Stomatal conductance, not biochemistry, drives low temperature acclimation of photosynthesis in <i>Populus balsamifera</i>, regardless of nitrogen availability
Bibliographic record
Abstract
ABSTRACT Low‐temperature thermal acclimation may require adjustments to N and water use to sustain photosynthesis because of slow enzyme functioning and high water viscosity. However, understanding of photosynthetic acclimation to temperatures below 11 °C is limited. We acclimated Populus balsamifera to 6 °C and 10 °C (6A and 10A, respectively) and provided the trees with either high or low N fertilizer. We measured net CO2 assimilation (Anet), stomatal conductance (gs), maximum rates of Rubisco carboxylation (Vcmax), electron transport (Jmax) and dark respiration (Rd) at leaf temperatures of 2, 6, 10, 14 and 18 °C, along with leaf N concentrations. The 10A trees had higher Anet than the 6A trees at warmer leaf temperatures, which was correlated with higher gs in the 10A trees. The instantaneous temperature responses of Vcmax, Jmax and Rd were similar for trees from both acclimation temperatures. While soil N availability increased leaf N concentrations, this had no effect on acclimation of photosynthesis or respiration. Our results indicate that acclimation below 11 °C occurred primarily through changes in stomatal conductance, not photosynthetic biochemistry, and was unaffected by short‐term N supply. Thermal acclimation of stomatal conductance should therefore be a priority for future carbon cycle model development.
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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 | 0.000 |
| 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".