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Mapping Dutch Elm Disease Risk on the Isle of Man Using an Agent-Based Model

This study investigates the correlation between beetle cluster origins and the mortality rate of elms affected by Dutch Elm Disease on the Isle of Man. Utilizing an agent-based model within the StarLogo TNG platform, the research analyzes how elevation (identified as 'elm-line' and 'beetle-line') influences disease spread and beetle proliferation. Results reveal potential ‘danger spots’ on the island where infestations might be more severe, facilitating targeted interventions to protect the local elm population.

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Mapping Dutch Elm Disease Risk on the Isle of Man Using an Agent-Based Model

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  1. Identifying Dutch elm disease ‘danger-spots’ on the Isle of Manwith an agent-based model Bruce Mitchell a, Joana Barros b,Daniel Wendelc Email a: bruce.birkbeck@ntlworld.comb: j.barros@bbk.ac.ukc: djwendel@gmail.com

  2. Suffolk, 1984

  3. StarLogo TNG - code blocks

  4. The Isle of Man in TNG SpaceLand

  5. Objective • Is there a relationship between where a beetle cluster originates and the number of agent elms that die? • If so, might that relationship be used to map the island to reveal locations where an infestation might potentially be more or less damaging?

  6. Isle of Man - Relief • Scolytusbeetles can only thrive where elms prosper • Landscape determineselm habitats • On Man, there is an ‘elm-line’ of ca. 160 metres a.s.l.. • Two ranges of hills exceed this elevation, restricting the flow of the disease across the island • In the model, a ‘beetle-line’ was set at an arbitrary 25 % higher (200m) than the elm-line. • The combination of DEM, elm-line and beetle-line places elevation at the model’s centre. • Beetles operating out of any given disease epicentre will find some areas accessible, others out of range.

  7. A single model run … • Sets up tree, beetle, forester, bird agents • ‘Infects’ up to 1.5% of elms with beetles • Runs an elm census at game turn (GT) 0 • Generates a single beetle cluster • Runs a beetle census at GT 60 • Continues to GT 1,000 • Exports results to .csv file • Resets to GT 0 and introduces next run

  8. Model agent flow chart

  9. The agents

  10. Model system flow chart

  11. SL-TNG Output • For each run: • cluster originates in random location; • number of elms surviving to GT1000 varies • .csv run results ported to MapInfo and SPSS for processing, then transferred to ArcGIS Spatial Analyst

  12. RCNS Calculation – step one

  13. RCNS Calculation – step two

  14. RCNS zones and regions

  15. Data processing flowchart

  16. Frequency of cell inclusion

  17. Cluster median centres and elm survival

  18. Modelled danger-spots

  19. Identifying Dutch elm disease ‘danger-spots’ on the Isle of Manwith an agent-based modelBruce MitchellData Visualisation CentreMethodology DirectorateOffice for National Statistics bruce.mitchell@ons.gov.ukJoana Barros Department of Geography, Environment and Development Studies, Birkbeck, University of London, Malet Street, London, WC1E 7HX j.barros@bbk.ac.ukDaniel WendelLead StarLogo TNG DeveloperScheller Teacher Education Program Massachusetts Institute of TechnologyBoston, MA, USA djwendel@gmail.com

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