Impact of superparasitism time intervals on progeny survival and fitness of an egg parasitoid
Bibliographic record
Abstract
1.Superparasitism influences the fitness of female parasitoids and their progeny, and increasing time interval between oviposition bouts generally reduces survival probabilities of the second clutch. However, the timing of superparasitism may, under certain conditions, favour the second clutch. 2. This study investigated the effects of superparasitism time intervals on survival and fitness of both clutches, allowing the egg parasitoid Trichogramma euproctidis to parasitise previously parasitised Ephestia kuehniella host eggs at different time intervals. 3. In short intervals (0–1 h), a significant advantage was found for the second clutch over the first clutch (93.1% survival). In contrast, the second clutch was outcompeted by the first clutch in 17–19 h and 27–29 h intervals. Females deposited their eggs into larvae (intraspecific hyperparasitism) in a 39–41 h interval with a probability of survival of 57.1%. Females mostly refused to hyperparasitise pupae (∼80% rejection at 124–126 h), and when they did, their progeny never survived. 4. Hyperparasitism significantly increased parasitoid mortality in both clutches from less than 20% (superparasitism only) to over 35%. 5. Except on newly laid eggs (0–1 h), superparasiting females were frequently observed attempting to stab immatures of all stages (from 36.4% to 89.4% of all ovipositions depending on treatment), but infanticide only appeared to succeed on larvae (39–41 h). 6. When the second clutch survived, emerging parasitoids were smaller than control individuals, probably due to resource depletion. 7. These results suggest that T. euproctidis females can detect that a host has been previously parasitised but they cannot perceive superparasitism time intervals.
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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 | 0.001 |
| 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) | 0.002 | 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".