The influence of landscape‐level heterogeneity in fire frequency on canopy composition in the boreal forest of eastern Canada
Bibliographic record
Abstract
Abstract Question Will a three‐fold variation in fire frequency among large patches (≈105–106 ha) of boreal forest generate differences in canopy composition and, more specifically, will it influence the relative abundance of species with regard to their typical position along the succession gradient. Location A landscape of 1.6 Mha in the boreal forest of eastern Canada (Quebec). Methods We sampled 160 circular plots in closed‐canopy forest in which we measured canopy vegetation composition, local fire history (time since last fire) and edaphic conditions. We conducted multivariate analyses (NMDS, multi‐response permutation procedure) and pair‐wise comparisons to probe differences in canopy composition between areas of contrasting fire frequency before and after controlling for the influence of local environmental factors. Results There are significant differences between areas of contrasting fire frequency in terms of relative species abundance, even after analytically removing the effect of important local environmental factors. In old stands,Picea marianais significantly more abundant in high fire frequency areas whileAbies balsameais significantly more abundant in low fire frequency areas, both before and after controlling for local environmental factors, including time since last fire. Young stands do not differ in terms of individual species relative abundance but show more variability among stands in low fire frequency areas. Main conclusion The low fire frequency areas allow late‐successional specialistA. balsameato dominate over ubiquitous successional generalistP. marianabecause of the typically longer time elapsed since the last fire. This suggests that succession fromP. marianatoA. balsameacan occur long after what is typically covered by dendroecologically reconstructed fire history in this type of boreal landscape (> 200–300 yr).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".