Elevated CO2 and ammonium nitrogen promoted the plasticity of two maple in great lakes region by adjusting photosynthetic adaptation
Bibliographic record
Abstract
Introduction Climate change-related CO2 increases and different forms of nitrogen deposition are thought to affect the performance of plants, but their interactions have been poorly studied. Methods This study investigated the responses of photosynthesis and growth in two invasive maple species, amur maple (Acer ginnala Maxim.) and boxelder maple (Acer negundo L.), to elevated CO2 (400 µmol mol-1 (aCO2) vs. 800 µmol mol-1 (eCO2) and different forms of nitrogen fertilization (100% nitrate, 100% ammonium, and an equal mix of the two) with pot experiment under controlled conditions. Results and discussion The results showed that eCO2 significantly promoted photosynthesis, biomass, and stomatal conductance in both species. The biochemical limitation of photosynthesis was switched to RuBP regeneration (related to Jmax) under eCO2 from the Rubisco carboxylation limitation (related to Vcmax) under aCO2. Both species maximized carbon gain by lower specific leaf area and higher N concentration than control treatment, indicating robust morphological plasticity. Ammonium was not conducive to growth under aCO2, but it significantly promoted biomass and photosynthesis under eCO2. When nitrate was the sole nitrogen source, eCO2 significantly reduced N assimilation and growth. The total leaf N per tree was significantly higher in boxelder maple than in amur maple, while the carbon and nitrogen ratio was significantly lower in boxelder maple than in amur maple, suggesting that boxelder maple leaf litter may be more favorable for faster nutrient cycling. The results suggest that increases in ammonium under future elevated CO2 will enhance the plasticity and adaptation of the two maple species.
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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".