Oviposition behavior of the mirid <i>Macrolophus pygmaeus</i> under risk of intraguild predation and cannibalism
Bibliographic record
Abstract
Zoophytophagous mirid species, that feed and develop either on prey or plant resources, are often found simultaneously on the same host. Hence, these species can engage in both intraguild predation and cannibalism, which can pose a threat to mirid eggs. Ovipositing females may respond to such risks of predation on their eggs by reducing the number of eggs laid or selecting safer oviposition sites. We tested the oviposition behavior of Macrolophus pygmaeus (Rambur) (Hemiptera: Miridae) females under the risk of cannibalism by M. pygmaeus males and intraguild predation by Nesidiocoris tenuis (Reuter) males (Hemiptera: Miridae) under laboratory conditions. Intraguild predators and cannibals were introduced during or after the oviposition period. The number of eggs laid (using counts of newly hatched nymphs) and their proportion on each part of a tomato plant were both measured. The results reveal that only cannibalism by M. pygmaeus males after the period of oviposition significantly decreased the number of hatched eggs. Cannibalism thus represents a greater risk to mirid eggs than intraguild predation. The M. pygmaeus female responded to the presence of potential intraguild predators (or competitors) by decreasing the number of eggs laid in the upper leaves. The results suggest that M. pygmaeus females avoid competition by N. tenuis, by laying fewer eggs on upper leaves. Cannibalism could regulate zoophytophagous predator populations under prey scarcity conditions and minimize the risk of crop damage associated with those biological control agents.
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 | 0.000 |
| 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".