Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to \'translate\' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

UNASSIGNED: The current paper presents a nomenclatural checklist for vascular plants validated being (sub)endemic to and present in the flora of the Ukrainian Carpathians. This checklist is a part of the work targeted on an inventory of endemic plants distributed in the Ukrainian Carpathians. It is mainly based on the analysis of primary sources (i.e. original protologues and monographic works), but also uses the data provided in the recent online taxonomic aggregators, such as the Global Biodiversity Information Facility (GBIF), Catalogue of Life (CoL), Plants of the World Online (POWO), Euro+Med PlantBase, World Flora Online (WFO) and others. Over 7,000 specimens deposited in the leading Ukrainian herbaria were also revised and used as a supporting data source during the work on the checklist.
UNASSIGNED: The checklist provides a revised nomenclature, including corrections on publication dates, rediscovered taxonomic protologues, corrected authorships and revised taxonomic status for (sub)endemic (sub)species of vascular plants occurring in the Ukrainian Carpathians. It contains 1,101 names, from which 78 species and subspecies have been accepted as valid and 1023 species and infraspecific taxa are provided as synonyms. It is completed with critical notes on the nomenclature of problematic taxa and brief annotations regarding their distribution in the Ukrainian Carpathians, indicating the endemicity range and sozological status for all analysed (sub)species.The current checklist is linked with the GBIF taxonomic backbone, provides notes on detected issues and primarily focuses on its update and correction of the nomenclatural issues and taxonomic inconsistencies, but also aims at discussing issues in other popular taxonomic databases.Sabulinapauciflora is proposed as a new combination to comply with a recent revision of the genus Sabulina.

A major gap in the biodiversity knowledge graph is a connection between taxonomic names and the taxonomic literature. While both names and publications often have persistent identifiers (PIDs), such as Life Science Identifiers (LSIDs) or Digital Object Identifiers (DOIs), LSIDs for names are rarely linked to DOIs for publications. This article describes efforts to make those connections across three large taxonomic databases: Index Fungorum, International Plant Names Index (IPNI) and the Index of Organism Names (ION). Over a million names have been matched to DOIs or other persistent identifiers for taxonomic publications. This represents approximately 36% of names for which publication data are available. The mappings between LSIDs and publication PIDs are made available through ChecklistBank. Applications of this mapping are discussed, including a web app to locate the citation of a taxonomic name and a knowledge graph that uses data on researcher ORCID ids to connect taxonomic names and publications to authors of those names.

We introduce a new method of estimating accepted species diversity by adapting mark-recapture methods to comparisons of taxonomic databases. A taxonomic database should become more complete over time, so the error bar on an estimate of its completeness and the known diversity of the taxon it treats will decrease. Independent databases can be correlated, so we use the time course of estimates comparing them to understand the effect of correlation. If a later estimate is significantly larger than an earlier one, the databases are positively correlated, if it is significantly smaller, they are negatively correlated, and if the estimate remains roughly constant, then the correlations have averaged out. We tested this method by estimating how complete MolluscaBase is for accepted names of terrestrial gastropods. Using random samples of names from an independent database, we determined whether each name led to a name accepted in MolluscaBase. A sample tested in August 2020 found that 16.7% of tested names were missing; one in July 2021 found 5.3% missing. MolluscaBase grew by almost 3,000 accepted species during this period, reaching 27,050 species. The estimates ranged from 28,409 ± 365 in 2021 to 29,063 ± 771 in 2020. All estimates had overlapping 95% confidence intervals, indicating that correlations between the databases did not cause significant problems. Uncertainty beyond sampling error added 475 ± 430 species, so our estimate for accepted terrestrial gastropods species at the end of 2021 is 28,895 ± 630 species. This estimate is more than 4,000 species higher than previous ones. The estimate does not account for ongoing flux of species into and out of synonymy, new discoveries, or changing taxonomic methods and concepts. The species naming curve for terrestrial gastropods is still far from reaching an asymptote, and combined with the additional uncertainties, this means that predicting how many more species might ultimately be recognized is presently not feasible. Our methods can be applied to estimate the total number of names of Recent mollusks (as opposed to names currently accepted), the known diversity of fossil mollusks, and known diversity in other phyla.

The taxonomic identification of organisms based on the amplification of specific genetic markers (metabarcoding) implicitly requires adequate discriminatory information and taxonomic coverage of environmental DNA sequences in taxonomic databases. These requirements were quantitatively examined by comparing the determination of cyanobacteria and microalgae obtained by metabarcoding and light microscopy. We used planktic and biofilm samples collected in 37 lakes and 22 rivers across the Alpine region. We focused on two of the most used and best represented genetic markers in the reference databases, namely the 16S rRNA and 18S rRNA genes. A sequence gap analysis using blastn showed that, in the identity range of 99-100%, approximately 30% (plankton) and 60% (biofilm) of the sequences did not find any close counterpart in the reference databases (NCBI GenBank). Similarly, a taxonomic gap analysis showed that approximately 50% of the cyanobacterial and eukaryotic microalgal species identified by light microscopy were not represented in the reference databases. In both cases, the magnitude of the gaps differed between the major taxonomic groups. Even considering the species determined under the microscope and represented in the reference databases, 22% and 26% were still not included in the results obtained by the blastn at percentage levels of identity ≥95% and ≥97%, respectively. The main causes were the absence of matching sequences due to amplification and/or sequencing failure and potential misidentification in the microscopy step. Our results quantitatively demonstrated that in metabarcoding the main obstacles in the classification of 16S rRNA and 18S rRNA sequences and interpretation of high-throughput sequencing biomonitoring data were due to the existence of important gaps in the taxonomic completeness of the reference databases and the short length of reads. The study focused on the Alpine region, but the extent of the gaps could be much greater in other less investigated geographic areas.

OBJECTIVE: 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.
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%.