Towards the implementation of a DNA barcode library for the identification of Peruvian species of Anastrepha (Diptera: Tephritidae)
Bibliographic record
Abstract
The genus Anastrepha is a diverse lineage of fruit-damaging tephritid flies widespread across the Neotropical Region. Accurate taxonomic identification of these flies is therefore of paramount importance in agricultural contexts. DNA barcoding libraries are molecular-based tools based on a short sequence of the mitochondrial COI gene enabling rapid taxonomic identification of biological species. In this study, we evaluate the utility of this method for species identification of Peruvian species of Anastrepha and assemble a preliminary barcode profile for the group. We obtained 73 individual sequences representing the 15 most common species, 13 of which were either assigned to previously recognized or newly established BINs. Intraspecific genetic divergence between sampled species averaged 1.01% (range 0-3.3%), whereas maximum interspecific values averaged 8.67 (range 8.26-17.12%). DNA barcoding was found to be an effective method to discriminate between many Peruvian species of Anastrepha that were tested, except for most species of the fraterculus species group, which were all assigned to the same BIN as they shared similar and, in some cases, identical barcodes. We complemented this newly produced dataset with 86 published sequences to build a DNA barcoding library of 159 sequences representing 56 Peruvian species of Anastrepha (approx. 58% of species reported from that country). We conclude that DNA barcoding is an effective method to distinguish among Peruvian species of Anastrepha outside the fraterculus group, and that complementary methods (e.g., morphometrics, additional genetic markers) would be desirable to assist sensu stricto species identification for phytosanitary surveillance and management practices of this important group of pestiferous flies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".