Shaking Youngsters and Shaken Adults: Female Beetles Eavesdrop on Larval Seed Vibrations to Make Egg-Laying Decisions
Bibliographic record
Abstract
Egg-laying decisions are critical for insects, and particularly those competing for limited resources. Sensory information used by females to mediate egg-laying decisions has been reported to be primarily chemical, but the role of vibration has received little attention. We tested the hypothesis that vibrational cues produced by feeding larvae occupying a seed influences egg-laying decisions amongst female cowpea beetles. This hypothesis is supported by three lines of evidence using two strains of the cowpea beetle (Callosobruchus maculatus), an Indian strain with choosy females and aggressively competing larvae and a Brazilian strain with less choosy females and larvae exhibiting an "accommodating" type of competition. First, in free-choice bioassays of seed selection, choosy Indian females selected control seeds (free of eggs, larvae, or egg-laying marker) over seeds with live larvae (free of eggs and egg-laying marker), but did not discriminate between control seeds and those with dead larvae. In contrast, less choosy Brazilian females showed no preference for seeds containing live or dead larvae over controls. Second, laser-doppler vibrometer recordings confirmed that larvae feeding inside seeds generate vibrations that are available to the female during egg-laying decisions. Third, during dichotomous choice experiments where artificial vibrations approximating those produced by feeding larvae were played back during seed selection, Indian females preferred immobile control seeds over vibrating seeds, but Brazilian females showed no preference. These results support the hypothesis that females use larval vibrations in their egg-laying decisions; whether these vibrations are passive cues exploited by the female, or active signals that 'steer' the behaviour of the female is unknown. We propose that vibration cues and signals could be important for host selection in insects, particularly those laying on substrates where visual or chemical cues may be unreliable. This seems to be the case with females of the cowpea beetle since visual cues are not important and chemical egg-marking does not last more than two weeks, allowing vibration cues to improve discrimination of egg-laying substrate particularly by choosy females.
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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 | 0.000 |
| 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) | 0.002 | 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".