Consequences of unequal population size, asymmetric gene flow and sex‐biased dispersal on population structure in brook charr (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Unravelling relationships between dispersal and population structure requires considering the impacts of assumption violations of indirect gene flow models in a given system. We combined temporal, individual and coalescent-based analyses of microsatellite DNA variation to explore the general hypothesis that unequal effective population size (Ne), asymmetric gene flow (m) and nonrandom (sex-biased) individual dispersal had an important effect on spatiotemporal population structuring in lake-dwelling brook charr (Salvelinus fontinalis). This integrative examination shed light on the dichotomous structuring observed between an outlet and three tributary-spawning populations and their potential for adaptive divergence. It revealed further that finer tributary population structuring incongruent with drainage structure has been shaped by asymmetric m from one population with a large Ne towards two populations of smaller Ne. Gene flow among the tributaries was also mediated mainly by male-biased dispersal. However, longer distance dispersal from tributaries to the outflow was female-biased. Spatially dependent sex-biased dispersal may have contributed therefore to gene flow at different levels of population structuring. Our results demonstrate how dispersal and population structure may interrelate to produce spatial variation in intraspecific diversity, and are therefore relevant for conservation programmes seeking to define conservation units or predict recolonization rates of extirpated populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".