The interplay between dispersal and gene flow in anadromous Arctic char (<i>Salvelinus alpinus</i>): implications for potential for local adaptation
Bibliographic record
Abstract
Dispersal can influence the process of local adaptation, particularly when the dispersers successfully breed in the non-natal habitat. Anadromous Arctic char (Salvelinus alpinus) display a complex migratory behaviour that makes the distinction between breeding and nonbreeding dispersal especially important. This species does not reproduce every year, but individuals must migrate to fresh water to overwinter such that a large proportion of fish running up-river are not in breeding condition and have no potential for gene flow. We used a genetic assignment approach to identify dispersers among populations of char from Baffin Island, Canada. Estimates of dispersal varied between 15.8% and 25.5% depending on the assignment method, suggesting that Arctic char disperse at a higher rate than other salmonids. Nonbreeding individuals were more likely to use non-natal habitats than breeding individuals, thus resulting in estimates of dispersal that overestimate the potential for gene flow among populations. Finally, we parameterized a population genetic model showing that gene flow is probably sufficiently low to allow for local adaptation among populations, given realistic selection coefficients. Our results underscore the importance of understanding patterns of dispersal to appropriately evaluate their potential consequences for local adaptation and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".