The effect of size and competition on tree growth rate in old-growth coniferous forests
Bibliographic record
Abstract
Tree growth and competition play central roles in forest dynamics. Yet models of competition often neglect important variation in species-specific responses. Furthermore, functions used to model changes in growth rate with size do not always allow for potential complexity. Using a large data set from old-growth forests in California, models were parameterized relating growth rate to tree size and competition for four common species. Several functions relating growth rate to size were tested. Competition models included parameters for tree size, competitor size, and competitor distance. Competitive strength was allowed to vary by species. The best ranked models (using Akaike’s information criterion) explained between 18% and 40% of the variance in growth rate, with each species showing a strong response to competition. Models indicated that relationships between competition and growth varied substantially among species. The results also suggested that the relationship between growth rate and tree size can be complex and that how we model it can affect not only our ability to detect that complexity but also whether we obtain misleading results. In this case, for three of four species, the best model captured an apparent and unexpected decline in potential growth rate for the smallest trees in the data set.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".