Bryophyte abundance, diversity and composition after retention harvest in boreal mixedwood forest
Bibliographic record
Abstract
Abstract Variable‐retention harvest is widely recognized as an alternative to more intensive methods such as clear‐cutting. However, present information is inadequate to judge the impact of variable retention on biodiversity of indigenous forest organisms intolerant of canopy removal, such as forest‐inhabiting bryophytes. We examined how bryophyte species cover, richness, diversity and composition change with time in response to a broad range of dispersed retention harvest treatments (2% [clear‐cut], 10%, 20%, 50%, 75% retention of original basal area) contrasted with uncut controls [100% retention]) in broadleaf deciduous, mixedwood and conifer‐dominated boreal forests in North West Alberta, Canada. Bryophytes were studied in 432 permanent sample plots within 72 compartments before harvest and at 3, 6 and 11 years after harvest. Clear‐cut and lower (10% and 20%) retention levels resulted in lower cover and richness of bryophytes than in unharvested control compartments in mixed and conifer‐dominated forests, but less so in deciduous‐dominated forests, which generally supported low cover and richness. Species composition in each forest type varied along the gradient of harvesting intensity; clear‐cuts and lower levels of retention supported similar composition, as did control plots and those representing higher retention levels. Over time, the retention harvest treatments became more similar to uncut controls. Synthesis and applications. Variable‐retention harvests can better maintain bryophyte biodiversity in managed boreal mixedwood forests, as compared to clear‐cuts. We found the efficacy of retention harvest scaled with harvest intensity. Higher levels of retention better moderated the negative impacts of harvesting on bryophyte assemblages across all forest types. Our results suggest, however, that even 10% retention will facilitate faster post‐harvest recovery of bryophytes, as compared to clear‐cutting.
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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 | 0.000 | 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".