Modeling stratified dispersal in forest pests: A case study of the mountain pine beetle in Alberta

Abstract

Forest pests pose critical threats to forest ecosystems worldwide, yet accurately predicting their spatial spread remains challenging due to complex dispersal behaviors, weather effects, and the inherent difficulty of tracking small organisms across large landscapes. These challenges have resulted in divergent estimates of typical dispersal distances across studies. Here, we use high-quality data from helicopter and field-crew surveys to parameterize dispersal kernels for the mountain pine beetle, a destructive pest that has recently expanded its range into Alberta, Canada. We find that fat-tailed kernels-those which allow for a small number of long-distance dispersal events-consistently provide the best fit to these data. Specifically, the radially symmetric Student's t-distribution with parameters ρ = 0.012 $$ \uprho =0.012 $$ km and ν = 1.45 $$ \upnu =1.45 $$ stands out as parsimonious and user-friendly; this model predicts a median dispersal distance of 60 m, with the 95 th $$ 95\mathrm{th} $$ percentile of dispersers traveling nearly 5 km. The best-fitting mathematical models have biological interpretations. The Student's t-distribution, derivable as a mixture of diffusive processes with varying settling times, is consistent with observations that mountain pine beetle adults fly short distances while few travel far; early-emerging beetles fly farther; and larger beetles from larger trees exhibit greater variance in flight distance. This phenotypic variability is mirrored in other forest pests, resulting in a stratified dispersal pattern where most individuals disperse locally while rare long-distance "jumpers" drive range expansion. Our approach demonstrates how aerial survey data can be used to characterize dispersal patterns, as many insects create diagnostic signatures-combining foliage damage patterns and host-tree preferences-that are visible from above. Since aerial surveys of North American forests are widely available, our methodology can be broadly used to create parsimonious dispersal models for many forest insects.