GBIF Rule Based Annotations

What this was about

John Waller presents experimental work at GBIF on rule-based annotations that capture 'rules in people's heads' about suspicious occurrences, e.g. 'amphibians in Antarctica are suspicious'. Rules are polygons plus taxon and optional scoping (dataset key, basis of record, fossils excluded) and apply to existing and future records. Examples include an elephant shrew record in Madagascar and axolotls outside Mexico; he argues that marking where a species is certainly not found (an 'anti-distribution') is much easier for non-experts than defining native ranges. A demo Download Annotator and an R package, gbifrules, apply rules to downloads.

Why it matters. It offers a lightweight, community-driven way to capture expert knowledge about data errors that survives unstable occurrence identifiers.

In the room

• Rules flag all current and future matching records (taxon within polygon) as suspicious.
• Anti-distributions (where a species will not occur) are easier to define than native ranges and need little expertise.
• Because GBIF occurrence IDs are not fully stable, single records are targeted by scoping rules to a small box plus dataset key and basis of record.
• Complex rules allow exceptions, e.g. no non-human primates in Cuba except fossils.
• Tools: an experimental web UI for making rules, a browser-based Download Annotator demo, and the gbifrules R package (GitHub, not CRAN).
• Main challenge: recruiting rule makers; the feature is not yet visible on gbif.org.

Notable moments