Selection of spatial-temporal lattice models: Assessing the impact of climate conditions on a mountain pine beetle outbreak.
Reyes, P. E., Zhu, J. and Aukema, B. H.
Journal of Agricultural, Biological, and Environmental Statistics 2012, 17-3, 508–525. DOI: 10.1007/s13253-012-0103-0
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.
KEYWORDS: Autoregressive models, Bark beetle, Lattice model, Model selection, Penalized maximum likelihood, Spatial-temporal process.