Artificial selection of zoophagous lines of the biological control agent <i>Dicyphus hesperus</i>
Bibliographic record
Abstract
Abstract Zoophytophagous predators can be beneficial for controlling crop pests in greenhouses. Yet, they can also cause significant economic damage. More zoophagous and effective predator lines can be developed by selectively breeding highly zoophagous individuals. Hence, artificial selection based on the degree of zoophagy in zoophytophagous predators can improve their efficiency as biocontrol agents while reducing the risk of crop damage. However, artificial selection on zoophagy could cause changes in other behavioral or life history traits due to genetic correlation or pleiotropy. These changes can affect the ecological conditions in which biological control agents work. We created highly and lowly zoophagous lines of Dicyphus hesperus Knight (Hemiptera: Miridae) using artificial selection. We tested genetic correlations between zoophagy and food patch exploitation equity in four generations of artificial selection. The results revealed that females were more zoophagous than males. The broad sense heritability ( H 2 ) of zoophagy was 0.38 in females and 0.29 in males. Artificial selection on zoophagy led to decreased food patch exploitation equity, yet the traits were not genetically correlated. Our results suggest that artificial selection can be used to develop lines of D. hesperus that enhance the benefits of biological control and modify ecological factors such as prey density and distribution.
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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) | 0.001 | 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 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".