Variability and dynamics of old-growth forests in the circumboreal zone: implications for conservation, restoration and management
Bibliographic record
Abstract
<ja:p>Due to the unprecedented loss of old-growth forests to harvesting throughout circumboreal regions an understanding of similarities and differences in old-growth dynamics is needed to design effective restoration, management and conservation efforts. This paper reviews concepts, prevalence and variability of old-growth forests across landscapes, and evaluates different stand scale dynamics at the old-growth stage across the circumboreal zone. Old-growth historically dominated many boreal forest landscapes in both Eurasia and North America. Throughout much of North America, and to some extent in western Siberia, the natural prevalence and development of old-growth forests are regulated by the occurrence of stand-replacing fires. In eastern North America and Siberia, insect outbreaks may, however, be more important. Insect outbreaks as well as recurrent non-stand replacing surface fires and windthrows, when occurring at the old-growth stage, often form stands characterized by several tree age-class cohorts. This multi age-class forest development type is common in Europe and eastern Siberia but its prevalence and importance in boreal North-America is not well documented. Similarities in successional dynamics across the circumboreal region are found in the development of mono-dominant even-aged stands, the replacement of shade intolerant tree species by shade tolerant species, as well as in all-aged stands driven by small-scale gap dynamics. The message to land managers is that the focus should not only be on setting aside remaining old-growth forests or in restoring static old-growth attributes, but also in emulating natural disturbances and successional dynamics at landscape and regional scales to maintain natural variability in old-growth attributes through time.</ja:p>
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".