A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion

Yuan Zhong,Xin Gao,Wei Xu,Farah Hoque,G Brent Hall,Baoxin Hu

doi:10.3390/ijgi9070414

Abstract

The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.

Highlights

The outbreak of the Emerald Ash Borer (EAB, Agrilus planipennis Fairmaire) in the Great Lakes States of the United States and southwestern Ontario, Canada was first discovered in 2002 [1,2]
This shows that most of the predictors were significantly associated with the presence–absence distribution of the EAB, and the estimated deviance showed that the model fit was similar
In the proposed generalized linear mixed model (GLMM), two types of random effects were established based on the geographic information provided with the EAB data

Summary

Introduction

The outbreak of the Emerald Ash Borer (EAB, Agrilus planipennis Fairmaire) in the Great Lakes States of the United States and southwestern Ontario, Canada was first discovered in 2002 [1,2]. Strategies for the detection and control of the EAB infestation in Canada have mainly depended on visual surveys and selective culling of trees [3], which are difficult strategies to conduct over large areas. Prevention and control of the beetle’s spread have become imperative. To achieve these goals, it is important to predict with high accuracy the spread of the EAB into currently unaffected Ash tree locations. It is important to predict with high accuracy the spread of the EAB into currently unaffected Ash tree locations In this regard, the use of species distribution models (SDMs) provides a useful means to predict areas with a high level of risk, as well as identifying the relevant risk factors

Objectives

Results

Conclusion