Clear-cutting of temperate forests in late successional stages triggers successional setbacks extending compositional recovery by an additional century
Bibliographic record
Abstract
Clear-cutting is used by foresters worldwide to harvest timber from forest ecosystems. Clear-cutting of cool–wet boreal forests in late successional stages can maintain successional trajectories pointed toward the composition of a late successional stages. However, clear-cutting of warm–dry boreal forests in late successional stages can trigger reversion of successional trajectories back toward forests of earlier stages because early-successional shade-intolerant species are more abundant than in cool–wet boreal forests, a process that is referred to as a successional setback. Such successional setbacks can generate sustainability issues by extending the expected time to compositional recovery after clear-cutting. This can lead to an overestimation of allowable cuts of economically important late-successional species and subsequently to a temporary forest composition conversion if the occurrence of successional setbacks remains unassessed. Temperate forests in late successional stages are warmer and drier than boreal forests and consequently include more early-successional shade-intolerant species susceptible to encroach clear-cut areas. Even if current ecological knowledge suggests that temperate forests could be susceptible to post-clearcut successional setbacks, a comprehensive assessment has yet to be undertaken. The objective of the present study was to assess whether clear-cutting has triggered successional setbacks in temperate forests. Therefore, we studied post-clearcut successional trajectories by using forest inventory data covering the entire temperate forest of the province of Québec, eastern Canada (209 000 km2). Clear-cutting triggered successional setbacks in both ecological regions forming the temperate forest. After clear-cutting, successional trajectories of trees pointed toward the composition of an early successional stage. To address this sustainable management issue in a comprehensive manner with clear-cutting, foresters could use partial cut approaches.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".