DNA barcodes for insect pest identification: a test case with tussock moths (Lepidoptera: Lymantriidae)
Bibliographic record
Abstract
Reliable and rapid identification of exotic pest species is critical to biosecurity. However, identification of morphologically indistinct specimens, such as immature life stages, that are frequently intercepted at borders is often impossible. Several DNA-based methods have been used for species identification; however, a more universal and anticipatory identification system is needed. Consequently, we tested the ability of DNA "barcodes" to identify species of tussock moths (Lymantriidae), a family containing several important pest species. We sequenced a 617 base pair fragment of the mitochondrial gene cytochrome c oxidase 1 for 20 lymantriid species. We used these, together with other Noctuoidea species sequences from GenBank and the Barcoding of Life Database to create a "profile" or reference sequence data set. We then tested the ability of this profile to provide correct species identifications for 93 additional lymantriid specimens from a data set of mock unknowns. Of the unknowns, 100% were correctly identified by the cytochrome c oxidase 1 profile. Mean interspecific sequence (Kimura 2-parameter) divergence was an order of magnitude greater (14%) than mean intraspecific divergence (<1%). Four species showed deeper genetic divergences among populations. We conclude that DNA barcodes provide a highly accurate means of identifying lymantriid species and show considerable promise as a universal approach to DNA-based identification of pest insects.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".