Coping with seasonality: dynamics of adult body mass and survival in an alpine hibernator
Bibliographic record
Abstract
Alpine mammals are highly vulnerable to current and projected climate change because they are confined to a certain elevation range. Physiological and behavioural adaptations in burrowing species, such as finding shelter in burrows when the summer conditions are unfavorable and hibernating in winter during the stressful period of resource shortage, could partly buffer the negative impacts of these forecasted changes. We studied the links between environmental factors and annual variations in adult mass and survival over 14 years in hoary marmots. We hypothesized that annual variation in seasonal environmental factors determines individual mass and survival through direct effects on food quality and availability, expecting greater survival when marmots reach higher mass before hibernation. We found that harsh winters decreased mass at emergence from hibernation by 47% compared with mild winters. Nonetheless, adult marmots had a greater mass gain in summers following harsh winters and reached a similar mass at the end of the summer compared with summers following mild winters. This suggests individuals can adopt a resource allocation strategy that allows maximizing summer mass gain to survive hibernation. Earlier springs also increased summer mass gain by 15 g day −1 , and tended to increase apparent adult survival by 23%, compared with late springs. While these findings suggest a warming climate could have positive effects on summer mass gain and survival, survival also tended to decrease by 24% in summers with more precipitation. This suggests the forecasted changes in precipitation extremes could also trigger considerable negative effects on the demography of burrowing species in the long term. Our study shows that, although burrowing and hibernating behaviours could buffer responses to environmental changes, these behaviours are not an indefectible shield against climate change.
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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".