The impact of patch encounter rate on patch residence time of female parasitoids increases with patch quality
Bibliographic record
Abstract
Abstract 1. For animal species that forage on patchily distributed resources, patch time allocation is of prime importance to their reproductive success. According to Charnov’s marginal value theorem (MVT), the rate of patch encounter should influence negatively the patch residence time: as the rate of patch encounter decreases, the patch residence time increases. Moreover, the MVT predicts that animals should stay longer in high quality patches. 2. Using the aphid parasitoid Aphidius rhopalosiphi (Hymenoptera: Aphidiinae), the effects of these two factors (patch encounter rate and host density) were combined in order to test if the increment in patch residence time for a given decrease in patch encounter rate was larger for high quality patches than for low quality patches. 3. The results show a significant effect of the interaction between the two factors. In high host density patches, parasitoids spent more time if they experienced a low patch encounter rate, while in low host density patches, patch encounter rate had no significant effect on the patch residence time. This suggests that the response of A. rhopalosiphi females to patch encounter rate varied with host density in the patch. Moreover, the same interaction effect was observed for the number of ovipositor contacts on aphids. 4. Parasitoid females can use patch encounter rate to estimate patch density in the habitat but the effect of this estimate on their patch residence time is modulated by patch quality. Staying longer in a patch when patches are rare is more advantageous when the fitness gained by doing so is large. In low quality patches, the expected fitness gain is small and the female may gain more by leaving and taking her chance at finding another patch.
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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 | 0.003 |
| 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".