Conundrums of competitive ability in plants: what to measure?
Bibliographic record
Abstract
A survey of recent literature indicates that competitive ability in plants has been measured, in most studies, only in terms of the relative intensity of size suppression experienced by competitors within one growing season. Far fewer studies have recorded relative success in terms of survival and even fewer studies have recorded fecundity under competition. Differences in size suppression are usually assumed to reflect differences in relative abilities to deny resources to competitors. However, most previous studies have failed to control or account for other sources of variation in the size suppression that plants experience under competition, i.e. variation between mixtures in the resource supply/demand ratio (approach to carrying capacity), or variation in the degree of niche overlap between competitors, or variation in the intensity of concurrent facilitative interactions between competitors. For future studies, much greater caution is required in recognizing these inherent limitations of traditional measures of competitive ability and, hence, guarding against unfounded conclusions or predictions about potential for competitive success that are based on these measures. There is also a significant challenge for future studies to adopt empirical approaches for minimizing these limitations. Some initial recommendations are considered here based on an emerging view of competitive ability measured in terms of traits associated with all three conventional components of Darwinian fitness, i.e. not just growth (plant size) but also survival and fecundity allocation (offspring production per unit plant size per unit time). According to this model, differences in competitive ability imply differences in the ability, despite intense competition (i.e. low resource supply/demand ratio), to recruit offspring into the next generation and thereby limit offspring recruitment by other plants. The important traits of competitive ability, therefore, are not only those that allow a plant to deny resources to competitors, suppress their sizes and hence, maximize the plant's own size, but also those traits that allow the plant to withstand suppression from competition enough to persist, both as an individual (through survival) and across generations (through descendants).
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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 | 0.079 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".