Genome-Wide Investigation of the Multiple Origins Hypothesis for Deep-Spawning Kokanee Salmon (<i>Oncorhynchus nerka</i>) across its Pan-Pacific Distribution
Bibliographic record
Abstract
Salmonids have emerged as important study systems for investigating molecular processes underlying parallel evolution given their tremendous life history variation. Kokanee, the resident form of anadromous sockeye salmon (Oncorhynchus nerka), have evolved multiple times across the species' pan-Pacific distribution, exhibiting multiple reproductive ecotypes including those that spawn in streams, on lake-shores, and at lake depths >50 m. The latter has only been detected in 5 locations in Japan and British Columbia, Canada. Here, we investigated the multiple origins hypothesis for deep-spawning kokanee, using 9721 single nucleotide polymorphisms distributed across the genome analyzed for the vast majority of known populations in Japan (Saiko Lake) and Canada (Anderson, Seton, East Barrière Lakes) relative to stream-spawning populations in both regions. We detected 397 outlier loci, none of which were robustly identified in paired-ecotype comparisons in Japan and Canada independently. Bayesian clustering and principal components analyses based on neutral loci revealed 6 distinct clusters, largely associated with geography or translocation history, rather than ecotype. Moreover, a high level of divergence between Canadian and Japanese populations, and between deep- and stream-spawning populations regionally, suggests the deep-spawning ecotype independently evolved on the 2 continents. On a finer level, Japanese kokanee populations exhibited low estimates of heterozygosity, significant levels of inbreeding, and reduced effective population sizes relative to Canadian populations, likely associated with transplantation history. Along with preliminary evidence for hybridization between deep- and stream-spawning ecotypes in Saiko Lake, these findings should be considered within the context of on-going kokanee fisheries management in Japan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".