Silvicultural disturbance severity and plant communities of the southern Canadian boreal forest
Bibliographic record
Abstract
Boreal forest ecosystems are adapted to periodic disturbance, but there is widespread concern that conventional forest practises degrade plant communities. We examined vegetation diversity and composition after clearcut logging, mechanical and chemical site preparation in eight 5- to 12-yr old studies located in southern boreal forests of British Columbia and Quebec, Canada to find useful indicators for monitoring ecosystem integrity and to provide recommendations for the development and testing of new silvicultural approaches. Community-wide and species-specific responses were measured across gradients of disturbance severity and the results were explained in terms of the intermediate disturbance hypothesis and a simple regeneration model based on plant life history strategies. Species richness was 30 to 35% higher 5 to 8 years after clearcut logging than in old forest. Total and vascular species diversity generally peaked on moderately severe site treatments, while non-vascular diversity declined with increasing disturbance severity. On more-or-less mesic sites, there was little evidence of diversity loss within the range of conventional silvicultural disturbances; however, there were important changes in plant community composition. Removing soil organic layers caused a shift from residual and resprouting understory species to ruderal species regenerating from seeds and spores. Severe treatments dramatically increased non-native species invasion. Two important challenges for the proposed natural dynamics-based silviculture will be 1) to find ways of maintaining populations of sensitive non-vascular species and forest mycoheterotrophs, and 2) to create regeneration niches for disturbance-dependent indigenous plants without accelerating non-native species invasion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".