Thermal tolerance patterns across latitude and elevation
Bibliographic record
Abstract
Linking variation in species' traits to large-scale environmental gradients can lend insight into the evolutionary processes that have shaped functional diversity and future responses to environmental change. Here, we ask how heat and cold tolerance vary as a function of latitude, elevation and climate extremes, using an extensive global dataset of ectotherm and endotherm thermal tolerance limits, while accounting for methodological variation in acclimation temperature, ramping rate and duration of exposure among studies. We show that previously reported relationships between thermal limits and latitude in ectotherms are robust to variation in methods. Heat tolerance of terrestrial ectotherms declined marginally towards higher latitudes and did not vary with elevation, whereas heat tolerance of freshwater and marine ectotherms declined more steeply with latitude. By contrast, cold tolerance limits declined steeply with latitude in marine, intertidal, freshwater and terrestrial ectotherms, and towards higher elevations on land. In all realms, both upper and lower thermal tolerance limits increased with extreme daily temperature, suggesting that different experienced climate extremes across realms explain the patterns, as predicted under the Climate Extremes Hypothesis. Statistically accounting for methodological variation in acclimation temperature, ramping rate and exposure duration improved model fits, and increased slopes with extreme ambient temperature. Our results suggest that fundamentally different patterns of thermal limits found among the earth's realms may be largely explained by differences in episodic thermal extremes among realms, updating global macrophysiological 'rules'. This article is part of the theme issue 'Physiological diversity, biodiversity patterns and global climate change: testing key hypotheses involving temperature and oxygen'.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".