Measuring the components of competition along productivity gradients
Bibliographic record
Abstract
Summary Controversy surrounds the measurement of competition intensity. Moreover, when biomass varies systematically along productivity and other environmental gradients, common indices of competitive outcome mask important ecological interactions. This study presents two indices derived from how neighbours interact with target plants. The first, relative crowding, increases directly with the abundance of neighbours present and decreases inversely with the potential size and vigour of the target plant itself. The second, interaction strength, is the integral of suppression of the target by neighbours over the range of neighbour abundance. Relative crowding and interaction strength are derived independently, but when multiplied produce the commonly used relative competitive index, showing the biological underpinnings of the relative competition index in terms of crowding and strength of interaction. Since the new indices of relative crowding and interaction strength explicitly account for the amount of neighbour biomass, they serve as a valid method to track the effects of changing habitat conditions on the components of competition. The new indices are applied to three published data sets. In each case, relative crowding increased with standing crop. In one case competition was reported as unchanged along a productivity gradient, whereas the new indices show that relative crowding and interaction strength both had significant patterns, but their effects were counteracting. These results do not fit current theories of competition. Further empirical studies are needed to see if competition theory needs revision. Separating the mechanisms of competition into relative crowding and strength of interaction reveals previously hidden patterns that help bring to light underlying processes of competition along productivity gradients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".