A Meta‐Regression of 18 Wildfire Chronosequences Reveals Key Environmental Drivers and Knowledge Gaps in the Boreal Nitrogen Balance
Bibliographic record
Abstract
ABSTRACT Climate change has increased the size and frequency of wildfires across the boreal biome. Severe wildfires in boreal forests have been found to trigger shifts from evergreen to deciduous canopies, which has cascading effects on carbon and nitrogen cycling. Ecosystem productivity and carbon uptake in boreal forests are strongly linked with nitrogen, and Earth system models increasingly depend on our understanding of the nitrogen balance to predict post‐fire carbon uptake. To investigate the post‐fire boreal nitrogen balance, we combined a mass balance approach and literature synthesis to estimate rates of nitrogen accumulation and nitrogen inputs across a network of 18 boreal wildfire chronosequences that varied in both wildfire regime and post‐fire canopy type, comprising 527 forest stands. We found that deciduous‐ or mixed‐dominance boreal forests establishing after severe, stand‐replacing fires had the highest nitrogen accumulation rates (15.7 ± 3.8 kg ha−1 year−1), while evergreen‐dominated forests establishing after surface‐ or mixed‐severity fires had the lowest nitrogen accumulation rates (1.4 ± 1.1 kg ha−1 year−1). Annual known inputs from nitrogen deposition and biological nitrogen fixation combined, estimated from published data, largely failed to explain the rate of nitrogen accumulation, particularly in deciduous or mixed‐dominance forests establishing after stand‐replacing fires, suggesting that the origins of most nitrogen in these forest types remain poorly understood. As the frequency of severe wildfires increases across the boreal biome and shifts toward deciduous canopies become more common, our study reveals a large knowledge gap in the resulting nitrogen balance that needs to be resolved in order to improve predictions of forest carbon uptake.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".