Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission

Abstract

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vector’s own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding–as well as potential effects of infection on vector population density–on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics.

A brief view of factors that affect plant virus evolution

“…In addition, viruses may evolve different ways to manipulate their plant hosts to generate compounds or morphological phenotypes that attract vectors (116,117), which will increase the chances of the virus being transmitted to a new host. The nature of the manipulation of the vector's choice and the success of the viral spread is complex (118,119) and has deep evolutionary consequences (120), especially since the feeding preference of the vector affects the types of hosts a virus encounters, and therefore shapes the evolution of a virus toward a specialist or a generalist pathogen (121).…”

Tolerant crops increase growers’ yields but promote selfishness: how the epidemiology of disease resistant and tolerant varieties affect grower behaviour

“…Previous epidemiological models including factors such as imperfect vaccination, risk-structure or re-infection (e.g. Arino et al (2003), Gumel & Song (2008), summarised in Gumel (2012)), vector dynamics (Garba et al (2008), Cunniffe et al (2022)), fungicide application (Castle & Gilligan (2012)) or aspects of individual behaviour (Ajbar et al (2021), Hadeler & Castillo-Chavez (1995)) have also identified such bistable regions. In our case, changing the rate of horizontal transmission ( β = δ β β for the tolerant parameterisation) induced this bistability, and whether the system went to a disease-free or disease-endemic equilibrium depended on the initial proportion of infectious fields and initial proportion of controllers (Fig.…”

How the epidemiology of disease-resistant and disease-tolerant varieties affects grower behaviour

“…Previous epidemiological models including factors such as imperfect vaccination, risk-structure or re-infection (e.g. [ 63 , 64 ], summarized in [ 58 ]), vector dynamics [ 65 , 66 ], fungicide application [ 67 ] or aspects of individual behaviour [ 68 , 69 ] have also identified such bistable regions. In our case, changing the rate of horizontal transmission ( β = δ β β for the tolerant parametrization) induced this bistability, and whether the system went to a disease-free or disease-endemic equilibrium depended on the initial proportion of infectious fields and initial proportion of controllers ( figure 4 b ).…”