Spatial Pest and Invasive Species Modeling

Cydia pomonella-Olei <>, via Wikimedia Commons

Leonard Coop | Associate Professor of Entomology | Department of Horticulture | Oregon IPM Center

Model Management

We develop models to assist in decision-making for integrated pest and invasive species management. These models use weather and climate data to predict the timing of pest behaviors such as flight times of insect pest species. We also develop and host plant disease models that largely predict the risk of disease infection. Over the last 25 years, this system has grown to include over 150 separate models linked to over 29,000 weather stations, supporting pest management and crop modeling in multiple cropping systems nationwide.

Making a Difference

As an undergraduate studying biology at a small university in Kansas, I (Len Coop) found two of my favorite courses were entomology and computer science. The study of insects was most fascinating and I decided to try graduate school in Entomology, which brought me to Oregon State University. As a graduate student, I became interested in quantitative ecology and continued the study of computer science, even though it did not contribute directly to my program in integrated pest management (IPM). This allowed me to do field and laboratory work with insects, and to also put the resulting data into computer programs. I decided to work on the applied side for its more immediate potential impacts and satisfaction of seeing long-standing research put into production.

IPM has increasingly relied on science-based decision support tools, and my career in developing such tools tracked the rise of standalone personal computers, followed by the world wide web and the use of email and text messaging. Once weather data became available in real time via the internet, I focused on developing weather-driven models that support IPM decision-making. I and my team try to stay current and relevant with evolving technologies to deliver model predictions to agriculturists of all types.

Figure 1. graph of codling moth development, shown here with our new mobile app version of the model. More than 130 additional degree-day models are accessible from this app for many insects, diseases, crops, and weeds, supporting many aspects of agricultural production and pest management.
Figure 2. Example plant disease infection risk model app for potato / tomato late blight. Users may subscribe to this information to arrive by email on a schedule of their own choosing. Most plant disease models are driven by the temperature and moisture conditions that are conducive to infection. It is assumed that disease inoculum is present.

Model Behavior

There are many points in agricultural production where pest models can help inform decision-makers about the activities of pests as a result of weather and climate impacts on their life cycles. Better informed decision-making can help make agricultural activities more efficient, reduce pest losses, and help the environment by decisions that lead to less pesticide usage.

A classic example is the codling moth model shown in Figure 1. The codling moth model has been used for many years to predict first egg-hatch and other events in this key apple pest’s life cycle, and is now more convenient than ever. We have over 130 degree-day models available using our mobile-enabled app including more than 75 insect models and more than 40 crop development models.  The crop development models will help growers time planting and harvest times to meet production needs more efficiently. This is part of the CROPTIME project led by Nick Andrews, OSU Center for Small Farms and Community Food Systems.

We also have over 20 hourly weather-driven plant disease infection risk models, which help inform growers about the need to apply fungicides and bactericides. Nine of these models are newly available as mobile-enabled apps. These include apple and pear scab, boxwood blight, cherry, hop, and grape powdery mildew, fireblight, grass seed stem rust, and potato-tomato late blight (Fig. 2). These mobile apps were just recently merged with an email notification system, whereby a subscribed user receives regular model predictions via email.

We have also developed a new spatial modeling and mapping platform, where you can see dates of predicted pest events for regions as large as the contiguous United States (Fig. 3), developed thus far for 15 invasive insects. This system was designed to help with invasive species surveillance programs supported by USDA Plant Protection and Quarantine (PPQ), and is also in use to aid in weed biological control programs, a project led by Dr. Fritzi Grevstad of Oregon State's Department of Botany and Plant Pathology.

Figure 3. New “Pest Event Map” which predicts dates of selected life stage events in the life cycle of insects, here for example for adult emergence of emerald ash borer, an invasive beetle that has already killed millions of ash trees in eastern and central U.S. states. The platform integrates climate suitability modeling. As seen here, the insect is predicted to be excluded from the hotter parts of Texas, Arizona, and California.

Teaming Up

Our team of modelers and programmers currently includes Len Coop, Entomologist and Associate Professor (Practice); and Brittany Barker, Research Associate, both working within the Oregon IPM Center and the Department of Horticulture; along with Chris Hedstrom, IPM Communications and Outreach Coordinator, and contract programmer Dan Upper. We have partnered with many researchers and extension agents working in pest management in Oregon and nationwide: The UC IPM Program, USDA Plant Protection and Quarantine, the National Plant Phenology Network, and many others.