Gene flow and genetic drift in urban environments
Bibliographic record
Abstract
Evidence is growing that human modification of landscapes has dramatically altered evolutionary processes. In urban population genetic studies, urbanization is typically predicted to act as a barrier that isolates populations of species, leading to increased genetic drift within populations and reduced gene flow between populations. However, urbanization may also facilitate dispersal among populations, leading to higher genetic diversity within, and lower differentiation between, urban populations. We reviewed the literature on nonadaptive urban evolution to evaluate the support for each of these urban fragmentation and facilitation models. In a review of the literature with supporting quantitative analyses of 167 published urban population genetics studies, we found a weak signature of reduced within-population genetic diversity and no evidence of consistently increased between-population genetic differentiation associated with urbanization. In addition, we found that urban landscape features act as barriers or conduits to gene flow, depending on the species and city in question. Thus, we speculate that dispersal ability of species and environmental heterogeneity between cities contributes to the variation exhibited in our results. However, >90% of published studies reviewed here showed an association of urbanization with genetic drift or gene flow, highlighting the strong impact of urbanization on nonadaptive evolution. It is clear that species biology and city heterogeneity obscure patterns of genetic drift and gene flow in a quantitative analysis. Thus, we suggest that future research makes comparisons of multiple cities and nonurban habitats, and takes into consideration species' natural history, environmental variation, spatial modelling and marker selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".