Geostatistical Model for Forecasting Spatial Dynamics of Defoliation Caused by the Gypsy Moth (Lepidoptera: Lymantriidae)

Abstract

Outbreaks of the gypsy moth, Lymantria dispar (L.), typically occur over large areas but are difficult to predict. Previously developed models forecast defoliation from preseason counts of egg masses in a given stand. In this study, we take a different approach to defoliation prediction: forecasts are based upon the statistical autocorrelation of defoliation through space and time. Spatial and temporal autocorrelation of defoliation in historical data was quantified at a variety of scales using variograms. We used a 30-yr time series of aerial sketch maps of gypsy moth defoliation in Massachusetts to calculate these variograms. The variograms were then used to parameterize a geostatistical estimation technique: three-dimensional simple kriging. Kriged estimates are weighed averages of values from nearby locations and are typically used to interpolate two-dimensional data. In this study, we used kriging to extrapolate future defoliation maps into a third dimension, time. Kriged estimates were expressed as probabilities of detectable defoliation. Predicted probabilities were estimated for each year of the time series and were compared with actual defoliation maps for that year. The kriging procedure usually performed well in predicting the spatial distribution of outbreaks in a given year, but the magnitude of regionwide outbreaks generally lagged a year behind actual values. Though this approachis not currently suitable for operational use, it represents a novel approach to landscape level forecasting of insect outbreaks. These models may ultimately outperform current forecasting systems.