Variation in leaf carbon economics, energy balance, and heat tolerance traits highlights differing timescales of adaptation and acclimation
Bibliographic record
Abstract
Multivariate leaf trait correlations are hypothesized to originate from natural selection on carbon economics traits that control lifetime leaf carbon gain, and energy balance traits governing leaf temperatures, physiological rates, and heat injury. However, it is unclear whether macroevolution of leaf traits primarily reflects selection for lifetime carbon gain or energy balance, and whether photosynthetic heat tolerance is coordinated along these axes. To evaluate these hypotheses, we measured carbon economics, energy balance, and photosynthetic heat tolerance traits for 177 species (157 families) in a common garden that minimizes co-variation of taxa and climate. We observed wide variation in carbon economics, energy balance, and heat tolerance traits. Carbon economics and energy balance (but not heat tolerance) traits were phylogenetically structured, suggesting macroevolution of leaf mass per area and leaf dry matter content reflects selection on carbon gain rather than energy balance. Carbon economics and energy balance traits varied along a common axis orthogonal to heat tolerance traits. Our results highlight a fundamental mismatch in the timescales over which morphological and heat tolerance traits respond to environmental variation. Whereas carbon economics and energy balance traits are constrained by species' evolutionary histories, photosynthetic heat tolerance traits are not and can acclimate readily to leaf microclimates.
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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 | 0.000 |
| 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) | 0.001 | 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".