Climate, topography, or fuels? Top‐down versus bottom‐up controls on fire refugia across British Columbia, Canada
Bibliographic record
Abstract
Abstract Surviving pockets of vegetation within fire perimeters, termed fire refugia, are an important component of ecological recovery following disturbance. Understanding the relative influence of the drivers of fire refugia throughout diverse landscapes and climate conditions can help identify areas that are conducive to their formation. We investigated the role of various top‐down (climate) and bottom‐up (fuels, physical setting) controls on fire refugia creation throughout 21 unique fire regime units in the forests of British Columbia, Canada, over a 20‐year (2000–2019) period. Boosted regression tree models were used to determine the relative influence of each of these controls and their associated variables on fire refugia, as well as to create predictive maps of fire refugia probabilities over a range of annual climate conditions. We found that the bottom‐up controls, particularly variables relating to physical setting, generally held the greatest influence on fire refugia creation, though those relating to fuels were of higher importance in the more disturbance‐prone forests of the boreal and central interior regions. These bottom‐up controls, however, can be overwhelmed by extreme climate conditions, which have variable effects on refugia depending on the region. There was an overall positive correspondence between locations of persistent (long‐term) fire refugia and mapped old‐growth, suggesting that strong, static terrain features may shelter some forests over the course of multiple fire events, allowing for the development of old‐growth stands. We concluded that, while strong topographic features confer the strongest measure of protection in some regions of the province, there are many areas in which fuel mitigation tactics (e.g., fuel thinning, prescribed and cultural burning) may be particularly useful for protecting areas of high human or ecological value in the face of increasingly extreme climate conditions. Although our maps can help predict where and when fire refugia may form under provided climatic and environmental conditions, they do not reflect real‐time conditions and are therefore not intended for risk assessment or for operational management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".