Plant interactions are unimportant in a subarctic–alpine plant community
Bibliographic record
Abstract
We investigated whether plant interaction intensity in a subarctic-alpine meadow is important for determining community structure and species abundance. Using two common species as phytometers, we measured interaction intensity using a neighbor removal approach. Eight biotic and abiotic variables known to influence species abundance and community structure were measured, with regression trees used to examine how plant interactions and the biotic and abiotic variables were related to species evenness, richness, and phytometer spatial cover. A range of interactions was present, with both strong competition and facilitation present over small-scale abiotic and biotic gradients. Despite the variation in interaction intensity, it was generally unrelated to either community structure or phytometer cover. In other words, plant interactions were intense in many cases but were not important to community structure. This may be due to the prevalence of clonal species in this system and the influence of previous year's interactions on plant survival and patterns of community structure. These results also suggest how conflicting theories of the role of competition in unproductive environments may be resolved. Our findings suggest that plant interactions may be intense in reducing individual growth, while simultaneously not important in the context of community structure. Plant interactions need to be viewed and tested relative to other factors and stresses to accurately evaluate their importance in plant communities, with continued differentiation between the intensity of plant interactions and their relative importance in communities.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".