Predictors of moss and liverwort species diversity of microsites in conifer‐dominated boreal forest
Bibliographic record
Abstract
Patterns of moss and liverwort species diversity — species richness and species turnover (β‐diversity) — in three conifer‐dominated boreal forest stands of northern Alberta, Canada are described. We examined the relationship between bryophyte species diversity and micro‐environment at two sample grains, the microsite — substrate types for moss colonization: logs, stumps, tree bases, undisturbed patches of forest floor (dominated by feather moss species), and disturbed patches of forest floor — and the mesosite (25 m × 25 m plots). Microsite type and properties (e.g. decay class, hardwood vs softwood, pH) were the principal predictors of bryophyte species diversity and not micro‐environment variation among mesosites. Microsite type was the strongest predictor of microsite species richness and β‐diversity was higher among microsites and types and within microsites than among mesosites or stands. Microsite properties were significant predictors of species richness for all microsite types. Log and stump decay classes, influenced also by hardwood vs softwood predicted species richness of woody microsite types and soil pH and moisture predicted species richness of forest floor microsites. β‐diversity was highest for tree bases and disturbed patches of forest floor and lowest for logs. Mesosite β‐diversity was lower than that among microsites, and mesosite species richness was not well explained by measured environmental parameters. Results suggest that in conifer‐dominated boreal stands, species richness of microsites is only negligibly influenced by within‐stand variation at the mesosite grain and that substrate characteristics are the most important predictors of bryophyte species diversity in this ecosystem.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".