The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment
Bibliographic record
Abstract
Because of its profound effects on the rates of biological processes such as aerobic metabolism, environmental temperature plays an important role in shaping the distribution and abundance of species. As temperature increases, the rate of metabolism increases and then rapidly declines at higher temperatures - a response that can be described using a thermal performance curve (TPC). Although the shape of the TPC for aerobic metabolism is often attributed to the competing effects of thermodynamics, which can be described using the Arrhenius equation, and the effects of temperature on protein stability, this account represents an over-simplification of the factors acting even at the level of single proteins. In addition, it cannot adequately account for the effects of temperature on complex multistep processes, such as aerobic metabolism, that rely on mechanisms acting across multiple levels of biological organization. The purpose of this review is to explore our current understanding of the factors that shape the TPC for aerobic metabolism in response to acute changes in temperature, and to highlight areas where this understanding is weak or insufficient. Developing a more strongly grounded mechanistic model to account for the shape of the TPC for aerobic metabolism is crucial because these TPCs are the foundation of several recent attempts to predict the responses of species to climate change, including the metabolic theory of ecology and the hypothesis of oxygen and capacity-limited thermal tolerance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".