Impacts of novel wildfire disturbance on landcover and wildlife in boreal North America
Bibliographic record
Abstract
The summer of 2023 was the first time the planet exceeded 1.5 °C above pre-industrial average temperatures and was recognized as the hottest year on record globally. In Canada, this translated to temperatures that were 2.2 °C above average, resulting in a record shattering fire season. Burning conditions were exceptional in many ways including large total burned area and individual fire size, vast swaths of short-interval reburns and severe combustion, fast-moving wildfires, and substantial burning outside of the typical fire season, reflecting climate warming-induced changes in fire regime. While we know how some components of biodiversity in the boreal biome have responded to historic burning, the 2023 fire season highlights that we are rapidly moving into a novel set of burning conditions and that we require better knowledge of how these conditions will alter landcover and the associated wildlife communities. Here, we synthesize our understanding of how changing fire regimes will affect (short- and long-term effects) boreal landscapes and the implications of these changes for terrestrial wildlife using the extreme burning conditions in subarctic forests of the Northwest Territories, Canada in 2023 as a case study. Our goals were to a) evaluate the impacts of the changing fire regime on habitat composition and/or structure in the short- and longer-term; and b) assess the potential responses of terrestrial vertebrates to these changes based on our understanding of their reliance on key aspects of habitat composition and/or structure. We describe impacts of changes in individual aspects of the fire regime on wildlife taxa but, overall, at the landscape level, changes in fire regime are altering the composition and structure of boreal forests, which will drive decreases in taxa requiring mature conifer forest and post-fire structural complexity while favoring early seral species or those that prefer broadleaf forests or open habitats. This review offers us a range of possibilities about the future landcover and northern wildlife communities under changing wildfires, however uncertainties about feedbacks and future conditions of this vast, diverse, and remote landscape present the greatest challenges for forecasting and other supports for adaptation planning.
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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 | 0.002 |
| 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".