Lakeside riparian forests support diversity of wood fungi in managed boreal forests
Bibliographic record
Abstract
Riparian forests often have a more diverse tree species composition and more woody debris than neighboring upland forests, but little is known about their importance for the conservation of deadwood-dependent species. We studied the forest characteristics and the diversity of wood fungi (poroid Aphyllophorales) in lakeside riparian (flat and sloping topography) and upland boreal forests in eastern Finland. Riparian forests had a higher density of broadleaved trees and broadleaved debris than did upland forests. A total of 48 species of wood fungi were recorded, including eight red-listed or old-growth forest indicator species. Overall, more species and records and greater diversity were observed in the flat riparian sites than in the sloped riparian and upland sites. The mean species richness did not differ significantly among site categories, indicating greater β diversity among the flat riparian sites. Species composition was more similar between the two riparian categories than between the riparian and upland sites. Riparian sites also hosted more fungal species associated with broadleaved trees. The results show that riparian forests support the diversity of wood fungi in managed boreal forests. Diversity of deadwood-dependent organisms can be promoted by leaving wider and completely unharvested riparian buffer zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".