1. By: Will Ayersman June 9, 2010 8:30 am

2. Outline 1. What is Emerald Ash Borer (EAB)? 2. Why should we be concerned? 3. What was the approach? 4. What results did we find?

3. Project Background - Ash Ash is a valuable hardwood species Provides value for timber, wildlife habitat, and shade trees Estimated 8 billion trees in the US

4. Project Background - EAB Introduced from Asia mid 1990’s First reported in Detroit summer 2002 Entry by wood packaging materials from Asia

5. 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

6. 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

7. Project Objectives 1. 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

8. Environmental Variables Business 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)

9. The Model: Maximum Entropy  Bayesian 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

10. Presence Points MAXENT Environmental Variables Convert to Raster Grid Convert to ASCII Convert to CSV Probability Map

11. Predictive Power Represents the true predictive power of the model Utilizes AUC statistic

12. Assessment of Environmental Variables

13. Recommended Treatment Locations StateCounties MarylandHoward, Montgomery, Washington, Wicomico New JerseyMorris, Passaic OhioAshtabula, Brown, Lawrence, Washington PennsylvaniaLackawanna, Wayne West VirginiaBerkeley, Cabell StateCounties 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 areaThousands of acres (25-50% prob)