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Using Simple Models to Predict Virus Epizootics in Gypsy Moth Populations

Greg Dwyer and Joseph S. Elkinton
Journal of Animal Ecology
Vol. 62, No. 1 (Jan., 1993), pp. 1-11
DOI: 10.2307/5477
Stable URL: http://www.jstor.org/stable/5477
Page Count: 11
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Using Simple Models to Predict Virus Epizootics in Gypsy Moth Populations
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Abstract

1. Biologists have made little use of recent advances in the mathematical theory of the dynamics of insect pathogens, because of difficulties with parameter estimation and misgivings about the simplicity of the models in question. 2. We use an existing simple model for the dynamics of insect pathogens, slightly modified both to provide greater accuracy and to allow for more straightforward parameter estimation. 3. Focusing on the nuclear polyhedrosis virus (NPV) of gypsy moth (Lymantria dispar (L.)), we estimated each of the model parameters independently, estimating three of the four model parameters from the literature. 4. To estimate the rate of transmission, we present an experimental protocol which involves fitting a reduced version of the model to data from a small-scale transmission experiment. 5. Without circularity or curve-fitting, we tested the model with literature data giving initial densities and weekly NPV mortality for epizootics in eight gypsy moth populations on 4--9 ha plots in Massachusetts, USA. 6. The model predictions are reasonably accurate for five of the eight populations, suggesting that gypsy moth NPV dynamics within a season are driven by a small number of biological processes. 7. The three populations for which the model did poorly began the season with low host densities yet gave rise to more severe epizootics than predicted by the model. This indicates that standard assumptions about disease transmission may not hold for gypsy moth NPV dynamics at low densities; specifically, we suspect that density-related changes in larval behaviour result in higher NPV transmission at low density. 8. These results suggest that simple models of the dynamics of animal pathogens can be used to make quantitative predictions about disease dynamics in the field.

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