Young and old forest in the boreal: critical stages of ecosystem dynamics and management under global change
Bibliographic record
Abstract
The circumboreal forest encompasses diverse landscape structures, dynamics and forest age distributions determined by their physical setting, and historical and current disturbance regimes. However, due to intensifying forest utilisation, and in certain areas due to increasing natural disturbances, boreal forest age-class structures have changed rapidly, so that the proportion of old forest has substantially declined, while that of young post-harvest and post-natural-disturbance forest proportions have increased. In the future, with a warming climate in certain boreal regions, this trend may further be enhanced due to an increase in natural disturbances and large-scale use of forest biomass to replace fossil-based fuels and products. The major drivers of change of forest age class distributions and structures include the use of clearcut short-rotation harvesting, more frequent and severe natural disturbances due to climate warming in certain regions. The decline in old forest area, and increase in managed young forest lacking natural post-disturbance structural legacies, represent a major transformation in the ecological conditions of the boreal forest beyond historical limits of variability. This may introduce a threat to biodiversity, ecosystem resilience and long-term adaptive capacity of the forest ecosystem. To safeguard boreal forest biodiversity and ecosystem functioning, and to maintain the multiple services provided to societies by this forest biome, it is pivotal to maintain an adequate share and the ecological qualities of young post-disturbance stages, along with mature forest stages with old-growth characteristics. This requires management for natural post-disturbance legacy structures, and innovative use of diverse uneven-aged and continuous cover management approaches to maintain critical late-successional forest structures in landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".