Fire activity in the northern Arctic tundra now exceeds late Holocene levels, driven by increasing dryness and shrub expansion
Bibliographic record
Abstract
Abstract. Tundra ecosystems are characterized by small, rare and infrequent fires due to cold, often waterlogged conditions, and limited biomass. However, ongoing climate warming and drying in northern soils and peatlands contribute to increasingly frequent and extensive wildfires. To place recent fire regimes in the context of long-term variability and to better understand interactions between moisture, vegetation, and fire, we reconstructed wildfire history over the past 3000 years using a network of charcoal records in combination with vegetation and hydrological datasets and satellite-derived fire datasets from northern Arctic Alaska. Composite charcoal records show minimal fire activity from ~1000 BCE to 500 CE, followed by a slight increase from 500 CE onwards. This long-term pattern shifted abruptly around 1880 CE, when fire activity exceeded any levels observed in the preceding three millennia. Individual charcoal records show a more heterogeneous fire pattern before 1880 CE and a more homogeneous one thereafter. Our findings suggest that deepening of water tables and peatland drying associated with permafrost thaw have facilitated woody encroachment, especially by more flammable Ericaceous shrubs. These vegetation changes have increased fuel availability and flammability, ultimately driving the recent surge in wildfire activity. This study highlights the importance of moisture–vegetation–fire feedback in shaping tundra fire regimes and the vulnerability of Arctic ecosystems to fire. This is particularly evident in areas experiencing pronounced drying and the expansion of flammable shrub taxa. We also found that the charcoal source area of our tundra fire encompasses broader landscapes over tens of kilometre.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".