Taxamatch, an algorithm for near ('fuzzy') matching of scientific names in taxonomic databases

Primary amino acid sequences of decapod (Na+, K+)-ATPase provide evolutionary insights into osmoregulatory mechanisms

Decapod Crustacea exhibit a marine origin, but many taxa have occupied environments ranging from brackish to fresh water and terrestrial habitats, overcoming their inherent osmotic challenges. Osmotic and ionic regulation is achieved by the gill epithelia, driven by two active ATP-hydrolyzing ion transporters, the basal (Na+, K+)-ATPase and the apical V(H+)-ATPase. The kinetic characteristic of gill (Na+, K+)-ATPase and the mRNA expression of its α subunit have been widely studied in various decapod species under different salinity challenges. However, the evolution of the primary structure has not been explored, especially considering the functional modifications associated with decapod phylogeny. Here, we proposed a model for the topology of the decapod α subunit, identifying the sites and motifs involved in its function and regulation, as well as the patterns of its evolution assuming a decapod phylogeny. We also examined both the amino acid substitutions and their functional implications within the context of biochemical and physiological adaptation. The α-subunit of decapod crustaceans shows greater conservation (∼94% identity) compared to the β-subunit (∼40%). While the binding sites for ATP and modulators are conserved in the decapod enzyme, the residues involved in the α-β interaction are only partially conserved. In the phylogenetic context of the complete sequence of (Na+, K+)-ATPase α-subunit, most substitutions appear to be characteristic of the entire group, with specific changes for different subgroups, especially among brachyuran crabs. Interestingly, there was no consistent separation of α-subunit partial sequences related to habitat, suggesting that the convergent evolution for freshwater or terrestrial modes of life is not correlated with similar changes in the enzyme's primary amino acid sequence.

Vertical and horizontal environmental DNA (eDNA) patterns of fish in a shallow and well-mixed North Sea area

The integration of eDNA metabarcoding into monitoring programs provides valuable information about fish community structures. Despite the growing body of evidence supporting the method's effectiveness in distinguishing fine-scale eDNA signals, there is a limited understanding of eDNA distribution in shallow, well-mixed environments, especially related to sampling depth. We analyzed 167 samples collected from the surface and bottom water at 17 locations of the Belgian Part of the North Sea (BPNS), where the deepest sampling point was 31 m, and compared this to beam trawl catch data collected simultaneously at the same locations. eDNA metabarcoding identified an additional 22 species compared to beam trawl catch data. Diversity measures and patterns were very similar between surface and bottom samples and revealed community patterns that were previously described by long-term beam trawl catch data. Surface and bottom samples had 39 fish species in common, while six and eight rare species were uniquely detected, respectively. Our results demonstrate that eDNA metabarcoding effectively identifies spatial community patterns of fishes in the highly dynamic environment of the BPNS regardless of sampling depth. Out of the six most common species tested, eDNA metabarcoding read abundances correlated strongly with catch-based abundance data for one species, but moderately for two others, indicating that inferring fish abundance and biomass via eDNA metabarcoding remains challenging.

Treemendous: an R package for integrating taxonomic information across backbones

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.

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing

BACKGROUND

The standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.

RESULT

To address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset.

CONCLUSIONS

We anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.

rWCVP: a companion R package for the World Checklist of Vascular Plants

The World Checklist of Vascular Plants (WCVP) is an extremely valuable resource that is being used to address many fundamental and applied questions in plant science, conservation, ecology and evolution. However, databases of this size require data manipulation skills that pose a barrier to many potential users. Here, we present rWCVP, an open-source R package that aims to facilitate the use of the WCVP by providing clear, intuitive functions to execute many common tasks. These functions include taxonomic name reconciliation, geospatial integration, mapping and generation of multiple different summaries of the WCVP in both data and report format. We have included extensive documentation and tutorials, providing step-by-step guides that are accessible even to users with minimal programming experience. rWCVP is available on cran and GitHub.

