Using tree rings to assess the potential of thinning to alleviate drought stress in a dry forest of western Canada
Bibliographic record
Abstract
Increasing frequency of severe drought events under climate change is a major cause for concern for millions of hectares of forested land. One practical solution to improving forest resilience may be thinning. There may be several potential benefits, chief of which is that drought tolerance could be improved in the remaining trees due to lower competition for resources and increased precipitation throughfall. By improving resilience to drought, this may increase productivity of the remaining trees while lowering risks of mortality. Such potential benefits can effectively be quantified with data from statistically-sound, long-term field experiments, and tree rings provide a suitable avenue to compare treatments. We work with an experiment that applied different levels of tree retention to mature interior Douglas fir (Pseudotsuga menziesii var. glauca) in a dry ecosystem of western Canada. The treatments were applied in the winter of 2002/2003, coinciding with the aftermath of a severe natural drought event in 2002. We used tree-rings to quantify the extent to which thinning improves recovery and resilience of treated trees as compared to non-thinned controls. Tree-ring samples as well as height and diameter data were obtained from 83 trees from 8 treatment units of the randomized experimental design. Indicators for resilience to drought were calculated based on basal area increments. Thinning substantially increased basal area increments at the individual tree level, but more importantly, led to significantly higher recovery and resilience relative to the control. The results of this tree-ring analysis suggest that thinning may be a viable silvicultural intervention to counteract effects of severe drought events and to maintain tree cover.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".