Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient
Notice bibliographique
Résumé
Abstract Cities are emerging as models for addressing the fundamental question of whether populations evolve in parallel to similar environments. Here, we examine the environmental factors that drive parallel evolutionary urban-rural clines in a Mendelian trait — the cyanogenic antiherbivore defense of white clover ( Trifolium repens ). We sampled over 700 urban and rural clover populations across 16 cities along a latitudinal transect in eastern North America. In each population, we quantified the frequency of genotypes that produce hydrogen cyanide (HCN), and in a subset of the cities we estimated the frequency of the alleles at the two genes ( CYP79D15 and Li ) that epistatically interact to produce HCN. We then tested the hypothesis that winter environmental conditions cause the evolution of clines in HCN by comparing the strength of clines among cities located along a gradient of winter temperatures and frost exposure. Overall, half of the cities exhibited urban-rural clines in the frequency of HCN, whereby urban populations evolved lower HCN frequencies. The weakest clines in HCN occurred in cities with the lowest temperatures but greatest snowfall, supporting the hypothesis that snow buffers plants against winter frost and constrains the formation of clines. By contrast, the strongest clines occurred in the warmest cities where snow and frost are rare, suggesting that alternative selective agents are maintaining clines in warmer cities. Additionally, some clines were driven by evolution at only CYP79D15 , consistent with stronger and more consistent selection on this locus than on Li . Together, our results demonstrate that both the agents and targets of selection vary across cities and highlight urban environments as large-scale models for disentangling the causes of parallel evolution in nature. Impact Summary Understanding whether independent populations evolve in the same way (i.e., in parallel) when subject to similar environments remains an important problem in evolutionary biology. Urban environments are a model for addressing the extent of parallel evolution in nature due to their convergent environments (e.g. heat islands, pollution, fragmentation), such that two distant cities are often more similar to one another than either is to nearby nonurban habitats. In this paper, we used white clover ( Trifolium repens ) as a model to study the drivers of parallel evolution in response to urbanization. We collected >11,000 plants from urban and rural habitats across 16 cities in eastern North America to examine how cities influence the evolution of a Mendelian polymorphism for an antiherbivore defense trait – hydrogen cyanide (HCN). This trait had previously been shown to exhibit adaptive evolution to winter temperature gradients at continental scales. Here we tested the hypothesis that winter environmental conditions cause changes in the frequency of HCN between urban and rural habitats. We found that half of all cities had lower frequency of HCN producing genotypes relative to rural habitats, demonstrating that cities drive parallel losses of HCN in eastern North America. We then used environmental data to understand why cities vary in the extent to which they drive reduction in HCN frequencies. The warmest cities showed the greatest reductions in HCN frequencies in urban habitats, while colder, snowier cities showed little change in HCN between urban and rural habitats. This suggests that snow weakens the strength of natural selection against HCN in cities. However, it additionally suggests alternative ecological or evolutionary mechanisms drive the strong differences in HCN between urban and rural habitats in the warmest cities. Overall, our work highlights urban environments as powerful, large-scale models for disentangling the causes of parallel and non-parallel evolution in nature.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».