Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately (Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down (Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology (Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to extending and expanding on how FAIR applies to biodiversity data and include practical targets and criteria for the publication and digitization of taxonomic concept representations and alignments in taxonomic treatments, checklists, and backbones. As a motivating case, consider the abundantly sampled North American deer mouse— Peromyscus maniculatus (Wagner 1845)—which was recently split from one continental species into five more narrowly defined forms, so that the name P. maniculatus is now only applied east of the Mississippi River (Bradley et al. 2019, Greenbaum et al. 2019). That single change instantly rendered ambiguous ~7% of North American mammal records in the Global Biodiversity Information Facility (n=242,663, downloaded 2021-06-04; GBIF.org 2021) and ⅓ of all National Ecological Observatory Network (NEON) small mammal samples (n=10,256, downloaded 2021-06-27). While this type of ambiguity is common in name-based databases when species are split, the example of P. maniculatus is particularly striking for its impact upon biological questions ranging from hantavirus surveillance in North America to studies of climate change impacts upon rodent life-history traits. Of special relevance to NEON sampling is recent evidence suggesting deer mice potentially transmit SARS-CoV-2 (Griffin et al. 2021). Automating the updating of occurrence records in such cases and others will require operational representations of taxonomic concepts—e.g., range maps, reference sequences, and diagnostic traits—that are FAIR in addition to taxonomic concept alignment information (Franz and Peet 2009). Despite steady progress, it remains difficult to find, access, and reuse authoritative information about how to apply taxonomic names even when it is already digitized. It can also be difficult to tell without manual inspection whether similar types of concept representations derived from multiple sources, such as range maps or reference sequences selected from different research articles or checklists, are in fact interoperable for a particular application. The issue is therefore different from important ongoing efforts to digitize trait information in species circumscriptions, for example, and focuses on how already digitized knowledge can best be packaged to inform human experts and artifical intelligence applications (Sterner and Franz 2017). We therefore propose developing community guidelines and criteria for FAIR taxonomic concept representations as "semantic artefacts" of general relevance to linked open data and life sciences research (Le Franc et al. 2020).
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Updating splits, lumps, and shuffles: Reconciling GenBank names with standardized avian taxonomies
Abstract Biodiversity research has advanced by testing expectations of ecological and evolutionary hypotheses through the linking of large-scale genetic, distributional, and trait datasets. The rise of molecular systematics over the past 30 years has resulted in a wealth of DNA sequences from around the globe. Yet, advances in molecular systematics also have created taxonomic instability, as new estimates of evolutionary relationships and interpretations of species limits have required widespread scientific name changes. Taxonomic instability, colloquially “splits, lumps, and shuffles,” presents logistical challenges to large-scale biodiversity research because (1) the same species or sets of populations may be listed under different names in different data sources, or (2) the same name may apply to different sets of populations representing different taxonomic concepts. Consequently, distributional and trait data are often difficult to link directly to primary DNA sequence data without extensive and time-consuming curation. Here, we present RANT: Reconciliation of Avian NCBI Taxonomy. RANT applies taxonomic reconciliation to standardize avian taxon names in use in NCBI GenBank, a primary source of genetic data, to a widely used and regularly updated avian taxonomy: eBird/Clements. Of 14,341 avian species/subspecies names in GenBank, 11,031 directly matched an eBird/Clements; these link to more than 6 million nucleotide sequences. For the remaining unmatched avian names in GenBank, we used Avibase’s system of taxonomic concepts, taxonomic descriptions in Cornell’s Birds of the World, and DNA sequence metadata to identify corresponding eBird/Clements names. Reconciled names linked to more than 600,000 nucleotide sequences, ~9% of all avian sequences on GenBank. Nearly 10% of eBird/Clements names had nucleotide sequences listed under 2 or more GenBank names. Our taxonomic reconciliation is a first step towards rigorous and open-source curation of avian GenBank sequences and is available at GitHub, where it can be updated to correspond to future annual eBird/Clements taxonomic updates.
