High-severity fires undermine resilience of black spruce-dominated boreal forests in eastern North America
Bibliographic record
Abstract
Abstract Climate-induced fire regime shifts may reduce post-fire resilience of black spruce-dominated (BS; Picea mariana ) North American boreal forests. While post-fire vulnerability of immature BS stands has been extensively studied, no study has evaluated simultaneous effects of fire severity and seasonality on the post-fire regeneration of mature (> 60-year-old) BS stands. This study aims to quantify post-fire regeneration levels of BS and co-occurring tree species to assess ecosystem recovery and possible loss of resilience due to regeneration failure. We analyzed effects of seed bank conditions, fire regime characteristics (fire severity and seasonality), and seedbed conditions on BS post-fire regeneration in mature forests in Quebec, Canada. Post-fire regeneration density was extensively surveyed across ∼50 400 km 2 through a network of 536 plots that were distributed in 21 fires, which burned between 1995 and 2016. One-third of plots failed to regenerate (< 1750 conifer seedlings/ha) at levels adequate to produce closed-crown forest, whereas one-fifth experienced compositional changes, mainly towards jack pine (JP; Pinus banksiana ) dominance. Pre-fire basal area of BS and living Sphagnum ground cover increased BS post-fire regeneration, whereas high-severity crown fires and spring fires reduced it. These findings suggest that mature BS-dominated forests may lose resilience in response to high-severity and spring fires. Given the projected increase in fire severity, and the extension towards an early-fire season in response to climate change, our study suggests that post-fire regeneration failure may become more frequent over the coming decades, with potential negative consequences on ecosystem services that are provided by BS-dominated boreal forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".