Data from: A field experiment assessing the roles of drought, herbivory, and local climate on cyanogenesis cline formation and local adaptation in <em>Trifolium repens</em>
Bibliographic record
Abstract
Projecting how a population will adapt to environmental changes requires a mechanistic understanding of the specific biotic and abiotic factors that impose selection on that population. In white clover (Trifolium repens), clines in an antiherbivore defense mechanism, hydrogen cyanide (HCN), form via variation in selection imposed by the environment. However, the specific environmental factors that select for or against chemical defense phenotypes in white clover remain unresolved. We performed a field experiment in high and low-latitude study sites, with a factorial manipulation of precipitation and herbivory at each site. These factors are hypothesized to be important in driving HCN cline formation, so we investigated their effects on the fitness of a white clover F3 recombinant population, segregating for the alleles underlying the HCN chemical defense phenotype. Surprisingly, we found that precipitation and herbivory either did not drive differential selection on HCN or its metabolic components, or did not impose selection in a manner consistent with the maintenance of observed HCN clines. Instead, we find that the production of only one of the metabolic components of HCN, cyanogenic glycosides, resulted in a fitness advantage, even when lacking the ability to produce HCN. This was most prominent at the northern study site, which is again contrary to expectations. These results suggest additional physiological roles that cyanogenic glycosides may play in adaptation and the evolutionary ecology of white clover. This study highlights the importance of experimental manipulations of environmental factors to understand how selection acts on genes underlying important phenotypic traits, often in unexpected ways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".