Location: Stored Product Insect and Engineering Research
Project Number: 3020-43000-034-056-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Jul 1, 2024
End Date: Jun 30, 2029
Objective:
This project includes the following objectives:
Objective 1. Optimize fumigant application of sulfuryl fluoride (SF) and other emerging fumigation technologies, for rapid response mitigation for LAGB and KB detections in shipping containers.
Objective 2. Upgrade and tailor enhanced biosurveillance systems for real-time monitoring of P. truncatus and T. granarium.
Objective 3: Predict the invasion risk of P. truncatus and T. granarium using thermal profiles and ecological variation.
Approach:
We will use a combination of laboratory, semi-field, field experiments, and modeling to develop improved risk assessment, interception, and mitigation strategies for P. truncatus and T. granarium. This includes evaluating sulfuryl fluoride against P. truncatus as a mitigation measure. In addition, it includes assaying a suite of remote, automated, real-time traps in the laboratory and semi-field to determine the most behaviorally-compatible traps for P. truncatus and T. granarium and using behaviorally-compatible traps to determine reliability in distinguishing both species from other stored product insects. This will be done with collaborators in Velastino, Greece, and in the laboratory at CGAHR in Manhattan, KS using a 3D camera setup. Finally, traps will be validated at food facilities for reliability in detecting P. truncatus and T. granarium. In Objective 3, we will predict the invasion risk of P. truncatus and T. granarium using thermal profiles and ecological variation. This will be done by establishing P. truncatus and T. granarium life history thermal reactions norms and using them to predict population peaks with current and predicted weather patterns. In addition, we will also model movement of P. truncatus and T. granarium across a variety of landscapes and human-mediated interactions. The model will include proxies for suitable areas in nature and considerations of seasonal variation, human-mediated movements, harvest schedules, and treatment schedules at different facilities. An extension of the model will also consider climate change scenarios and how population growth and harvesting schedules may impact overall movement patterns in these insects. Finally, we will implementing community ecology, movement, thermal profiles, and ecosystem variation in interception risk models. By combining movement models, population growth models, and species distribution models, we can better target high risk areas and corridors of movement that can lead to better monitoring and management of these insects. We will start with supervised learning models such as linear regression, logistic regression, and decision trees. If enough training data is available, we will also expand to using neural network models and random forest models to aid in making connections.
