A meta-analysis of management effects on forest carbon storage
Bibliographic record
Abstract
Forest management can have substantial impacts on ecosystem carbon storage, but those effects can vary significantly with management type and species composition. We used systematic review methodology to identify and synthesize effects of thinning and/or burning, timber harvesting, clear-cut, and wildfire on four components of ecosystem carbon: aboveground vegetation, soil, litter, and deadwood. We performed a meta-analysis on studies from the United States and Canada because those represented 85% of the studies conducted worldwide. We found that the most important variables in predicting effect sizes (ratio of carbon stored in treated stands versus controls) were, in decreasing order of importance, ecosystem carbon component, time since treatment, and age of control. Management treatment was the least important of all the variables we examined, but the trends we found suggest that thinning and/or burning treatments resulted in less carbon loss than wildfire or clear-cut. This finding is consistent with recent modeling studies indicating that forest management is unimportant to long-term carbon dynamics relative to the effects of large-scale natural disturbances (e.g., drought, fire, pest outbreak). However, many data gaps still exist on total ecosystem carbon, particularly in regions other than North America, and in timber production forests and plantations.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".