ADAPTIVE POPULATION DIVERGENCE IN CRYPTIC COLOR-PATTERN FOLLOWING A REDUCTION IN GENE FLOW
Bibliographic record
Abstract
Adaptive population divergence is often driven by divergent natural selection, but can be constrained by the homogenizing effect of gene flow between populations. Indeed, a common pattern in nature is an inverse correlation between the degree of adaptive phenotypic divergence between populations and levels of gene flow between populations. However, there is essentially no experimental data on whether this correlation arises because gene flow constrains adaptation or, conversely, because adaptive divergence causes barriers to gene flow (ecological speciation). Here, I report increased adaptive divergence in cryptic color pattern between a pair of Timema insect populations following an experimental reduction in between-population gene flow. The reduction in gene flow arose due to a natural experiment, and thus was not replicated at a second site. However, temporal replication of the trends among six generations of data, coupled with a lack of increased adaptive divergence for two other population pairs where gene flow was not manipulated (i.e., control sites), argues that the results did not arise by chance. Estimates of dispersal ability and population size further support reduced gene flow, rather than increased genetic drift, as the cause of divergence. Thus, the findings provide experimental evidence that gene flow constrains adaptation in nature.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".