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Record W4410423407 · doi:10.1016/j.foreco.2025.122788

Potential of thinning to increase forest resilience and resistance to drought, pest, windstorm and fire: A meta-analysis

2025· article· en· W4410423407 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueForest Ecology and Management · 2025
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThinningResilience (materials science)PEST analysisResistance (ecology)Environmental scienceAgroforestryForestryEcologyGeographyBiologyBotany

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.225
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it