How climate change might affect tree regeneration following fire at northern latitudes: a review
Bibliographic record
Abstract
Abstract Climate change is projected to increase fire severity and frequency in the boreal forest, but it could also directly affect post-fire recruitment processes by impacting seed production, germination, and seedling growth and survival. We reviewed current knowledge regarding the effects of high temperatures and water deficits on post-fire recruitment processes of four major tree species (Picea mariana, Pinus banksiana, Populus tremuloides and Betula papyrifera) in order to anticipate the effects of climate change on forest recovery following fire in the boreal biome. We also produced maps of future vulnerability of post-fire recruitment by combining tree distributions in Canada with projections of temperature, moisture index and fire regime for the 2041–2070 and 2071–2100 periods. Although our review reveals that information is lacking for some regeneration stages, it highlights the response variability to climate conditions between species. The recruitment process of black spruce is likely to be the most affected by rising temperatures and water deficits, but more tolerant species are also at risk of being impacted by projected climate conditions. Our maps suggest that in eastern Canada, tree species will be vulnerable mainly to projected increases in temperature, while forests will be affected mostly by droughts in western Canada. Conifer-dominated forests are at risk of becoming less productive than they currently are, and eventually, timber supplies from deciduous species-dominated forests could also decrease. Our vulnerability maps are useful for prioritizing areas where regeneration monitoring efforts and adaptive measures could be developed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".