The Impact of Variable Retention Harvesting on Growth and Carbon Sequestration of a Red Pine (Pinus resinosa Ait.) Plantation Forest in Southern Ontario, Canada
Bibliographic record
Abstract
As atmospheric carbon dioxide concentrations continue to rise and global temperatures increase, there is growing concern about the sustainability, health, and carbon sequestration potential of forest ecosystems. Variable retention harvesting (VRH) has been suggested to be a potential method to increase forest biodiversity, growth, and carbon (C) sequestration. A field trial was established in an 88-year-old red pine (Pinus resinosa Ait.) plantation in southern Ontario, Canada, using a completely randomized design to examine the response of tree productivity and other forest values to five harvesting treatments: 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) in comparison to an unharvested control (CN). In this study, we explored the impacts of VRH on aboveground stem radial growth and annual C increment. Standard dendrochronological methods and allometric equations were used to quantify tree- and stand-level treatment effects during a five-year pre-harvest (2009–2013) and post-harvest (2014–2018) period. Tree-level growth and C increment were increased by the dispersed retention pattern regardless of retention level. At the stand level, the total C increment was highest at greater retention levels and did not vary with retention pattern. These results suggest that the choice of retention level and pattern can have a large influence on management objectives as they relate to timber production, climate change adaptation, and/or climate change mitigation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".