Burning at the Edge: Integrating Biophysical and Eco-Cultural Fire Processes in Canada’s Parks and Protected Areas
Bibliographic record
Abstract
Currently, high intensity, large-area lightning fires that burn during droughts dominate Canada’s fire regimes. However, studies from several disciplines clearly show that humans historically ignited burns within this matrix of large fires. Two approaches for fire research and management have arisen from this pattern: a “large-fire biophysical paradigm” related to lightning-ignited fires, and an “eco-cultural paradigm” related to human-caused burning. Working at the edge between biophysically driven fires and eco-cultural burns, and their associated management and research paradigms, presents unique challenges to land managers. We proceed by describing fire frequency trends across Canada, and how an interaction between changing climatic and cultural factors may provide better causal explanations for observed patterns than either group of factors alone. We then describe four case histories of fire restoration into Canadian landscapes moving through evolution, or deliberate intent, towards increasing emphasis on an eco-cultural paradigm. We show that use of cultural burns maintains this facet of the long-term regime while providing greater capacity for larger, higher intensity fires to occur with fewer negative ecological and socio-economic implications. Key lessons learned by practitioners restoring fire to landscapes include: 1) fire is only one process in ecosystems that also include other complex interactions, and thus restoration of fire alone could have unintended consequences in some ecosystems; 2) recognizing long-term human roles of not only fire managers, but also hunters and gatherers is critical in restoration programs; and 3) this diversity of past, present, and future ecological and cultural interactions with fire can link managers to a broad constituency of stakeholders. Bringing this variety of people and interests into the decision-making processes is a necessary pre-requisite to successful fire management at the edge.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".