Effects of post‐windthrow salvage logging on microsites, plant composition and regeneration
Bibliographic record
Abstract
Abstract Questions How does windthrow influence plant diversity and composition as well as regeneration and microsite characteristics? What are the consequences of post‐windthrow salvage logging on these key attributes? Location Eastern black spruce–moss forest,Quebec,Canada. Methods A total of 92 plots were sampled, each with a radius of 11.28 m; 49 of these plots were salvaged while 43 were unsalvaged. Regeneration density, plant diversity and seedbeds were characterized. We tested the effect of microtopography and windthrow severity on species richness and Shannon diversity index for salvaged and unsalvaged windthrows using a mixed model. Partial redundancy analysis (RDA) determined which environmental and stand characteristics were most important in explaining differences in plant species and forest floor types among the treatments. The effects of treatments (salvaged and unsalvaged windthrows), microtopography attributes, windthrow severity and regeneration species on seedling and sapling abundance were tested using a linear mixed model. Results Salvaged windthrow, with a large proportion of skid trails, dead mosses andSphagnum, had a lower degree of seedbed heterogeneity. Also, some understorey species present in the unsalvaged ecosystem were absent from the salvaged windthrow.Sphagnumand other moss species were clearly associated with the unsalvaged treatment. White birches were positively associated with mound microtopography in the unsalvaged windthrow. Conclusion From an ecosystem‐based forest management perspective, natural post‐windthrow understorey conditions and microsite heterogeneity can be in part maintained in salvaged cut blocks by incorporating retention patches that include downed and standing dead wood and living trees of diverse sizes. These steps should favour plant regeneration and augment diversity for salvage logging after wind disturbance.
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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) | 0.002 | 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".