Substrates mediate responses of forest bryophytes to a gradient in overstory retention
Bibliographic record
Abstract
Forest bryophytes are sensitive to the disturbances and environmental changes associated with forest management. We asked whether the substrates on which bryophytes grow mediate responses to exposure following canopy removal. We measured bryophyte cover and richness in 0.1 m2 quadrats on the forest floor, decayed logs, and tree bases along a gradient of dispersed overstory retention (100%, 40%, and 15% of initial basal area) 7 to 8 years after harvest of mature Pseudotsuga forests. Cover, local richness, and, to a lesser degree, species evenness declined steeply across the retention gradient on decayed logs and tree bases but not on the forest floor. Liverworts were more sensitive than mosses, particularly on decayed logs and on the southwestern aspects of trees (>97% declines in cover under 15% retention). Richness and evenness at the treatment scale also declined sharply on decayed logs and on the southwestern aspects of trees but changed little or increased under 40% retention on the forest floor. Our results indicate that even moderate levels of dispersed retention cannot sustain the abundance and overall diversity of wood-associated bryophytes in these forests. During regeneration harvests, conservation of these species may require retention of intact forest aggregates in which substrate quality and microclimatic stability can be maintained.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".