Ash dieback and Dutch elm disease have already reshaped British woodlands, and mathematical models are now being built to predict how the next forest epidemic will unfold before it strikes. This research tackles a blind spot in existing disease models. Most either account for where trees are located or for seasonal changes in transmission—but rarely both at once. A fungus that spreads in summer rains behaves very differently in a dense plantation versus a scattered woodland, and ignoring either factor makes predictions unreliable. The project will combine spatial structure and seasonality in a single mathematical framework, then test it against Dothistroma needle blight, a disease that costs the UK forestry industry millions each year. If the models work, forest managers will gain a practical tool for comparing interventions—such as culling infected trees or adjusting planting density—before committing resources. An interactive web-modeller will put this directly into the hands of nurseries and land managers. The fundamental mathematics developed here, including analysis of chaotic cycles and disease persistence thresholds, could also transfer to other plant-pathogen systems where spatial and seasonal dynamics interact.
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Infectious diseases pose a major threat to forests and woodlands. Diseases such as Ash dieback and Dutch elm disease have drastically altered the natural environment. With sustainable forest management being central to both DEFRA and UN sustainability goals, it is vital that we have scientifically informed management strategies for when diseases are identified in forests and nurseries. In this proposal, I will develop tailored mathematical and computational models to examine the dynamics of disease in forest systems. Crucially, the models will include both spatially-structured populations and seasonality, two sources of complexity that have rarely been combined in previous mathematical models. I will develop both systems of non-linear ordinary differential equations and stochastic simulations to take a robust approach to exploring the dynamics. Further to the mathematical advancements in developing and analysing these complex models, I will develop a Knowledge Exchange collaboration with an external project partner, Forest Research, to apply the models. Specifically, we will use the case study of Dothistroma needle blight in Scots Pines, a disease of considerable economic importance to the UK forestry industry. I will work with the partner to include key details of the system and parameterise the models. We will then use these models to compare different management strategies for the outbreak of Dothistroma in a forest nursery. The proposal therefore has two objectives: O1 - Develop a general theoretical model of disease spread including both spatial structure and seasonality. Undertake bifurcation analysis to explore the outcomes, such as where the disease does and does not persist, and the potential for limit cycles, quasi-periodic cycles and chaos. O2 - Apply the model to the specific system of Dothistroma needle blight in Scots Pines. Work with the project partner to use the models to implement proposed management strategies to advise on best practice in forest nurseries. Additionally develop an interactive web-modeller.
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