Parallel evolution of lake whitefish dwarf ecotypes in association with limnological features of their adaptive landscape
Bibliographic record
Abstract
Sympatric fish populations observed in many north temperate lakes are among the best models to study the processes of population divergence and adaptive radiation. Despite considerable research on such systems, little is known about the associations between ecological conditions and the extent of ecotypic divergence. In this study, we examined the biotic and abiotic properties of postglacial lakes in which lake whitefish, Coregonus clupeaformis, occur as a derived dwarf ecotype in sympatry with an ancestral normal ecotype. We compared 19 limnological variables between two groups of lakes known from previous studies to harbour sympatric dwarf and normal ecotypes with high and low levels of phenotypic and genetic differentiation respectively. We found clear environmental differences between the two lake groups. Namely, oxygen was the most discriminant variable, where lakes harbouring the most divergent populations were characterized by the greatest hypolimnetic oxygen depletion. These lakes also had lower zooplankton densities and a narrower distribution of zooplantonic prey length. These results suggest that the highest differentiation between sympatric ecotypes occurs in lakes with reduced habitat and prey availability that could increase competition for resources. This in turns supports the hypothesis that parallelism in the extent of phenotypic divergence among sympatric whitefish ecotypes is associated with parallelism in adaptive landscape in terms of differences in limnological characteristics, as well as availability and structure of the zooplanktonic community.
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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".