modelling the spread of sudden oak death in the uk

Richard StuttNik Cunniffe

Erik DeSimoneMatt Castle

Chris Gilligan

February 2012

Example results from landscape-scale models◦ SOD in California (precursor to this model)◦ SOD in UK

How the model works◦ Host landscape◦ Environmental conditions◦ Pathogen dispersal

Uses of the model◦ Predictions of spread◦ Effects of control

Key components:◦ Host◦ Environment◦ Pathogen dynamics and dispersal

Expressed as a compartmental model

Susceptible hosts in the landscape are divided into a metapopulation at a chosen resolution (250m)

UK Sudden Oak death landscape assembled from:◦ National Inventory of Woodland Trees (NIWT)◦ Forestry Commission commercial Larch data◦ Maximum Entropy suitability models for Rhododendron

and Vaccinium (FERA/JNCC)

Different hosts have different weightings for sporulation and susceptibility

Broadleaved

Young Trees Felled

Coniferous

Identify favourable conditions for P. ramorum◦ moisture ◦ temperature

Parameterise using experimental results

Calculate underlying suitability of locations in the landscape

Statistical used to model future conditions

Dispersal kernel is a statistical description oftransport of inoculum between locations

Implicitly incorporates many mechanisms

Fit model using historic spread data

Used Maximum Likelihood to assess goodness of fit

Predicted probability of infection by 2010 given starting conditions in 2004

Survey Positive for P. ramorum

Survey Negative for P. ramorum

Prediction in the absence of control Effect of controls

◦ Felling infected stands◦ Felling infected stands + proactive control

Effect of any delay in implementing control Application to surveying for P. Ramorum

Total Infection

Symptomatic

Symptomatic at time of Survey

Total Infection

Symptomatic

Symptomatic at time of Survey

Total Infection

Symptomatic

Symptomatic at time of Survey

Total Infection

Symptomatic

Symptomatic at time of Survey

Examine region of South Wales

Cull: no delay after survey 6 month delay

Key Questions When Surveying for Disease:◦ Where is the disease likely to be?◦ Where is it likely to be most severe and spread

most rapidly?◦ How to optimise the sampling?

Uses:• Currently known outbreaks • Predicted severity of

outbreaks• => Sampling weighting

Survey pattern formed• => sampling from

weightings Map shows a weighting and

a set of survey points (green)

Continue to improve the model Refinement of country wide strategies: Region specific control Effect of non compliance User friendly models

Frank van den Bosch, Stephen Parnell◦ Rothamsted Research

Forestry Commission, FERA◦ (in particular Bruce Rothnie and Keith Walters)

Funding from DEFRA, BBSRC and USDA