Tuesday, 19 November 2002
D0409

This presentation is part of : Display Presentations, Subsection Eb. Regulatory and Extension Entomology - Regulatory

An internet-based climatological model for predicting the distribution of invasive insect pests

Glenn Fowler, USDA-APHIS-PPQ-CPHST, Plant Epidemiology and Risk Analysis Laboratory, 1017 Main Campus Drive, Partners Building I, Suite 2500, Raleigh, NC and Roger Magarey, North Carolina State University, Department of Plant Pathology, 1530 Gardner Hall, Box 7616, Raleigh, NC.

An internet-based climatological model is being developed for predicting the distribution of invasive insect pests. The work is a cooperative project between APHIS, North Carolina State University and a commercial weather provider ZedX, Inc. The components of the system are an internet-based interface, a ‘fill-in-blanks’ template and a knowledge base. The web-based graphical user interface allows a user to click on a map and zoom down from the national scale to a zip-code. The weather databases are available at a 10-km2 resolution, for current, climatic or forecast weather data. Risk assessments can be displayed in map (at a regional or national scale) or in tabular form (for a specified location). The pest models will be created from an interactive ‘fill-in-the blanks’ template. The template speeds the addition of new pest species by providing a series of ready prompts and default values. The template will include a knowledge base consisting of a comprehensive encyclopedia of biological data for a range of plant pests, including the choice of variables and their thresholds. The algorithm for predicting exotic insect distributions will be based on relatively simple degree-day accumulation models and is currently under construction. Other digital geographic data including soil temperature and texture, crop distribution and phenology will also be included. Once the prototype is completed, its predictions will be tested against the documented geographic ranges of known exotic insects that have become established in the United States to evaluate its accuracy. A parallel system for plant diseases is also under construction.

Keywords: Prediction, modeling

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