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talk · Tuesday 22 September · 11:30–⁠11:45 · SAL C

Managing observations when taxonomy changes: the case of Artportalen and Dyntaxa

Johan Liljeblad · National & Global Taxonomic Backbones

Recording time 3:04:07–3:15:25Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionConcept-based taxon IDs with traceable history let a large observation system absorb taxonomic change pragmatically.

Overview

What this was about

Johan Liljeblad explained how Sweden's 26-year-old citizen observatory Artportalen stays aligned with the national taxonomic database (Dyntaxa, started 2004), which uses stable taxon-concept identifiers rather than names. Taxonomy is published continuously and harvested nightly, but Artportalen applies changes every two months with news notes: name changes need no action, lumps move observations to the new taxon ID, and splits keep observations on a 'collective taxon' unless information (e.g. geography or phenology) allows assignment to one segregate. Edge cases include chained lumps, excluded taxa and fully revised concepts, so the process remains semi-manual with expert review, which is manageable at about 100 new taxa and 10-15 reassignments per cycle.

Why it matters. A working national model for keeping millions of observations consistent with an evolving taxonomy, relevant to any portal linked to a changing backbone.

Key ideas

In the room

  • Every taxon has an ID and at least one recommended name; the ID stays stable to the concept while names change, and changes are traceable.
  • Taxonomy is harvested nightly (Darwin Core file) but applied to Artportalen every second month to manage changes and inform users.
  • Not every taxon in the database is reportable; a filter controls what can be reported.
  • Lumps move observations to the replacing ID; splits keep observations on a collective taxon unless extra information allows reassignment.
  • Complications include multi-step lumps, taxa later excluded from Sweden, and totally revised concepts with no single target.
  • Around a million records are added per two months; about 100 new taxa and 10-15 reassignments per cycle are checked by organism-group experts.
  • Sweden has about 60,000 species in the database and observations of about 40,000.
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Notable moments

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Transcript

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Programme abstract

Managing observations when taxonomy changes: the case of Artportalen and Dyntaxa

Johan Liljeblad

In the Swedish taxonomic database Dyntaxa (www.dyntaxa.se), updates are published continuously. This taxonomy is used by the Swedish citizen observatory Artportalen (www.artportalen.se). However, while about one million observations are added to Artportalen every two months, updates of taxonomy are only made once during this time.

In Dyntaxa, the taxon, with its corresponding taxonID, is the basis. The name for such a taxon can change without changing taxonID. Lumps are easy to deal with by pointing an unaccepted taxon (with its taxonID) to the accepted one. Technically, splits can also be dealt with automatically by keeping all observations on the old (sensu lato) taxon, even though it is no longer considered a species but essentially a collective taxon. However, this is usually unsatisfactory. When there is more information about the split you might be able to move observations to one or several of the new species. In Sweden, these cases are relatively few despite the volume of observations.

Another way is to inform the reporter of such observations of these changes. They will be presented with the option to move them if they now know what new species they observed.

The Dyntaxa data model can handle not only these relations between taxa, such as splits and different kinds of lumps, but also convey the reason for them and the statuses of the taxa involved. When an editor is working in Dyntaxa, lumping a species with another, an automated text is generated. This text summarizes which taxonIDs were involved and that, e.g., taxonID 25 was merged into taxonID 42. This is also stored in a machine-readable format in the database.

We show examples where automation works fine, and some where we currently assess them manually but could possibly automate more in the future.

Submitted to the conference programme before the talk, so the talk itself may differ.