Climate‐vegetation‐fire interactions and their impact on long‐term carbon dynamics in a boreal peatland landscape in northern Manitoba, Canada
Bibliographic record
Abstract
Climate warming may increase the size and frequency of fires in the boreal biome, possibly causing greater carbon release that amplifies warming. However, in peatlands, vegetation change may also control long‐term fire and carbon accumulation, confounding simple relationships between climate, fire, and carbon accumulation. Using 17 peat cores dating to 8000 cal years B.P. from northern Manitoba, Canada, we addressed the following questions: (1) Do past climate changes correlate with shifts in peatland vegetation? (2) What is the relationship between peatland vegetation and fire severity? (3) What is the mean return interval for boreal peat fires, and how does it change across fires of different severities? (4) How does fire severity affect carbon accumulation rates? (5) Do fire and long‐term carbon accumulation change directly in response to climate or indirectly though climate‐driven changes in vegetation? We measured carbon accumulation rates, fire severity, and return intervals using macroscopic charcoal and changes in vegetation using macrofossils. Climate and vegetation changes covaried, with shifts from wetter fen to drier, forested bog communities during the Holocene Thermal Maximum (HTM). Fires became more severe following the shift to forested bogs, with fire severity peaking after 4000 cal years B.P. rather than during the HTM. Rising fire severity, in turn, was correlated with a significant decrease in carbon accumulation from ∼6000 to 2000 cal years B.P. The Medieval Warm Period and Little Ice Age affected vegetation composition and permafrost, further impacting fire and carbon accumulation. Our results indicate that long‐term changes in fire and carbon dynamics are mediated by climate‐driven changes in vegetation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".