Drivers of postfire soil organic carbon accumulation in the boreal forest
Bibliographic record
Abstract
The accumulation of soil carbon (C) is regulated by a complex interplay between abiotic and biotic factors. Our study aimed to identify the main drivers of soil C accumulation in the boreal forest of eastern North America. Ecosystem C pools were measured in 72 sites of fire origin that burned 2-314 years ago over a vast region with a range of ∆ mean annual temperature of 3°C and one of ∆ 500 mm total precipitation. We used a set of multivariate a priori causal hypotheses to test the influence of time since fire (TSF), climate, soil physico-chemistry and bryophyte dominance on forest soil organic C accumulation. Integrating the direct and indirect effects among abiotic and biotic variables explained as much as 50% of the full model variability. The main direct drivers of soil C stocks were: TSF >bryophyte dominance of the FH layer and metal oxide content >pH of the mineral soil. Only climate parameters related to water availability contributed significantly to explaining soil C stock variation. Importantly, climate was found to affect FH layer and mineral soil C stocks indirectly through its effects on bryophyte dominance and organo-metal complexation, respectively. Soil texture had no influence on soil C stocks. Soil C stocks increased both in the FH layer and mineral soil with TSF and this effect was linked to a decrease in pH with TSF in mineral soil. TSF thus appears to be an important factor of soil development and of C sequestration in mineral soil through its influence on soil chemistry. Overall, this work highlights that integrating the complex interplay between the main drivers of soil C stocks into mechanistic models of C dynamics could improve our ability to assess C stocks and better anticipate the response of the boreal forest to global 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".