The big four of plant taxonomy - a comparison of global checklists of vascular plant names

Taxonomic checklists used to verify published plant names and identify synonyms are a cornerstone of biological research. Four global authoritative checklists for vascular plants exist: Leipzig Catalogue of Vascular Plants, World Checklist of Vascular Plants, World Flora Online (successor of The Plant List, TPL), and WorldPlants. We compared these four checklists in terms of size and differences across taxa. We matched taxon names of these checklists and TPL against each other, identified differences across checklists, and evaluated the consistency of accepted names linked to individual taxon names. We assessed geographic and phylogenetic patterns of variance. All checklists differed strongly compared with TPL and provided identical information on c. 60% of plant names. Geographically, differences in checklists increased from low to high latitudes. Phylogenetically, we detected strong variability across families. A comparison of name-matching performance on taxon names submitted to the functional trait database TRY, and a check of completeness of accepted names evaluated against an independent, expert-curated checklist of the family Meliaceae, showed a similar performance across checklists. This study raises awareness on the differences in data and approach across these checklists potentially impacting analyses. We propose ideas on the way forward exploring synergies and harmonizing the four global checklists.

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature

Summary

Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of the National Center for Biotechnology Information Taxonomy and Global Biodiversity Information Facility databases to find associated synonyms with given target species names.

Availability and implementation

PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page.

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature

Summary

Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of two commonly used taxonomic databases to find associated synonyms with given target species names.

Availability and implementation

PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page.

Contact

Lswhiteh@unc.edu.

Supplemental Information

We provide documentation for PhyloMatcher, including walkthrough instructions for the GUI application on the Releases page of https://github.com/Lswhiteh/PhyloMatcher.

Geographic name resolution service: A tool for the standardization and indexing of world political division names, with applications to species distribution modeling

Massive biological databases of species occurrences, or georeferenced locations where a species has been observed, are essential inputs for modeling present and future species distributions. Location accuracy is often assessed by determining whether the observation geocoordinates fall within the boundaries of the declared political divisions. This otherwise simple validation is complicated by the difficulty of matching political division names to the correct geospatial object. Spelling errors, abbreviations, alternative codes, and synonyms in multiple languages present daunting name disambiguation challenges. The inability to resolve political division names reduces usable data, and analysis of erroneous observations can lead to flawed results. Here, we present the Geographic Name Resolution Service (GNRS), an application for correcting, standardizing, and indexing world political division names. The GNRS resolves political division names against a reference database that combines names and codes from GeoNames with geospatial object identifiers from the Global Administrative Areas Database (GADM). In a trial resolution of political division names extracted from >270 million species occurrences, only 1.9%, representing just 6% of occurrences, matched exactly to GADM political divisions in their original form. The GNRS was able to resolve, completely or in part, 92% of the remaining 378,568 political division names, or 86% of the full biodiversity occurrence dataset. In assessing geocoordinate accuracy for >239 million species occurrences, resolution of political divisions by the GNRS enabled the detection of an order of magnitude more errors and an order of magnitude more error-free occurrences. By providing a novel solution to a significant data quality impediment, the GNRS liberates a tremendous amount of biodiversity data for quantitative biodiversity research. The GNRS runs as a web service and is accessible via an API, an R package, and a web-based graphical user interface. Its modular architecture is easily integrated into existing data validation workflows.

Match Algorithms for Scientific Names in FlorItaly, the Portal to the Flora of Italy

Scientific names are not part of everyday language in any modern country, and their input as strings in a query system can be easily associated with typographical errors. While globally unique identifiers univocally address a taxon name, they can hardly be used for querying a database manually. Thus, matching algorithms are often used to overcome misspelled names in query systems in several data repositories worldwide. In order to improve users’ experience in the use of FlorItaly, the Portal to the Flora of Italy, a near match algorithm to resolve misspelled scientific names has been integrated in the query systems. In addition, a novel tool in FlorItaly, capable of rapidly aligning any list of names to the nomenclatural backbone provided by the national checklists, has been developed. This manuscript aims at describing the potential of these new tools.

Soft Bigram distance for names matching

BACKGROUND

Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately. BI-DIST distance is considered the best orthographic measure for names identification; nevertheless, it lacks a distance scale between the name bigrams.

METHODS

In this research, the Soft Bigram Distance (Soft-Bidist) measure is proposed. It is an extension of BI-DIST by softening the scale of comparison among the name Bigrams for improving the name matching. Different datasets are used to demonstrate the efficiency of the proposed method.

RESULTS

The results show that Soft-Bidist outperforms the compared algorithms using different name matching datasets.

Survey of Automatic Spelling Correction

Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

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%.

