Epixylic vegetation abundance, diversity, and composition vary with coarse woody debris decay class and substrate species in boreal forest
Bibliographic record
Abstract
Although the importance of coarse woody debris (CWD) to understory species diversity has been recognized, the combined effects of CWD decay and substrate species on abundance and species diversity of epixylic vegetation have received little attention. We sampled a wide range of CWD substrate species and decay classes, as well as forest floors in fire-origin boreal forest stands. Percent cover, species richness, and evenness of epixylic vegetation differed significantly with both CWD decay class and substrate species. Trends in cover, species richness, and evenness differed significantly between nonvascular and vascular taxa. Cover, species richness, and species evenness of nonvascular species were higher on CWD, whereas those of vascular plants were higher on the forest floor. Epixylic species composition also varied significantly with stand ages, overstory compositions, decay classes, substrate species, and their interactions. Our findings highlight strong interactive influences of decay class and substrate species on epixylic plant communities and suggest that conservation of epixylic diversity would require forest managers to maintain a diverse range of CWD decay classes and substrate species. Because stand development and overstory compositions influence CWD decay classes and substrate species, as well as colonization time and environmental conditions in the understory, our results indicate that managed boreal landscapes should consist of a mosaic of different successional stages and a broad suite of overstory types to support diverse understory plant communities.
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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 | 0.000 | 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) | 0.000 | 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".