Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow
Bibliographic record
Abstract
Divergent natural selection, adaptive divergence and gene flow may interact in a number of ways. Recent studies have focused on the balance between selection and gene flow in natural populations, and empirical work has shown that gene flow can constrain adaptive divergence, and that divergent selection can constrain gene flow. A caveat is that phenotypic diversification may be under the direct influence of environmental factors (i.e. it may be due to phenotypic plasticity), in addition to partial genetic influence. In this case, phenotypic divergence may occur between populations despite high gene flow that imposes a constraint on genetic divergence. Plasticity may dampen the effects of natural selection by allowing individuals to rapidly adapt phenotypically to new conditions, thus slowing adaptive genetic divergence. On the other hand, plasticity may promote future adaptive divergence by allowing populations to persist in novel environments. Plasticity may promote gene flow between selective regimes by allowing dispersers to adapt to alternate conditions, or high gene flow may result in the selection for increased plasticity. Here I expand frameworks for understanding relationships among selection, adaptation and gene flow to include the effects of phenotypic plasticity in natural populations, and highlight its importance in evolutionary diversification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".