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- Award ID(s):
- 1655683
- NSF-PAR ID:
- 10451918
- Date Published:
- Journal Name:
- Ornithology
- Volume:
- 139
- Issue:
- 4
- ISSN:
- 0004-8038
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Classification of the biological diversity on Earth is foundational to all areas of research within the natural sciences. Reliable biological nomenclatural and taxonomic systems facilitate efficient access to information about organisms and their names over time. However, broadly sharing, accessing, delivering, and updating these resources remains a persistent problem. This barrier has been acknowledged by the biodiversity data sharing community, yet concrete efforts to standardize and continually update taxonomic names in a sustainable way remain limited. High diversity groups such as arthropods are especially challenging as available specimen data per number of species is substantially lower than vertebrate or plant groups. The Terrestrial Parasite Tracker Thematic Collections Network project developed a workflow for gathering expert-verified taxonomic names across all available sources, aligning those sources, and publishing a single resource that provides a model for future endeavors to standardize digital specimen identification data. The process involved gathering expert-verified nomenclature lists representing the full taxonomic scope of terrestrial arthropod parasites, documenting issues experienced, and finding potential solutions for reconciliation of taxonomic resources against large data publishers. Although discordance between our expert resources and the Global Biodiversity Information Facility are relatively low, the impact across all taxa affects thousands of names that correspond to hundreds of thousands of specimen records. Here, we demonstrate a mechanism for the delivery and continued maintenance of these taxonomic resources, while highlighting the current state of taxon name curation for biodiversity data sharing.
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The taxonomic foundation of a new regional flora or monograph is the reconciliation of pre-existing names and taxonomic concepts (i.e., variation in usage of those names). This reconciliation is traditionally done manually, but the availability of taxonomic resources online and of text manipulation software means that some of the work can now be automated, speeding up the development of new taxonomic products. As a contribution to developing a new Flora of Alaska (floraofalaska.org), we have digitized the main pre-existing flora (Hultén 1968) and combined it with key online taxonomic name sources (Panarctic Flora, Flora of North America, International Plant Names Index - IPNI, Tropicos, Kew’s World Checklist of Selected Plant Families), to build a canonical list of names anchored to external Globally Unique Identifiers (GUIDs) (e.g., IPNI URLs). We developed taxonomically-aware fuzzy-matching software ( matchnames , Webb 2020) to identify cognates in different lists. The taxa for which there are variations between different sources in accepted names and synonyms are then flagged for review by taxonomic experts. However, even though names may be consistent across previous monographs and floras, the taxonomic concept (or circumscription) of a name may differ among authors, meaning that the way an accepted name in the flora is applied may be unfamiliar to the users of previous floras. We therefore have begun to manually align taxonomic concepts across five existing floras: Panarctic Flora, Flora of North America, Cody’s Flora of the Yukon (Cody 2000), Welsh’s Flora (Welsh 1974) and Hultén’s Flora (Hultén 1968), analysing usage and recording the Region Connection Calculus (RCC-5) relationships between taxonomic concepts common to each source. So far, we have mapped taxa in 13 genera, containing 557 taxonomic concepts and 482 taxonomic concept relationships. To facilitate this alignment process we developed software ( tcm , Webb 2021) to record publications, names, taxonomic concepts and relationships, and to visualize the taxonomic concept relationships as graphs. These relationship graphs have proved to be accessible and valuable in discussing the frequently complex shifts in circumscription with the taxonomic experts who have reviewed the work. The taxonomic concept data are being integrated into the larger dataset to permit users of the new flora to instantly see both the chain of synonymy and concept map for any name. We have also worked with the developer of the Arctos Collection Management Solution (a database used for the majority of Alaskan collections) on new data tables for storage and display of taxonomic concept data. In this presentation, we will describe some of the ideas and workflows that may be of value to others working to connect across taxonomic resources.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/ornithology/ukac045
