The consequences of phenotypic plasticity for ecological speciation
Bibliographic record
Abstract
We use an individual-based numerical simulation to study the effects of phenotypic plasticity on ecological speciation. We find that adaptive plasticity evolves readily in the presence of dispersal between populations from different ecological environments. This plasticity promotes the colonization of new environments but reduces genetic divergence between them. We also find that the evolution of plasticity can either enhance or degrade the potential for divergent selection to form reproductive barriers. Of particular importance here is the timing of plasticity in relation to the timing of dispersal. If plasticity is expressed after dispersal, reproductive barriers are generally weaker because plasticity allows migrants to be better suited for their new environment. If plasticity is expressed before dispersal, reproductive barriers are either unaffected or enhanced. Among the potential reproductive barriers we considered, natural selection against migrants was the most important, primarily because it was the earliest-acting barrier. Accordingly, plasticity had a much greater effect on natural selection against migrants than on sexual selection against migrants or on natural and sexual selection against hybrids. In general, phenotypic plasticity can strongly alter the process of ecological speciation and should be considered when studying the evolution of reproductive barriers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".