Defensive posture in a terrestrial salamander deflects predatory strikes irrespective of body size
Bibliographic record
Abstract
Abstract A wide variety of prey use defensive postures as a means of protection from predators. Many salamanders engage in broadly similar defensive postures, which may function as a warning signal and reduce the probability of attack, or may deflect predator attacks away from vital body parts. The extent to which these strategies (i.e., aposematism and deflection) act exclusively or synergistically, however, remains unknown. We deployed clay salamanders in the field, manipulating size (small, large) and posture (resting, defensive), and documented attack rates across three predator types. Competing risks analysis revealed that attack rates were affected by model size, deployment period, and leaf litter depth at the site of deployment, whereas model posture had no significant effect. Model size and posture did not interact, indicating that defensive posture was ineffective in deterring attack irrespective of prey size. Model prey in the defensive posture received significantly more attacks on the tail irrespective of size, and the defensive posture was more effective at deflecting avian attacks compared to mammal predation. We conclude that defensive posture increases tail conspicuousness without increasing predation risk, and primarily functions to deflect attacks away from vital body parts. The efficacy of defection may be further increased by tail undulation, however our use of static models means that we cannot exclude aposematic or deimatic functions for such movements. Our results provide important support for the deflection hypothesis in explaining antipredator behavior, and thereby set the stage for additional research targeting the functionality of attack deflection in natural predator–prey encounters.
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.
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 | 0.000 |
| 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".