Incomplete estimates of genetic diversity within species: Implications for <scp>DNA</scp> barcoding
Bibliographic record
Abstract
DNA barcoding has greatly accelerated the pace of specimen identification to the species level, as well as species delineation. Whereas the application of DNA barcoding to the matching of unknown specimens to known species is straightforward, its use for species delimitation is more controversial, as species discovery hinges critically on present levels of haplotype diversity, as well as patterning of standing genetic variation that exists within and between species. Typical sample sizes for molecular biodiversity assessment using DNA barcodes range from 5 to 10 individuals per species. However, required levels that are necessary to fully gauge haplotype variation at the species level are presumed to be strongly taxon-specific. Importantly, little attention has been paid to determining appropriate specimen sample sizes that are necessary to reveal the majority of intraspecific haplotype variation within any one species. In this paper, we present a brief outline of the current literature and methods on intraspecific sample size estimation for the assessment of COI DNA barcode haplotype sampling completeness. The importance of adequate sample sizes for studies of molecular biodiversity is stressed, with application to a variety of metazoan taxa, through reviewing foundational statistical and population genetic models, with specific application to ray-finned fishes (Chordata: Actinopterygii). Finally, promising avenues for further research in this area are highlighted.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".