Does Urbanization Alter Purifying Selection? A Case Study in the Burrowing Owl
Bibliographic record
Abstract
Urbanization is one of the most striking examples of anthropogenic disturbance dramatically altering ecosystems and evolutionary processes. In particular, natural selection and genetic drift are expected to be affected by the drastic changes in urban environmental conditions and landscape fragmentation. Whether selection strength increases or decreases in cities remains to be elucidated, especially since it is profoundly dependent on the strength of genetic drift. Using a previously published genomic dataset of 3 replicated pairs of urban and rural Argentinian populations of burrowing owls (Athene cunicularia), I investigate if urbanization affects genetic drift and the strength of purifying selection. Through genome-wide measures of ratios of deleterious to neutral diversity, I searched for potential accumulation of deleterious mutations associated with increased drift or decreased purifying selection, as well as measured the strength of purifying selection in each population by computing the distribution of fitness effects of mutations. Urban burrowing owls overall maintained nucleotide diversity levels similar to rural populations despite their small effective population sizes. Additionally, I found no evidence of genomic accumulation of deleterious mutations in urban populations, consistent with maintained genetic diversity, both suggesting a low or not yet visible, effect of genetic drift on urban populations. In contrast, the distribution of fitness effects of segregating variation revealed that the strength of purifying selection was reduced in cities, sometimes drastically (>50% weaker), compared to rural areas. These results provide new insight into how urbanization shapes natural selection and drift and show that the strength of selection can overall be reduced in cities, either because of the buffering environmental conditions or because of increased genetic drift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".