Factors affecting white spruce and aspen survival after partial harvest
Bibliographic record
Abstract
Summary 1. Variable retention harvest is an increasingly popular management alternative to clear‐cutting in boreal forest ecosystems. This harvest system retains a portion of the mature trees to maintain structural elements of the forest and preserve biodiversity. Residual trees must, however, survive in the post‐harvest environment to realize these benefits. Here, we investigate which tree and stand characteristics are associated with high survival to aid foresters in designing harvesting prescriptions. 2. We conducted an operational‐scale experiment broadly representative of western North American boreal forests. Treatments were four overstorey compositions (ranging from deciduous to conifer dominated) and five rates of canopy retention (10%, 20%, 50%, 75% and a 100% control). Factors affecting mortality were analysed for the leading tree species (aspen Populus tremuloides Michx. and white spruce Picea glauca (Moench) Voss 5 and 10 years after harvest. 3. Mortality of residual aspen was highest in 10% and 20% retention treatments with c. 30% mortality after 5 years and c. 50% after 10 years. At year 5, most of the dead trees remained upright as snags (87%), but by year 10, c. 40% of dead trees had fallen. By contrast, mortality was much lower in the 50% and 75% retention treatments, and similar to that of the unharvested control. Results for white spruce mortality were similar, except the shallow‐rooted spruce that was more susceptible to be blown down. 4. Regression tree analysis was used to identify individual tree attributes that predict 5‐ and 10‐year survival. Mortality was higher for trees with a low percentage of live crown and high height/diameter ratio, when trees were very tall or were close to machine corridors. Smaller residual spruce trees from deciduous‐dominated stands had the lowest mortality rates. 5. Synthesis and application . Where live trees are critical elements of wildlife habitat, forest managers should maintain ≥50% in retention harvest systems. At lower levels of retention (≤20%), mortality can be reduced if foresters select retention trees that are stout, possess large live crowns and are small to intermediate in size. These factors can be used to help design partial harvest systems for effective biodiversity conservation strategies.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".