Relative size matters: eyespots on large insect prey deter small arthropod predators
Bibliographic record
Abstract
Circular chromatic patterns that appear to resemble vertebrate eyes (‘eyespots’) are commonplace in the animal kingdom and are widely believed to have evolved as an anti-predator defence. For example, experiments have shown that eyespots on caterpillar-like pastry baits can deter predation by birds. However, little is known about the extent to which eyespots deter (or promote) attack by arthropod predators. Here, we describe two separate experiments in which salticid spiders ( Salticus scenicus ) and Chinese mantids ( Tenodera sinensis ) were presented with a choice of mealworms ( Tenibrio molitor ) with or without eyespots. In a complementary experiment, we observed the time taken for adult Chinese mantids to attack hawkmoth ( Manduca quinquemaculata ) larvae of two different sizes, with and without eyespots. All three experiments indicate that eyespots on insect larvae can deter predation, so long as the larvae are sufficiently large compared with the size of the arthropod predator. However, when larvae are small relative to the arthropod predator, eyespots cease to be protective and may even promote attacks. Our results suggest that small arthropods can show an aversion to large prey with eyespots and help explain the presence of eyespots in medium-sized caterpillars, because these traits are unlikely to deter avian predators.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".