Sexual and vegetative recruitment of trembling aspen following a high-severity boreal wildfire
Bibliographic record
Abstract
Abstract Background High-severity fire is rare in trembling aspen-dominated forests of the boreal region. The post-fire recruitment strategy of aspen, by either vegetative suckering or sexually (i.e., by seed), has considerable implications for subsequent forest structure, genetic diversity, and ecological resilience to shifting climatic and disturbance regimes. In this study, we take advantage of the unique opportunity provided by the Chuckegg Creek Wildfire Fire (310,000 ha) in northern Alberta, Canada, which burned at high severity through aspen stands before and after spring green-up, to document how phenology, fire severity, and stand characteristics affect recruitment one year following the fire. Results We found sites were dominated either by high-density patches of seedlings or a fairly uniform density of suckers, with few sites occupied by both. Sites dominated by seedlings burned predominantly after green-up. Using boosted regression trees, we found that surface fire severity best predicted both aspen seedling and sucker density at sites. Seedlings were favoured at sites that burned at high surface severity and after spring green-up, whereas suckering density was highest at sites that burned at moderate-high surface severity before green-up. Conclusion Our research highlights the influence of surface fire severity and phenology on aspen recruitment. High fire severity, particularly after aspen green-up, reduced suckering while promoting seedling recruitment. Aspen seedlings filled the recruitment gap caused by this lowered, suckering response, providing an alternate route for aspen forest adaptive capacity after high-severity surface fire.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".