The Signature of Endemic Populations in the Spread of Mountain Pine Beetle Outbreaks

The mountain pine beetle (MPB) is among the most destructive eruptive forest pests in North America. A recent increase in the frequency and severity of outbreaks, combined with an eastward range expansion towards untouched boreal pine forests, has spurred a great interest by government, industry and academia into the population ecology of this tree-killing bark beetle. Modern approaches to studying the spread of the MPB often involve the analysis of large-scale, high-resolution datasets on landscape-level damage to pine forests. This creates a need for new modelling tools to handle the unique challenges associated with large sample sizes and spatial effects. In two companion papers (Koch et al. in Environ Ecol Stat. https:// doi.org/10.1007/s10651-02000456-2, 2020a; J R Soc Interface 17(170):20200434, 2020b), we explain how the computational challenges of dispersal and spatial autocorrelation can be addressed using separable kernels. In this paper, we use these ideas to capture nonstationary patterns in the dispersal flights of MPB. This facilitates a landscape-level inference of subtle properties of MPB attack behaviour based on aerial surveys of killed pine. Using this model, we estimate the size of the cryptic endemic MPB population, which formerly has been measurable only by means of costly and time-intensive ground surveys.

Automated summary

The mountain pine beetle is a destructive forest pest in North America, with an increasing frequency and severity of outbreaks prompting interest in its population ecology. Modern approaches involve analyzing large-scale datasets and developing new modeling tools to address challenges associated with large sample sizes and spatial effects. Research has focused on using separable kernels to address computational challenges in dispersal and spatial autocorrelation of the MPB.