Project Overview

Reports

Commodities

• Additional Plants: conifers, trees

• Agronomic: corn, soybeans

• Natural Areas: forestry (conifer)

• Vegetables: cabbages

Target Pests

Practices

• Pest Management: decision support system, forecasting, pesticide application timing, precision pesticide use, prevention

Problem or Opportunity
Migratory insects play critical roles in agriculture and forestry, but some migratory species become destructive pests to crops and agriculture when they arrive in new areas either directly or as vector of pathogens. In the North Central region, migratory insect pests such as spruce budworm, or corn earworm pose growing threats for natural resources, especially as climate change alters migration patterns and outbreak dynamics.

Migratory insect pests are typically monitored by counting adult in seasonal traps. While counts reveal population trends, they cannot distinguish between local population growth or migratory arrival. This distinction is critical for predicting outbreaks, identifying insect-borne pathogens and planning timely Integrated Pest Management (IPM) interventions. Existing tools like genetic or isotope analysis can trace insect origins, but they are expensive, slow, and require specialized laboratories making them impractical for large-scale IPM programs. There is an urgent need for a rapid, affordable, and field-ready method to identify non-local insects in real time.

Approach
This project will test portable, fast and non-destructive X-ray fluorescence (XRF) to measure metal concentrations in insect tissues. Metal concentrations reflect the local environment where insects developed, creating “fingerprints” indicating whether an insect is local or from another region. We will build upon previous work and use the spruce budworm as a model species and corn earworm as a test species.

Project Objectives

1. Calibrate portable XRF measurements against state-of-the-art instrument for concentration data to ensure accuracy.
2. Compare elemental fingerprints of migratory and resident spruce budworm populations to identify markers of origin.
3. Develop classification models to distinguish local and migratory individuals based on XRF data.
4. Test feasibility of applying this method to identify migrants from bulk pheromone trap samples from Indiana.

Anticipated Impact
This project will provide the first proof-of-concept for using portable XRF to rapidly identify migratory insects in IPM monitoring systems. By enabling near real-time detection of newly arrived pests directly from bulk trap samples, this technology offers key applications.

Short-Term Operational Use:
For key pest species such as spruce budworm and corn earworm, identifying adult migration events shortly after arrival coupled with early warning systems can inform timely pesticide applications for the following season. This proactive approach could significantly reduce crop and forest losses and minimize reliance on broad-spectrum insecticides.

Long-Term Scientific and Strategic Use:
Elemental fingerprinting data will help researchers understand the drivers and patterns of insect migration, including how climate, landscape, and environmental changes influence dispersal. These insights will support the development of predictive models of pest population dynamics, enabling more strategic and sustainable IPM planning across seasons and regions.

Once validated on spruce budworm and tested on corn earworm, the method can be extended to other migratory pests in the North Central region to strengthen early detection, rapid response, and biosecurity preparedness for crops and forests under changing environmental conditions.

• Calibrate Portable XRF Against ICP-MS

  • Ensure that portable XRF devices can reliably measure trace metal concentrations in insect tissues by comparing them to laboratory-based ICP-MS data.

• Compare Migratory vs. Resident Insects

  • Use elemental fingerprints to distinguish migratory spruce budworm (Choristoneura fumiferana) from local populations, based on specimens collected during known migration events.

• Develop Classification Models

  • Create statistical classification models to distinguish local and migratory insects based on their XRF-derived elemental profiles.

• Field Test in Bulk Pheromone Traps

  • Apply the method to CAPS samples from Indiana to assess its feasibility in distinguishing migrants from locals in bulk trap collections.