Does riparian disturbance alter stream amphibian antipredator behaviors?
Bibliographic record
Abstract
Ecological traps occur when a species makes maladaptive habitat-selection decisions. Human-modified environments including deforested riparian habitats can change how organisms respond to environmental cues. Stream amphibians alter their habitat selection in response to abiotic cues associated with riparian clearing, but little research exists to determine if behavioral shifts to abiotic cues may make them more susceptible to predation. To evaluate if deforested habitats create ecological traps, we studied habitat-selection behavior of larval Black-bellied Salamander (Desmognathus quadramaculatus (Holbrook, 1840)) when given conflicting environmental cues. We also evaluated the potential for learning or adaptation to cues in deforested reaches by evaluating individuals from forested and deforested reaches. We anticipated that individuals from deforested reaches would make adaptive antipredator choices when presented with well-lit habitat, whereas individuals from forested reaches would select shaded habitat closer to a predator. We found that habitat origin, light, and predator presence all interacted to influence habitat selection. Although individuals from forested habitats selected shaded environments, all observed individuals adaptively avoided a predator. Individuals from deforested reaches were more willing to enter well-lit habitat to avoid the predator. Despite documented declines of salamanders associated with forest removal, it appears that individuals are capable of making adaptive antipredator decisions in degraded habitats.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".