Small changes in species composition despite stand-replacing bark beetle outbreak in <i>Picea abies</i> mountain forests
Bibliographic record
Abstract
In the mid-1990s, a Spruce Bark Beetle (Ips typographus L.) outbreak affected the Norway spruce (Picea abies (L.) Karst.) forests in the higher elevations of the Bavarian Forest National Park in Germany. To monitor the effect of this large-scale disturbance on the understory vegetation, a series of permanent plots was established in 1998. Until 2000, most of the trees of the spruce-dominated forests at elevations > 1.100 m a.s.l. died. To explore the long-term vegetation development after the disturbance, we resurveyed these plots in 2010. We hypothesised that as a consequence of the stand-replacing disturbance, (i) species composition would change substantially, (ii) shade-tolerant forest species would be replaced by light-demanding species, and (iii) the expansion of the light-demanding species would homogenize species composition. Comparing the 1998 and 2010 situations, we found that species composition did not change substantially after the dieback of the tree layer, shade-tolerant species survived, light-demanding species increased only marginally, and vegetation heterogeneity did not change. The main reason is the missing forest floor disruption: there is no open space emerging for species to establish when trees die standing. As a consequence, a dramatic change in ecosystem structure (loss of the whole canopy layer) is not necessarily connected with a general change in species composition.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".