Testing for local adaptation in <i>Avena barbata</i>: a classic example of ecotypic divergence
Bibliographic record
Abstract
Forty years ago, Robert Allard and colleagues documented that the slender wild oat, Avena barbata, occurred in California as two multi-locus allozyme genotypes, associated with mesic and xeric habitats. This is arguably the first example of ecotypes identified by molecular techniques. Despite widespread citation, however, the inference of local adaptation of these ecotypes rested primarily on the allozyme pattern. This study tests for local adaptation of these ecotypes using reciprocal transplant and quantitative trait locus (QTL) mapping techniques. Both ecotypes and 188 recombinant inbred lines (RILs) derived from a cross between them were grown in common garden plots established at two sites representative of the environments in which the ecotypes were first described. Across four growing seasons at each site, three observations consistently emerged. First, despite significant genotype by environment interaction, the mesic ecotype consistently showed higher lifetime reproductive success across all years and sites. Second, the RILs showed no evidence of a trade-off in performance across sites or years, and fitness was positively correlated across environments. Third, at QTL affecting lifetime reproductive success, selection favoured the same allele in all environments. None of these observations are consistent with local adaptation but suggest that a single genotype is selectively favoured at both moist and dry sites. I propose an alternative hypothesis that A. barbata may be an example of contemporary evolution--whereby the favoured genotype is spreading and increasing in frequency--rather than local adaptation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".