Beyond trait distances: Functional distinctiveness captures the outcome of plant competition
Bibliographic record
Abstract
Abstract Functional trait distances between coexisting organisms reflect not only complementarity in the way they use resources, but also differences in their competitive abilities. Accordingly, absolute and relative trait distances have been widely used to capture the effects of niche dissimilarity and competitive hierarchies, respectively, on the performance of plants in competition. However, multiple dimensions of the plant phenotype are involved in these plant–plant interactions (PPI), challenging the use of relative trait distances to predict their outcomes. Furthermore, estimating the effects of competitive hierarchy on the performance of a group of coexisting plants remains particularly difficult since relative trait distances relate to the effects of a focal plant on another. We argue that trait distinctiveness, an emerging facet of functional diversity that characterizes the eccentric position of a species (or genotype) in a phenotypic space, can reveal the unique role played by a given individual plant in a group of competing plants. We used the model crop species Oryza sativa spp. japonica to evaluate the ability of trait distances and trait distinctiveness to predict the outcome of intraspecific PPI on the performance of single genotype and genotype mixtures. We performed a screening experiment to characterize the phenotypic space of 49 rice genotypes based on 11 above‐ground and root traits. We selected nine genotypes with contrasting positions in the phenotypic space and grew them in pots following a complete pairwise interaction design. Relative distances and distinctiveness based on traits associated with light competition were by far the best predictors of the performance of single genotypes—taller genotypes that acquired resource faster being the best competitors—while absolute trait distances had no effect. These results indicate that competitive hierarchy for light dominates PPI in this experiment. Consistently, trait distinctiveness in plant height and age at flowering had the strongest, positive effects on mixture performance, confirming that functional distinctiveness captures the effects of trait hierarchies and asymmetric PPI at this scale. Our findings shed new light on the role of trait diversity in regulating PPI and ecosystem processes and call for a greater consideration of functional distinctiveness in studies of coexistence mechanisms. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".