A neighborhood analysis of canopy tree competition: effects of shading versus crowding
Bibliographic record
Abstract
We have developed extensions of traditional distance-dependent, spatial competition analyses that estimate the magnitude of the competitive effects of neighboring trees on target tree growth as a function of the species, size, and distance to neighboring trees. Our analyses also estimate inter- and intra-specific competition coefficients and explicitly partition the competitive effects of neighbors into the effects of shading versus crowding. We tested the method using data from forests of northern, interior British Columbia dominated by western hemlock (Tsuga heterophylla (Raf.) Sarg.) and western redcedar (Thuja plicata Donn ex D. Don). For both species, the most parsimonious regression models included terms for the effects of tree size, crowding, and shading and separate competitive effects of four different groups of competing species. The models explained 33%59% of the variation in radial growth of the two species. For both species, growth declined more steeply as a function of crowding than shading. There was striking asymmetry in the strength of interspecific competition between hemlock and redcedar, with crowding by hemlock having a strong per capita effect on redcedar, while crowding by redcedar had relatively little effect on the radial growth of hemlock.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".