Plant diseases belong to the largest threats of food security and human health worldwide. Many plant diseases are caused by pathogens that are transmitted by insect vectors. It is increasingly recognized that pathogens modify the behavior of their vectors to increase transmission, and there is compelling empirical evidence that transmission by vectors depends on the infection status of host plants and of the vectors themselves. Modeling the effects of vector preferences on disease transmission is a current research front, because this has an impact in crop protection strategies. However, there is a lack of incorporating spatial vector dispersal and including preference in vector movement. We propose to construct and analyze a spatiotemporal model of plant infection that includes spatial movement of vectors, especially movement that depends on infection status of hosts and vectors. This leads to systems of nonlinear partial differential equations, where the movement will be described by diffusion and advection terms, whereas transmission and population dynamics will be described by reaction terms. The work will build on the applicants’ extensive experience and expertise in the mathematical modeling of plant and vector-borne diseases, movement preferences and movement behavior, as well as the analysis of partial differential equation models. The work will be the first to study partial differential equations for plant disease models with vector preferences.