Evidence of elevation-specific growth changes of spruce, fir, and beech in European mixed mountain forests during the last three centuries
Bibliographic record
Abstract
In Europe, mixed mountain forests, primarily comprised of Norway spruce (Picea abies (L.) Karst.), silver fir (Abies alba Mill.), and European beech (Fagus sylvatica L.), cover about 10 × 10 6 ha at elevations between ∼600 and 1600 m a.s.l. These forests provide invaluable ecosystem services. However, the growth of these forests and the competition among their main species are expected to be strongly affected by climate warming. In this study, we analyzed the growth development of spruce, fir, and beech in moist mixed mountain forests in Europe over the last 300 years. Based on tree-ring analyses on long-term observational plots, we found for all three species (i) a nondecelerating, linear diameter growth trend spanning more than 300 years; (ii) increased growth levels and trends, the latter being particularly pronounced for fir and beech; and (iii) an elevation-dependent change of fir and beech growth. Whereas in the past, the growth was highest at lower elevations, today’s growth is superior at higher elevations. This spatiotemporal pattern indicates significant changes in the growth and interspecific competition at the expense of spruce in mixed mountain forests. We discuss possible causes, consequences, and silvicultural implications of these distinct growth changes in mixed mountain forests.
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 | 0.001 |
| 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) | 0.000 | 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".