Challenge to the model of lake charr evolution: shallow- and deep-water morphs exist within a small postglacial lake
Bibliographic record
Abstract
All examples of lake charr (Salvelinus namaycush) diversity occur within the largest, deepest lakes of North America (i.e. > 2000 km2). We report here Rush Lake (1.3 km2) as the first example of a small lake with two lake charr morphs (lean and huronicus). Morphology, diet, life history, and genetics were examined to demonstrate the existence of morphs and determine the potential influence of evolutionary processes that led to their formation or maintenance. Results showed that the huronicus morph, caught in deep-water, had a deeper body, smaller head and jaws, higher eye position, greater buoyancy, and deeper peduncle than the shallow-water lean morph. Huronicus grew slower to a smaller adult size, and had an older mean age than the lean morph. Genetic comparisons showed low genetic divergence between morphs, indicating incomplete reproductive isolation. Phenotypic plasticity and differences in habitat use between deep and shallow waters associated with variation in foraging opportunities seems to have been sufficient to maintain the two morphs, demonstrating their important roles in resource polymorphism. Rush Lake expands previous explanations for lake charr intraspecific diversity, from large to small lakes and from reproductive isolation to the presence of gene flow associated with strong ecological drivers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".