Detecting Compensatory Growth in Silviculture Trials: Empirical Evidence From Three Case Studies Across Canada
Bibliographic record
Abstract
Compensatory growth (CG) appears common in biology and is defined as accelerated growth after experiencing a period of unfavorable conditions. It usually leads to an increase in biomass that may eventually equal or even surpass that of sites not experiencing disturbance. In forestry, with sufficient time the stand volume lost in a disturbance such as a thinning operation could match or even exceed those from undisturbed sites, respectively called exact and overcompensation. The forest sector could benefit from enhanced productivity and associated ecosystem services such as carbon storage through overcompensation. Therefore, detection of CG in different types of forests becomes important for taking advantage of it in forest management. However, compensatory growth has not been reported widely in forestry, partially due to the paucity of long-term observations and lack of proper indicators. Legacy forest projects can provide a suitable data source, though they may be originally designed for other purposes. Three case studies representing different data structures of silviculture trials are investigated to evaluate if compensatory growth is common in forest stands. Our results showed that compensatory growth occurred in all three cases, and thus suggested that the compensatory growth might indeed be common in forest stands. We found that the relative growth (RG) can serve as a universal indicator to examine stand-level compensatory growth in historical long-term silviculture datasets. When individual tree-based measurements are available, both volume and value-based indicators can be used in detecting compensatory growth, and lumber value-based indicators could be more sensitive in detecting overcompensation.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".