Long-term effects of fire frequency on carbon storage and productivity of boreal forests: a modeling study
Bibliographic record
Abstract
Climate change is predicted to shorten the fire interval in boreal forests. Many studies have recorded positive effects of fire on forest growth over a few decades, but few have modeled the long-term effects of the loss of carbon and nitrogen to the atmosphere. We used a process-based, dynamic, forest ecosystem model, which couples the carbon, nitrogen and water cycles, to simulate the effects of fire frequency on coniferous forests in the climate of Prince Albert, Saskatchewan. The model was calibrated to simulate observed forest properties. The model predicted rapid short-term recovery of net primary productivity (NPP) after fire, but in the long term, supported the hypotheses that (1) current NPP and carbon content of boreal forests are lower than they would be without periodic fire, and (2) any increase in fire frequency in the future will tend to lower NPP and carbon storage. Lower long-term NPP and carbon storage were attributable to (1) loss of carbon on combustion, equal to about 20% of NPP over a 100-200 year fire cycle, (2) loss of nitrogen by volatilization in fire, equal to about 3-4 kg N ha(-1) year(-1) over a 100-200 year fire cycle, and (3) the fact that the normal fire cycle is much shorter than the time taken for the forest (especially the soil) to reach an equilibrium carbon and nitrogen content. It was estimated that a shift in fire frequency from 200 to 100 years over 1000 Mha of boreal forest would release an average of about 0.1 Gt C year(-1) over many centuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".