Urban expansion has widespread impacts on wildlife species globally, including the transmission and emergence of infectious diseases. However, there is almost no information about how urban landscapes shape transmission dynamics in wildlife. Using an innovative phylodynamic approach combining host and pathogen molecular data with landscape characteristics and host traits, we untangle the complex factors that drive transmission networks of feline immunodeficiency virus (FIV) in bobcats (Lynx rufus). We found that the urban landscape played a significant role in shaping FIV transmission. Even though bobcats were often trapped within the urban matrix, FIV transmission events were more likely to occur in areas with more natural habitat elements. Urban fragmentation also resulted in lower rates of pathogen evolution, possibly owing to a narrower range of host genotypes in the fragmented area. Combined, our findings show that urban landscapes can have impacts on a pathogen and its evolution in a carnivore living in one of the most fragmented and urban systems in North America. The analytical approach used here can be broadly applied to other host–pathogen systems, including humans.
Bibliographical noteFunding Information:
Division of Environmental Biology, Grant/ Award Number: DEB 1413925; National Science Foundation Ecology of Infectious Diseases, Grant/Award Number: EF 0723676/DEB 1413925; University of Minnesota’s Office of the Vice President for Research and Academic Health Center Seed Grant
This project was funded by two National Science Foundation Ecology of Infectious Diseases research programme grants (EF 0723676/ DEB 1413925). MC was funded by the University of Minnesota’s Office of the Vice President for Research and Academic Health Center Seed Grant. We also thank Paul Cross (USGS) for helpful comments on a draft of this manuscript and Roman Biek for advice on the analysis. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
- disease biology
- feline immunodeficiency virus
- machine learning