Beyond black spruce: shift in plant communities after frequent fire in a Yukon subarctic boreal forest
Bibliographic record
Abstract
Rapid warming in northern climates is altering plant successional trajectories at their northern extent. Changing fire regimes under ongoing climate change are predicted to further influence shifts in vegetation successional trajectories in boreal forests. New fire regimes impact ecosystem vegetation legacies, which dictate the regeneration success of forests and can rapidly change ecosystem states to non-forested trajectories. Two closely timed fires (1990/1, 2005) in the Eagle Plains region of northern Yukon resulted in a failure of black spruce (Picea mariana) regeneration. Our study characterized the alternate regeneration trajectories in the absence of black spruce regeneration and examined possible abiotic factors driving those changes. We found evidence of alternate regeneration trajectories favouring tall shrub growth in sites experiencing a shortened fire return interval. Particularly, denser tall-shrub regeneration occurred in sites with deeper active layers. Increased shrub dominance may have implications on culturally significant species such as barren-ground caribou (Rangifer tarandus), berry producing plants, and those that depend on these species. Increased shrub growth will impact ecological processes like carbon sequestration, nutrient cycling, and permafrost dynamics. As disturbance regimes evolve, divergent post-fire successional pathways will continue to emerge, influencing other landscape processes, and impact important species to Indigenous communities of the area.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".