reliability and scalability of ecological research.