A direct comparison of the effectiveness of two anti‐predator strategies under field conditions
Bibliographic record
Abstract
Abstract Aposematism and crypsis are two widespread defensive strategies that have evolved in organisms to reduce attacks by predators. However, although both have been studied extensively, predation rates on unpalatable conspicuous prey have seldom been directly compared to those on palatable cryptic prey, and never in the field. In this study, we use established methods to compare the effectiveness of both defensive traits, by presenting artificial prey targets on trees where they were subject to attack by wild avian predators in a natural field setting. When partially consumed prey and those that had been completely removed were both treated as attacked by predators, there were no differences in attack rates between targets with the two defensive strategies. However, aposematic prey were completely consumed less often than cryptic prey, and partially consumed more often. This suggests that predators engage in taste rejection of unpalatable prey and/or feed on conspicuous prey more cautiously (‘go‐slow’ predation). We also observed significant differences in predation among experimental sites, in spite of their similarity and relatively close proximity, and among trials, which suggests that prey may experience highly variable predation in the wild. If aposematic prey are capable of surviving attacks by predators, then this represents a potential defensive benefit of aposematism over crypsis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".