No increased drought-related mortality after thinning: a meta-analysis
Bibliographic record
Abstract
Abstract Context Forest scientists are challenged to identify and propose evidence-based silvicultural options to mitigate the impacts of drought events induced by climate change. For example, it has been suggested that thinning increases soil water availability for individual trees by reducing stand density and stand-level transpiration. Many studies have assessed the impact of thinning on stem growth and transpiration of individual trees during and after drought events. Often, growth increases were observed, but not consistently, and their impact on tree survival following drought has rarely been addressed. Aims We aimed to assess the effect of thinning on tree mortality, the ultimate indicator of tree resistance to soil water deficit induced by drought, with a focus on dominant trees. Methods We conducted a risk ratio meta-analysis on tree mortality before and after an extreme drought event with 32 thinning experiments from nine studies in Europe and North America. Results We showed that thinning reduced the overall mortality risk of trees. However, the lower mortality rate in thinned stands relative to unthinned stands in pre-drought periods was not further reduced during and after extreme drought events ( p > 0.05). This may be due to the large heterogeneity and inconsistent reporting of mortality across the studies included in our analysis. Thinning did not exacerbate mortality among dominant trees. Conclusion Since thinning did not increase mortality, its application can still be recommended for many other management objectives such as maintaining tree species richness or lower disturbance risks from windthrow. We propose better documentation of thinning trials to improve the data base for systematic reviews.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".