Potential of thinning to increase forest resilience and resistance to drought, pest, windstorm and fire: A meta-analysis
Bibliographic record
Abstract
As the pressure on forest ecosystems increases with the occurrence of more severe and frequent natural disturbances, the need for silvicultural treatments to mitigate multiple risks is becoming increasingly apparent. Thinning has been identified as a means of managing stands to enhance resilience and resistance to disturbances. However, the underlying mechanisms vary depending on the disturbance types and tree species and there is a lack of empirical evidence that thinning can effectively mitigate these risks at a broad scale. We conducted a meta-analysis of 50 studies quantifying the effects of thinning treatments on the resilience and resistance of forest stands to four categories of natural disturbances: drought, insects and pathogens, wind, and fire. Meta-analyses were conducted to examine the influence of various moderators, namely the response type (growth, survival, damage), thinning intensity, thinning type, time since the first treatment, stand age and pest type (for insects and pathogens). We found a positive broad-scale effect of thinning on forest resilience and resistance, while the disturbance-specific effect was positive for reducing the impact of drought, pests, and in some cases fire, but not significant for windstorms. Although responses varied among disturbance types, and in some cases response type, thinning type, and time since treatment, a key finding of this study is that no statistically significant negative effect of thinning has been detected with respect to our resilience and resistance indicators. Although thinning should not be considered as a tool that will singlehandedly increase the resilience of forests, our results suggest that for temperate and boreal ecosystems of North America and Europe, thinning can be expected to increase the resilience and resistance of forests to multiple stressors, in a wide range of sites and stand characteristics. Yet, empirical data from Asia, southern hemisphere and tropical forests are needed to enable global-scale conclusions. Moreover, potential detrimental effects of thinning on forest ecology should be carefully assessed before prioritizing thinning as a means of increasing forest resilience and resistance.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".