Species-Level Para- and Polyphyly in DNA Barcode Gene Trees: Strong Operational Bias in European Lepidoptera
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of DNA data is revolutionizing all fields of systematic research. DNA barcode sequences, now available for millions of specimens and several hundred thousand species, are increasingly used in algorithmic species delimitations. This is complicated by occasional incongruences between species and gene genealogies, as indicated by situations where conspecific individuals do not form a monophyletic cluster in a gene tree. In two previous reviews, non-monophyly has been reported as being common in mitochondrial DNA gene trees. We developed a novel web service "Monophylizer" to detect non-monophyly in phylogenetic trees and used it to ascertain the incidence of species non-monophyly in COI (a.k.a. cox1) barcode sequence data from 4977 species and 41,583 specimens of European Lepidoptera, the largest data set of DNA barcodes analyzed from this regard. Particular attention was paid to accurate species identification to ensure data integrity. We investigated the effects of tree-building method, sampling effort, and other methodological issues, all of which can influence estimates of non-monophyly. We found a 12% incidence of non-monophyly, a value significantly lower than that observed in previous studies. Neighbor joining (NJ) and maximum likelihood (ML) methods yielded almost equal numbers of non-monophyletic species, but 24.1% of these cases of non-monophyly were only found by one of these methods. Non-monophyletic species tend to show either low genetic distances to their nearest neighbors or exceptionally high levels of intraspecific variability. Cases of polyphyly in COI trees arising as a result of deep intraspecific divergence are negligible, as the detected cases reflected misidentifications or methodological errors. Taking into consideration variation in sampling effort, we estimate that the true incidence of non-monophyly is ∼23%, but with operational factors still being included. Within the operational factors, we separately assessed the frequency of taxonomic limitations (presence of overlooked cryptic and oversplit species) and identification uncertainties. We observed that operational factors are potentially present in more than half (58.6%) of the detected cases of non-monophyly. Furthermore, we observed that in about 20% of non-monophyletic species and entangled species, the lineages involved are either allopatric or parapatric-conditions where species delimitation is inherently subjective and particularly dependent on the species concept that has been adopted. These observations suggest that species-level non-monophyly in COI gene trees is less common than previously supposed, with many cases reflecting misidentifications, the subjectivity of species delimitation or other operational factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it