DNA-based species delineation in tropical beetles using mitochondrial and nuclear markers
Bibliographic record
Abstract
DNA barcoding has been successfully implemented in the identification of previously described species, and in the process has revealed several cryptic species. It has been noted that such methods could also greatly assist in the discovery and delineation of undescribed species in poorly studied groups, although to date the feasibility of such an approach has not been examined explicitly. Here, we investigate the possibility of using short mitochondrial and nuclear DNA sequences to delimit putative species in groups lacking an existing taxonomic framework. We focussed on poorly known tropical water beetles (Coleoptera: Dytiscidae, Hydrophilidae) from Madagascar and dung beetles (Scarabaeidae) in the genus Canthon from the Neotropics. Mitochondrial DNA sequence variation proved to be highly structured, with >95% of the observed variation existing between discrete sets of very closely related genotypes. Sequence variation in nuclear 28S rRNA among the same individuals was lower by at least an order of magnitude, but 16 different genotypes were found in water beetles and 12 genotypes in Canthon, differing from each other by a minimum of two base pairs. The distribution of these 28S rRNA genotypes in individuals exactly matched the distribution of mtDNA clusters, suggesting that mtDNA patterns were not misleading because of introgression. Moreover, in a few cases where sequence information was available in GenBank for morphologically defined species of Canthon, these matched some of the DNA-based clusters. These findings demonstrate that clusters of close relatives can be identified readily in the sequence variation obtained in field collected samples, and that these clusters are likely to correspond to either previously described or unknown species. The results suggest that DNA-assisted taxonomy will not require more than a short fragment of mtDNA to provide a largely accurate picture of species boundaries in these groups. Applied on a large scale, this DNA-based approach could greatly improve the rate of species discovery in the large assemblages of insects that remain undescribed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".