Phytopthora alni ssp alni [Brasier & S. A. Kirk]

A water-born pathogen infecting Alnus spp. (Alders) Michael Tuffly

Environmental Resources Inventory Analysis (ERIA) Consultants http://www.eriaconsultants.com

Key Contributors •Dr. Thomas Jung, Forest Pathologist, Germany •Marla Downing, Biologist, USDA Forest Service, Forest Health Technology Enterprise Team (FHTET), Fort Collins, CO •Dr. Frank Koch, Research Assistant Professor, Department of Forestry and Environmental Resources, North Carolina State University. •Vern Thomas, Remote Sensing Specialist, USDA Forest Service, Forest Health Technology Enterprise Team (FHTET), Fort Collins, CO

Forest pests and pathogens are costing businesses, property owners, and government agencies in the United States billions of dollars

annually (Yemshanov et al. 2009).

This cost is due to: • Lost timber revenues

• Decreased property values

• Eradication and control efforts

These forest pests and pathogens are split into two general categories: invasive and native.

Invasive forest pests are:

• Emerald ash borer (Agrilus planipennis Fairmaire.)

• Gypsy moth (Lymantria dispar L.)

• Asian long horn beetle (Anoplophora glabripennis)

Native pest are: • Mountain pine beetle (Dendroctonus ponderosae Hopkins.)

• Spruce beetle (Dendroctonus rufipennis Kirby.)

• Western spruce bud worm (Choristoneura occidentalis Freeman.)

Invasive forest pathogens are: • White pine blister rust (Cronartium ribicola Fisch.)

• Phytopthora’s

What are Phytopthora’s

• Phytopthora’s commonly known as water molds.

• Some of invasive Phytopthora’s currently found in the United States that are:

1) Phytopthora lateralis (Port-Orford-Cedar Root Rot)

2) Phytopthora ramorium (Sudden Oak Death)

Phytophthora alni ssp alni potential host species in the United States

Arizona alder European alder

gray alder green alder hazel alder

mountain alder red alder

seaside alder speckled alder thinleaf alder

Sitka alder white alder

If P. alni was released into the lower 48 states many riparian forest habitats would be destroyed.

Examples of Phytopthora alni ssp alni

Goals

• To create a series of models that predict

– The Introduction Potential

– The Establishment Potential

– The Susceptibility Potential

Phytophthora alni ssp alni: Introduction Risk

• Spores (oogonium) are easily transported via water, soil particles, or plant roots.

• Introduction parameters: Equal Weighted Overlay of:

– Metropolitan Areas

– Wholesale and retail plant nurseries

Introduction Potential

Introduction Potential

Phytophthora alni ssp alni: Establishment Risk

• Establishment parameters

– Alder host (Alnus spp.) on slope less than 11 percent* • Need to be modeled 1) Herbarium Data

2) Occurrence of Alder from the Forest Inventory and Analysis (FIA) data 3) Perennial Streams

– Flood-prone areas • Need to be modeled via logistic regression using:

1) Soil Dryness Index** 2) Aspect 3) Land Surface Curvature 4) Slope 5) Topographic Position 6) Solar Radiation Attributes were extracted from all six data sets that were coincident with

freshwater wetlands (n = 3,130 ) as defined by the National Wetlands Inventory (USGS)

*Thoirain,. B., C. Husson, and B. Marçais 2007. Risk Factors for the Phytophthora-Induced Decline of Alder in Northeastern France. Ecology and Epidemiology Vol 97, 1: 99 – 105

** Schaetzl, R.J. 1986. A soilscape analysis of contrasting glacial terrains in Wisconsin. Annals Assoc. Am. Geogs. 76:414-425.

Establishment Potential, Alder Host

Establishment Potential, Alder Host

Establishment Potential, Alder

Host

Establishment Potential, Alder

Host

Perennial

Establishment Potential, Flood-Prone Areas

Establishment Potential, Flood-Prone Areas

Establishment Potential, Flood-Prone Areas

Establishment Potential, Flood-Prone Areas

Establishment Potential, Flood-Prone Areas

Establishment Potential, Flood-Prone Area

Logistic Regression Equation

Exp(- 3.45045 + (0.0475 * [di]) + (0.000130 * [aspect]) - (0.00022 * [cur]) - (0.00016 * [slope]) - (0.00691 * [topo]) + (0.000674 * [solar]))

Kappa = 0.575 Ability to predict a wetland (Assumption that 0.5 is a threshold value):

• 77.4% wetland • 80.3% non-wetland

Establishment Potential