Clearcut Solutions? An Evaluation of Partial Harvesting in the Black Spruce Boreal Forest
Bibliographic record
Abstract
Bringing together field-based empirical studies, a simulation modelling experiment, and a critical analysis of the natural disturbance emulation paradigm, this thesis evaluates partial harvesting in the black spruce boreal forest. Forest management in Ontario is required to emulate natural disturbances, but in regions of the boreal forest where fire cycles are long, regulated even-aged management by clearcutting has truncated forest age-class distributions. Partial harvesting has been proposed as a means to maintain the structural complexity and biodiversity associated with old forests while allowing continued timber production. Despite the potentially important role of partial harvesting in a strategy for sustainable boreal forest management, little research has examined post-harvest stand development, a critical determinant both of habitat and timber supplies.I used a chronosequence approach in combination with dendroecological techniques, a neighbourhood modelling framework, and maximum likelihood statistical methods to quantify stand dynamics over the first decade after partial harvest in the black spruce (Picea mariana) boreal forest of northeastern Ontario, Canada. Residual trees displayed large but time-lagged growth responses to partial harvest. The largest responses were found in young trees, while old trees were largely unable to react to improved post-harvest resource availability. Growth responses were offset by elevated rates of residual-tree mortality, which peaked in the first year after harvest at nearly 13 times the pre-harvest level. Proximity to harvest machinery trails severely escalated the risk of mortality for residual trees. Considering growth and mortality responses together in a forest simulator model, I found that stand development proceeded most rapidly where skidding intensity was reduced and retention areas were aggregated. Given appropriate prescriptions, my results indicate that partial harvesting can be a viable silvicultural option for black spruce boreal forests. However, the ability of partially harvested stands to emulate natural disturbance is questionable, particularly given the strong influence of harvest machinery impacts on post-harvest stand development. I argue that the natural disturbance emulation framework has important flaws and falls short of a justifiable approach for forest management in Ontario.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".