The importance of temporal heterothermy in bats
Bibliographic record
Abstract
Abstract Animals must balance their energy budgets even when confronted with periodic food shortages and/or adverse environmental conditions. Especially, small endothermic animals require large amounts of energy to maintain high and stable body temperatures (Tb) via endogenous heat production. To deal with energetic challenges, many small endotherms are heterothermic, abandon regulation of highTband enter a state of torpor resulting in large energy savings. Torpor is used by many bat species because they are small, have high rates of heat loss and rely on fluctuating food resources (e.g. insects, fruit, nectar). Many bats use torpor all year, but the expression of temporal heterothermy can be strongly seasonal especially for temperate and subtropical species, which may hibernate for long periods. Recent advances in our understanding of torpor expression in bats have been made using temperature telemetry for remote data collection ofTbin free‐ranging wild individuals from all climate zones. This new knowledge on free‐ranging bats has revealed the importance of torpor expression not only for energy conservation but also for other benefits, such as reduction of extrinsic mortality (e.g. predation). On the contrary, dense clustering during hibernation, important for minimizing energy and water loss, may also expose bats to infectious disease. An emerging, cold‐tolerant fungal pathogen of bats causes a new disease called white‐nose syndrome (WNS), which is devastating populations of multiple species in easternNorthAmerica. Given the importance of temporal heterothermy to their biology, and links between torpor expression and mortality fromWNS, it is becoming increasingly important to understand the ecology and physiology of torpor in this largely understudied and cryptic mammalian group. Here, we review past and current literature to summarize the importance and evolution of heterothermy in bats.
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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) | 0.002 | 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".