Contrasting acclimation responses to elevated CO<sub>2</sub> and warming between an evergreen and a deciduous boreal conifer
Bibliographic record
Abstract
Abstract Rising atmospheric carbon dioxide (CO2) concentrations may warm northern latitudes up to 8°C by the end of the century. Boreal forests play a large role in the global carbon cycle, and the responses of northern trees to climate change will thus impact the trajectory of future CO2 increases. We grew two North American boreal tree species at a range of future climate conditions to assess how growth and carbon fluxes were altered by high CO2 and warming. Black spruce (Picea mariana, an evergreen conifer) and tamarack (Larix laricina, a deciduous conifer) were grown under ambient (407 ppm) or elevated CO2 (750 ppm) and either ambient temperatures, a 4°C warming, or an 8°C warming. In both species, the thermal optimum of net photosynthesis (ToptA) increased and maximum photosynthetic rates declined in warm‐grown seedlings, but the strength of these changes varied between species. Photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax, and of electron transport, Jmax) was reduced in warm‐grown seedlings, correlating with reductions in leaf N and chlorophyll concentrations. Warming increased the activation energy for Vcmax and Jmax (EaV and EaJ, respectively) and the thermal optimum for Jmax. In both species, the ToptA was positively correlated with both EaV and EaJ, but negatively correlated with the ratio of Jmax/Vcmax. Respiration acclimated to elevated temperatures, but there were no treatment effects on the Q10 of respiration (the increase in respiration for a 10°C increase in leaf temperature). A warming of 4°C increased biomass in tamarack, while warming reduced biomass in spruce. We show that climate change is likely to negatively affect photosynthesis and growth in black spruce more than in tamarack, and that parameters used to model photosynthesis in dynamic global vegetation models (EaV and EaJ) show no response to elevated CO2.
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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) | 0.000 | 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".