Heat limits scale with metabolism in ectothermic animals
Bibliographic record
Abstract
Ectotherms given time to acclimate to warmer environments, habitats or experimental treatments tend to tolerate higher maximum temperatures, but only slightly higher. This means warmer acclimated organisms live closer to their physiological temperature limits (their 'critical temperatures'). The reason for this modest-and often highly variable-plasticity of heat limits is debated but raises concerns for resilience to future climate warming. Experiments have shown heat tolerance is dependent not just on the magnitude of thermal stress but also on time via exposure duration. This implicates rate processes in the regulation of heat limits, yet few studies have explored this possibility. Invoking biological rates (such as metabolic rate) to explain the plasticity of critical temperatures is complicated by the need to account for temperature, time and the nonlinear dependence of rates on temperature. We developed a new approach to explore whether incorporating estimated metabolic rate and its thermal scaling could explain the apparently modest and highly variable capacities of ectotherms to adjust their heat limits. To do this, we re-evaluate a large thermal tolerance dataset for diverse ectothermic animals heated from different acclimation temperatures up to their critical temperature. By integrating temperature, time and the exponential relationship between temperature and metabolic rate, we compute a cumulative 'metabolic currency' that ectotherms expend (or accumulate) before reaching their heat limits. We then explore how this quantity varies for ectotherms acclimated to different temperatures. Our 'metabolic rescaling' has a dramatic impact on explaining variation in heat limits, revealing that heating tolerance is effectively fixed within a species such that heat limits from any acclimation temperature can be predicted with remarkable accuracy by measuring heat limits at any other acclimation temperature. Heating rate also has a strong, consistent, influence. Evidently, warmer-acclimated organisms only marginally elevate their critical temperatures because they have a fixed amount of energy to spend during heating, and they spend it at a faster rate in warmer temperatures. This provides a very different perspective to leading explanations that organismal heat limits are constrained by hard physiological boundaries and instead encourages unification of thermal tolerance and metabolic scaling theory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".