Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data‐poor organisms like cetaceans
Bibliographic record
Abstract
Abstract Obtaining the representative morphological data required for traditional taxonomy is difficult, and sometimes impossible, for cetaceans, especially large ones. As a result, three quarters of the 88 currently recognized extant species have no subspecies and 40 taxa likely have additional unnamed taxa. Conservation needs give urgency to improving taxonomy because unnamed taxa are unlikely to receive protection equivalent to that received by named taxa. Genetic data can improve efforts to delimit subspecies, but the markers and methods used have varied and the magnitude of genetic difference used to justify subspecies distinctions across studies has also varied. Here, we define the concepts of populations, subspecies, and species to establish a foundation for developing guidelines (data to include and analyses to conduct) and quantitative standards (the magnitude of differentiation expected at different taxonomic levels) for using genetic data to support taxonomic recognition. Our definition is particularly applicable to data‐poor groups because it allows for naming a subspecies when there is uncertainty about whether lineages have diverged sufficiently for species‐level recognition. This allows a species that lacks convincing data for lineage divergence to be recognized as a subspecies while sufficient data are accrued, which could take decades for some cetaceans.
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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 | 0.478 | 0.572 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.012 | 0.023 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".