Are plant species inherently harder to discriminate than animal species using DNA barcoding markers?
Bibliographic record
Abstract
The ability to discriminate between species using barcoding loci has proved more difficult in plants than animals, raising the possibility that plant species boundaries are less well defined. Here, we review a selection of published barcoding data sets to compare species discrimination in plants vs. animals. Although the use of different genetic markers, analytical methods and depths of taxon sampling may complicate comparisons, our results using common metrics demonstrate that the number of species supported as monophyletic using barcoding markers is higher in animals (> 90%) than plants (~70%), even after controlling for the amount of parsimony-informative information per species. This suggests that more than a simple lack of variability limits species discrimination in plants. Both animal and plant species pairs have variable size gaps between intra- and interspecific genetic distances, but animal species tend to have larger gaps than plants, even in relatively densely sampled genera. An analysis of 12 plant genera suggests that hybridization contributes significantly to variation in genetic discontinuity in plants. Barcoding success may be improved in some plant groups by careful choice of markers and appropriate sampling; however, overall fine-scale species discrimination in plants relative to animals may be inherently more difficult because of greater levels of gene-tree paraphyly.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".