Body size affects the evolution of eyespots in caterpillars
Bibliographic record
Abstract
Many caterpillars have conspicuous eye-like markings, called eyespots. Despite recent work demonstrating the efficacy of eyespots in deterring predator attack, a fundamental question remains: Given their protective benefits, why have eyespots not evolved in more caterpillars? Using a phylogenetically controlled analysis of hawkmoth caterpillars, we show that eyespots are associated with large body size. This relationship could arise because (i) large prey are innately conspicuous; (ii) large prey are more profitable, and thus face stronger selection to evolve such defenses; and/or (iii) eyespots are more effective on large-bodied prey. To evaluate these hypotheses, we exposed small and large caterpillar models with and without eyespots in a 2 × 2 factorial design to avian predators in the field. Overall, eyespots increased prey mortality, but the effect was particularly marked in small prey, and eyespots decreased mortality of large prey in some microhabitats. We then exposed artificial prey to naïve domestic chicks in a laboratory setting following a 2 × 3 design (small or large size × no, small, or large eyespots). Predators attacked small prey with eyespots more quickly, but were more wary of large caterpillars with large eyespots than those without eyespots or with small eyespots. Taken together, these data suggest that eyespots are effective deterrents only when both prey and eyespots are large, and that innate aversion toward eyespots is conditional. We conclude that the distribution of eyespots in nature likely results from selection against eyespots in small caterpillars and selection for eyespots in large caterpillars (at least in some microhabitats).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".