Does rainfall or temperature influence antipredator vigilance in a hibernating mammal?
Bibliographic record
Abstract
Abstract As the global climate changes, temperatures are rising, snow is melting earlier, and rainfall is becoming more variable, and these climatic changes may create an ecological mismatch. While prior work has shown how animals respond to these changes physiologically and behaviorally, few have specifically investigated antipredator behavior, an essential activity. In many species, there are direct fitness tradeoffs between allocating time and energy to antipredator vigilance and foraging. To discover how these tradeoffs are affected by climate change, we studied how temperature, snowmelt date, and rainfall affected the proportion of time yellow-bellied marmots (Marmota flaviventer) allocated to vigilance during bouts of foraging. While snowmelt and temperature did not explain variation in vigilance, rainfall did. Higher rainfall in the week prior to a focal observation was associated with higher vigilance, possibly reflecting more abundant food that affords the luxury of increasing antipredator vigilance while foraging. Such an effect might be consequential at the population level given the importance of foraging and antipredator behaviors for a highly time restrictive hibernating species. Further research is necessary to determine consequences at the population level and whether and how these findings extend to other species.
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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 | 0.002 |
| 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".