Solr-Plant: efficient extraction of plant names from text

Background

The retrieval of plant-related information is a challenging task due to variations in species name mentions as well as spelling or typographical errors across data sources. Scalable solutions are needed for identifying plant name mentions from text and resolving them to accepted taxonomic names.

Results

An Apache Solr-based fuzzy matching system enhanced with the Smith-Waterman alignment algorithm (“Solr-Plant”) was developed for mapping and resolution to a plant name and synonym thesaurus. Evaluation of Solr-Plant suggests promising results in terms of both accuracy and processing efficiency on misspelled species names from two benchmark datasets: (1) SALVIAS and (2) National Center for Biotechnology Information (NCBI) Taxonomy. Additional evaluation using S800 text corpus also reflects high precision and recall. The latest version of the source code is available at https://github.com/bcbi/SolrPlantAPI. A REST-compliant web interface and service for Solr-Plant is hosted at http://bcbi.brown.edu/solrplant.

Conclusion

Automated techniques are needed for efficient and accurate identification of knowledge linked with biological scientific names. Solr-Plant complements the current state-of-the-art in terms of both efficiency and accuracy in identification of names restricted at species level. The approach can be extended to identify broader groups of organisms at different taxonomic levels. The results reflect potential utility of Solr-Plant as a data mining tool for extracting and correcting plant species names.

Annotated checklist for stony corals of American Sāmoa with reference to mesophotic depth records

An annotated checklist of the stony corals (Scleractinia, Milleporidae, Stylasteridae, and Helioporidae) of American Sāmoa is presented. A total of 377 valid species has been reported from American Sāmoa with 342 species considered either present (251) or possibly present (91). Of these 342 species, 66 have a recorded geographical range extension and 90 have been reported from mesophotic depths (30-150 m). Additionally, four new species records (Acanthastreasubechinata Veron, 2000, Favitesparaflexuosus Veron, 2000, Echinophylliaechinoporoides Veron & Pichon, 1980, Turbinariairregularis Bernard, 1896) are presented. Coral species of concern include species listed under the US Endangered Species Act (ESA) and the International Union for Conservation of Nature's (IUCN) Red List of threatened species. Approximately 17.5% of the species present or possibly present are categorized as threatened by IUCN compared to 27% of the species globally. American Sāmoa has seven ESA-listed or ESA candidate species, including Acroporaglobiceps (Dana, 1846), Acroporajacquelineae Wallace, 1994, Acroporaretusa (Dana, 1846), Acroporaspeciosa (Quelch, 1886), Fimbriaphylliaparadivisa (Veron, 1990), Isoporacrateriformis (Gardiner, 1898), and Pocilloporameandrina Dana, 1846. There are two additional species possibly present, i.e., Pavonadiffluens (Lamarck, 1816) and Poritesnapopora Veron, 2000.

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

Background Species occurrence records are very important in the biodiversity domain. While several available corpora contain only annotations of species names or habitats and geographical locations, there is no consolidated corpus that covers all types of entities necessary for extracting species occurrence from biodiversity literature. In order to alleviate this issue, we have constructed the COPIOUS corpus-a gold standard corpus that covers a wide range of biodiversity entities. Results Two annotators manually annotated the corpus with five categories of entities, i.e. taxon names, geographical locations, habitats, temporal expressions and person names. The overall inter-annotator agreement on 200 doubly-annotated documents is approximately 81.86% F-score. Amongst the five categories, the agreement on habitat entities was the lowest, indicating that this type of entity is complex. The COPIOUS corpus consists of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities. Named entity recognisers trained on the corpus could achieve an F-score of 74.58%. Moreover, in recognising taxon names, our model performed better than two available tools in the biodiversity domain, namely the SPECIES tagger and the Global Name Recognition and Discovery. More than 1,600 binary relations of Taxon-Habitat, Taxon-Person, Taxon-Geographical locations and Taxon-Temporal expressions were identified by applying a pattern-based relation extraction system to the gold standard. Based on the extracted relations, we can produce a knowledge repository of species occurrences. Conclusion The paper describes in detail the construction of a gold standard named entity corpus for the biodiversity domain. An investigation of the performance of named entity recognition (NER) tools trained on the gold standard revealed that the corpus is sufficiently reliable and sizeable for both training and evaluation purposes. The corpus can be further used for relation extraction to locate species occurrences in literature-a useful task for monitoring species distribution and preserving the biodiversity.

