Different artificial lighting spectra changes the mating behavior of the generalist predator Orius insidiosus (Say), and photoperiod extension promotes its development
Bibliographic record
Abstract
In protected cropping systems such as greenhouses and indoor farming, augmentative biological control depends on release rates, establishment, and reproduction of natural enemies. Light-emitting diodes (LEDs) are widely used to enhance plant growth in these systems and are increasingly implemented in mass-rearing facilities for natural enemies. However, the impacts of LEDs on the life cycle of beneficial predators remain insufficiently explored. This study examined the mating behaviors and developmental performance of generalist predator Orius insidiosus under light spectra previously shown to support its predation of the pest thrips Frankliniella occidentalis . In laboratory experiments, predator pairs were exposed to artificial light sequences starting with a 12 h baseline solar light condition simulating a cloudy winter day, supplemented by 8 h photoperiod extensions (blue, blue-red, or blue-green–red spectra), or a control without extension. Mating occurred under all tested conditions, but blue light reduced mating frequency and duration. Photoperiod extension improved fecundity, fertility, and resulted in the highest number of second-generation O. insidiosus adults, with blue light favoring egg laying and hatching but not metamorphosis into adults. The second-generation sex ratio was unaffected by light sequence, maintaining population viability with a balanced proportion of females. Our findings demonstrate that O. insidiosus can successfully mate, reproduce, and develop under artificial lighting and highlight the potential of modulating light spectrum to optimize both mass-rearing and establishment in protected crops.
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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".