The space‐time continuum: the effects of elevated <scp><scp>CO</scp></scp><sub>2</sub> and temperature on trees and the importance of scaling
Bibliographic record
Abstract
To predict how forests will respond to rising temperatures and atmospheric CO₂ concentrations, we need to understand how trees respond to both of these environmental factors. In this review, we discuss the importance of scaling, moving from leaf-level responses to those of the canopy, and from short-term to long-term responses of vegetation to climate change. While our knowledge of leaf-level, instantaneous responses of photosynthesis, respiration, stomatal conductance, transpiration and water-use efficiency to elevated CO₂ and temperature is quite good, our ability to scale these responses up to larger spatial and temporal scales is less developed. We highlight which physiological processes are least understood at various levels of study, and discuss how ignoring differences in the spatial or temporal scale of a physiological process impedes our ability to predict how forest carbon and water fluxes forests will be altered in the future. We also synthesize data from the literature to show that light respiration follows a generalized temperature response across studies, and that the light compensation point of photosynthesis is reduced by elevated growth CO₂. Lastly, we emphasize the need to move beyond single factorial experiments whenever possible, and to combine both CO₂ and temperature treatments in studies of tree performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".