Characterizing the contribution of plasticity and genetic differentiation to community‐level trait responses to environmental change
Bibliographic record
Abstract
Abstract The match between functional trait variation in communities and environmental gradients is maintained by three processes: phenotypic plasticity and genetic differentiation (intraspecific processes), and species turnover (interspecific). Recently, evidence has emerged suggesting that intraspecific variation might have a potentially large role in driving functional community composition and response to environmental change. However, empirical evidence quantifying the respective importance of phenotypic plasticity and genetic differentiation relative to species turnover is still lacking. We performed a reciprocal transplant experiment using a common herbaceous plant species (Oxalis montana) among low‐, mid‐, and high‐elevation sites to first quantify the contributions of plasticity and genetic differentiation in driving intraspecific variation in three traits: height, specific leaf area, and leaf area. We next compared the contributions of these intraspecific drivers of community trait–environment matching to that of species turnover, which had been previously assessed along the same elevational gradient. Plasticity was the dominant driver of intraspecific trait variation across elevation in all traits, with only a small contribution of genetic differentiation among populations. Local adaptation was not detected to a major extent along the gradient. Fitness components were greatest in O. montana plants with trait values closest to the local community‐weighted means, thus supporting the common assumption that community‐weighted mean trait values represent selective optima. Our results suggest that community‐level trait responses to ongoing climate change should be mostly mediated by species turnover, even at the small spatial scale of our study, with an especially small contribution of evolutionary adaptation within species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".