Temporal change in genetic structure and effective population size in steelhead trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
There is a wealth of published molecular population genetic studies, however, most do not include historic samples and thus implicitly assume temporal genetic stability. We tested for changes in genetic diversity and structure in three populations of steelhead trout (Oncorhynchus mykiss) from a northern British Columbia watershed using seven microsatellite loci over 40 years. We found little change in genetic diversity (mean allele numbers and observed and expected heterozygosity), despite large variation in the estimated numbers of steelhead returning to the watershed over the same time period. However, the temporal stability in genetic diversity is not reflected in population structure, which appears to be high among populations, yet significantly variable over time. The neighbour-joining tree showed that, overall, two of the populations (Zymoetz and Kispiox) clustered separately from the third (Babine); a finding which was not consistent with their geographical separation. The clustering pattern was also not temporally consistent. We used the temporal method to estimate the effective number of breeders (Nb ) for the three populations; our values (Nb = 17-102) were low for the large and presumed vigorous populations of steelhead trout sampled. The low Nb values were also not consistent with the generally high genetic diversity estimates, suggesting the possibility of intermittent gene flow among the three populations. The use of temporal analyses in population genetic samples should be a priority; first, to verify observed patterns in contemporary data, and second, to build a dataset of temporal analyses to allow generalizations to be made concerning temporal genetic stability and effective population size in natural 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 | 0.001 | 0.001 |
| 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".