Examining forest resilience to changing fire frequency in a fire‐prone region of boreal forest
Bibliographic record
Abstract
Future changes in climate are widely anticipated to increase fire frequency, particularly in boreal forests where extreme warming is expected to occur. Feedbacks between vegetation and fire may modify the direct effects of warming on fire activity and shape ecological responses to changing fire frequency. We investigate these interactions using extensive field data from the Boreal Shield of Saskatchewan, Canada, a region where >40% of the forest has burned in the past 30 years. We use geospatial and field data to assess the resistance and resilience of eight common vegetation states to frequent fire by quantifying the occurrence of short-interval fires and their effect on recovery to a similar vegetation state. These empirical relationships are combined with data from published literature to parameterize a spatially explicit, state-and-transition simulation model of fire and forest succession. We use this model to ask if and how: (a) feedbacks between vegetation and wildfire may modify fire activity on the landscape, and (b) more frequent fire may affect landscape forest composition and age structure. Both field and GIS data suggest the probability of fire is low in the initial decades after fire, supporting the hypothesis that fuel accumulation may exert a negative feedback on fire frequency. Field observations of pre- and postfire composition indicate that switches in forest state are more likely in conifer stands that burn at a young age, supporting the hypothesis that resilience is lower in immature stands. Stands dominated by deciduous trees or jack pine were generally resilient to fire, while mixed conifer and well-drained spruce forests were less resilient. However, simulation modeling suggests increased fire activity may result in large changes in forest age structure and composition, despite the feedbacks between vegetation-fire likely to occur with increased fire activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".