Morphological variation over ontogeny and environments in resource polymorphic arctic charr (<i>Salvelinus alpinus</i>)
Bibliographic record
Abstract
Natural selection requires genetically based phenotypic variation to facilitate its action and cause adaptive evolution. It has become increasingly recognized that morphological development can become canalized likely as a result of selection. However, it is largely unknown how selection may influence canalization over ontogeny and differing environments. Changes in environments or colonization of a novel one is expected to result in adaptive divergence from the ancestral population when selection favors a new phenotypic optimum. In turn, a novel environment may also expose variation previously hidden from natural selection. We tested for changes in phenotypic variation over ontogeny and environments among ecomorphs of Arctic charr (Salvelinus alpinus) from two Icelandic lakes. Populations represented varying degrees of ecological specialization, with one lake population possessing highly specialized ecomorphs exhibiting a large degree of phenotypic divergence, whereas the other displayed more subtle divergence with more ecological overlap. Here we show that ecomorphs hypothesized to be the most specialized in each lake possess significant reductions in shape variation over ontogeny regardless of environmental treatment suggesting canalized development. However, environments did change the amount of shape variation expressed in these ecomorphs, with novel environments slowing the rate at which variation was reduced over ontogeny. Thus, environmental conditions may play an important role in determining the type and amount of genetically based phenotypic variation exposed to natural selection.
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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".