Butterfly–parasitoid–hostplant interactions in Western Palaearctic Hesperiidae: a DNA barcoding reference library
Bibliographic record
Abstract
Abstract The study of ecological interactions between plants, phytophagous insects and their natural enemies is an essential but challenging component for understanding ecosystem dynamics. Molecular methods such as DNA barcoding can help elucidate these interactions. In this study, we employed DNA barcoding to establish hostplant and parasitoid interactions with hesperiid butterflies, using a complete reference library for Hesperiidae of continental Europe and north-western Africa (53 species, 100% of those recorded) based on 2934 sequences from 38 countries. A total of 233 hostplant and parasitoid interactions are presented, some recovered by DNA barcoding larval remains or parasitoid cocoons. Combining DNA barcode results with other lines of evidence allowed 94% species-level identification for Hesperiidae, but success was lower for parasitoids, in part due to unresolved taxonomy. Potential cases of cryptic diversity, both in Hesperiidae and Microgastrinae, are discussed. We briefly analyse the resulting interaction networks. Future DNA barcoding initiatives in this region should focus attention on north-western Africa and on parasitoids, because in these cases barcode reference libraries and taxonomy are less well developed.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".