Harvesting Intensity and Aridity Are More Important Than Climate Change in Affecting Future Carbon Stocks of Douglas-Fir Forests
Bibliographic record
Abstract
Improved forest management may offer climate mitigation needed to hold warming to below 2°C. However, uncertainties persist about the effects of harvesting intensity on forest carbon sequestration, especially when considering interactions with regional climate and climate change. Here, we investigated the combined effects of harvesting intensity, climatic aridity, and climate change on carbon stocks in Douglas-fir [ Pseudotsuga menziesii Mirb. (Franco)] stands. We used the Carbon Budget Model of the Canadian Forest Sector to simulate the harvest and regrowth of seven Douglas-fir stand types covering a 900 km-long climate gradient across British Columbia, Canada. In particular, we simulated stand growth under three regimes (+17%, −17% and historical growth increment) and used three temperature regimes [historical, representative concentration pathways (RCP) 2.6 and RCP 8.5]. Increasing harvesting intensity led to significant losses in total ecosystem carbon stocks 50 years post-harvest. Specifically, forests that underwent clearcutting were projected to stock about 36% less carbon by 2,069 than forests that were left untouched. Belowground carbon stocks 50 years into the future were less sensitive to harvesting intensity than aboveground carbon stocks and carbon losses were greater in arid interior Douglas-fir forests than in humid, more productive forests. In addition, growth multipliers and decay due to the RCP’s had little effect on total ecosystem carbon, but aboveground carbon declined by 7% (95% confidence interval [−10.98, −1.81]) in the high emissions (RCP8.5) scenario. We call attention to the implementation of low intensity harvesting systems to preserve aboveground forest carbon stocks until we have a more complete understanding of the impacts of climate change on British Columbia’s forests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".