Mitigating post-fire regeneration failure in boreal landscapes with reforestation and variable retention harvesting: At what cost?
Bibliographic record
Abstract
Successive disturbances such as fire can affect post-disturbance regeneration density, with documented adverse effects on subsequent stand productivity. We conducted a simulation study to assess the potential of reactive (reforestation) and proactive (variable retention harvesting) post-fire regeneration failure mitigation strategies in a 1.37 Mha fire-prone boreal landscape dominated by black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.). We quantified their respective capacity to maintain landscape productivity and post-fire resilience, as well as their associated financial returns under current and projected (RCP 8.5) fire regimes. While post-fire reforestation with jack pine revealed to be the most effective strategy to maintain potential production, associated costs quickly became prohibitive when applied over extensive areas. Proactive strategies such as an extensive use of variable retention harvesting, combined with replanting of fire-adapted jack pine only in easily accessible areas, appeared as a more promising approach. Despite this, our results suggest an inevitable erosion of forest productivity due to post-fire regeneration failure events, highlighting the importance of integrating fire a priori in strategic forest management planning as well as its effects on long-term regeneration dynamics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".