Predator diet and prey adaptive responses: Can tadpoles distinguish between predators feeding on congeneric vs. conspecific prey?
Bibliographic record
Abstract
Predator diet can play an important role in facilitating detection of predation risk among prospective prey, and such detection should have adaptive significance in reducing mortality in environments where not all predators confer similar risk. In the laboratory, we tested behavioural and morphological responses of tadpoles from two congeneric frog species (bullfrog ( Rana catesbeiana Shaw, 1802) and mink frog ( Rana septentrionalis Baird, 1854)) to cues from an odonate predator (genus Aeshna Fabricius, 1775). In a separate experiment we found that both frog species had similar baseline vulnerability to Aeshna predation, implying that species’ responses to predators feeding on conspecific vs. congeneric prey also would be similar. Both species reduced their activity in the presence of predators feeding on tadpoles of either species vs. those fed invertebrates (Libellulidae) or not subjected to predators (controls). Bullfrog tadpoles grew bigger than controls when exposed to predators fed mink frog tadpoles only, whereas mink frogs failed to show a comparable response. Neither species exhibited changes in shape that were attributable to predator diet. Our results suggest that closely related frog species do not distinguish between predators feeding on conspecific vs. congeneric prey, implying that selection favours generalized antipredator responses when prey species are subject to similar predation risk.
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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) | 0.001 | 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".