Background

This project is a broadly interdisciplinary effort that brings together ecologists, computer scientists, statisticians, remote sensing scientists, natural resource managers, policy makers, and others who share an interest in the invasive species challenge. This section provides an overview of the converging science themes represented in the project and access to the work products we are generating.

Invasive Species

Nonindigenous invasive species may pose the single most formidable threat of natural disaster of the 21st century. The direct cost to the American economy alone is estimated at $100-200 billion per year, greater than all other natural disasters combined. The spread of invasive species is growing as globalization increases the movements of pest and disease organisms. The issue has developed diverse stakeholder support, ranging from land management agencies, states, the agricultural industry, conservation organizations, and private landowner groups. The National Research Council's Committee on Grand Challenges in Environmental Sciences has identified increased understanding of biodiversity and ecosystem functioning as one of eight "Grand Challenges in Environmental Science" facing our nation and the world today and has emphasized the need for developing an ecological forecasting capability and improved management techniques for nonindigenous invasive species.

Geostatistical Modeling

High resolution mapping of biological resources is central to confronting the invasive species threat and forms the basis of the Invasive Species Forecasting System. The ability to model smallscale variability in landscape characteristics requires the generation of fullcoverage maps depicting characteristics measured in the field. While many spatial datasets describing land characteristics have proven reliable for macroscale ecological monitoring, these relatively coarse scale data fall short in providing the precision required by more refined ecosystem resource models. Spatial statistics and geostatistics provide a means of developing spatial models that can be used to correlate coarse scale geographical data with microscale field measurements of biotic variables.