IAP-25-056
Mathematical models for treescape resilience in a changing climate
Climate change is amplifying the threat to our native treescapes from devastating outbreaks of invasive pests and diseases. As temperatures rise and ecosystems shift, these biological invasions are expected to become more frequent, severe, and widespread. Strategic forest planning and adaptive management are therefore essential for enhancing the resilience of treescapes and containing outbreaks when they occur. This project will address a pressing and timely question: What are effective and cost-efficient strategies for developing climate-resilient treescapes? To answer this, we will employ advanced spatio-temporal models to simulate the spread of tree diseases under a range of climate and management scenarios, providing a robust basis for policy and practical intervention.
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Image Captions
Example computational model output simulating the spread of a tree disease.
Methodology
The project aims to identify practical strategies to build resilience to tree disease outbreaks in the face of climate change. We will develop spatio-temporal models of disease spread, utilising partial differential equations and individual-based techniques. The models will be informed by real data through modern statistical inference techniques, using up-to-date disease and pest data, national tree maps, and projected changes in pathogen transmission over the next decade. This approach will be applied to existing pests and diseases, along with hypothetical future ones, and will incorporate effects such as seasonal weather patterns, year-on-year climate trends (following the latest projection scenarios) and regional variability. Importantly, we will use the model to test the effectiveness of a variety of scenarios designed to build resilience against outbreaks of tree disease. This project is in collaboration with the Department for Environment, Food and Rural Affairs (Defra), ensuring direct engagement with government policymakers and alignment with national environmental policy priorities.
Project Timeline
Year 1
In the first few months, the focus will be on conducting a literature review and consolidating datasets including national tree distribution maps, climate projections, and existing field data on key pests and diseases. Then, a baseline spatio-temporal model of disease spread will be developed, building upon previous work, using a combination of partial differential equations and individual based techniques. Engagement with Defra and other stakeholders will begin early to align project outputs with policy needs.
Year 2
In Year 2, modern statistical inference techniques will be employed to estimate model parameters for various case study pests and diseases. Validation of the baseline models will be carried out using historical outbreak data. They will then be further developed to incorporate seasonal, regional, and landscape variability. The model will be used to simulate multiple outbreak scenarios, including hypothetical future pathogens and under various climate change trajectories.
Year 3
In Year 3, mitigation and resilience-building strategies will be evaluated to determine which are most effective and cost-efficient under projected future climate conditions. Using the spatio-temporal models developed in earlier stages, a range of interventions (e.g., targeted surveillance, felling, and spatial planting configurations) will be tested across multiple outbreak scenarios. This analysis will focus on a set of key case study pests and diseases that represent a range of transmission mechanisms and ecological impacts.
Year 3.5
The final six months will be dedicated to completing the PhD thesis and submitting key research papers for publication. A policy-focused report will be produced in collaboration with Defra to summarise actionable findings. Results will be presented at both academic and stakeholder-facing conferences. Time will also be allocated for final model documentation and code sharing, ensuring transparency and reusability.
Training
& Skills
The student will receive training in spatio-temporal modelling, statistical inference, and geospatial data processing using tools such as R, Python, and GIS. They will be exposed to forest epidemiology, climate scenario analysis, and resilience planning, supported by interdisciplinary supervision and collaboration with Defra. Policy engagement and science communication skills will be strengthened through direct interaction with policymakers. The student will also benefit from transferable skills training in scientific writing, public speaking, and project management, and will have opportunities to present research at national and international conferences. Participation in relevant DTP workshops and short courses will further support professional development.
References & further reading
Previous work by the Newcastle group:
• McKeown, J. P., Wadkin, L. E., Parker, N. G., Golightly, A., & Baggaley, A. W. (2025). Reaction-diffusion models of invasive tree pest spread: quantifying the spread of oak processionary moth in the UK. arXiv preprint arXiv:2509.14166.
• Wadkin, L. E., Holden, J., Ettelaie, R., Holmes, M. J., Smith, J., Golightly, A., … & Baggaley, A. W. (2024). Estimating the reproduction number, R0, from individual-based models of tree disease spread. Ecological Modelling, 489, 110630.
• Wadkin, L. E., Golightly, A., Branson, J., Hoppit, A., Parker, N. G., & Baggaley, A. W. (2023). Quantifying invasive pest dynamics through inference of a two-node epidemic network model. Diversity, 15(4), 496.
• Wadkin, L. E., Branson, J., Hoppit, A., Parker, N. G., Golightly, A., & Baggaley, A. W. (2022). Inference for epidemic models with time‐varying infection rates: Tracking the dynamics of oak processionary moth in the UK. Ecology and Evolution, 12(5), e8871.
