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Biosecurity: models to predict and manage emerging pests and diseases of natural vegetation

Supervisors: Prof. Chris Gilligan (Principal Supervisor), with Dr Nik Cunniffe and Dr Joao Filipe

Reference Code: B120

Importance of the area of research concerned: Newly emerging pests and diseases pose an increasing threat to natural vegetation and ecosystem services in the UK. The risks are exacerbated by climate change, urbanisation, increased recreational use of the countryside and failure to regulate trade and personal imports. Modelling serves to predict risks and inform control strategies. Building on recent work by the Cambridge group to model and inform Government policy on ash dieback in the UK and ramorum disease in the UK and US, the studentship is designed to develop and test models for risk.

Project summary: The project will develop and parameterise models for the spread and control of two classes of pest and disease exemplified by acute oak decline and emerald ash borer.  Acute oak decline is already here, while emerald ash borer has been widely studied and poses a threat to the UK. The primary objective of the project is to integrate knowledge on the distribution of the host, with meteorological driving variable, including the potential for long distance movement of inoculum and pests, together with epidemiological and population dynamics to predict spread.

What the student will actually do: Working with the supervisor and members of the Epidemiology and Modelling Group, the student will develop a stochastic, spatially-explicit model for pest and disease spread through heterogeneous host populations. Scales of interest will vary from local spread within a woodland, along hedgerows to regional and countrywide spread. The student will use mapped data for spread to infer dispersal parameters. S/he will use the models to construct risk and hazard maps to inform practical survey and control strategies. There will be opportunities to work with government agencies and other stakeholders.

Training to be provided: The student will be trained in epidemiological and population modelling, stochastic simulation, meteorological modelling and in the biology of the hosts, pests and pathogens. Training will be provided in methods for parameter estimation and in geographical information systems. There will be an opportunity to advance fundamental theory in epidemiology as well as in the use of models for practical disease control and engagement with stakeholders.


  1. Boyd, I.L., Freer-Smith, P.H., Gilligan, C.A. &  Godfray, H. C. J. (2013). The consequence of tree pests and diseases for ecosystem services. Science vol. 342, 1235773-1-8.
  2. Parry, M.F., Gibson, G.J., Parnell, S., Gottwald, T.R., Irey, M.S., Gast, T.C. & Gilligan, C.A.  (2014).  Bayesian inference for an emerging arboreal epidemic in the presence of control. Proceedings of the National Academy of Science USA vol. 117: 6258–6262.
  3. Meentemeyer, R.K., Cunniffe, N.J., Cook, A.R., Filipe, J.A.N., Hunter, R.D., Rizzo, D.M. & Gilligan, C.A. (2011). Application of stochastic epidemiological models to realistic landscapes: spread of the sudden oak death pathogen in California 1990–2030. Ecosphere 2: art17.