Gene flow increases temporal stability of Chinook salmon (Oncorhynchus tshawytscha) populations in the Upper Fraser River, British Columbia, Canada
Bibliographic record
Abstract
Temporal instability in population genetic structure has significant implications for management and conservation decisions. Here, we evaluate temporal stability in five populations of Chinook salmon ( Oncorhynchus tshawytscha ) from the Upper Fraser River, British Columbia, Canada, based on estimates of temporal allelic variance and effective population size (Ne) at 11 microsatellite loci. Significant temporal variation in allele frequencies was found within individual populations sampled at 5- to 12-year intervals. Removal of migrant fish or correcting for migrants resulted in higher allelic variance or reduced Ne. Populations with higher levels of temporally consistent gene flow show reduced temporal allelic variance (i.e., reduced genetic drift) and higher Ne. This study is an important empirical example of the effect of gene flow on genetic stability and Ne. In salmonids, low straying levels may have evolved to favor local adaptation; however, we show that even such low levels of gene flow can elevate effective population sizes and preserve genetic variability. This study highlights the importance of considering gene flow acting to temporally stabilize populations, particularly small ones, and should migration be interrupted, Nelevels may decline with no obvious change in census population sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".