The relative influence of natural selection and geography on gene flow in guppies
Bibliographic record
Abstract
Two general processes may influence gene flow among populations. One involves divergent selection, wherein the maladaptation of immigrants and hybrids impedes gene flow between ecological environments (i.e. ecological speciation). The other involves geographic features that limit dispersal. We determined the relative influence of these two processes in natural populations of Trinidadian guppies (Poecilia reticulata). If selection is important, gene flow should be reduced between different selective environments. If geography is important, gene flow should be impeded by geographic distance and physical barriers. We examined how genetic divergence, long-term gene flow, and contemporary dispersal within a watershed were influenced by waterfalls, geographic distance, predation, and habitat features. We found that waterfalls and geographic distance increased genetic divergence and reduced dispersal and long-term gene flow. Differences in predation or habitat features did not influence genetic divergence or gene flow. In contrast, differences in predation did appear to reduce contemporary dispersal. We suggest that the standard predictions of ecological speciation may be heavily nuanced by the mating behaviour and life history strategies of guppies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".