Human augmentation of historical red pine fire regimes in the Boundary Waters Canoe Area Wilderness
Bibliographic record
Abstract
Abstract The Border Lakes Region of Minnesota and Ontario has long been viewed as a fire‐dependent ecosystem. High‐severity fire in the region's near‐boreal forests has been a focus of ecological research and public fascination. However, the surface fire history within this transnational wilderness landscape has received more limited attention. We used an interdisciplinary, dendroecological approach to characterize the surface fire history of the region, assess potential drivers of historical surface fires, and document the ecological legacies of frequent fires within the red pine forests of the Boundary Waters Canoe Area Wilderness (BWCAW) in northern Minnesota. We used tree‐ring and fire atlas data to reconstruct multi‐century surface fire records for 101 sites and document age structure and composition at 32 sites across the BWCAW. Stratification of these sites relative to their proximity to a primary travel and trade corridor used first by Indigenous groups and later by Euro‐American fur traders through the late 1800s provided strong evidence of human augmentation of fires. The patterns of fire activity, fire–climate relationships, and forest development indicate that traditional landuse by Anishinaabeg (Ojibwe) increased rates of local surface fire and played an important role in shaping the landscape. The decline of traditional subsistence practices by the Border Lakes Anishinaabeg coincided with a sharp decline in surface fires and a period of abundant tree establishment. In the absence of repeat surface fires, many red pine sites have shifted in composition, increased in stem density, and grown vulnerable to forest‐type conversion through future high‐severity fire. These results highlight the need for active fire reintroduction to red pine forests of the Great Lakes Region and underscore the importance of collaboration and guidance from Indigenous Knowledge Keepers in this process. A blended knowledge approach to fire restoration that directly engages with Indigenous perspectives and cultural practices can perpetuate the distinctive character of the largest remaining tracts of long‐lived pine forest in the Great Lakes Region. Carefully developed fire restoration practices would enhance the visitor use experience within one of the most frequently visited wilderness areas in the United States while re‐engaging directly with Indigenous knowledge and traditional cultural practices.
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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 | 0.000 |
| 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) | 0.002 | 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".