Effects of thinning-induced changes in structural heterogeneity on growth, ingrowth, and mortality in secondary coastal Douglas-fir forests
Bibliographic record
Abstract
Thinning is believed to accelerate the development of late-successional attributes, thereby enhancing stand structural heterogeneity in young, secondary forests. By making use of a large-scale experiment implemented in 40- to 60-year-old coastal Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) forests, we addressed the following objectives: (i) determine the effect of three thinning treatments on the temporal dynamics (first 11 years after thinning) of key forest structure measures, (ii) evaluate the relationships between spatially explicit structural diversity measures and spatially nonexplicit stand metrics, and (iii) test the relationships between stand structure and observed periodic stand volume growth, ingrowth, and mortality. Treatments consisted of high-density, moderate-density, and variable-density thinnings-from-below, as well as a control. Differences in stand structural heterogeneity between treatments were mostly nonsignificant. However, our results suggest that variable-density stands displayed structural enrichment as tree size and tree species diversity increased throughout the study period as a result of continuous ingrowth of species other than Douglas-fir. Simple spatially nonexplicit metrics could not be used to reliably model spatially explicit structural diversity measures. The inclusion of structural and species diversity measures only rarely improved accuracy of sample plot level growth, ingrowth, and mortality prediction models. Despite the short-term nature of this study, we conclude that variable-density thinning shows promise in increasing structural heterogeneity in young even-aged stands. The inclusion of structural diversity measures in growth and mortality models may be beneficial, but further work is needed to clarify the underlying relationships, particularly at the individual-tree level.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".