Size-symmetric versus size-asymmetric competition and growth partitioning among trees in forest stands along an ecological gradient in central Europe
Bibliographic record
Abstract
Current individual tree growth models rarely consider the mode of tree competition, which can be size-asymmetric when growth is limited by light or size-symmetric when belowground resources are scarce. Even with the same competition index, growth reactions may vary considerably due to a prevailing resource limitation, as the dominant trees in a stand benefit disproportionately more on light-limited sites. To scrutinize and model the relationship between mode of competition and site conditions, 34 long-term experiments with 120 plots dating back to 1871 were used. The data cover the dominating tree species in central Europe along a broad range of ecological conditions. For Norway spruce ( Picea abies (L.) Karst.), Scots pine ( Pinus sylvestris L.), and sessile oak ( Quercus petrea (Matt.) Liebl.), stronger light competition can be shown on fertile sites compared with sites with poorer conditions. Based on these findings, we constructed an enhanced version of a classic potential modifier growth model. Simulations for archetypical stands yield a transition from size-asymmetric to size-symmetric competition along the gradient from fertile to poor sites that is not covered by traditional models. It was concluded that by integrating the interaction between competition and site quality, individual tree models become more site sensitive, a prerequisite for their application under fluctuating environmental conditions.
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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 | 0.001 | 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 | 0.001 | 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".