Seed and bud legacies interact with varying fire regimes to drive long-term dynamics of boreal forest communities
Bibliographic record
Abstract
Sustainable forest management that employs the emulation of natural disturbance paradigm develops plans based on disturbance-driven forest succession. However, most research on forest succession has focused primarily on postdisturbance species change, often ignoring predisturbance legacies. We used the FORECAST ecosystem management model and a newly created natural regeneration submodel to examine the interaction of fire severity, fire frequency, and natural regeneration to produce multicycle dynamics of white spruce (Picea glauca (Moench) Voss), lodgepole pine (Pinus contorta Dougl. ex Loud.), and trembling aspen (Populus tremuloides Michx.) in a western Canadian boreal forest. We simulated 19 different scenarios in a factorial disturbance experiment of three severities and six frequencies and a no-disturbance scenario. Our simulations resulted in a wide diversity of boreal forest community types, including trembling aspen dominated mixedwoods with high-frequency (75 year), high-severity (100% mortality) fires, and nearly pure lodgepole pine stands at midfrequency (100125 year) and high-severity fires. With the unvarying disturbance regimes we used, a variety of different but recurring temporal patterns emerged. We show that the loss of seed source legacies reinforces the disturbance-driven species dominance, demonstrating community inertia. This study provides a long-term perspective to boreal forest management that demonstrates the role of disturbance and legacies in long-term 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".