Landscape Framework To Predict Phenological Events for Gypsy Moth (Lepidoptera: Lymantriidae) Management Programs

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Abstract
Ability to predict the timing of phenological events (herein termed target events) is an important component of gypsy moth, Lymalltria dispar (L.) management programs and integrated pest management programs in general. Several simulation models have been developed that, in part, demonstrate their validity for predicting events at individual locations. The framework described in this article extends the use of these models to be able to make predictions (i.e., create maps) for heterogeneous landscapes. An algorithm is presented that can predict the time that a target event will occur anywhere in a landscape using temperature, a digital elevation model, linked egg hatch and larval development models, and a linear function that relates elevation to the Julian date when a given target event will occur. The algorithm was validated with four data sets collected from Virginia, West Virginia/Pennsylvania, and Utah. Model predictions were satisfactory for the Virginia data sets and differed significantly from those for West Virginia/Pennsylvania and Utah data sets. Potential sources of error are discussed. Target event maps are presented that demonstrate how this landscape framework can be used in gypsy moth management programs.

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