Impact of root-rot pathogens on forest succession in unmanaged <i>Pinus mugo</i> stands in the Central Alps
Bibliographic record
Abstract
In the mountain pine (Pinus mugo subsp. uncinata (DC.) Domin) forests of the Swiss National Park in the Central Alps, disease centers associated with the root-rot fungi Heterobasidion annosum (Fr.) Bref. and Armillaria spp. are characteristic elements. We assessed the impact of these pathogens on forest dynamics by studying transects running across disease centers into the adjacent forest. Overall, mountain pine was the most abundant regenerating tree species and accounted for 84% of all seedlings (<20 cm high) and 93% of all saplings (20–130 cm high), whereas Swiss stone pine (Pinus cembra L.) was less frequent (15% seedlings : 7% saplings). The density of mountain pine seedlings did not differ significantly between the disease centers and the adjacent forest, whereas mountain pine saplings were more frequent within the disease centers, indicating that growth from the seedling to the sapling stage was favoured in disease centers. There was significantly more dead wood and a greater diversity of plant species in the disease centers than in the adjacent forest. The results suggest that root-rot fungi slow down succession towards stands with a higher proportion of P. cembra by causing premature mortality of mountain pines and creating disease centers with dense mountain pine regeneration.
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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 | 0.000 |
| 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".