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)