Phenotypic Selection Favors Missing Trait Combinations in Coexisting Annual Plants
Bibliographic record
Abstract
Trade-offs among traits are important for maintaining biodiversity, but the role of natural selection in their construction is not often known. It is possible that trade-offs reflect fundamental constraints, negative correlational selection, or directional selection operating on costly, redundant traits. In a Sonoran Desert community of winter annual plants, we have identified a trade-off between relative growth rate and water-use efficiency among species, such that species with high relative growth rate have low water-use efficiency and vice versa. We measured selection on water-use efficiency, relative growth rate, and underlying traits within populations of four species at two study sites with different average climates. Phenotypic trait correlations within species did not match the among-species trade-off. In fact, for two species with high water-use efficiency, individuals with high relative growth rate also had high water-use efficiency. All populations experienced positive directional selection for water-use efficiency and relative growth rate. Selection tended to be stronger on water-use efficiency at the warmer and drier site, and selection on relative growth rate tended to be stronger at the cooler and wetter site. Our results indicate that directional natural selection favors a phenotype not observed among species in the community, suggesting that the among-species trade-off could be due to pervasive genetic constraints, perhaps acting in concert with processes of 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".