Integrated analytic spatial framework to manage invasive species

Non-native pests cause tremendous economic and ecological damages to managed and natural U.S. forests and agricultural landscapes each year.  Many insects and diseases are currently under regulatory control in an effort to prevent outbreaks.  However, it is difficult to control and minimize the damage once a non-native pest is established. Therefore, quick detection and response are required to mitigate invasive species when they are introduced into the United States. To ensure more timely responses to pest threats, there should be a system to identify, describe, monitor and forecast global threats and U.S. high-risk areas.  An integrated analytic spatial framework was developed to allow for integration of critical forecast elements to address pest-forecasting needs associated with prevention and exclusion efforts.  This framework contains critical datasets, such as weather data, host distribution, traffic information, and biological information, and analytic tools and forecast models to identify risks caused by invasive species, but it is flexible to modify for specific species in a short time without sacrificing critical elements.

The framework was evaluated by using Asian gypsy moth (Lymantria dispar L.) as a case study.  Asian gypsy moth is native to Northern China, Russia, and Japan and has not been established in the United States.  Unlike European gypsy moth, female Asian gypsy moth is capable of flying for a long distance; however, the main long distance transportation is by human activities.  The spread of Asian gypsy moth via human activities was simulated by using the analytic spatial framework to identify the risk areas within continental United States.