A molecular approach to unravel trophic interactions between parasitoids and hyperparasitoids associated with pecan aphids
Bibliographic record
Abstract
Advances in molecular ecology can overcome many challenges in understanding host-parasitoid interactions. Genetic characterization of the key-players in systems helps to confirm species and identify trophic linkages essential for ecological service delivery by biological control agents; however, relatively few agroecosystems have been explored using this approach. Pecan production consists of a large tree perennial system containing an assortment of seasonal pests and natural enemies. As a first step to characterizing host-parasitoid associations in pecan food webs, we focus on aphid species and their parasitoids. Based on DNA barcoding of field-collected and reared specimens, we confirmed the presence of 3 species of aphid, one family of primary parasitoids, and 5 species of hyperparasitoids. By applying metabarcoding to field-collected aphid mummies, we were able to identify multiple species within each aphid mummy to unravel a complex food web of 3 aphids, 2 primary parasitoids, and upward of 8 hyperparasitoid species. The results of this study demonstrate that multiple hyperparasitoid species attack a single primary parasitoid of pecan aphids, which may have negative consequences for successful aphid biological control. Although further research is needed on a broader spatial scale, our results suggest multiple species exist in this system and may suggest a complex set of interactions between parasitoids, hyperparasitoids, and the 3 aphid species. This was the first time that many of these species have been characterized and demonstrates the application of novel approaches to analyze the aphid-parasitoid food webs in pecans and other tree crop systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".