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Department of Plant Sciences

Plant pathogens can't read maps

Robin Thompson, Richard Cobb (University of California, Davis), Chris Gilligan and Nik Cunniffe  published a paper in Ecological Modelling analysing how control of plant diseases can potentially be made more efficient. For many plant diseases the spatial extent of management is informed solely by administrative geography: control is deployed uniformly across an entire county, state or country. The study focused on whether this could be improved by taking better account of plant disease epidemiology.

The analysis used sudden oak death in California as a case study, a disease that has killed millions of oak and tanoak trees in California since its first detection in 1995. Importantly plants that are traded by nurseries can also be infected. Current legislation in California therefore mandates that - directly after first detection of infected plants or trees - a trade quarantine is applied to all nurseries across the entire county.

However plant pathogens are not able to read maps and so do not respect administrative borders. This means that simply quarantining the entire county might be too severe or too lax, depending on predicted rates of spread and the relative costs of control and of disease escape. For systems like sudden oak death - for which the epidemiological dynamics are well-characterised and parameterised mathematical models are available - the study shows how basing control solely on administrative boundaries leads to predictable inefficiencies that could be improved upon.

  • Thompson RN, Cobb RC, Gilligan CA, Cunniffe NJ. (2016) Management of invading pathogens should be informed by epidemiology rather than administrative boundaries. Ecological Modelling.  doi:10.1016/j.ecolmodel.2015.12.014


Click on image to enlarge.

Fig. 1. (a) Location of P. ramorum hosts (Meentemeyer et al., 2011) and current quarantine counties in California; (b) a schematic showing how partial quarantine could be deployed if the pathogen appears in a new county. This indicates the consequences of sub-optimal and super-optimal sizes of the quarantine region