The multifarious effects of dispersal and gene flow on contemporary adaptation
Bibliographic record
Abstract
Summary Dispersal and gene flow can have a variety of interacting effects on evolution. These effects can either promote or constrain adaptive divergence through either genetic or demographic routes. The relative importance of these effects is unknown because few attempts have been made to conceptually integrate and test them. We draw a broad distinction between situationswithvs.withoutstrong coevolutionary dynamics. This distinction is important because the adaptive peak for a given population is more mobile in the former than in the latter. This difference makes ongoing evolutionary potential more important in the presence of strong coevolutionary dynamics than in their absence. We advance a conceptual integration of the various effects of gene flow and dispersal on adaptive divergence. In line with other authors, but not necessarily for the same reasons, we suggest that an intermediate level of gene flow will allow the greatest adaptive divergence. When dispersal is quite low, we predict that an increase will have positive effects on adaptive divergence, owing to genetic/demographic rescue and ‘reinforcement.’ The rescue effect may be more important in small populations and in homogeneous environments. The reinforcement effect may be more common in large populations and in heterogeneous environments. Once a certain level of dispersal is reached, we predict that a further increase may have negative effects on adaptive divergence. These effects may arise if carrying capacity is exceeded or maladaptive genes are introduced. Many additional effects remain to be integrated into this framework, and doing so may yield novel insights into the factors influencing evolution on ecological time‐scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 | 0.000 |
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".