Effects of harvest treatments on forest carbon pools in eastern North America: A meta‐analysis
Bibliographic record
Abstract
Understanding carbon dynamics in managed forest ecosystems is increasingly crucial for formulating informed recommendations in the context of climate change. Silviculture significantly impacts forest carbon pools, though these effects can vary depending on the type of treatment applied. In recent decades, partial cuttings have been proposed as an alternative to more intensive treatments like clearcutting to mitigate negative impacts on forest function and enhance carbon sequestration. In this study, we conducted a meta-analysis comparing the effects of clearcutting and partial cuttings across North America on six forest carbon pools: live trees, snags, understory vegetation, coarse woody debris, forest floor, and soil mineral horizons. The analysis was based on a database of 558 carbon observations from temperate and boreal forests in eastern North America. Our findings indicate a -30% difference in total carbon post-harvesting, predominantly influenced by changes in the overstory carbon pool. Only the live tree carbon pool was significantly affected by cutting intensity, with clearcutting resulting in lower total carbon values (-78% relative to the reference) compared to partial cuttings (-45%). However, after 30-40 years, live tree carbon levels were similar between clearcutting and partial cuttings. The primary factor influencing differences in deadwood carbon pools was the time since treatment, while soil carbon pools showed minimal variation with no significant differences compared to unmanaged forests. This meta-analysis suggests that using partial cuttings instead of clearcutting to mitigate the effects of forest management on carbon pools may be more complex than previously thought and will depend on site conditions and allowing sufficient time for the forest to recover. Further studies are needed to identify suitable forest stands for partial cuttings and evaluate tree selection strategies that optimize forest productivity and carbon sequestration.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.033 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".