The Physiology of Exercise in Free-Living Vertebrates: What Can We Learn from Current Model Systems?
Bibliographic record
Abstract
SYNOPSIS: Many behaviors crucial for survival and reproductive success in free-living animals, including migration, foraging, and escaping from predators, involve elevated levels of physical activity. However, although there has been considerable interest in the physiological and biomechanical mechanisms that underpin individual variation in exercise performance, to date, much work on the physiology of exercise has been conducted in laboratory settings that are often quite removed from the animal's ecology. Here we review current, laboratory-based model systems for exercise (wind or swim tunnels for migration studies in birds and fishes, manipulation of exercise associated with non-migratory activity in birds, locomotion in lizards, and wheel running in rodents) to identify common physiological markers of individual variation in exercise capacity and/or costs of increased activity. Secondly, we consider how physiological responses to exercise might be influenced by (1) the nature of the activity (i.e., voluntary or involuntary, intensity, and duration), and (2) resource acquisition and food availability, in the context of routine activities in free-living animals. Finally, we consider evidence that the physiological effects of experimentally-elevated activity directly affect components of fitness such as reproduction and survival. We suggest that developing more ecologically realistic laboratory systems, incorporating resource-acquisition, functional studies across multiple physiological systems, and a life-history framework, with reproduction and survival end-points, will help reveal the mechanisms underlying the consequences of exercise, and will complement studies in free-living animals taking advantage of new developments in wildlife-tracking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".