Using leaf economic spectrum and photosynthetic acclimation to evaluate the potential performance of wintersweet under future climate conditions
Bibliographic record
Abstract
Abstract The function of landscape plants on the ecosystem can alleviate environmental issues of urbanization and global change. Global changes due to elevated CO2 affect plant growth and survival, but there is a lack of quantitative methods to evaluate the adaptability of landscape plants to future climate conditions. Leaf traits characterized by leaf economic spectrum (LES) are the universal currency for predicting the impact on plant ecosystem functions. Elevated CO2 usually leads to photosynthetic acclimation (PC), characterised by decreased photosynthetic capacity. Here, we proposed a theoretical and practical framework for the use of LES and PC to project the potential performance of landscape plants under future climatic conditions through principal component analysis, structural equation modelling, photosynthetic restriction analysis and nitrogen allocation analysis. We used wintersweet (an important landscaping species) to test the feasibility of this framework under elevated CO2 and different nitrogen (N) supplies. We found that elevated CO2 decreased the specific leaf area but increased leaf N concentration. The results suggest wintersweet may be characterized by an LES with high leaf construction costs, low photosynthetic return, and robust stress resistance. Elevated CO2 reduced photosynthetic capacity and stomatal conductance but increased photosynthetic rate and leaf area. These positive physio‐ecological traits, e.g., larger leaf area (canopy), higher water use efficiency and stress resistance, may lead to improved performance of wintersweet under the predicted future climatic conditions. The results suggest planting more wintersweet in urban landscaping may be an effective adaptive strategy to climate change.
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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".