Evidence of introgression, ecological divergence and adaptation in<i>Asterias</i>sea stars
Bibliographic record
Abstract
Hybrid zones are important windows into the evolutionary dynamics of populations, revealing how processes like introgression and adaptation structure population genomic variation. Importantly, they are useful for understanding speciation and how species respond to their environments. Here, we investigate two closely related sea star species, Asterias rubens and A. forbesi, distributed along rocky European and North American coastlines of the North Atlantic, and use genome-wide molecular markers to infer the distribution of genomic variation within and between species in this group. Using genomic data and environmental niche modelling, we document hybridization occurring between northern New England and the southern Canadian Maritimes. We investigate the factors that maintain this hybrid zone, as well as the environmental variables that putatively drive selection within and between species. We find that the two species differ in their environmental niche breadth; Asterias forbesi displays a relatively narrow environmental niche while conversely, A. rubens has a wider niche breadth. Species distribution models accurately predict hybrids to occur within environmental niche overlap, thereby suggesting environmental selection plays an important role in the maintenance of the hybrid zone. Our results imply that the distribution of genomic variation in North Atlantic sea stars is influenced by the environment, which will be crucial to consider as the climate changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".