Does host plant quality affect the oviposition decisions of an omnivore?
Bibliographic record
Abstract
Optimal oviposition theory predicts a positive relationship between female preference for oviposition hosts and offspring performance. Interspecies effects on oviposition preference have been widely investigated, especially for herbivores. However, intraspecies variation, such as nitrogen content, might also influence female preference for oviposition hosts and subsequent offspring performance. To evaluate this possibility, we investigated the oviposition preference of a zoophytophagous omnivore and the development and survival of its nymphs on a single species of host plant that varied in nitrogen content. In choice and no-choice experiments without prey, female omnivores were allowed to oviposit on plants that had been fertilized using 4 rates of nitrogen fertilizer (39, 78, 156, and 311 mg/L nitrogen) for 72 h. After 72 h, the most females were found on tomato plants receiving high concentrations of nitrogen fertilizer and more eggs were laid on those plants. First instar nymphs developed more rapidly on high-nitrogen plants and third instar nymphs developed faster on low-nitrogen plants. Plant nitrogen did not affect nymph survival to the adult stage, or the probability of survival over time. Although female omnivores did discriminate between potential oviposition hosts based on plant nitrogen, their choices did not significantly impact nymph development or survival. This is the first study to show that intraspecies variation in nitrogen content between plants affects the oviposition preference of female omnivores, but not offspring performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".