When Competition Does Not Matter: Grassland Diversity and Community Composition
Bibliographic record
Abstract
We examined whether the intense root competition in a rough fescue grassland plant community in central Alberta, Canada, was important in structuring plant species diversity or community composition. We measured competition intensity across gradients of species richness, evenness, and community composition, using pairs of naturally occurring plants of 12 species. One plant in each pair was isolated from neighbors to measure competition; community structure and environmental conditions were also measured at each pair. We used structural equation modeling to examine how competition influenced community structure. Competition intensity was unrelated to species richness and community composition, but increased competition intensity was associated with a slight decline in evenness. Size-symmetric root competition was probably unimportant in structuring this plant community because there are no feedback mechanisms through which size-symmetric competition can magnify small initial differences and eventually lead to competitive exclusion. In plant communities with little shoot competition, competition and community structure should be unlinked regardless of competition intensity. In more productive systems, we propose that interactions between root and shoot competition may indirectly structure communities by altering the overall asymmetry of competition.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".