Body size affects the evolution of eyespots in caterpillars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it