PARALLEL AND NONPARALLEL ASPECTS OF ECOLOGICAL, PHENOTYPIC, AND GENETIC DIVERGENCE ACROSS REPLICATE POPULATION PAIRS OF LAKE AND STREAM STICKLEBACK
Bibliographic record
Abstract
Parallel (or convergent) evolution provides strong evidence for a deterministic role of natural selection: similar phenotypes evolve when independent populations colonize similar environments. In reality, however, independent populations in similar environments always show some differences: some nonparallel evolution is present. It is therefore important to explicitly quantify the parallel and nonparallel aspects of trait variation, and to investigate the ecological and genetic explanations for each. We performed such an analysis for threespine stickleback (Gasterosteus aculeatus) populations inhabiting lake and stream habitats in six independent watersheds. Morphological traits differed in the degree to which lake-stream divergence was parallel across watersheds. Some aspects of this variation were correlated with ecological variables related to diet, presumably reflecting the strength and specifics of divergent selection. Furthermore, a genetic scan revealed some markers that diverged between lakes and streams in many of the watersheds and some that diverged in only a few watersheds. Moreover, some of the lake-stream divergence in genetic markers was associated within some of the lake-stream divergence in morphological traits. Our results suggest that parallel evolution, and deviations from it, are primarily the result of natural selection, which corresponds in only some respects to the dichotomous habitat classifications frequently used in such studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".