A method to implement continuous characters in digital identification keys that estimates the probability of an annotation

Pytaxon: A Python software for resolving and correcting taxonomic names in biodiversity data

Background

The standardisation and correction of taxonomic names in large biodiversity databases remain persistent challenges for researchers, as errors in species names can compromise ecological analyses, land-use planning and conservation efforts, particularly when inaccurate data are shared on global biodiversity portals.

New information

We present pytaxon, a Python software designed to resolve and correct taxonomic names in biodiversity data by leveraging the Global Names Verifier (GNV) API and employing fuzzy matching techniques to suggest corrections for discrepancies and nomenclatural inconsistencies. The pytaxon offers both a Command Line Interface (CLI) and a Graphical User Interface (GUI), ensuring accessibility to users with different levels of computing expertise. Tests on spreadsheets derived from datasets published in the Global Biodiversity Information Facility (GBIF) demonstrated its effectiveness in identifying and resolving taxonomic errors. By mitigating the propagation of inaccuracies from researchers' datasets to global biodiversity databases, pytaxon supports more reliable conservation decisions and robust scientific investigations. Its contributions enhance data integrity and promote informed biodiversity management in a rapidly evolving global environment.

WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data

PREMISE

The standardization of plant names is a critical step in various fields of biology, including biodiversity, biogeography, and vegetation research. The WorldFlora package is introduced here to help achieve this goal by matching lists of plant names with a static copy from World Flora Online (WFO), an ongoing global effort to complete an online flora of all known vascular plants and bryophytes by 2020.

METHODS AND RESULTS

Based on direct and fuzzy matching, WorldFlora inserts matching cases from the WFO to a submitted data set containing taxonomic names. The results and success rates for selecting the expected best single matches are presented for four data sets, including two data sets used in recent comparisons of software tools for correcting taxon names.

CONCLUSIONS

WorldFlora offers a straightforward pipeline for semi-automatic plant name checking. For the four data sets, the success rate of credible matches ranged from 94.7% to 99.9%.