The Role of Energy Availability in Mammalian Hibernation: A Cost‐Benefit Approach
Bibliographic record
Abstract
Hibernation is widely regarded as an adaptation to seasonal energy shortage, but the actual influence of energy availability on hibernation patterns is rarely considered. Here we review literature on the costs and benefits of torpor expression to examine the influence that energy may have on hibernation patterns. We first establish that the dichotomy between food- and fat-storing hibernators coincides with differences in diet rather than body size and show that small or large species pursuing either strategy have considerable potential scope in the amount of torpor needed to survive winter. Torpor expression provides substantial energy savings, which increase the chance of surviving a period of food shortage and emerging with residual energy for early spring reproduction. However, all hibernating mammals periodically arouse to normal body temperatures during hibernation. The function of these arousals has long been speculated to involve recovery from physiological costs accumulated during metabolic depression, and recent physiological studies indicate these costs may include oxidative stress, reduced immunocompetence, and perhaps neuronal tissue damage. Using an optimality approach, we suggest that trade-offs between the benefits of energy conservation and the physiological costs of metabolic depression can explain both why hibernators periodically arouse from torpor and why they should use available energy to minimize the depth and duration of their torpor bouts. On the basis of these trade-offs, we derive a series of testable predictions concerning the relationship between energy availability and torpor expression. We conclude by reviewing the empirical support for these predictions and suggesting new avenues for research on the role of energy availability in mammalian hibernation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".