Can Boreal and Temperate Forest Management be Adapted to the Uncertainties of 21st Century Climate Change?
Bibliographic record
Abstract
Considerable uncertainties remain about magnitude and character, if not general direction of anthropogenic climate change. Global mean temperature could increase by 1.5–4.5°C or more over historic levels, and extreme weather events—drought, storms, and flooding—are likely to increase greatly in frequency. Although ecologists and foresters agree that the practice of forestry will be transformed under climate change, these uncertainties compound the challenge of achieving sustainable, adaptive forest management. In this aritcle, we (i) present a multidisciplinary synthesis of current knowledge of responses of temperate and boreal tree species and forest communities to climate change, and (ii) outline silvicultural strategies for adapting temperate and boreal forests to confront climate change. Our knowledge synthesis proceeds through critical appraisals of efforts to model future tree distributions and responses to climate change, and reviews physiological, phenological, acclimation, and epigenetic responses to climate. As is the case of climate change itself, there are numerous uncertainties about tree species and provenance responses to climate change. For example, acclimation of respiration and epigenetic conditioning of seed embryos has the potential to buffer species against limited warming. Provenances within species also display idiosyncratic responses to altered climates, implying that soemm varieties will be more resilient or resistant to climate change than others. Genetically determined limits to climatic tolerance, and the limits of tree community resistance and resilience (speed of recovery from disturbance) in the face of climate-related disturbances are largely unknown. These unknowns require managers to adopt a portfolio of silvicultural strategies, which may range from minor modifications of current practices to design of novel multi-species stands that may have no historical analogue. Forest managers must be prepared to respond nimbly as they develop, incorporate new insights about climate change and species responses to warming into their practices. Marshalling all strategies and sources of knowledge should enable forest managers to mount (at least) a partially successful response to the challenges of climate change.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".