Differential effects of temperature on the feeding kinematics of the tadpoles of two sympatric anuran species
Bibliographic record
Abstract
Temperature impacts ectotherm performance by influencing many biochemical and physiological processes. When well adapted to their environment, ectotherms should perform most efficiently at the temperatures they most commonly encounter. In the present study, we tested how differences in temperature affects the feeding kinematics of tadpoles of two anuran species: the benthic tadpole of Rhinella schneideri and the nektonic tadpole of Trachycephalus typhonius. Benthic and nektonic tadpoles have segregated distributions within ponds and thus tend to face different environmental conditions, such as temperature. Muscle contractile dynamics, and thus whole organism performance, is primarily temperature dependent for ectotherms. We hypothesized that changes in mean temperatures would have differential effects on the feeding kinematics of these two species. We conducted a laboratory experiment in which we used high-speed videography to record tadpoles foraging at cold and warm temperatures. In general, tadpoles filmed at warm temperatures opened their jaws faster, attained maximum gape earlier, and exhibited shorter gape cycles than tadpoles in cold temperatures, irrespective of species. We also found species x temperature interactions regarding the closing phase velocity, and the percentage of time it takes tadpoles to achieve maximum gape and to start closing their jaws. These interactions could indicate that these two co-occurring species differ in their sensitivity to differences in water temperature and have temperature-dependent feeding strategies that maximize feeding performance in their preferred environment.
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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 | 0.000 |
| 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".