Shared Dispersal Patterns but Contrasting Levels of Gene Flow in Two Anadromous Salmonids Along a Broad Subarctic Coastal Gradient
Bibliographic record
Abstract
Dispersal is a highly variable trait influenced by life history and ecological factors, affecting gene flow when dispersers successfully reproduce. Anadromous salmonids, with their diverse migratory strategies and ecological traits, serve as an ideal model for studying dispersal evolution, showcasing significant inter- and intraspecific variation. Although environmental factors like temperature likely influence dispersal propensity, their effects remain poorly documented. This study compares dispersal patterns and population structure in lake whitefish (Coregonus clupeaformis) and brook charr (Salvelinus fontinalis) along the subarctic coastline of James Bay, covering four degrees of latitude. These species differ in life history and population size, representing contrasting ends of a continuum influencing dispersal and gene flow. We hypothesised that lake whitefish, with shorter freshwater residency and potentially reduced olfactory imprinting, would disperse more frequently than brook charr. Using low-coverage whole-genome sequencing, we found that lake whitefish exhibited broader-scale population structure and greater long-distance dispersal capacity than brook charr. Surprisingly, both species showed similar dispersal rates and population differentiation levels. However, lake whitefish had effective population sizes approximately 10 times larger than brook charr, indicating that while their dispersal is common, it results in lower effective gene flow. Moreover, dispersal rates in both species were lower in the northern study area, likely due to colder temperatures, delayed ice break and shorter growing seasons. These findings yield insights into how life history and environmental variation shape dispersal evolution in migratory species.
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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 | 0.001 |
| 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) | 0.001 | 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".