Data from: Adaptation with gene flow across the landscape in a dune sunflower
Bibliographic record
Abstract
Isolation by adaptation increases divergence at neutral loci when natural selection against immigrants reduces the rate of gene flow between different habitats. This can occur early in the process of adaptive divergence and is a key feature of ecological speciation. Despite the ability of isolation by distance and other forms of landscape resistance to produce similar patterns of neutral divergence within species, few studies have used landscape genetics to control for these other forces. We have studied the divergence of Helianthus petiolaris ecotypes living in active sand dunes and adjacent non-dune habitat, using landscape genetics approaches, such as circuit theory and multiple regression of distance matrices, in addition to coalescent modelling. Divergence between habitats was significant, but not strong, and was shaped by isolation by distance. We expected that increased resistance due to patchy and unfavourable habitat in the dunes would contribute to divergence. Instead, we found that landscape resistance models with lower resistance in the dunes performed well as predictors of genetic distances among subpopulations. Nevertheless, habitat class remained a strong predictor of genetic distance when controlling for isolation by resistance and isolation by distance. We also measured environmental variables at each site and confirmed that specific variables, especially soil nitrogen and vegetation cover, explained a greater proportion of variance in genetic distance than did landscape or the habitat classification alone. Asymmetry in effective population sizes and numbers of migrants per generation was detected using coalescent modelling with Bayesian inference, which is consistent with incipient ecological speciation being driven by the dune habitat.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".