Correlated trait response: comparing amphibian defense strategies across a stress gradient
Bibliographic record
Abstract
Animals inhabiting complex environments often contend with multiple stressors that can select for conflicting responses. Individuals can mediate these conflicts by utilizing correlated responses across multiple traits. In aquatic habitats, larval amphibians often face conflicting, simultaneous pressures, such as ultraviolet-B (UV-B) radiation and predators. UV-B radiation and predation risk influence behavior and body color in many amphibian species, altering activity rates, refuge use, and coloration. When both UV-B and predators are present, individuals can avoid conflicts by coupling behavior with body color to form a correlated response. UV-B exposure rates vary along an elevation gradient, thus trait combinations may also vary. We quantified changes in activity rates and body color in two anuran species, the red-legged frog ( Rana aurora Baird and Girard, 1852) (low elevations) and the cascades frog ( Rana cascadae Slater, 1939) (high elevations), during exposure to predator chemical cues (rough-skinned newt, Taricha granulosa (Skilton, 1849)) and UV-B radiation. Rana aurora decreased activity in response to UV-B and became more cryptic over time, while R. cascadae coupled decreased activity rates in response to predators with dark body coloration to screen out UV-B. Both species responded with a correlated trait response, yet employed opposite strategies. This observed species difference may be reflective of differences in stress across habitats and availability of alternative defenses.
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 | 0.001 |
| 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 | 0.000 | 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".