Yu Takeuchi

The primary purpose of this agreement is to develop modeling approaches for plant pests to inform decision making and surveillance efforts by USDA APHIS Plant Protection and Quarantine (PPQ). This includes the development of new and innovative data analytic approaches or algorithms to improve existing modeling approaches. To help PPQ respond more quickly to pest threats, PPQ and NC State University have developed an integrated predictive mapping system called the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS). SAFARIS provides a web-based interface for accessing a suite of pest predictive models linked to various weather databases for use by researchers, risk analysts, decision/policy makers, rapid-responders, and land managers.

The main goals of this project are to develop methods to assess climate suitability and to incorporate potential climate change impacts for the Cooperative Agricultural Pest Survey (CAPS) pests to support USDA APHIS Plant Protection and Quarantine (PPQ) by providing scientific information to conduct exotic plant pest surveys. This proposal includes the evaluation of climate conditions to estimate potential establishment areas and the development of innovative analytic approaches to evaluate potential pest establishment areas and climate change impacts.

The primary purpose of this agreement is to provide the Identification Technology Program (ITP) with programming support for the ongoing development and maintenance of ITP������������������s online systems, helping to increase the availability and efficiency of pest, disease, and weed identification resources for PPQ and its partners. A related agreement with Colorado State University will support non-IT tasks related to ITP������������������s online systems, including but not limited to data entry and maintenance, product and interface testing, and taxonomy curation. This work plan is part of a multi-year project to create a fully integrated IT system that links data across all ITP products to maximize the value of ITP products and data. Once complete, ITP������������������s data system will allow PPQ and its partners and cooperators to create customized identification output in a range of formats and host these for easy access and distribution.

The primary goal of this agreement is to characterize global agricultural trade pathways and evaluate the likelihood of alien species introductions. The TRACE (Trade Route Analytic Computation and Evaluation) framework assists with characterizing global scale pathways and determining the likelihood of new pest introductions, which have intensified in recent years due to increased global trade, transport, and travel. The multilateral pathways allow non-indigenous pests to move easily and expand habitats. Development of TRACE will establish the analytical foundations to help PPQ evaluate and manage global pathways by incorporating analytic tools. These tools assist analyzing global scale plant pest pathways to prevent pest and disease introductions.

The primary purpose of this agreement is to develop modeling approaches for plant pests to inform decision making and surveillance efforts by USDA APHIS Plant Protection and Quarantine (PPQ). This includes the development of new and innovative data analytic approaches or algorithms to improve existing modeling approaches.

The main purpose of the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS) project is to generate and maintain a web-based framework that meets the predictive modeling, spatial analyses, and mapping needs of the USDA, Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ) by providing models, tools, and data to inform policy and operations. SAFARIS is designed to provide robust spatial modeling and mapping capabilities, data storage, easy accessibility, and transparency in data and modeling methods. The SAFARIS IT project is to develop a web-based framework by implementing web-based model tools and connecting to input data driver databases and managing the IT capacity to ensure network bandwidth and hardware/software requirements.

The primary purpose of this agreement is to support further development of the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS). This framework integrates a vast amount of abiotic/biotic data with the capacity and flexibility to produce forecast and analysis models to support Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ).

The proposed work will provide stakeholders with the critical scientific information for combating the emerging issue of the Oriental Fruit Fly (OFF). Specifically, this project has four well-defined objectives: 1) Collect OFF biological, distribution, occurring data in China and elsewhere. These data are critical for developing mechanistic models to predict the geographic range and relative abundance of OFF; 2) analyze and visualize the potential global OFF suitable areas by using the newly updated OFF biological and occurring data; 3) evaluate likelihood of OFF entry in the continental United States, and 4) co-relate OFF abundance with interception, outbreak, and quarantine in the United States.

The primary purpose of this agreement is to characterize global scale pathways and determine the likelihood of new pest introductions. The globalization of agricultural trade has increasingly moved from bilateral agreements between two trading partners to multilateral agreements between three or more trading partners. Multilateral trade and increases in international travel have assisted the movement of plant pests between regions while simultaneously complicating PPQ���s ability to analyze pathways for pest risk. The system we are developing, ���TRACE���, will establish an analytical framework, using a network analysis approach to help PPQ evaluate and manage the plant pest risk associated with global scale trade networks.

The primary purpose of this agreement is to develop algorithms and ensemble predictions that 1) fully quantify uncertainty in host map distributions, 2) are continuously updated as new data sources become available, 3) have full accuracy statistics, and 4) are fully open-source and able to be used and built on by other researchers and analysts. These algorithms will be tested on host species across 3 use types: annual crop, perennial crop, and forest host. By examining hosts across a wide range of crop and forest hosts we can ensure that the algorithms and ensembles are generalized enough to be used beyond the specified species examined during the project. This is the second year of the project.