Effects of silvicultural treatments on post-harvesting residual tree mortality
Bibliographic record
Abstract
In recent decades, retention forestry has become an increasingly popular management approach. Previous research has demonstrated that persistence of retained live trees (residuals) is higher when they are larger and more densely packed. Beyond density, minimal research has been done on how differing silviculture treatments post-harvest affect the persistence of these residual trees. Here, we use a silviculture experiment, fully replicated within five sites across Ontario, Canada, to examine how changes in silviculture treatments post-harvest affect residual tree mortality. The five sites in this study include three sites harvested using clearcut with seed tree and two sites harvested using shelterwood systems. Each site included unharvested control plots and a gradient of four silviculture treatments, but harvest intensity did not change within site. We considered mortality to include trees that died post-harvest and either remained standing or blew down. Overall, we found that probability of residual mortality was highly dependent on hierarchical interactions between linked-press (i.e., climate, edaphic conditions, and silvicultural system) and compounded-pulse (i.e., post-harvest silviculture treatments) disturbances. Based on outputs from a structural equation model, press disturbances affected residual tree mortality more than post-harvest pulse disturbances. Post-harvest pulse disturbances increased residual mortality probabilities in seed tree clearcuts but not shelterwoods. However, in seed tree clearcuts, moderate pulse disturbances had similar effects on residual mortality relative to the least intense pulse disturbances. In summary, forest managers can implement moderate intensity pulse disturbances after harvesting without increasing residual tree mortality relative to untreated areas. However, the highest intensities of post-harvest pulse disturbances did not result in complete loss of residuals and, depending on management objectives, the increased loss in residuals and associated loss of ecosystem services may be offset by benefits of higher yield of desired tree species.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".