How does dispersal shape the genetic patterns of animal populations in European cities? A simulation approach
Notice bibliographique
Résumé
Context and objectives: Although urbanization is a major driver of biodiversity erosion, it does not affect all species equally. The neutral genetic structure of populations in a given species is affected by both genetic drift and gene flow processes. In cities, the size of animal populations determines drift and can depend on multiple processes, whereas gene flow essentially depends on the ability of species to disperse across urban areas. Considering this, we tested whether variations in dispersal constraints alone could explain the variability of neutral genetic patterns commonly observed in urban areas. Besides, we assessed how the spatial distribution of urban green spaces (UGS) and peri-urban forests acts on these patterns. Methods: We simulated multi-generational genetic processes in virtual populations of animal species occupying either UGS or forest areas (both considered as a virtual species habitat) within and around 325 European cities. We used three dispersal cost scenarios determining the ability of species to cross the least favorable land cover types, while maintaining population sizes constant among scenarios. We then assessed genetic diversity and genetic differentiation patterns for each city and each habitat types across the three cost scenarios. Results: Overall, as dispersal across the least favorable land cover types was more constrained, genetic diversity decreased and genetic differentiation increased. Across scenarios, the scale and strength of the relationship between genetic differentiation and dispersal cost-distances varied substantially, alike previously observed empirical genetic patterns. Forest areas contributed more to habitat connectivity than UGS, due to their larger area and mostly peri-urban location. Hence, population-level genetic diversity was higher in forests than in UGS and genetic differentiation was higher between UGS populations than between forest populations. However, interface habitat patches allowing individuals to move between different habitat types seemed to locally buffer these contrasts by promoting gene flow. Discussion and conclusion: Our results showed that variations in spatial patterns of dispersal, and thus gene flow, could explain the variability of empirically observed genetic patterns in urban contexts. Besides, the largest habitat areas and biodiversity sources are likely to be found in areas surrounding city centers. This should encourage urban planners to pay attention to the areas promoting dispersal movements between urban habitats (e.g., UGS) and peri-urban habitats (e.g., forests), rather than among urban habitats, when managing urban biodiversity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».