Regional-scale hydrologic settings buffer black spruce regeneration in the presence of post-fire droughts
Bibliographic record
Abstract
Increasingly severe wildfires and droughts are reducing black spruce recruitment and favouring early successional species like jack pine and trembling aspen in Canada’s western boreal forests. Adjacent peatlands may mitigate these changes, depending on topographic position and soil texture, which influence groundwater connectivity. We examined tree regeneration in 58 post-fire upland forest stands (5–20 years old) across various local (adjacent peatland) and regional (relative to a regional low) topographic positions, under different post-fire drought conditions (i.e., post-fire climate moisture deficit). We hypothesized that regenerating forests at lower topographic positions, supported by primarily groundwater-fed (largely rich fen) peatlands, would be relatively buffered against post-fire drought as primarily precipitation-fed (bog and poor fen) peatlands at higher positions are more drought-sensitive. Regenerating black spruce proportions were negatively correlated with post-fire drought at regional high topographic positions, across soil textures. Post-fire stem density, tree volume, and proportions of jack pine and aspen were not correlated with post-fire drought. This study highlights that areas of Alberta’s boreal forest with large-scale hydrological connectivity may act as drought refugia for post-fire black spruce, while jack pine, and aspen are likely to remain resilient across a range of physical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".