Using three pairs of competitive indices to test for changes in plant competition under different resource and disturbance levels
Bibliographic record
Abstract
Questions: How do different resource and disturbance levels interact to affect competition? How do different indices of competition change the interpretation of how competition changes under different resource and disturbance conditions? Location: Greenhouse, Thompson Rivers University, Kamloops, British Columbia, Canada. Methods: Three pairs of indices that have been used to differentiate the predictions of Grime (CSR) and Tilman's (R*) theories were used to assess competition on two species of temperate bunchgrass, (Pseudorogeneria spicata and Festuca campestris) grown in a greenhouse on stress and disturbance gradients. Stress was created by manipulating the amount of water (high, low) and concentration of nutrient solution (high, low) added to pots, while disturbance was created by clipping (clipped, unclipped) in a fully factorial design. Plants were grown individually or with a single neighbour. The three pairs of indices were: (1) absolute and relative competition; (2) competitive effect and response; and, (3) competitive importance and intensity. Results: Absolute competition and competitive importance were the only indices responsive to the resource gradient, which supports CSR theory, and also the only ones to record an effect of disturbance on the strength of competition – under high resource conditions. The other indices showed few responses along the gradients, which supports R* theory. Measures of competitive effect and response did not differentiate the two theories. Conclusion: We show that some indices of competition show a decline with increased stress and disturbance, while other indices do not. Therefore, it is necessary to choose a competition index appropriate to the question being asked. Competitive importance and absolute competition were responsive to changes in stress and disturbance, while the other indices were not.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".