GISRUK Presentation, UCL
Thursday, 15 April 2010
Good afternoon.
During my MSc at Birkbeck last year, we were tasked with a team
assignment to create a model of a natural phenomenon using the
agent-based modelling framework StarLogo. It came to me suddenly,
driving through Suffolk, that DED had all the agents and processes
required: landscape, trees, beetles, foresters and birds.
When my family moved there in 1983, the county still had masses of
elm and our area seemed to have barely been touched. But within a
year [SLIDE 2] the landscape began to change very dramatically.
Dutch elm disease (DED) is the most destructive tree disease known,
and has been transported around the globe by international trade. It
arrived in the UK in 1967, and has destroyed in excess of 25 million
trees here. The global total is probably near 300 million. In most of
Europe, the mature elm is now a rarity.
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But some areas – e.g. East Sussex, Aberdeen and the Isle of Man –
retain large populations and are still fighting to protect them.
Discussions with authorities on the Isle and in East Sussex highlighted
that elevation might be a factor in its epidemiology.
This made the then brand-new StarLogo TNG an obvious tool as it
operates in a 2.5/3D environment. TNG comprises [SLIDE 3] two
elements, a coding window and a ‘SpaceLand’ where the action pans
out.
SpaceLand is a 101x101-cell raster, into which external
graphical data can be imported. We located a 75m DEM of the island
on EDINA, and generalised it to 400 m2 cells to fit into SpaceLand
[SLIDE 4].
We then populated this model DEM with four agents (elm trees,
beetles, foresters and birds) and set up a dozen or so adjustable
parameters to reflect a variety of disease management scenarios –
from intensely managed to hands-off, and compared the outcomes.
—= —
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For my dissertation, I took this much further. The model now runs for
a set duration, resets and runs again repeatedly, producing a
distribution of data which are carried over into and analysed in
ArcGIS Spatial Analyst. Most of the variability within the former
model has been locked down, and the role of elevation
strengthened.
The principal idea here is that [SLIDE 5], dependent upon terrain,
infestations originating in one area might be more or less easily
contained, leading to variations in the number of elms at risk. It is
proposed that a correlation may exist between original epicentres of
infection, terrain and the number of elms destroyed.
The Scolytus beetles that spread the disease do not [SLIDE 6] fly
much beyond the maximum elevation locally colonised by elms (the
‘elm-line’). Landscape above the elm line can therefore act as a
cordon sanitaire for DED control. Manx data support an elm line of
160 metres – and on the Isle, two ranges of hills exceed that height
and restrict the flow of the disease across the island. The ‘beetle-line’
was set at an arbitrary 25 % higher (200m)).
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The combination of DEM, elm-line and beetle-line places elevation at
the model’s centre. Beetles will find some areas more accessible
than others, depending on where they start from.
—= —
At setup [SLIDE 7], the ‘island’ is randomly populated with two
foresters, four ‘boids’ and 800 elm trees (up to the elm line). Up to
1.5% of the elms are randomly infected with beetles. When the
model begins to run, a tree census is executed, recording the
locations of all trees. Beetles then [SLIDE 8] issue from the infected
elm trees and seek out healthy ones to perpetuate the disease. The
foresters try to fell the diseased trees before the next generation of
beetles hatches [SLIDE 9].
The model pauses at game turn 60 to record the number of beetles
and where they are located (they always operate in a single cluster
for each run) [SLIDE 10]. It then runs on for an arbitrary 1,000 game
turns, resets and starts again. Each run produces [SLIDE 11] an
outbreak of DED originating in a different location, and with
particular outcomes at game turns 60 and 1000. Each run is
therefore a different outworking of interactions between the four
agents, and between the agents and landscape. Data for each run is
output to .csv format.
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In ArcGIS Spatial Analyst, each of the island’s 400x400m raster cells
is given a value of 0 or 1 to reflect whether it falls above or below the
beetle line. A 5x5 cell grid is then [SLIDE 12] passed over the raster
and, for every island cell in the raster, the sum of these binary values
for the 25 grid cells is calculated. The grid is passed again [SLIDE 13]
and, for every island raster cell, the median of the total values for the
25 grid cells is determined. This is the Raster Cell Neighbourhood
Statistic (RCNS). When this RCNS is mapped [SLIDE 14], it falls into
zones and regions, which show the relative ease with which the
modelled beetles can gain access to any cell – and consequently
indicate the vulnerability of local elms.
—= —
The StarLogo TNG output data are [SLIDE 15] processed in Mapinfo
and SPSS and then combined with the RCNS data. This reveals that
beetle clusters are not evenly distributed but are [SLIDE 16] more
likely to be generated in certain parts of the island. Further, a direct
relationship may be observed to exist between the location of a
game turn 60 beetle-cluster and the game turn 1,000 elm mortality
for that run [SLIDE 17].
 Most clusters in the northern plain cleared that region, but
failed to break through the pinch points to elm populations
beyond. This is demonstrated by a generally moderate
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mortality of less than one SD from the mean in that part of the
island.
 Clusters originating near Douglas, with easier access to the
secondary region of Peel, were on average more destructive.
 Clusters originating close to the hills, especially where these
most nearly approach the coast were often less so.
Consequently, the number of elms that on average survived when
any given cell was included in a cluster also varied spatially [SLIDE
18]. Areas which produced the highest mortality obtain the darker
shades: these are the danger-spots we have been looking for.
Thus the model results conform to the pattern anticipated – that
parts of the island may be more conducive to beetle generation and
the onward spread of DED than others.
The model’s strength would be greatly enhanced if ported to a
dedicated ABM 3D modelling framework such as Repast. This would
also facilitate the production of thousands of runs representing a
considerably more robust dataset than the 106 which completed in
TNG. Some aspects of the current model also include too much
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variability, which ought to be locked down. Most significantly, the
model would benefit from the use of real data on the Isle’s elm
population rather than the random generation which assumes the
tree to be distributed throughout the island – which is not the case.
The complex matter of climate has not been addressed.
Nevertheless, in its present prototype state, the model provides a
useful basis for identifying locations where an outbreak of disease
might lead to higher mortality. Further development could transform
it into an analytical tool capable of supporting the fight against DED
or other forest diseases - either on the Isle of Man or beyond.
[SLIDE 19]
Thank you.
I should like to record my thanks to Joana Barros, at Birkbeck and
Daniel Wendel at MIT for invaluable support. Also Denis Rotheray
and our other two partners in the original assignment.
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