Competitive interactions across a soil fertility gradient in a multispecies forest
Bibliographic record
Abstract
Summary Whether plant competition grows stronger or weaker across a soil fertility gradient is an area of great debate in plant ecology. We examined the effects of competition and soil fertility and their interaction on growth rates of the four dominant tree species in the sub‐boreal spruce forest of British Columbia. We tested separate soil nutrient and moisture indices and found much stronger support for models that included the nutrient index as a measure of soil fertility. Competition, soil fertility and their interaction affected radial growth rates for all species. Each species supported a different alternate hypothesis for how competitive interactions changed with soil fertility and whether competition intensity was stronger or weaker overall as soil fertility increased depended on the context, specifically, species, neighbourhood composition and type of competition (shading vs. crowding). The four species varied slightly in their growth response to soil fertility. Individual species had some large variations in the shapes of their negative relationships between shading, crowding and tree growth, with one species experiencing no net negative effects of crowding at low soil fertility. Goodness‐of‐fit was not substantially increased by models including competition–soil fertility interactions for any species. Tree size, soil fertility, shading and crowding predicted most of the variation in tree growth rates in the sub‐boreal spruce forest. Synthesis. The intensity of competition among trees across a fertility gradient was species‐ and context‐specific and more complicated than that predicted by any one of the dominant existing theories in plant ecology.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".