Does water depth or diet divergence predict progress toward ecological speciation in whitefish radiations?
Bibliographic record
Abstract
Question: Is the extent of genetic divergence between sympatric whitefish ecotypes – a proxy for progress toward speciation – related to the extent of ecological divergence in spawning depth or diet? Study System: Whitefish (Coregonus spp.) that have diversified into two or more sympatric ecotypes in subalpine Swiss lakes. Sympatric ecotypes vary in the extent of reproductive isolation. Analytical Methods: We measured the degree of spawning depth differentiation based on the depth-at-capture of different ecotypes. We estimated diet differentiation between ecotypes as Mahalanobis distances from stable isotopes. We compared each of these to genetic differentiation measured from AFLP data, using modified correlation tests and phylogenetically independent contrasts to account for nonindependence of comparisons in lakes with more than two ecotypes. Results: We found that the magnitude of divergence in spawning depth was generally – albeit only marginally significantly – associated with the extent of genetic divergence between sympatric ecotypes. This effect was clearly stronger than the effect of diet divergence, which was not associated with genetic differentiation. Furthermore, there was no evidence for an interactive effect of depth and diet divergence on progress toward 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".