Potential biodiversity impacts of forest biofuel harvest: lichen assemblages on stumps and slash of Scots pine
Bibliographic record
Abstract
Harvesting stumps and logging residues for energy production may have negative impacts on forest species, especially those associated with dead wood. We assessed the potential impact of biofuel harvest on epiphytic lichens by studying the lichen assemblages on stumps and downed fine woody debris (FWD) of Scots pine (Pinus sylvestris L.) in clear-cut, mature managed, and old-growth forest stands in eastern Finland. We also examined the impact of tree retention level and prescribed burning on these assemblages. A total of 102 lichen species (including 13 red-listed species) were observed, with 95 species on stumps and 69 species on downed FWD. Composition of the species assemblages differed between stumps and downed FWD and between stumps of different age. Tree retention (in comparison with clear-cut sites) and prescribed burning resulted in a slightly higher species richness on cut stumps 12 years after harvest but did not affect the assemblages on downed FWD or older stumps. We conclude that stumps and downed FWD of Scots pine can host high numbers of lichen species, including red-listed ones. Most of the species occurred also on other substrates and are, therefore, not likely to be affected by biofuel harvest. However, for dead wood dependent lichen species, intensive biofuel harvest is potentially harmful, though the severity of this impact likely depends on the landscape-level availability of other woody substrates.
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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 | 0.000 | 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".