Urban landscapes shift wildlife distribution and behaviour, but how these landscapes modify pathogen spread remains obscure. Individual host traits and regional landscape structure (natural and anthropogenic) combine to shape pathogen spread, yet demonstrating disease spread over meaningful timescales has been a major limitation to comprehending urban impacts. Here we bring together multiple sources of information (host, landscape, and pathogen) using a novel “landscape phylodynamics” approach, coupling machine learning with Bayesian phylogeography, to understand pathogen gene flow and transmission of the fast-evolving feline immunodeficiency virus (FIV) in an urban bobcat (Lynx rufus) population.
Preferred host habitat types (forest and grassland) and host relatedness best predicted FIV gene flow, with FIV transmission events most likely to occur in more natural habitat at greater distances from the urban edge and among more related individuals. Urban habitat fragmentation dramatically slowed FIV movement and rates of evolution. Together we show that urban landscapes can have a profound impact on pathogen gene flow and spread in one of the most fragmented and urban systems in North America.