Rapid Climate Acclimation (Not Traits or Phylogeny) Drives Variation in Photosynthesis Temperature Response
Bibliographic record
Abstract
ABSTRACT Understanding variation in plant assimilation‐temperature (AT) responses is essential for improving forecasts of climate change feedbacks and their impacts on the biosphere. Previous studies have focused on acclimation to weather or adaptation to climate of origin, but relationships between AT response parameters and leaf functional traits or phylogeny have received little attention. To evaluate the influence of climate, traits, and phylogeny on AT response, we used the new Fast Assimilation‐Temperature Response (FAsTeR) gas exchange method to measure 243 AT response curves in 102 species from 96 families grown in a common garden. We also quantified climate variables, saturating light intensity, and key leaf functional traits. Local environmental conditions were the strongest predictors of AT response parameters. The optimal temperature for photosynthesis responded positively to recent air temperature and light exposure (partial r2 = 0.27 and 0.53, respectively), and was best predicted by mean air temperature on the day of measurement; other AT parameters exhibited weak or no relationships with recent air temperature (all partial r2 < 0.1). AT response parameters showed no phylogenetic structure and only modest variation with leaf functional traits or climate of origin (all partial r2 < 0.07). Plant AT responses were primarily driven by acclimation to local climate variables, rather than adaptation to climate of origin. Thermal acclimation of photosynthesis occurred on much shorter timescales than expected (≤ 1 day). These findings underscore the need to account for rapid acclimation in Earth system models and climate change forecasts.
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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 | 0.000 | 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".