Selection for zoophagy influences biocontrol efficacy and fruit damage by <i>Dicyphus hesperus</i> in greenhouses
Bibliographic record
Abstract
Abstract The zoophytophagous predator Dicyphus hesperus Knight (Hemiptera: Miridae) is effective in the biological control of whiteflies in greenhouses, but it can also cause damage to fruits and plants. Artificial selection on foraging behavior allows the development of more zoophagous lines that have the potential to be both more effective and less likely to cause damage. Moreover, highly zoophagous lines could affect other biological control agents through increased intraguild predation or competition. This study tests the biological control efficacy against tobacco whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) and damage by highly and lowly zoophagous lines of D. hesperus in tomato greenhouses. The effect of these lines on Encarsia formosa Gahan (Hymenoptera: Aphelinidae) parasitoid wasp populations was also tested. In cage tests, we introduced D. hesperus from lowly or highly zoophagous and non‐selected lines. In half of the cage, E. formosa was introduced. The ability of predators and parasitoids to reduce B. tabaci populations was monitored for 12 weeks. Tomatoes produced were harvested and graded according to damage by D. hesperus . Highly zoophagous lines had a rapid and lasting impact on pest populations. Lowly zoophagous lines take longer to achieve the same level of pest control as highly zoophagous lines. Introductions of E. formosa also reduce populations, but without interacting with D. hesperus . Dicyphus hesperus did not affect E. formosa abundance. Lowly zoophagous lines generated higher proportions of damage. The results show that artificial selection based on zoophagy produces more efficient and less damaging lines in the greenhouse tomato crop. Over time, lines with low zoophagy compensated for low individual efficiency by increasing their numbers. Highly zoophagous lines are compatible with parasitoid wasps, which were little affected by D. hesperus .
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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) | 0.000 | 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".