Selective effects of floral food sources and honey on life‐history traits of a pest–parasitoid system
Bibliographic record
Abstract
Abstract Many parasitoids and their herbivorous hosts forage on the same floral resources in agroecosystems. Floral resources that benefit natural enemies without supporting pests can improve the efficacy of biological control agents. Here, we report the results of a study on selective effects of floral and non‐floral food sources on the life‐history traits of the parasitoid Diadegma insulare (Cresson) (Hymenoptera: Ichneumonidae) and its host Plutella xylostella (L.) (Lepidoptera: Plutellidae). Under standard laboratory conditions, insects were exposed to four flowering plant species, 10% honey solution, and water. All food sources increased the longevity of the herbivore and its parasitoid by as much as four‐ to nine‐fold, respectively, compared to the water control diet. Diadegma insulare survived the longest on Thlaspi arvense L. (Brassicaceae) and P. xylostella on Sinapis arvensis L. (Brassicaceae). However, none of the food sources tested was beneficial to the parasitoid alone, though Lobularia maritima L. (Brassicaceae) was found to selectively favor the longevity of D. insulare. Diadegma insulare adults were heavier when fed on S. arvensis, whereas feeding on the honey solution led to higher body weights of P. xylostella. In conclusion, we demonstrated that floral and non‐floral food sources varied in their suitability and acted differently on life‐history traits of a host–parasitoid system. The selective characteristics of nectar‐producing plants and their influence on the herbivore‐natural enemy combination can, therefore, be employed to increase their impact in integrated P. xylostella management.
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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 | 0.000 |
| 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) | 0.001 | 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".