Anthropogenic Landscape Alteration, but Not Urbanization, Influences Non‐Adaptive Evolution in Common Milkweed (<scp><i>Asclepias syriaca</i></scp> L.)
Bibliographic record
Abstract
ABSTRACT Urbanization can alter mating and dispersal, with consequences for non‐adaptive evolution in populations. Potential outcomes vary widely due to the heterogeneity of urban landscapes and the diverse life history strategies of taxa. Furthermore, it is unclear how plants, which are significantly understudied in this context, are impacted. To better understand how urbanization influences non‐adaptive evolution in a native plant of conservation importance, we analyzed patterns of neutral genetic variation in common milkweed (Asclepias syriaca). From 256 individuals sampled across 122 locations throughout the Greater Toronto Area, Canada, we created two datasets of 2,835 and 972 single nucleotide polymorphisms through genotype‐by‐sequencing. Genetic diversity and effective population size Ne were mostly consistent between urban and rural habitats. Genetic differentiation between urban and rural habitats was low, and samples originated from a single genetic population. Demographic analysis indicated that Ne decreased by > 99% within the past 800 years, with the rate of loss accelerating over time. These findings suggest that this A. syriaca population was little affected by the transition from rural to urban habitat; rather, anthropogenic activity prior to urbanization, such as precontact Indigenous inhabitation and colonial settlement, had observable effects on population demography. This study demonstrates how anthropogenic factors can modify the degree to which urbanization impacts evolution and emphasizes the importance of contextualizing results with demographic, ecological, and cultural histories.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".