Assessing the cumulative effects of postfire management on forest landscape dynamics in northeastern China
Bibliographic record
Abstract
We used the LANDIS model to study the long-term cumulative effects of postfire 10-year management (harvest and reforestation) on species abundance, age structure, and spatial pattern in the Tuqiang Forest Bureau on the northern slopes of the Great Hing'an Mountains after a catastrophic fire in 1987. Two simulation scenarios were constructed: the actual postfire management scenario and the natural regeneration scenario that assumed no postfire management activities occurred after the 1987 fire. Both scenarios were run with 10 replicated simulations per scenario over a 300-year period. Our results indicated that postfire management had a significant influence on species abundance, age structure, and spatial pattern. Postfire management effectively increased the abundance of coniferous trees (larch (Larix gmelinii) and Mongolian Scotch pine (Pinus sylvestris var. mongolica)), increased the abundance of white birch in the short-term simulation stage, and decreased the abundance of white birch (Betula platyphylla) in the long run. The aggregation level of white birch responded similarly to postfire management — increasing initially, and then decreasing over time. However, compared with the natural regeneration scenario, postfire management resulted in more fragmented larch and Mongolian Scotch pine, which could last for about 100–150 years because of timber harvesting in the first 10 years postfire. In addition, the age structure of larch forests under the postfire management scenario changed dramatically during the 300 simulation years: the abundance of mature and old-growth age classes of larch forests decreased dramatically in the first 10 years, but then increased and exceeded that under the natural regeneration scenario after about 100 simulation years. Therefore, although postfire management had a positive cumulative effect (less fragmented and more larch abundance) on forest recovery at the long-term successional stage, postfire management, especially timber harvesting within the first 10 years after the 1987 fire, posed negative effects (more fragmented and less mature forests) at short- and mid-term successional stages (about 100 years).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".