The increasing threat posed by invasive tree pests and pathogens is creating major challenges for Great Britain. Effective response depends on making a series of difficult decisions under substantial uncertainty. Following the detection of a new outbreak, plant health authorities must determine the extent of infestation, decide whether eradication is feasible, evaluate whether management actions are working, and identify when a change in strategy is justified. These decisions are often made rapidly using limited evidence, despite the fact that surveillance information accumulates throughout the response.
This project will develop an adaptive decision-support framework that enables outbreak management strategies to be continually updated as new evidence becomes available. It will explore how data should be collected in outbreak situations for optimal surveillance and management. The research will focus on four key questions. First, how can surveillance resources be allocated most efficiently to delimit a newly detected outbreak? Second, when should decision-makers pursue eradication rather than containment? Third, how can ongoing surveillance data be used to determine whether a management strategy is achieving its objectives? Fourth, when is there sufficient evidence to continue, intensify or change course?
The doctoral researcher will combine epidemiological modelling, statistics and decision theory to create methods that determine optimal and roubust data collection in outbreak situations that account for predictions of pest spread and economic costs. Emerald ash borer (Agrilus planipennis), one of the most significant threats to ash trees worldwide, will provide a worked case study, although the methods will be designed to be transferable across a wide range of invasive tree pests and diseases.
The project will benefit from close links with the Horizon Europe PhytoPRISM programme, a €6 million consortium involving 15 partners across Europe led by the Wariwck PI. PhytoPRISM is developing modelling and decision-support tools for EU national plant health authorities and runs from September 2026 to 2030. The student will therefore benefit from access to extensive datasets, modelling expertise, stakeholder networks and close engagement with plant health practitioners. The outcome will be a scientifically robust and operationally relevant framework capable of strengthening preparedness and improving responses to future tree health outbreaks in GB.
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The project will develop a suite of linked modelling and decision-support tools for adaptive outbreak management. Bayesian statistical methods will be used to estimate outbreak extent and update uncertainty as surveillance data accumulate. Epidemiological models of pest spread will be combined with value-of-information approaches to optimise delimitation surveys and identify efficient surveillance strategies. Decision-theoretic and economic models will evaluate trade-offs between eradication, containment and long-term management under different outbreak scenarios. To assess management effectiveness, species distribution and climate suitability models will be integrated with surveillance data to distinguish changes in pest or disease prevalence caused by interventions from those arising from variation in environmental conditions, host availability or climate. Sequential monitoring methods will quantify confidence in observed trends and management outcomes. Finally, optimal stopping and adaptive management approaches will be developed to determine when management strategies should be maintained, intensified or replaced. Methods will be tested using simulated and historical outbreak data, with emerald ash borer as a primary case study on exisiting maps of UK ash distribution.
DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.
The student will receive advanced training in mathematical and computational modelling of biological invasions and plant pests, with a particular focus on emerald ash borer (EAB). Training will include the development and analysis of spatial, stochastic and risk-based models, integration of surveillance and ecological data, and techniques for uncertainty and sensitivity analysis. The student will gain strong programming skills in R and Python, alongside training in reproducible research, data visualisation and scientific communication. They will work closely with plant health scientists and policy stakeholders, gaining experience in translating modelling evidence into practical recommendations for pest surveillance, management and biosecurity decision-making.
DEFRA Plant Health is a key partner in the project. Through a long-standing collaboration with DEFRA Plant Health, I have provided modelling evidence to support plant health risk assessment, preparedness planning and policy development for a range of emerging pest and pathogen threats. This partnership will give the student valuable exposure to the policy and operational contexts in which modelling informs decision-making. Regular engagement with DEFRA scientists and plant health professionals will help the student develop skills in translating research into practical, evidence-based recommendations for biosecurity and pest management.
Year 1: Develop and test the surveillance and epidemiological modelling framework, using simulated and historical data alongside the emerald ash borer case study. Establish how surveillance resources can be optimally allocated to delimit new outbreaks and quantify uncertainty in outbreak extent.
Year 2: Integrate Bayesian inference, decision theory and economic analysis to compare eradication, containment and longer-term management options. These will include the costs of surveillance and costs of subsequent management options within demarcated areas. Evaluate how effectively different strategies reduce outbreak prevalence while accounting for environmental conditions, host distribution, costs and operational feasibility.
Year 3: Develop adaptive rules for determining when to continue, intensify, modify or switch management strategies as new evidence accumulates. Validate the framework with stakeholders and produce practical decision-support tools, publications and thesis chapters for application to tree health outbreaks in Great Britain.
Chávez, V.A., Brown, N., van den Bosch, F., Parnell, S., Dyke, A., Hall, C., Karlsdottir, B., Marzano, M., Morris, J., O’Brien, L., Williams, D. and Milne, A.E. (2025) ‘Early detection strategies for invading tree pests: Targeted surveillance and stakeholder perspectives’, Journal of Applied Ecology, 62, pp. 857–871.
Koh, J.M.J., Cunniffe, N.J. and Parnell, S. (2025) ‘Assessing delimiting strategies to identify the infested zones of quarantine plant pests and diseases’, Scientific Reports, 15.
For any enquiries related to this project please contact Stephen Parnell, University of Warwick, [email protected]
https://warwick.ac.uk/fac/sci/lifesci/people/sparnell/
To apply to this project:
Applications must be submitted by 23:59 GMT on Wednesday 6th January 2027.