MODEL TO PREDICT GYPSY-MOTH (LEPIDOPTERA, LYMANTRIIDAE) DEFOLIATION USING KRIGING AND LOGISTIC-REGRESSION

Outbreaks of the gipsy moth, Lymantria dispar (L), typically occur ove r large areas but are difficult to predict. Most gypsy moth management programs base suppression decisions on models that predict defoliatio n from preseason counts of egg masses in a given stand. In this study we developed a statistical model that used spatially stratified egg ma ss samples to predict gypsy moth defoliation on a regional scale, rath er than on a stand level. The model was developed from historical defo liation sketch-map data and counts of gypsy moth egg masses under burl ap bands at irregularly distributed plots in Massachusetts. These coun ts were used to generate interpolated surfaces of egg mass counts in g rid cells (2 by 2 km) throughout the state. Maximum-likelihood procedu res were used to parameterize a logistic regression model that predict ed the probability of defoliation in each grid cell as a function of i nterpolated egg mass counts, the presence of defoliation in the previo us year, and the 30-yr frequency of defoliation. Predicted probability surfaces tended to align mostly with the distribution of actual defol iation in each year. The model appeared to perform better than a previ ous model that was based on three-dimensional kriging of defoliation.