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.
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Reyes, P.E., Zhu, J. & Aukema, B.H. Selection of Spatial-Temporal Lattice Models: Assessing the Impact of Climate Conditions on a Mountain Pine Beetle Outbreak. JABES 17, 508–525 (2012). https://doi.org/10.1007/s13253-012-0103-0
- Autoregressive models
- Bark beetle
- Lattice model
- Model selection
- Penalized maximum likelihood
- Spatial-temporal process