Genomic basis of deep‐water adaptation in Arctic Charr (Salvelinus alpinus) morphs
Bibliographic record
Abstract
Colonization of extreme habitats requires extensive adaptation to novel environmental challenges. Deep-water environments (>50 m) have high hydrostatic pressure, low temperature, and low light, requiring physiological and visual system adaptation, but genomic mechanisms underlying evolution in these environments are rarely known. Post-glacial colonization of Gander Lake in Newfoundland, Canada, by Arctic Charr (Salvelinus alpinus) provides the opportunity to study the genomic basis of adaptation to extreme deep-water environments. Here, we compare genomic and morphometric divergence between a phenotypically divergent deep-water, demersal morph adapted to depths of up to 288m and a larger, piscivorous morph occupying shallower depths. Using a SNP array and resequencing of nuclear and mitochondrial genomes, we find moderate genetic divergence (FST = 0.15 - 0.11) between morphs, consistent with divergence in body shape and size, despite absence of mitochondrial genome divergence. Outlier analyses identified three key genes with very high divergence against a genome-wide distribution of diverged genomic islands containing genes with functions related to deep-water adaptation such as sensory processes, ligand binding and regulation of transcription. Quantification of SNP array signal intensity variation associated with complex polymorphisms (e.g. copy number variants), similarly uncovered genetic separation of morphs and coincided with several islands of genomic divergence, but also revealed additional genomic regions and molecular mechanisms associated with depth adaptation. Together, these results show that adaptation to an extreme deep-water environment has been facilitated by multiple polymorphism types with roles in cellular and physiological processes, providing insight into the genomic basis of adaptation in extreme environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".