Drivers of post-fire vascular plant regeneration in the conifer-dominated boreal forest of southern Northwest Territories
Bibliographic record
Abstract
In recent years, climate warming has led to an increase in the severity and frequency of naturally occurring fires in boreal ecosystems globally. In 2014, an unprecedented 3.4 million hectares of boreal forest burned in the Northwest Territories (NWT). While much research has focused on post-fire succession of serotinous tree species such as Picea mariana (black spruce) and Pinus banksiana (jack pine), the understory community of vascular plants play an important role in ecosystem functioning but less is known about the response of this component of the system to changing fire regimes. Regeneration strategies such as the ability to resprout from underground rhizomes or disperse an abundance of seeds following fire are examples of plant traits that are adapted to fire regimes and have supported patterns of early recovery of boreal plant communities. Environmental factors such as surficial moisture and soil substrate conditions can also impact post-fire community assembly. Vascular plant community responses to changing fire severity and frequency will shape patterns of succession; understanding changes in these patterns in vascular plant assembly immediately following disturbance will enable future predictions to be made regarding forest recovery. Understanding the patterns of early recovery of plant communities is of interest both for humans and wildlife that depend on self-recovery of vegetation in this region.\nDuring the summer of 2015, information was collected on the presence of plant species and their regeneration modes in 212 sample plots throughout the NWT. Our objectives were to 1) quantify the role of environmental variables and fire characteristics on taxa richness and regeneration traits following an extreme wildfire event; and 2) characterize and investigate vascular plant species composition immediately following fire, with a view to understanding the environmental variables and plant traits underlying post-fire assembly processes.\nWe found that plant community recovery in the southern boreal forest of the NWT is rapid and dominated primarily by rhizomatic species present pre-fire. Our findings suggest that environmental characteristics influenced patterns of community assembly across multiple spatial scales. Poorly drained areas with greater surficial moisture and associated soil characteristics strongly supported self-replacement of plant communities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".