Initial Succession After Wildfire in Dry Boreal Forests of Northwestern North America
Bibliographic record
Abstract
Abstract Wildfires in the boreal forest of North America are generally stand renewing, with the initial phase of vegetation recovery often governing the vegetation trajectory for decades. Here, we investigate post-fire vegetation changes in dry boreal forests of the Northwest Territories, Canada, during the first five years following the unusually severe 2014 wildfire season. We sampled post-fire tree regeneration and the overall plant community at one, three, and five years post-fire across different fire severities and stand types within fires that burned in 2014. Post-fire trajectories of tree recruitment, cover by plant functional types, and plant diversity varied widely among sampled stands, as well as among years post-fire. Tree seedling density reaches relative equilibrium by three years post-fire, whereas trends in understory plant cover and understory species assemblages suggest an ongoing change that will extend beyond five years of observation. In almost half of sampled stands, the composition of recruited trees differs from that of the pre-fire stand, suggesting a change in tree-species dominance. An analysis of regional climate reveals a significant, albeit spatially variable, warming and drying trend that will further accelerate forest-stand transformation through both climate drivers of plant community composition and indirectly through increasing fire activity. While the 2014 wildfires enhanced the structural and compositional heterogeneity of the region, they also triggered vegetation changes that are likely to be persistent. As such, this study exemplifies the speed and variability that characterizes post-fire stand development in a strongly moisture-limited part of North America.
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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 | 0.001 | 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".