An Integrated Spatial Analytic Framework to Manage Invasive Species in Regulatory Phytosanitary Applications.

TAKEUCHI, YU. An Integrated Spatial Analytic Framework to Manage Invasive Species in Regulatory Phytosanitary Applications (Under the direction of Dr. Frank Koch and Dr. Stacy Nelson) Non-native pests cause economic and ecological damage to managed and natural U.S. forests and agricultural landscapes. Many insects and diseases are currently under regulatory control in an effort to prevent and manage outbreaks. However, it is difficult to control and minimize the damage once a non-native pest is established. Quick detection and response are required to mitigate invasive species when they are introduced into the United States. Alternatives to rapid response efforts include port inspection, surveillance and monitoring, shipment treatments and pre-clearance programs; however, the international nature of this approach makes it highly complex, operationally difficult and challenging to coordinate. To advance more timely responses to pest threats, I developed an integrated spatial analytic framework named Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS) to manage invasive species for phytosanitary management agencies. The SAFARIS is designed to provide a seamless environment for pest predictive models. It supports pest forecast models and tools for researchers, risk analysts, decision/policy makers, rapid-responders, and land managers in need of streamlined and tractable system. The system was designed to assist pest surveys, pest forecasts, pest risk analyses, emergency response, and economic analyses. It allows users to include a variable number of drivers as well as models and can be modified for specific pests in a short time without sacrificing critical elements that serve as the foundation for a science-based process, especially consideration of epidemiology, population dynamics, impact and overall risk dynamics, including characterization of uncertainty. The SAFARIS has three main components: 1) databases containing pest model input drivers, 2) databases containing pest biology information and expert-derived parameters; and 3) pest forecast models and related analytic tools. The key characteristics of SAFARIS include 1) streamlining algorithms for data pre-processing, 2) plug and play approach to data drivers and models, 3) algorithmic and data transparency, 4) enabling quantitative evaluation of sources of uncertainty associated with pest forecasts, and 5) integration of climate change effects into pest predictions. I developed SAFARIS by establishing a base framework, identifying and collecting key bioclimatic data drivers and biological information and integrating pest forecast models and analytic tools within the framework. The current system is designed to grow with the inclusion of more models. It already includes phenology models, insect population dynamics models, and suitable/unsuitable-conditions mapping tools as well as associated data drivers (bioclimatic drivers, pest biology, and host information). The system was evaluated by assessing the likelihood of Asian gypsy moth (AGM) (Lymantria dispar L.) introduction into the conterminous United States from Asian countries. The likelihood of introduction was determined within the SAFARIS environment by analyzing AGM phenology, population dynamics, human-assisted spread, and host availability. Despite the large quantity of materials transported into U.S. ports through Western coastal areas, the highest likelihood of introduction was identified in the Eastern