Selection of Spatial-Temporal Lattice Models: Assessing the Impact of Climate Conditions on a Mountain Pine Beetle Outbreak

Article

Abstract

Insects are among the most significant indicators of a changing climate. Here we evaluate the impact of temperature, precipitation, and elevation on the tree-killing ability of an eruptive species of bark beetle in pine forests of British Columbia, Canada. We consider a spatial-temporal linear regression model and in particular, a new statistical method that simultaneously performs model selection and parameter estimation. This approach is penalized maximum likelihood estimation under a spatial-temporal adaptive Lasso penalty, paired with a computationally efficient algorithm to obtain approximate penalized maximum likelihood estimates. A simulation study shows that finite-sample properties of these estimates are sound. In a case study, we apply this approach to identify the appropriate components of a general class of landscape models which features the factors that propagate an outbreak. We interpret the results from ecological perspectives and compare our method with alternative model selection procedures.

Key Words

Autoregressive models Bark beetle Lattice model Model selection Penalized maximum likelihood Spatial-temporal process 

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Copyright information

© International Biometric Society 2012

Authors and Affiliations

  1. 1.Department of StatisticsKansas State UniversityManhattanUSA
  2. 2.Department of Statistics and Department of EntomologyUniversity of WisconsinMadisonUSA
  3. 3.Department of EntomologyUniversity of MinnesotaSt. PaulUSA

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