Can Boreal and Temperate Forest Management be Adapted to the Uncertainties of 21st Century Climate Change?
Bibliographic record
Abstract
AbstractConsiderable 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.Keywords: acclimationadaptationclimate changeecophysiologyepigeneticsforest managementforestryphenologypredictive modellingsilviculture ACKNOWLEDGMENTSWe are very grateful to Dr. Jo Clark of Earth Trust UK for providing the initial review of this manuscript. Her many helpful suggestions for both content and structure greatly improved the later drafts of our paper. We also thank the anonymous reviewer, who provided a number of fresh perspectives that found their way into the final version of the article.NotesNicotra et al. (Citation2010) define phenotypic plasticity as an epigenetic response: a mechanism of gene regulation that leads to heritable, but potentially reversible changes in gene expression.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".