DNA barcodes and cryptic species of skipper butterflies in the genus <i>Perichares</i> in Area de Conservación Guanacaste, Costa Rica
Bibliographic record
Abstract
DNA barcodes can be used to identify cryptic species of skipper butterflies previously detected by classic taxonomic methods and to provide first clues to the existence of yet other cryptic species. A striking case is the common geographically and ecologically widespread neotropical skipper butterfly Perichares philetes (Lepidoptera, Hesperiidae), described in 1775, which barcoding splits into a complex of four species in Area de Conservación Guanacaste (ACG) in northwestern Costa Rica. Three of the species are new, and all four are described. Caterpillars, pupae, and foodplants offer better distinguishing characters than do adults, whose differences are mostly average, subtle, and blurred by intraspecific variation. The caterpillars of two species are generalist grass-eaters; of the other two, specialist palm-eaters, each of which feeds on different genera. But all of these cryptic species are more specialized in their diet than was the morphospecies that held them. The four ACG taxa discovered to date belong to a panneotropical complex of at least eight species. This complex likely includes still more species, whose exposure may require barcoding. Barcoding ACG hesperiid morphospecies has increased their number by nearly 10%, an unexpectedly high figure for such relatively well known 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".