Different mixtures of Norway spruce, silver fir, and European beech modify competitive interactions in central European mature mixed forests
Bibliographic record
Abstract
The influence of forest ecology and strategic planning has increased in importance to support the management of mixed-species forests to enhance biodiversity. However, little is known about competitive and facilitative interactions between trees and species in mixed fir–beech–spruce forests, mostly because of a lack of long-term experimental research. In the 1960s, long-term sample plots were established in the Western Carpathians to develop region-specific yield models. Trees in the plots were measured at 5- to 16-year intervals from 1967(69). In 2010, the positions of standing trees in all plots were identified spatially. Stump positions were also identified to record the coordinates of trees that had been removed or had died. The objectives of this study were to evaluate the applicability of widely used competition indices for mature fir–beech–spruce mixed forests and to test whether the tree competition zone changes among species and forest stands of different stocking densities. Results showed that the best competition index was based on the comparison of the basal area of competitors and the subject tree in the radius, which was defined as a function of stand density and species. In addition, beech was found to be a strong self-competitor, which was not the case for silver fir (Abies alba Mill.). Results suggest that simpler competition indices are better suited for such diverse forests, as more complex indices do not describe the competition interactions sufficiently well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".