Which ecological factors influence the level of intraspecific diversity within post‐glacial fishes? A case study using <i>Coregonus</i> and <i>Salvelinus</i>
Bibliographic record
Abstract
Abstract While it is likely that ecological context is important, the factors that facilitate and maintain variable levels of intraspecific diversity in Salmonidae fishes across environments remain unclear. Using a meta‐analysis of sympatric ecotype assemblages from two salmonid genera—Salvelinus and Coregonus—we evaluated the importance of ecological factors determining the number of sympatric ecotypes (i.e. 2–7) and the level of trait divergence between them. We found that ecotype diversity increased with lake depth and surface area in both Coregonus and Salvelinus. Further, diversity in Coregonus increased with latitude, while the number of ecotypes in Salvelinus assemblages was linked to climatic seasonality. In comparing the two genera, we found elevated divergence in traits related to ontogeny (i.e. age and body shape) in Salvelinus and gill raker count in Coregonus. Trait divergence in life history traits (i.e. age and body length) in Salvelinus increased with seasonality, whereas contrasting relationships of latitude to body length and gill rakers were found in Coregonus. We also found similar levels of divergence in trait variance in the two genera, suggesting that among‐ecotype differences in phenotypic variability are not more common in one genus than the other. Overall, ecosystem characteristics, including lake location, climate and morphometry, are clearly important for where these genera have diversified, but the variables that are most closely associated with intraspecific diversity differ between the two genera studied and depend on whether diversity is quantified using number of ecotypes or trait divergence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".