Lack of parallel genetic patterns underlying the repeated ecological divergence of beach and stream‐spawning kokanee salmon
Bibliographic record
Abstract
Recent progress in methods for detecting adaptive population divergence in situ shows promise for elucidating the conditions under which selection acts to generate intraspecific diversity. Rapid ecological diversification is common in fishes; however, the role of phenotypic plasticity and adaptation to local environments is poorly understood. It is now possible to investigate genetic patterns to make inferences regarding phenotypic traits under selection and possible mechanisms underlying ecotype divergence, particularly where similar novel phenotypes have arisen in multiple independent populations. Here, we employed a bottom-up approach to test for signatures of directional selection associated with divergence of beach- and stream-spawning kokanee, the obligate freshwater form of sockeye salmon (Oncorhynchus nerka). Beach- and stream-spawners co-exist in many post-glacial lakes and exhibit distinct reproductive behaviours, life-history traits and spawning habitat preferences. Replicate ecotype pairs across five lakes in British Columbia, Canada were genotyped at 57 expressed sequence tag-linked and anonymous microsatellite loci identified in a previous genome scan. Fifteen loci exhibited signatures of directional selection (high FST outliers), four of which were identified in multiple lakes. However, the lack of parallel genetic patterns across all lakes may be a result of: 1) an inability to detect loci truly under selection; 2) alternative genetic pathways underlying ecotype divergence in this system; and/or 3) phenotypic plasticity playing a formative role in driving kokanee spawning habitat differences. Gene annotations for detected outliers suggest pathogen resistance and energy metabolism as potential mechanisms contributing to the divergence of beach- and stream-spawning kokanee, but further study is required.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".