120°W
50°N
30°N
100°W
80°W
Emerald Ash Borer (EAB) Detection Likelihood
Agrilus planipennis Fairmaire 2002 to 2013 Positives
50°N
30°N
Detection Likelihood
Positives 2002 to 2013
Low
Moderate
Moderate High
High
Emerald Ash Borer (EAB) Detection Likelihood
This map depicts the likelihood of a positive EAB detection and
is based on the associations of nine independent variables with
EAB presence data from 2002 to 2013. Detection likelihood
values were generated using the bootstrap option of the MaxEnt
statistical technique. The analysis was limited to the Ash range
and included urban areas. Likelihood values are classified
into: Low, Moderate, Moderate High, and High classes.
0 50 100
200
300
400
500
Miles
Albers Equal Area Conic Projection
100°W
Map produced by FHTET, IL
Fort Collins, CO on 11-26-2013
File: EAB_Detection_Likelihood_All_2014.mxd
Project: EAB_2013.
80°W
Emerald Ash Borer Agrilus planipennis Fairmaire
Detection Likelihood 2002 to 2013 Positives Model Summary;
November 26, 2013
The 2002 to 2013 Detection Likelihood model for the Emerald Ash Borer (EAB), Agrilus planipennis, was produced for the Contiguous 48 United States
(CONUS) at a 1 square kilometer (1 km2) resolution by the U.S. Forest Service, Forest Health Protection, Forest Health Technology Enterprise Team (FHTET), in
collaboration with Animal and Plant Health Service, Plant Protection and Quarantine (APHIS PPQ) staff members. The intended use of the 2002 to 2013 Detection
Likelihood product is to inform the 2014 EAB sample design.
The likelihood of detecting EAB was modeled using the Maximum Entropy (MaxEnt) interpolation technique. MaxEnt identifies significant associations
between EAB presence data from 2002 to 2013 with selected site variables and then maps the probability of EAB presence in a geographic context. This nonparametric approach has been demonstrated to have utility for generating species distributions, because of its treatment of locations where a species has not been
observed as pseudo-absence locations. Pseudo-absence is defined as a species being absent from an area for one of three reasons: 1) the habitat cannot support the
species, 2) the species has not completely filled its geographic niche, or 3) the species is present but was not detected. For a species such as EAB that is still emerging
and expanding into its full geographic range this is an appropriate modeling approach.
EAB presence data from 2002 to 2013 was provided by APHIS PPQ and state cooperators. Independent variables were investigated and selected to represent
where EAB is likely to establish (Table 1). A bias raster, based on the autocorrelation of positive EAB locations, was used to correct for sampling biases. The
MaxEnt bootstrap option was used to generate 100 iterations to develop an average Detection Likelihood model. For display purposes and ease of interpretation the
data were partitioned into four classes (Table 2).
Table 1: Variables used in the Detection Likelihood 2002 to 2013 Positives MaxEnt model.
Variable
Description and Source
Elevation
Digital Elevation Model (DEM 30m) from National Elevation Dataset (NED) resampled (bilinear interpolation) to
1km resolution and converted from meters to feet.
Distance to Rest Euclidean distance from Rest Areas (POI Factory 2012 http://www.poi-factory.com/node/14656). Rest Area point
Area
locations were identified as potential introduction locations.
Traffic Volume
Traffic Volume point locations, from across the U.S., were compiled from TrafficMetrix and used to interpolate a
Traffic Volume raster using simple variable distance kriging.
Drainage Index
Drainage Index is based on soil taxonomy and drainage classes from SSURGO and STATSGO soil data.
Road Density
Developed from rasterized 2003 Tele Atlas Dynamp Transportation v. 5.2 layer for each state at 100m. Density was
calculated by summing the number of 100m road pixels within a 1 km pixel.
Housing Density Housing density data developed using the 2000 U.S. census data.
Position
Index
Distance to
Campground
Urban Areas
Terrain index ranging from 0 (flat areas) to 100 (ridge tops). Low values represent valley bottoms. Position index is
generated on a 7x7 kernel and scales the mean elevation to the surrounding elevation to separate lowlands from
uplands.
Euclidean distance from campgrounds identified by federal and state cooperators. Campgrounds were identified as
potential introduction locations.
Urban areas within the ash range identified as having a population greater than 100 persons per square mile from the
2000 census (Withrow 2010).
Table 2: Detection Likelihood Classes
MaxEnt
Value
0 to 0.05
0.05 to 0.25
0.25 to 0.50
0.50 to 1
Likelihood
Class
Low
Moderate
Moderate High
High
Contractor Support
Ian Leinwand
John Withrow
Point of Contact
Marla C. Downing
Forest Health Technology Enterprise
Team (FHTET), Forest Health
Protection, USDA Forest Service
2150 Centre’ Ave., Bldg A, Suite 331
Fort Collins, CO 80526-8121
Phone: 970-295-5843
mdowning@fs.fed.us
Downing, M. C., I. I. F. Leinwand, P. H. Chaloux, J. R. Withrow, and F. J. Sapio. 2013. Emerald Ash Borer (EAB) “Agrilus planipennis Fairmaire Risk Assessment” U.S. Forest Service Forest Health Technology
Enterprise Team (FHTET) Forest Health Protection USDA Forest Service, http://www.fs.fed.us/foresthealth/technology/invasives_agrilusplanipennis_riskmaps.shtml