Peatlands promote fire refugia in boreal forests of northern Alberta, Canada
Bibliographic record
Abstract
Abstract In the boreal forests of North America, large wildfires often leave residual patches of unburned vegetation, termed fire refugia, which can affect post‐fire ecosystem processes. Although topographic complexity is a major driver of fire refugia in mountainous terrain, refugia in boreal plains are more likely driven by a combination of other bottom‐up controls on fuel configuration as well as top‐down climate controls. In this study, we investigated the role of hydrological, ecological, and topographic heterogeneity, as well as climate moisture patterns, on the presence of fire refugia in forested upland and peatland ecosystems within Alberta's subhumid boreal forests over a 33‐year (1985–2018) period. Generalized linear models were used to model the probability of refugia in forested stands as a function of bottom‐up (vegetation, topography, site moisture, and ecosystem) and top‐down (normal and annual climate moisture deficit) controls. We then developed predictive maps of refugia probability for a range of normal and interannual climate moisture deficit values. We found that forested fens had a probability of refugia that was 64% higher than upland forests, while forested bogs did not differ from forested uplands in refugia likelihood. Climate and physical setting presented the strongest controls on fire refugia in uplands and peatlands, respectively. Increasing amounts of adjacent bogs, but not fens, produced a sixfold increase in refugia probability in uplands, while increasing amounts of adjacent bogs and fens produced roughly two times the refugia probability in forested peatlands. In these upland forest stands, fire refugia probability was negatively related to the interaction between regional climate moisture deficits and interannual deviations from these norms, thus increasing the probability of fire refugia during more severe drought conditions in areas with less arid climates, while decreasing refugia probabilities in drier climates. However, in peatlands themselves, neither regional climate moisture conditions nor the interannual deviations affected refugia. Fire size had a negative effect on fire refugia in all upland‐based models and a positive effect in all peatland‐based models. Our results suggest that large areas of intact peatlands may be capable of promoting fire refugia and thereby slowing climate‐driven, fire‐mediated vegetation transitions in surrounding forest ecosystems.
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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 | 0.000 |
| 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".