Identifying epidemiological predictors for quantitative host plant resistance : application to the sunflower-phoma pathosystem

Phoma black stem (BS) is caused by the fungus Leptosphaeria lindquistii, and is an important disease in France. The study presented in this dissertation provides useful information on BS epidemiology and sunflower quantitative resistance against the disease. Experiments were conducted on plants grown in small plots (field), adult plants (greenhouse), and seedlings (growth chamber) in order to (1) characterize the spatiotemporal dynamics of BS, (2) identify morphological traits affecting BS through disease escape processes and utilizing a standardised disease assessment procedure, and (3) identify predictors of quantitative resistance to BS. This study suggests that (1) BS is primarily associated to monocyclic epidemics in south west France, (2) low BS levels are associated with sunflower plants characterized by a large number of green leaves and large height, and (3) predictors of quantitative resistance to BS can be experimentally identified

Forest Pathology and Plant Health

Every year, a number of new forest pathosystems are discovered as the result of introduction of alien pathogens, host shifts and jumps, hybridization and recombination among pathogens, etc. Disease outbreaks may also be favored by climate change and forest management. The mechanisms driving the resurgence of native pathogens and the invasion of alien ones need to be better understood in order to draft sustainable control strategies. For this Special Issue, we welcome population biology studies providing insights on the epidemiology and invasiveness of emergent forest pathogens possibly by contrasting different scenarios varying in pathogen and host populations size, genetics, phenotype and phenology, landscape fragmentation, occurrence of disturbances, management practices, etc. Both experimental and monitoring approaches are welcome. In summary, this special issue focuses on how variability in hosts, pathogens, or ecology may affect the emergence of new threats to plant species

Modelling spatial strategies for the durable deployment of crop disease resistance

Maximising the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialised. The factors that interact with and determine the magnitude of this spatial effect are not currently well understood however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We initially use a spatially explicit model, incorporating seasonality and localised reservoirs of inoculum, to explore disease dynamics within landscapes containing a mixture of fields planted with either susceptible or resistant cultivars. When the spatial diversification of these fields is maximised, with lower aggregation of similar fields, the overall intensity of the landscape scale epidemic is reduced. The strength of this spatial effect however depends strongly on the pathogen dispersal characteristics, any fitness cost(s) of the resistance breaking trait, the efficacy of host resistance, and the length of the timeframe of interest. The conclusions drawn from this initial work, about how multi-strain disease dynamics respond to the scale of spatial diversification in a multi-host landscape, allow us to construct a general spatially implicit model that captures these fundamental dynamics. This new model features a novel method for incorporating spatial structure using an intuitive spatial aggregation metric that can be easily estimated from spatially explicit landscape data. The model is simple enough to be amenable to mathematical invasion analysis, while being flexible enough that questions of resistance durability can be thoroughly explored. In particular, results demonstrating interaction between spatial heterogeneity and cultivar cropping ratio are presented, an investigation that was not easily possible in our earlier more complex model. These results indicate that optimal spatial deployment strategies depend on a variety of factors, and may not necessarily be constant over time. Overall, these models allow us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modelling and pathosystem specific evaluation. In addition, this approach for capturing detailed spatial structure and multi-species interactions within simple mathematical models could be applied to a wide variety of ecological and evolutionary systems. This study highlights the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems, as well as providing new mathematical tools to do so

Modelling spatio-temporal tree disease epidemics in Great Britain

Presently, tree populations worldwide face unprecedented threats from invasive pests and pathogens endangering biodiversity, timber production and human wellbeing. From first principles, this thesis incrementally extends a simple percolation model of forest-based epidemics into a more involved stochastic dispersal framework combined with tree canopy data. The approach developed here couples two spatially-explicit epidemic models at different scales. First, a non-local stochastic model of pathogen dispersal between trees is constructed. Second, the small-scale epidemic model is projected onto a large-scale distribution of host abundance, resulting in an $R_0$-map across Great Britain. Subsequently, a clustering algorithm is employed to identify high-risk regions in the $R_0$-map. Initial results indicate a global epidemic phase transition across the distribution, conditional on an infectivity parameter. The approach to `spatially scale-up' an epidemic model over the entire landscape is computationally efficient, flexible and adaptable to many pests and pathogens. In addition, numerous studies have sought to understand and optimise epidemic control in botanical populations. The mainstream control paradigm generally seeks to optimise an `eradication radius' about infected symptomatic trees over a relatively small spatial scale. However, large-scale epidemic control based solely on the spatial distribution of hosts has yet to be explored in depth. As such, this thesis will also examine how host heterogeneity, combined with targeted epidemic control, can give rise to natural `pinch-points' that may slow the epidemic spread between regions. Ultimately, this investigation intends to help policymakers reach informed decisions about where to focus control in the landscape of Great Britain