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Gentrification drives patterns of alpha and beta diversity in cities.

Mason FidinoHeather A SanderJesse S LewisElizabeth W LehrerKimberly RiveraMaureen H MurrayHenry C AdamsAnna KaseAndrea FloresTheodore StankowichChristopher J SchellCarmen M SalsburyAdam T RohnkeMark J JordanAustin M GreenAshley R GramzaAmanda J ZellmerJacque WilliamsonThilina D SurasingheHunter StormKimberly L SparksTravis J RyanKatie R RemineMary E PendergastKayleigh MullenDarren E MinierChristopher R MiddaughAmy L MertlMaureen R McClungRobert A LongRachel N LarsonMichel T KohlLavendar R HarrisCourtney T HallJeffrey D HaightDavid DrakeAlyssa M DavidgeAnn O CheekChristopher P BlochElizabeth G BiroWhitney J B AnthonysamyJulia L AngstmannMaximilian L AllenSolny A AdalsteinssonAnne G Short GianottiJalene M LaMontagneTiziana A Gelmi-CandussoSeth B Magle
Published in: Proceedings of the National Academy of Sciences of the United States of America (2024)
While there is increasing recognition that social processes in cities like gentrification have ecological consequences, we lack nuanced understanding of the ways gentrification affects urban biodiversity. We analyzed a large camera trap dataset of mammals (>500 g) to evaluate how gentrification impacts species richness and community composition across 23 US cities. After controlling for the negative effect of impervious cover, gentrified parts of cities had the highest mammal species richness. Change in community composition was associated with gentrification in a few cities, which were mostly located along the West Coast. At the species level, roughly half (11 of 21 mammals) had higher occupancy in gentrified parts of a city, especially when impervious cover was low. Our results indicate that the impacts of gentrification extend to nonhuman animals, which provides further evidence that some aspects of nature in cities, such as wildlife, are chronically inaccessible to marginalized human populations.
Keyphrases
  • healthcare
  • mental health
  • genetic diversity
  • climate change
  • risk assessment
  • convolutional neural network
  • high speed