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B327: If we can’t stop it, what can we do? Slowing the spread of well-established plant disease epidemics (Lead Supervisor: Nik Cunniffe, Plant Sciences)

Supervisors: Nik Cunniffe (Plant Sciences) and Chris Gilligan (Plant Sciences)

Importance of the area of research:

Invading plant pests and diseases threaten the important ecosystem services that are provided by forests. Pest and pathogen introductions occur at ever-increasing rates, driven by changes to patterns of global trade, travel and climate. Recent high profile examples, particularly chalara ash dieback, but also other diseases and pests including sudden oak death and the oriental chestnut gall wasp, focus attention on how to manage introductions effectively. Mathematical modelling allows us to understand the spread of a new invader, providing a rational basis to make decisions on where, why, how - and even whether or not - management should be attempted. The likely importance of the area going forward is reflected in recent developments that place mathematical modelling in general – and work done in Cambridge in particular – at the very centre of policy responses by DEFRA and other governmental agencies (see for e.g. DEFRA’s recent Tree Health Management Plan

Project summary:

Management to slow the spread of an established pest or pathogen – rather than to eradicate it entirely – is not well understood. However, detection of pests and diseases in forests is difficult, and large areas are often infected/infested by the time of discovery. Outbreaks are then typically too large for eradication to be realistic, and the default is to not attempt management, since the epidemic is already “out of control”. We need to understand how to slow spread of well-established epidemics. This would allow particularly high-value habitats and ecosystems (e.g. a national park, SSSI, or heritage garden) to be protected.

What the student will do:

The student will use sudden oak death in California and chalara ash dieback in the United Kingdom as exemplars of epidemics in natural woodland that are now too well-advanced to attempt to eradicate. The student will develop spatially-explicit stochastic models of the spread in and nearby to particular regions that are to be protected, and by parameterising and running these models, will assess a range of strategies for management. This will involve decisions on optimal spatial-targeting of management intervention, on how control should be deployed at local scales, and on how the available budget can be partitioned between spending on detection and control. It will also require decisions on whether more resources should be devoted locally to the region of interest, or whether concentrating control in – potentially very distant – highly infected regions are the core of the epidemic or infestation is more likely to be successful and cost-effective.

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.


Cunniffe, N.J., Cobb, R.C., Meentemeyer, R.K., Rizzo, D.R. and Gilligan, C.A. (2016). Modeling when, where and how to manage a forest epidemic, motivated by sudden oak death in California. Proceedings of the National Academy of Sciences. 113:5640-5645.

Thompson, R.N., Cobb, R.C., Gilligan, C.A. and Cunniffe, N.J. (2016). Management of invading pathogens should be informed by epidemiology rather than administrative boundaries. Ecological Modelling. 324:28-32.

Cunniffe, N.J., Stutt, R.O.J.H., DeSimone, R.E., Gottwald, T.R. and Gilligan, C.A. (2015). Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty. PLoS Computational Biology. 11:e1004211.

Follow this link to find out about applying for this project

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