Age-dependent fitness costs of alarm signaling in aphids
Bibliographic record
Abstract
For an alarm signal to evolve, the benefits to the signaler must outweigh the costs of sending the signal. Research has largely focused on the benefits of alarm signaling, and the costs to an organism of sending an alarm signal are not well known. When attacked by a predator, aphids secrete cornicle droplets, containing an alarm pheromone, for individual protection and to warn clonemates. As aphid alarm pheromone is synthesized de novo in a feedback loop with juvenile hormone, we hypothesized that the secretion of cornicle droplets may result in a direct fitness cost to the emitter. We show that the secretion of a single cornicle droplet by pre-reproductive (third- and fourth-instar) pea aphids, Acyrthosiphon pisum, directly altered the timing and number of offspring produced. Third-instar pea aphids delayed offspring production but produced more offspring overall than non-secreting aphids, demonstrating a life-history shift but no significant fitness cost of droplet secretion. Fourth-instar pea aphids also delayed offspring production but produced the same number of offspring as non-secretors, resulting in a direct fitness cost of droplet secretion. Offspring production by adult, reproductive pea aphids that secreted a cornicle droplet did not differ from that of non-secretors. Thus, the fitness costs of secreting cornicle droplets containing an alarm signal are age-dependent.
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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) | 0.001 | 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".