Does urbanisation lead to parallel demographic shifts across the world in a cosmopolitan plant?
Bibliographic record
Abstract
Abstract Urbanisation is occurring globally, leading to dramatic environmental changes that are altering the ecology and evolution of species. In particular, the expansion of human infrastructure and the loss and fragmentation of natural habitats in cities is predicted to increase genetic drift and reduce gene flow by reducing the size and connectivity of populations. Alternatively, the ‘urban facilitation model’ suggests that some species will have greater gene flow into and within cities leading to higher diversity and lower differentiation in urban populations. These alternative hypotheses have not been contrasted across multiple cities. Here, we used the genomic data from the GLobal Urban Evolution project (GLUE), to study the effects of urbanisation on non‐adaptive evolutionary processes of white clover (Trifolium repens) at a global scale. We found that white clover populations presented high genetic diversity and no evidence of reduced Ne linked to urbanisation. On the contrary, we found that urban populations were less likely to experience a recent decrease in effective population size than rural ones. In addition, we found little genetic structure among populations both globally and between urban and rural populations, which showed extensive gene flow between habitats. Interestingly, white clover displayed overall higher gene flow within urban areas than within rural habitats. Our study provides the largest comprehensive test of the demographic effects of urbanisation. Our results contrast with the common perception that heavily altered and fragmented urban environments will reduce the effective population size and genetic diversity of populations and contribute to their isolation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".