Tool for validation and import in herbarium database

Abstract Many biological collections databases feature data quality problems. On the existing computational resources, we present an import tool and data validation. The program applies filters to data submitted through a spreadsheet at the time of data import, streamlining the error-checking process. The validations presented were divided into three categories according to the taxonomic, geographical and general specimen collection data. Its implementation eliminated the errors in the data entry of new vouchers in the Herbarium of the Botanical Garden of Rio de Janeiro.

A story of data won, data lost and data re-found: the realities of ecological data preservation

This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been published, the raw data had not. In the original study, publications containing plot data collected from the 1880s onwards had been collected, interpreted, digitised and integrated for the classification of vegetation and analysis of its conservation status across Australia. These compiled data are an extremely valuable national collection that demanded publishing in open, readily accessible online repositories, such as the Terrestrial Ecosystem Research Network (http://www.tern.org.au) and the Atlas of Living Australia (ALA: http://www.ala.org.au), the Australian node of the Global Biodiversity Information Facility (GBIF: http://www.gbif.org). It is hoped that the lessons learnt from this project may trigger a sober review of the value of endangered data, the cost of retrieval and the importance of suitable and timely archiving through the vicissitudes of technological change, so the initial unique collection investment enables multiple re-use in perpetuity.

A decade of the World Register of Marine Species - General insights and experiences from the Data Management Team: Where are we, what have we learned and how can we continue?

The World Register of Marine Species (WoRMS) celebrated its 10th anniversary in 2017. WoRMS is a unique database: there is no comparable global database for marine species, which is driven by a large, global expert community, is supported by a Data Management Team and can rely on a permanent host institute, dedicated to keeping WoRMS online. Over the past ten years, the content of WoRMS has grown steadily, and the system currently contains more than 242,000 accepted marine species. WoRMS has not yet reached completeness: approximately 2,000 newly described species per year are added, and editors also enter the remaining missing older names-both accepted and unaccepted-an effort amounting to approximately 20,000 taxon name additions per year. WoRMS is used extensively, through different channels, indicating that it is recognized as a high-quality database on marine species information. It is updated on a daily basis by its Editorial Board, which currently consists of 490 taxonomic and thematic experts located around the world. Owing to its unique qualities, WoRMS has become a partner in many large-scale initiatives including OBIS, LifeWatch and the Catalogue of Life, where it is recognized as a high-quality and reliable source of information for marine taxonomy.

Constructing a biodiversity terminological inventory

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.

Two Influential Primate Classifications Logically Aligned

Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence in ways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications. We demonstrate that such alignments are feasible for two classifications of primates corresponding to the second and third MSW editions. Our approach has three main components: (i) use of taxonomic concept labels, that is name sec. author (where sec. means according to), to assemble each concept hierarchy separately via parent/child relationships; (ii) articulation of select concepts across the two hierarchies with user-provided Region Connection Calculus (RCC-5) relationships; and (iii) the use of an Answer Set Programming toolkit to infer and visualize logically consistent alignments of these input constraints. Our use case entails the Primates sec. Groves (1993; MSW2-317 taxonomic concepts; 233 at the species level) and Primates sec. Groves (2005; MSW3-483 taxonomic concepts; 376 at the species level). Using 402 RCC-5 input articulations, the reasoning process yields a single, consistent alignment and 153,111 Maximally Informative Relations that constitute a comprehensive meaning resolution map for every concept pair in the Primates sec. MSW2/MSW3. The complete alignment, and various partitions thereof, facilitate quantitative analyses of name:meaning dissociation, revealing that nearly one in three taxonomic names are not reliable across treatments-in the sense of the same name identifying congruent taxonomic meanings. The RCC-5 alignment approach is potentially widely applicable in systematics and can achieve scalable, precise resolution of semantically evolving name usages in synthetic, next-generation biodiversity, and phylogeny data platforms.

Integrating Biodiversity Data into Botanic Collections

BACKGROUND

Today's species names are entry points into a web of publicly available knowledge and are integral parts of legislation concerning biological conservation and consumer safety. Species information usually is fragmented, can be misleading due to the existence of different names and might even be biased because of an identical name that is used for a different species. Safely navigating through the name space is one of the most challenging tasks when associating names with data and when decisions are made which name to include in legislation. Integrating publicly available dynamic data to characterise plant genetic resources of botanic gardens and other facilities will significantly increase the efficiency of recovering relevant information for research projects, identifying potentially invasive taxa, constructing priority lists and developing DNA-based specimen authentication.

