Estimating carbon stocks and fluxes in an experimental logging trial within British Columbia’s inland temperate rainforest
Bibliographic record
Abstract
,Temperate rainforests in British Columbia, Canada, are recognized for providing a suitable habitat for fauna and flora and storing a significant amount of carbon above and below ground. However, silviculture practices such as clearcuts have affected these forests, in some cases shifting their role from carbon sinks to carbon sources. To avoid some of the impacts of clearcutting, partial harvest practices have been implemented, which aim to leave some pre-harvest biological legacies behind to promote faster ecosystem recovery and provide a habitat for biodiversity. Carbon-related research in these systems has focused either on short-term studies or analyzing only a few pools, highlighting the need for long-term studies with a comprehensive analysis of carbon dynamics above and below ground. Here, I studied the long-term effect of partial harvest and clearcutting on forest carbon stocks by examining the carbon dynamics in multiple pools above (trees, downed woody material) and below ground (forest floor and roots) in the Northern Wetbelt, a longterm, replicated, research trial. I used historical databases and empirical data collected 19 years after harvest to estimate total forest carbon stocks and their change over time in mature and old-growth Interior Cedar-Hemlock forests. In addition, I identified the treatment that promotes the greatest carbon accumulation rates in the live trees given different canopy conditions. Within 19 years of harvest forest carbon stocks were much higher in partial harvest with high forest retention (449.49 ± 32.22 Mg C ha-1) and low retention (192.86 ± 14.63 Mg C ha-1) compared to clearcut conditions (136.29 ± 11.94 MgC ha-1). The carbon dynamics showed that pools can have opposite trends affecting differently the overall forest carbon stores. Specifically, while the carbon stocks in live trees increased, the coarse woody debris decreased. This interaction kept forest carbon stocks stable with no recovery toward the preharvest values within 19 years. Finally, the partial harvest treatment with higher forest retention had the canopy conditions that promoted the greatest carbon accumulation rates in live trees. Furthermore, the trees located at the edge of a managed opening showed a more rapid annual growth (0.0023 ± 0.0001 Mg C year-1) than the ones at the interior of the sampling plots (0.0017 ± 0.0001 Mg C year-1). This thesis contributes to forest management by providing long-term carbon estimates from different harvesting practices in mature and old-growth inland temperate rainforests to support decision-making toward more sustainable use of natural resources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".