By: Will Ayersman June 9, 2010 8:30 am
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Transcript of By: Will Ayersman June 9, 2010 8:30 am
By: Will AyersmanJune 9, 2010
8:30 am
Outline1. What is Emerald Ash Borer
(EAB)?
2. Why should we be concerned?
3. What was the approach?
4. What results did we find?
Project Background - AshAsh is a valuable hardwood species
Provides value for timber, wildlife habitat, and shade trees
Estimated 8 billion trees in the US
Project Background - EABIntroduced from
Asia mid 1990’s
First reported in Detroit summer 2002
Entry by wood packaging materials from Asia
Why Be Concerned?EAB could potentially affect 30-90 million
urban trees
$20-60 billion in costs associated in damages
Nurseries produce roughly 2 million ash trees each year
Ash accounts for $100-140 million annually
Why Be Concerned?In Michigan alone, eradication efforts have
cost over $328 million as of 2003
For Ohio, it is estimated that roughly $2-8 billion in losses
Project Objectives1. Create spatial data layers related to spread
and establishment of EAB through anthropogenic criteria
2. Implement an appropriate modeling framework in order to utilize these data layers using GIS
3. Map and identify new high risk areas for EAB for management and monitoring
Environmental VariablesBusiness Information
- campgrounds, nurseries, sawmills, and firewood dealers (Iverson et al. 2006; Campbell, 2001; Minnesota Dept. of Agriculture, 2006)
Census Data - urban areas (Iverson et al. 2006; Poland & McCullough, 2006) - human population, seasonal homes (Minnesota Dept. of Agriculture, 2006) - housing density (US Forest Service)
Transportation Data - rest areas, major roads, harbors (Haack, 2003; Work et al. 2005)
Ash basal area (Iverson et al. 2006)
The Model: Maximum EntropyBayesian Statistical Model
Better suited for making predictions with limited observations
Uses presence-only data, doesn’t require absence data
Provides statistical outputs for analysis
Determines which variables make a contribution
Presence Points
MAXENT
Environmental Variables
Convert to Raster Grid
Convert to ASCII
Convert to CSV
Probability Map
Predictive PowerRepresents the
true predictive power of the model
Utilizes AUC statistic
Recommended Treatment Locations
State Counties
Maryland Howard, Montgomery, Washington, Wicomico
New Jersey Morris, Passaic
Ohio Ashtabula, Brown, Lawrence, Washington
Pennsylvania Lackawanna, Wayne
West Virginia Berkeley, Cabell
State Counties
Delaware Kent
Maryland Anne Arundel, Charles
New Jersey Atlantic, Bergen, Cumberland, Monmouth, Ocean
Ohio Clinton, Crawford, Darke
Pennsylvania Blair, Cumberland, Lebanon, Luzerne
West Virginia Putnam, Wayne, Wood
Contain 26 sq km of high risk area
Thousands of acres (25-50% prob)