Trait plasticity is more important than genetic variation in determining species richness of associated communities
Bibliographic record
Abstract
Abstract Intraspecific variation can be an important driver of ecological interactions in species‐rich communities. Predicting the effects of intraspecific variation in different environments, however, remains a major challenge. This is because we often do not quantify both the effects of functional traits on associated communities and the extent to which trait variation is due to genetics (genotype effects) vs. plasticity (environment effects). As a consequence, the relative importance of trait plasticity vs. genetic variation in structuring associated communities remains unclear. We sought to fill this gap by conducting common garden experiments with the plant Salix hookeriana across biotic (ant–aphid interactions) and abiotic (wind exposure) environmental gradients in a coastal dune ecosystem. In each experiment, we simultaneously measured plant traits and species richness of associated above‐ and below‐ground communities. We then used statistical models to quantify the relative importance of trait plasticity vs. genetic variation in structuring communities. Our major finding was that trait plasticity was more important than genetic variation in determining the number of species in associated communities. This result was consistent across different environmental contexts (experimental manipulations of ant–aphid interactions and wind exposure), multiple years, and for above‐ground arthropods and root microbes. This occurred because the traits that had the largest effect on species richness were also the most plastic. Synthesis. These results indicate that trait plasticity can be a dominant driver of above‐ and below‐ground biodiversity.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".