NEW INFORMATION

To demonstrate information availability and discuss integration into botanic collections, scientific names derived from botanic gardens were evaluated using the Encyclopedia of Life, The Catalogue of Life and The Plant List. 98.5% of the names could be verified by the combined use of these providers. Comparing taxonomic status information 13 % of the cases were in disagreement. About 7 % of the verified names were found to be included in the International Union for Conservation of Nature Red List, including one extinct taxon and three taxa with the status "extinct in the wild". As second most important factor for biodiversity loss, potential invasiveness was determined. Approximately 4 % of the verified names were detected using the Global Invasive Species Information Network, including 208 invasive taxa. According to Delivering Alien Invasive Species Inventories for Europe around 20 % of the verified names are European alien taxa including 15 of the worst European invasive taxa. Considering alternative names in the data recovery process, success increased up to 18 %.

A Standardized Reference Data Set for Vertebrate Taxon Name Resolution

Taxonomic names associated with digitized biocollections labels have flooded into repositories such as GBIF, iDigBio and VertNet. The names on these labels are often misspelled, out of date, or present other problems, as they were often captured only once during accessioning of specimens, or have a history of label changes without clear provenance. Before records are reliably usable in research, it is critical that these issues be addressed. However, still missing is an assessment of the scope of the problem, the effort needed to solve it, and a way to improve effectiveness of tools developed to aid the process. We present a carefully human-vetted analysis of 1000 verbatim scientific names taken at random from those published via the data aggregator VertNet, providing the first rigorously reviewed, reference validation data set. In addition to characterizing formatting problems, human vetting focused on detecting misspelling, synonymy, and the incorrect use of Darwin Core. Our results reveal a sobering view of the challenge ahead, as less than 47% of name strings were found to be currently valid. More optimistically, nearly 97% of name combinations could be resolved to a currently valid name, suggesting that computer-aided approaches may provide feasible means to improve digitized content. Finally, we associated names back to biocollections records and fit logistic models to test potential drivers of issues. A set of candidate variables (geographic region, year collected, higher-level clade, and the institutional digitally accessible data volume) and their 2-way interactions all predict the probability of records having taxon name issues, based on model selection approaches. We strongly encourage further experiments to use this reference data set as a means to compare automated or computer-aided taxon name tools for their ability to resolve and improve the existing wealth of legacy data.

The use and limits of scientific names in biological informatics

Scientific names serve to label biodiversity information: information related to species. Names, and their underlying taxonomic definitions, however, are unstable and ambiguous. This negatively impacts the utility of names as identifiers and as effective indexing tools in biological informatics where names are commonly utilized for searching, retrieving and integrating information about species. Semiotics provides a general model for describing the relationship between taxon names and taxon concepts. It distinguishes syntactics, which governs relationships among names, from semantics, which represents the relations between those labels and the taxa to which they refer. In the semiotic context, changes in semantics (i.e., taxonomic circumscription) do not consistently result in a corresponding and reflective change in syntax. Further, when syntactic changes do occur, they may be in response to semantic changes or in response to syntactic rules. This lack of consistency in the cardinal relationship between names and taxa places limits on how scientific names may be used in biological informatics in initially anchoring, and in the subsequent retrieval and integration, of relevant biodiversity information. Precision and recall are two measures of relevance. In biological taxonomy, recall is negatively impacted by changes or ambiguity in syntax while precision is negatively impacted when there are changes or ambiguity in semantics. Because changes in syntax are not correlated with changes in semantics, scientific names may be used, singly or conflated into synonymous sets, to improve recall in pattern recognition or search and retrieval. Names cannot be used, however, to improve precision. This is because changes in syntax do not uniquely identify changes in circumscription.

These observations place limits on the utility of scientific names within biological informatics applications that rely on names as identifiers for taxa. Taxonomic systems and services used to organize and integrate information about taxa must accommodate the inherent semantic ambiguity of scientific names. The capture and articulation of circumscription differences (i.e., multiple taxon concepts) within such systems must be accompanied with distinct concept identifiers that can be employed in association with, or in replacement of, traditional scientific names.