The reciprocal relationship between competition and intraspecific trait variation
Bibliographic record
Abstract
Summary Trait differences among plants are expected to influence the outcome of competition; competition should be strongest between similar species (or individuals) under limiting similarity, and between dissimilar species within competitive hierarchies. These hypotheses are often used to infer competitive dynamics from trait patterns within communities. However, plant traits are frequently plastic in response to competition. This variation is poorly accounted for in trait‐based studies of competition and community assembly. To explore the relationship between trait responses and competitive outcomes, we grew 15 species alone, in monoculture and in mixture. We measured traits relating to leaf and root tissue morphology as well as biomass allocation and related competition‐induced changes in these traits to intra‐ and interspecific competition using multi‐model inference. Additionally, we tested how traits from different competitive environments influenced potential community assembly inferences. The competitive environment had large effects on species’ traits, although many effects were species specific. Differences among species in how competition affected trait expression were linked to both intra‐ and interspecific competition, frequently affecting competitive hierarchies. Intraspecific competition was lower for species that limited competition‐induced increases in root allocation and had less variability in this trait overall. Interspecific competition was lower for species with larger leaves and lower specific leaf area than their neighbours. Switching to more stress‐tolerant strategies by increasing root diameter and leaf tissue density also reduced competition. However, dissimilarity in root tissue density also minimized competition, consistent with limiting similarity affecting competitive outcomes. Moreover, changes in these traits were linked to changes in functional diversity, suggesting that competition affects functional diversity by affecting trait expression. Synthesis . Both trait hierarchies and trait dissimilarity affect the outcome of competition by acting on different traits, although competition‐induced changes in trait expression can alter competitive outcomes. Moreover, the magnitude of these trait changes suggests that the source environment where plant traits are collected can affect the inferences drawn from trait patterns within communities. Combined, our results suggest that considering the effect of competition on trait expression is critical to understanding the relationship between traits and community assembly.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".