Carbon fluxes acclimate more strongly to elevated growth temperatures than to elevated <scp>CO</scp><sub>2</sub> concentrations in a northern conifer
Bibliographic record
Abstract
Increasing temperatures and atmospheric CO2 concentrations will affect tree carbon fluxes, generating potential feedbacks between forests and the global climate system. We studied how elevated temperatures and CO2 impacted leaf carbon dynamics in Norway spruce (Picea abies), a dominant northern forest species, to improve predictions of future photosynthetic and respiratory fluxes from high-latitude conifers. Seedlings were grown under ambient (AC, c. 435 μmol mol(-1) ) or elevated (EC, 750 μmol mol(-1) ) CO2 concentrations at ambient, +4 °C, or +8 °C growing temperatures. Photosynthetic rates (Asat ) were high in +4 °C/EC seedlings and lowest in +8 °C spruce, implying that moderate, but not extreme, climate change may stimulate carbon uptake. Asat , dark respiration (Rdark ), and light respiration (Rlight ) rates acclimated to temperature, but not CO2 : the thermal optimum of Asat increased, and Rdark and Rlight were suppressed under warming. In all treatments, the Q10 of Rlight (the relative increase in respiration for a 10 °C increase in leaf temperature) was 35% higher than the Q10 of Rdark , so the ratio of Rlight to Rdark increased with rising leaf temperature. However, across all treatments and a range of 10-40 °C leaf temperatures, a consistent relationship between Rlight and Rdark was found, which could be used to model Rlight in future climates. Acclimation reduced daily modeled respiratory losses from warm-grown seedlings by 22-56%. When Rlight was modeled as a constant fraction of Rdark , modeled daily respiratory losses were 11-65% greater than when using measured values of Rlight . Our findings highlight the impact of acclimation to future climates on predictions of carbon uptake and losses in northern trees, in particular the need to model daytime respiratory losses from direct measurements of Rlight or appropriate relationships with Rdark .
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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 | 0.001 | 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".