What happens to the traditional taxonomy when a well-known tropical saturniid moth fauna is DNA barcoded?
Bibliographic record
Abstract
Biodiversity of tropical Saturniidae, as measured through traditionally described and catalogued species, strongly risks pooling cryptic species under one name. We examined the DNA barcodes, morphology, habitus and ecology of 32 ‘well known’ species of dry forest saturniid moths from Area de Conservacion Guanacaste (ACG) in north-western Costa Rica and found that they contain as many as 49 biological entities that are probably separate species. The most prominent splitting of traditional species – Eacles imperialis, Automeris zugana, Automeris tridens, Othorene verana, Hylesia dalina, Dirphia avia, Syssphinx molina, Syssphinx colla, and Syssphinx quadrilineata – is where one species was believed to breed in dry forest and rain forest, but is found to be two biological entities variously distinguishable by DNA barcodes and morphology, habitus, and/or microecological distribution. This implies that ‘standard’ biological information about each traditional species may be an unconscious mix of interspecific information, and begs renewed DNA barcoding, closer attention to so-called intraspecific variation, and increased museum collection and curation of specimens from more individual and ecologically characterised sites – as well as eventually more species descriptions. Simultaneously, this inclusion of sibling species as individual entities in biodiversity studies, rather than pooled under one traditional name, reduces the degree of ecological and evolutionary generalisation perceived by the observer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.139 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".