Modeling the effects of individual-tree size, distance, and species on understory vegetation based on neighborhood analysis
Bibliographic record
Abstract
Quantifying how understory vegetation responds to individual neighboring trees is critical to understanding forest dynamics. To do so, we used a spatial neighborhood approach to quantify the competitive effect of individual trees on the density and height of dwarf bamboo (Sasa senanensis (Franch. et Savat.) Rehder) in a mixed conifer–broadleaf forest on the island of Hokkaido, northern Japan. Using hierarchical Bayesian models, we analyzed how the effect of neighboring trees varies with stem size, distance to the dwarf bamboo, and tree species. The effect of neighbors peaked when the tree reached a medium size (33.0–45.0 cm in diameter at breast height) and decreased for larger trees. The effect of neighbors decreased with increasing distance to the dwarf bamboo. The slope of the decrease was gentler for larger trees. Conifers exerted an average of 7.2 times the effect of broadleaved trees. Species with higher shade tolerance exerted larger effects. Species with late leaf flush and early defoliation tended to exert smaller effects. Our results provide evidence that neighborhood analysis is an effective approach for quantifying the effects of individual trees on understory vegetation and represents a critical step toward understanding how fine-scale interactions between understory vegetation and trees influence overall forest dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".