Effects of climate and forest composition on soil carbon cycling, soil organic matter stability and stocks in a humid boreal region
Bibliographic record
Abstract
Abstract. The maintenance of the large soil organic carbon (SOC) stocks of the boreal forest under climate change is a matter of concern. In this study, major soil carbon pools and fluxes were assessed in twenty-two closed-canopy forests located along an elevation and latitudinal climatic gradient expanding 4 °C in mean annual temperature (MAT) for two important boreal conifer forest stand types: balsam fir (Abies balsamea) a fire avoider and black spruce (Picea mariana) a fire-tolerant species. SOC stocks were not influenced by climate or forest type. However, carbon fluxes, including aboveground litterfall rates as well as total soil respiration (Rs), heterotrophic (Rh) and autotrophic soil respiration (Ra) were linearly related to climate (cumulative degree days >5 °C). The sensitivity of SOM degradation to temperature, assessed by comparing Q10 (rate of change for a T increase of 10 °C) of soil respiration and Rs10 (soil respiration rates corrected to 10 °C) did not vary across the climate gradient, while the proportion of labile carbon and nitrogen showed higher values for balsam fir and for warmer sites. Balsam fir forests showed a greater litterfall rate, a better litter quality (lower C:N ratio) as well as a higher Rs10 than black spruce ones, suggesting that their soils cycle a larger amount of C and N under a similar climate regime. Altogether, these results suggest that a warmer climate and balsam fir forest composition induce a more rapid SOC turnover. Contrary to common soil organic matter stabilization hypotheses, greater SOC cycling rates did not lead to higher total SOC stocks nor to the depletion of labile soil C and N. Positive effects of warming both on fluxes to and from the soil as well as a potential saturation of stabilised SOC could explain these results which apply to the context of this study: a cold and wet environment and a stable vegetation composition along the climate gradient.
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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".