Molecular detection of trophic links in a complex insect host–parasitoid food web
Bibliographic record
Abstract
Previously, host-parasitoid links have been unveiled almost exclusively by time-intensive rearing, while molecular methods were used only in simple agricultural host-parasitoid systems in the form of species-specific primers. Here, we present a general method for the molecular detection of these links applied to a complex caterpillar-parasitoid food web from tropical rainforest of Papua New Guinea. We DNA barcoded hosts, parasitoids and their tissue remnants and matched the sequences to our extensive library of local species. We were thus able to match 87% of host sequences and 36% of parasitoid sequences to species and infer subfamily or family in almost all cases. Our analysis affirmed 93 hitherto unknown trophic links between 37 host species from a wide range of Lepidoptera families and 46 parasitoid species from Hymenoptera and Diptera by identifying DNA sequences for both the host and the parasitoid involved in the interaction. Molecular detection proved especially useful in cases where distinguishing host species in caterpillar stage was difficult morphologically, or when the caterpillar died during rearing. We have even detected a case of extreme parasitoid specialization in a pair of Choreutis species that do not differ in caterpillar morphology and ecology. Using the molecular approach outlined here leads to better understanding of parasitoid host specificity, opens new possibilities for rapid surveys of food web structure and allows inference of species associations not already anticipated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".