Our native trees and forests are under constant threat from emerging diseases, as exemplified by recent outbreaks of ash dieback which could kill 80% of ash trees across the UK. The Woodland Trust says, “At a cost of billions, the effects will be staggering. It will change the landscape forever and threaten many species which rely on ash.”
The Department for Environment, Food and Rural Affairs have highlighted the importance of mathematical modelling in developing robust plans and management policies for minimising the impacts of these disease threats. We are working to identify practical strategies to build resilience to tree disease outbreaks in the face of rapid ecological changes through mathematical modelling. To do so, we are exploring spatio-temporal models of disease based upon high-resolution predictive maps of the national tree distribution (as shown in the figure above). We use a variety of mathematical techniques, including agent-based modelling, statistical inference and partial differential equations.
Supported by a three-year NERC Knowledge Exchange Fellowship, and in collaboration with DEFRA and the Forestry Commission, my research in this area will focus on developing mathematical prediction tools to protect urban trees and woodland from invasive pests, particularly the oak processionary moth in London.
L E Wadkin, J Branson, A Hoppit, N G Parker, A Golightly and A W Baggaley (2022). Inference for epidemic models with time-varying infection rates: Tracking the dynamics of oak processionary moth in the UK. Ecology and Evolution, 12, e8871.
Research team: Dr Andrew Baggaley (Newcastle), Prof Nick Parker (Newcastle), Dr Andy Golightly (Durham)