Patterns and repeatability of multi‐ecotype assemblages of sympatric salmonids
Bibliographic record
Abstract
Abstract Aim High repeatability among assemblages of closely related but ecologically distinct ecotypes implies predictability in evolution and assembly of communities. The conditions under which ecotype assemblages form predictably, and the reasons, have been little investigated. Here, we test whether repeatability declines as the number of ecotypes builds. Location Postglacial lakes with a circumboreal distribution. Time Period Data were extracted from studies published between 1982 and 2019. Major Taxa Studied Ecotype assemblages from two Salmonid genera – Salvelinus and Coregonus. Fish in postglacial lakes commonly occur as pairs of ecotypes, typically with a pelagic and a littoral/benthic form, but in Salvelinus and Coregonus, assemblages commonly contain multiple sympatric ecotypes. Methods We used a meta‐analysis of Salvelinus and Coregonus to empirically assess how repeatability varies across assemblages of two to seven ecotypes. We examined repeatability of use of broad niche categories as well as underlying phenotypic traits. Results Within Coregonus, repeatability across multi‐ecotype assemblages did not break down with the addition of a third or fourth ecotype. However, in Salvelinus, repeatability was largely absent and independent of the number of ecotypes. Repeatability of trait frequency distributions was absent in both genera, yet associations between trait means and niche categories were evident, especially in Coregonus. Main Conclusions These results show that repeatability can vary greatly between lineages; that repeatability need not break down as the number of ecotypes builds; and that high repeatability of broad niche categories may result despite marked differences in the underlying frequency distribution of trait means. These findings not only affirm the presence of repeatable ecotype assembly and early stages of divergence in postglacial fishes at a global scale, but also highlight variability among taxa and underlying phenotypic traits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".