DNA barcoding of Northern Nearctic Muscidae (Diptera) reveals high correspondence between morphological and molecular species limits
Bibliographic record
Abstract
BACKGROUND: Various methods have been proposed to assign unknown specimens to known species using their DNA barcodes, while others have focused on using genetic divergence thresholds to estimate "species" diversity for a taxon, without a well-developed taxonomy and/or an extensive reference library of DNA barcodes. The major goals of the present work were to: a) conduct the largest species-level barcoding study of the Muscidae to date and characterize the range of genetic divergence values in the northern Nearctic fauna; b) evaluate the correspondence between morphospecies and barcode groupings defined using both clustering-based and threshold-based approaches; and c) use the reference library produced to address taxonomic issues. RESULTS: Our data set included 1114 individuals and their COI sequences (951 from Churchill, Manitoba), representing 160 morphologically-determined species from 25 genera, covering 89% of the known fauna of Churchill and 23% of the Nearctic fauna. Following an iterative process through which all specimens belonging to taxa with anomalous divergence values and/or monophyly issues were re-examined, identity was modified for 9 taxa, including the reinstatement of Phaonia luteva (Walker) stat. nov. as a species distinct from Phaonia errans (Meigen). In the post-reassessment data set, no distinct gap was found between maximum pairwise intraspecific distances (range 0.00-3.01%) and minimum interspecific distances (range: 0.77-11.33%). Nevertheless, using a clustering-based approach, all individuals within 98% of species grouped with their conspecifics with high (>95%) bootstrap support; in contrast, a maximum species discrimination rate of 90% was obtained at the optimal threshold of 1.2%. DNA barcoding enabled the determination of females from 5 ambiguous species pairs and confirmed that 16 morphospecies were genetically distinct from named taxa. There were morphological differences among all distinct genetic clusters; thus, no cases of cryptic species were detected. CONCLUSIONS: Our findings reveal the great utility of building a well-populated, species-level reference barcode database against which to compare unknowns. When such a library is unavailable, it is still possible to obtain a fairly accurate (within ~10%) rapid assessment of species richness based upon a barcode divergence threshold alone, but this approach is most accurate when the threshold is tuned to a particular taxon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".