Large, sustained soil CO2 efflux but rapid recovery of CH4 oxidation in post-harvest and post-fire stands in a mixedwood boreal forest
Bibliographic record
Abstract
The net effect of forest disturbances, such as fires and harvesting, on soil greenhouse gas fluxes is determined by their impacts on both biological and physical factors, as well as the temporal dynamics of these effects post-disturbance. Although harvesting and fire may have distinct effects on soil carbon (C) dynamics, the temporal patterns in soil CO2 and CH4 fluxes and the potential differences between types of disturbances, remain poorly characterized in boreal forests. In this study, we measured soil CO2 and CH4 fluxes using a cavity ring-down spectroscopy system in snow-free seasons over two years in post-harvest and post-fire chronosequence sites within a mixedwood boreal forest in northwestern Ontario, Canada. Soil CO2 efflux showed a post-disturbance peak, with differing dynamics depending on the disturbance type: post-harvest stands exhibited a nearly tenfold increase (from ~1 to ~11 μmol CO2.m-2.s−1) from 1 to 9–10 years post-disturbance, followed by a steep decline; post-fire stands showed a more gradual increase, peaking at ~6–7.2 μmol CO2.m-2.s−1 after ~12–15 years. The youngest post-harvest stands were net sources of CH4,whereas post-fire stands were never net CH4 sources. In both disturbance types, the strength of the CH4 sink increased with stand age, approaching ~2.4 nmol.m-2.s−1 by 15 years post-disturbance. Volumetric water content, bulk density, litter depth, and pH were significant predictors of CO2 fluxes; for CH4 fluxes, litter depth, pH, and the interaction of VWC and soil temperature were significant predictors in both disturbance types, with EC also showing a relationship in post-harvest stands. Our findings indicate that while soil CH4 oxidation rapidly recovers following disturbance, both post-harvest and post-fire stands show a multi-decade release of soil CO2 that is too large to be offset by C gains over this period.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".