Learning provides mating opportunities for males of a parasitoid wasp
Bibliographic record
Abstract
Abstract The ability of insects to learn locations of future resources has rarely been studied. Here, we show that males of the solitary parasitoid wasp Pimpla disparis Viereck (Hymenoptera: Ichneumonidae) learn locations of future mates. Male P. disparis reportedly arrest on parasitized pupae of wax moth, Galleria mellonella L. (Lepidoptera: Pyralidae), and gypsy moth, Lymantria dispar L. (Lepidoptera: Erebidae), when mate emergence is imminent. We tested the hypothesis that male P. disparis identify, memorize, and revisit the location(s) of parasitized host pupae as a strategy to attain mates. We colour‐coded P. disparis males in the field and noticed that they revisit parasitized moth pupae on consecutive days, and arrest on those pupae with a near‐emergence P. disparis parasitoid. In a laboratory experiment with two large corrugated cardboard cylinders (CCCs) as surrogate trees, each CCC bearing two parasitized moth pupae with a near‐emergence P. disparis parasitoid or two pupae not parasitized, males on day 1 of the experiment visited parasitized pupae more often than pupae not parasitized. On day 2, when each CCC had been replaced and now carried pupae that were not parasitized, males returned to the same CCC, or the same micro‐location on that CCC, which on day 1 had carried parasitized pupae. Field and laboratory data combined indicate that male P. disparis learn the location of future mates. With female P. disparis being haplodiploid and capable of reproducing without mating experience, the onus to find a mate is on males. They accomplish this by detecting parasitized pupae, learning their location, revisiting them frequently, and then arresting on them when the prospective mate nears emergence, taking a 50% chance that it is indeed a female.
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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".