Adaptation to distinct habitats is maintained by contrasting selection at different life stages in sunflower ecotypes
Bibliographic record
Abstract
Conspecific populations living in adjacent but contrasting microenvironments represent excellent systems for studying natural selection. These systems are valuable because gene flow is expected to force genetic homogeneity except at loci experiencing divergent selection. A history of reciprocal transplant and common garden studies in such systems, and a growing number of genomic studies, have contributed to understanding how selection operates in natural populations. While selection can vary across different fitness components and life stages, few studies have investigated how this ultimately affects allele frequencies and the maintenance of divergence between populations. Here, we study two sunflower ecotypes in distinct, adjacent habitats by combining demographic models with genome-wide sequence data to estimate fitness and allele frequency change at multiple life stages. This framework allows us to estimate that only local ecotypes are likely to experience positive population growth (λ > 1) and that the maintenance of divergent adaptation appears to be mediated via habitat- and life stage-specific selection. We identify genetic variation, significantly driven by loci in chromosomal inversions, associated with different life history strategies in neighbouring ecotypes that optimize different fitness components and may contribute to the maintenance of distinct ecotypes.
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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 | 0.000 |
| 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".