Inequality of Size and Size Increment in Pinus banksiana in Relation to Stand Dynamics and Annual Growth Rate
Bibliographic record
Abstract
BACKGROUND AND AIMS: Changes in size inequality in tree populations are often attributed to changes in the mode of competition over time. The mode of competition may also fluctuate annually in response to variation in growing conditions. Factors causing growth rate to vary can also influence competition processes, and thus influence how size hierarchies develop. METHODS: Detailed data obtained by tree-ring reconstruction were used to study annual changes in size and size increment inequality in several even-aged, fire-origin jack pine (Pinus banksiana) stands in the boreal shield and boreal plains ecozones in Saskatchewan and Manitoba, Canada, by using the Gini and Lorenz asymmetry coefficients. KEY RESULTS: The inequality of size was related to variables reflecting long-term stand dynamics (e.g. stand density, mean tree size and average competition, as quantified using a distance-weighted absolute size index). The inequality of size increment was greater and more variable than the inequality of size. Inequality of size increment was significantly related to annual growth rate at the stand level, and was higher when growth rate was low. Inequality of size increment was usually due primarily to large numbers of trees with low growth rates, except during years with low growth rate when it was often due to small numbers of trees with high growth rates. The amount of competition to which individual trees were subject was not strongly related to the inequality of size increment. CONCLUSIONS: Differences in growth rate among trees during years of poor growth may form the basis for development of size hierarchies on which asymmetric competition can act. A complete understanding of the dynamics of these forests requires further evaluation of the way in which factors that influence variation in annual growth rate also affect the mode of competition and the development of size hierarchies.
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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 | 0.002 | 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 | 0.000 |
| 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 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".