Modeling Agrilus planipennis F. (Coleoptera: Buprestidae) Spread in New Jersey

Abstract

Pests and disease have become an increasingly common issue as globalized trade brings non-native species into unfamiliar systems. Emerald ash borer (Agrilus planipennis), is an Asiatic species of boring beetle currently devastating the native population of ash (Fraxinus) trees in the northern forests of the United States, with 85 million trees having already succumbed across much of the Midwest. We have developed a reaction-diffusion partial differential equation model to predict the spread of emerald ash borer over a heterogeneous 2-D landscape, with the initial ash tree distribution given by data from the Forest Inventory and Analysis. As expected, the model predictions show that emerald ash borer consumes ash which causes the local ash population to decline, while emerald ash borer spreads outward to other areas. Once the local ash population begins to decline emerald ash borer also declines due to the loss of available habitat. Our model’s strength lies with its focus on the county scale and its linkage between emerald ash borer population growth and ash density. This enables one to make accurate predictions regarding emerald ash borer spread which allows one to consider various methods of control as well as to accurately study the economic effects of emerald ash borer spread.

Predicting the potential for spread of emerald ash borer (Agrilus planipennis) in Great Britain: What can we learn from other affected areas?

“…Economic analyses, using an exponential dispersion kernel to simulate the long-distance spread of the pest, demonstrated the importance of investing in surveillance measures for rapid identifications of new incursions of EAB and in control measures to slow, and where possible eradicate, the beetle (Kovacs et al, 2010). As more data have become available on the dynamics of EAB and the host landscape, models have been used to explore options for slowing down the spread of EAB given that eradication is no longer feasible (McCullough & Mercader, 2012;Mercader et al, 2011Mercader et al, , 2016Kovacs et al, 2014;Lyttek et al, 2019).…”