Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Previous empirical work has shown that feedbacks between fire severity, soil organic layer thickness, tree recruitment, and forest growth are important factors controlling carbon accumulation after fire disturbance. However, current boreal forest models inadequately simulate this feedback. We address this deficiency by updating the ED2 model to include a dynamic feedback between soil organic layer thickness, tree recruitment, and forest growth. The model is validated against observations spanning monthly to centennial time scales and ranging from Alaska to Quebec. We then quantify differences in forest development after fire disturbance resulting from changes in soil organic layer accumulation, temperature, nitrogen availability, and atmospheric CO 2 . First, we find that ED2 accurately reproduces observations when a dynamic soil organic layer is included. Second, simulations indicate that the presence of a thick soil organic layer after a mild fire disturbance decreases decomposition and productivity. The combination of the biological and physical effects increases or decreases total ecosystem carbon depending on local conditions. Third, with a 4°C temperature increase, some forests transition from undergoing succession to needleleaf forests to recruiting multiple cohorts of broadleaf trees, decreasing total ecosystem carbon by ∼40% after 300 years. However, the presence of a thick soil organic layer due to a persistently mild fire regime can prevent this transition and mediate carbon losses even under warmer temperatures. Fourth, nitrogen availability regulates successional dynamics; broadleaf species are less competitive with needleleaf trees under low nitrogen regimes. Fifth, the boreal forest shows additional short‐term capacity for carbon sequestration as atmospheric CO 2 increases.
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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 | 0.001 | 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 | 0.001 |
| 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 it