Physiological differences between wild and captive animals: a century-old dilemma
Bibliographic record
Abstract
Laboratory-based research dominates the fields of comparative physiology and biomechanics. The power of lab work has long been recognized by experimental biologists. For example, in 1932, Georgy Gause published an influential paper in Journal of Experimental Biology describing a series of clever lab experiments that provided the first empirical test of competitive exclusion theory, laying the foundation for a field that remains active today. At the time, Gause wrestled with the dilemma of conducting experiments in the lab or the field, ultimately deciding that progress could be best achieved by taking advantage of the high level of control offered by lab experiments. However, physiological experiments often yield different, and even contradictory, results when conducted in lab versus field settings. This is especially concerning in the Anthropocene, as standard laboratory techniques are increasingly relied upon to predict how wild animals will respond to environmental disturbances to inform decisions in conservation and management. In this Commentary, we discuss several hypothesized mechanisms that could explain disparities between experimental biology in the lab and in the field. We propose strategies for understanding why these differences occur and how we can use these results to improve our understanding of the physiology of wild animals. Nearly a century beyond Gause's work, we still know remarkably little about what makes captive animals different from wild ones. Discovering these mechanisms should be an important goal for experimental biologists in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".