The effects of gap size in a group selection silvicultural system on the growth response of young, planted Douglas-fir: a sector plot analysis
Bibliographic record
Abstract
The growth response of planted 7- and 11-year-old Douglas-fir was measured in a series of 11 group selection harvest gaps ranging in size from 0.05 to 1.1 ha repeated on two study sites. The sites are part of the Silviculture Treatments for Ecosystem Management in the Sayward experiment on central-eastern Vancouver Island, British Columbia, Canada. In each gap, trees were measured in four 9° sector plots oriented in orthogonal cardinal directions from a central vertex. A non-linear two-parameter model was used to examine relationships between per tree and unit area measures and gap size. Despite high levels of growth variability, there was a general, consistent asymptotic growth response to increasing gap size. The minimum gap size required for adequate Douglas-fir sapling height growth was between 0.24 and 0.33 ha and the gap diameter divided by surrounding residual tree dominant height (Dgap/Hgap) at the two sites was 1.5 and 2.2. The site with the smaller minimum gap size had taller surrounding residual trees and thus lower light levels, but had higher relative soil moisture and nutrients and was a younger age at the time of sampling. The largest gap size sampled in this study was relatively small (1.1 ha) and greater growth responses are likely in gaps larger than this. The results of this study suggest that gap sizes below a minimum will not create conditions to ensure adequate growth of Douglas-fir regeneration in group selection systems. In addition, only one group selection pass is examined here: under full implementation several group selection passes are envisaged leading to further changes in the gap-level environment throughout. Further work is needed to confirm the localized relationships found here including: greater replication across a range of site quality, sampling of larger gap sizes and examination of older ages of both regeneration and surround trees.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".