The Charr Problem Revisited: Exceptional Phenotypic Plasticity Promotes Ecological Speciation in Postglacial Lakes
Bibliographic record
Abstract
The salmonid arctic charr Salvelinus alpinus (L.) is one of the most widespread fishes in the world and is found farther north than any other freshwater or diadromous fish, but also in cool water farther south. It shows a strong phenotypic, ecological, and life history diversity throughout its circumpolar range. One particular side of this diversity is the frequent occurrence of two or more distinct charr morphs in the same lake. This polymorphism has been termed ‘the charr problem’. Similar cases are found in other postglacial fishes, but not with the extent and diversity as with the arctic charr. This review first treats the classical case, pioneered in an advanced way by Winifred Frost, of autumn and winter spawning charr in Windermere, England, and three other cases that have received much research interest in recent years: Thingvallavatn, Iceland; Loch Rannoch, Scotland; and Fjellfrøsvatn, Norway. Then a special kind of sympatry with one morph living permanently in the profundal zone, known from a few lakes in Europe, Russia and Canada and unique for arctic charr among postglacial fishes, is reviewed. Among them is a recently discovered charr at 450 m depth in Tinnsjøen, Norway, one of the few very deep lakes in the world. With examples, the concluding discussion focuses on the variation of arctic charr polymorphisms which extends from early stages of ecological segregation to cases of reproductive isolation and speciation; and on models to explain the charr problem. The exceptional diversity of arctic charr provides a unique potential for further progress in studies on ecologically driven evolution within the frames of modern theory of developmental plasticity, adaptive radiation and adaptive speciation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".