Investigating the extent of parallelism in morphological and genomic divergence among lake trout ecotypes in Lake Superior
Bibliographic record
Abstract
Abstract Understanding the emergence of species through the process of ecological speciation is a central question in evolutionary biology which also has implications for conservation and management. Lake trout (Salvelinus namaycush) is renowned for the occurrence of different ecotypes linked to resource and habitat use throughout North America. We aimed to unravel the fine genetic structure of the four lake trout ecotypes in Lake Superior. A total of 486 individuals from four sites were genotyped at 6822 filtered SNPs using RADseq technology. Our results revealed different extent of morphological and genetic differentiation within the different sites. Overall, genetic differentiation was weak but significant and was on average three times higher between sites (mean FST = 0.016) than between ecotypes within sites (mean FST = 0.005) indicating higher level of gene flow or a more recent shared ancestor between ecotypes within each site than between populations of the same ecotype. Evidence of divergent selection was also found between ecotypes and/or in association with morphological variation. Outlier loci found in genes related to lipid metabolism and visual acuity were of particular interest in this context of ecotypic divergence. However, we did not find clear indication of parallelism at the genomic level, despite the presence of phenotypic parallelism among some ecotypes from different sampling sites. Overall, the occurrence of different levels of both genomic and phenotypic differentiation between ecotypes within each site with several differentiated loci linked to relevant biological functions supports the presence of a continuum of divergence in lake trout.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".