Artificial lighting affects the predation performance of the predatory bug <i>Orius insidiosus</i> (Say) against the Western flower thrips <i>Frankliniella occidentalis</i> (Pergande)
Bibliographic record
Abstract
Abstract Protected crops, such as greenhouses and indoor farming environments, using light-emitting diodes (LEDs) enable the modulation of the light spectrum, intensity, and photoperiod for agronomic purposes. This creates dynamic artificial light conditions for insects and arachnids, including predators used in biological control. Despite increasing interest, the effects of LEDs on predator behavior and control performance remain poorly understood. In the laboratory, we examined the locomotion and predation behaviors of the generalist predator Orius insidiosus against the pest Frankliniella occidentalis under various light spectra and intensities. We tested narrowband blue, green, and red spectra, three ratios of red and blue light, and a spectrum combining all three colors across a gradient of light intensity in microcosms. Orius insidiosus was active and successfully attacked prey under all lighting conditions, with 70% of individuals engaging in predation during the observation period. The light spectrum significantly influenced all recorded behaviors, while light intensity had negligible effects. Narrowband spectra led to the highest attack probabilities, but the mixed blue-red spectrum with a higher proportion of red light yielded the highest prey capture rates. The spectrum with all three colors showed intermediate capture success. These trends were consistent regarding capture probability in more complex environments with cucumber plants, where thrips were exposed to 24-hour artificial light sequences before and during predator releases. However, thrips survival rates remained similar across all lighting treatments. Our results demonstrated that while lighting treatments affect predator behavior, Orius insidiosus retains its ability to capture prey under various light conditions. This paves the way for developing lighting strategies that balance plant productivity with effective biological control in protected crop environments.
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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".