Boreal forests will be more severely affected by projected anthropogenic climate forcing than mixedwood and northern hardwood forests in eastern Canada
Bibliographic record
Abstract
Abstract Context Increased anthropogenic climate forcing is projected to have tremendous impacts on global forest ecosystems, with northern biomes being more at risk. Objectives To model the impacts of harvest and increased anthropogenic climate forcing on eastern Canada’s forest landscapes and to assess the strong spatial heterogeneity in the severity, the nature and direction of the impacts expected within northern forest regions. Methods We used LANDIS-II to project species-specific aboveground biomass (AGB) between 2020 and 2150 under three climate (baseline, RCP 4.5 and RCP 8.5) and two harvest (baseline harvest, no harvest) scenarios within four forest regions (boreal west, boreal east, mixedwood and northern hardwood). Results Climate change impacts within the boreal forest regions would mainly result from increases in wildfires activity which will strongly alter total AGB. In the mixedwood and northern hardwood, changes will be less important and will result from climate-induced growth constraints that will alter species composition towards more thermophilous species. Climate-induced impacts were much more important and swifter under RCP 8.5 after 2080 suggesting that eastern Canada’s forests might cross important tipping points under strong anthropogenic climate forcing. Conclusions Boreal forest regions will be much less resilient than mixedwood or northern hardwoods to the projected changes in climate regimes. Current harvest strategies will interact with anthropogenic climate forcing to further modify forest landscapes, notably by accelerating thermophilous species AGB gain in southernmost regions. Major changes to harvest practices are strongly needed to preserve the long-term sustainability of wood supply in eastern Canada. Adaptation strategies should be region-specific.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".