Do endotherms have thermal performance curves?
Bibliographic record
Abstract
Temperature is an important environmental factor governing the ability of organisms to grow, survive and reproduce. Thermal performance curves (TPCs), with some caveats, are useful for charting the relationship between body temperature and some measure of performance in ectotherms, and provide a standardized set of characteristics for interspecific comparisons. Endotherms, however, have a more complicated relationship with environmental temperature, as endothermy leads to a decoupling of body temperature from external temperature through use of metabolic heat production, large changes in insulation and variable rates of evaporative heat loss. This has impeded our ability to model endothermic performance in relation to environmental temperature as well as to readily compare performance between species. In this Commentary, we compare the strengths and weaknesses of potential TPC analogues (including other useful proxies for linking performance to temperature) in endotherms and suggest several ways forward in the comparative ecophysiology of endotherms. Our goal is to provide a common language with which ecologists and physiologists can evaluate the effects of temperature on performance. Key directions for improving our understanding of endotherm thermoregulatory physiology include a comparative approach to the study of the level and precision of body temperature, measuring performance directly over a range of body temperatures and building comprehensive mechanistic models of endotherm responses to environmental temperatures. We believe the answer to the question posed in the title could be 'yes', but only if 'performance' is well defined and understood in relation to body temperature variation, and the costs and benefits of endothermy are specifically modelled.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".