Does damage-dependent long-distance dispersal explain mountain pine beetle spread?

PhD project. University of Toronto. Supervisor: Marie-Josée Fortin.
In preparation for publication.  Meantime, find the details in my dissertation.

Quick summary: I investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicts the occurrence of new infestations in British Columbia.

Abstract

Mountain pine beetles can disperse long distances, but the role of long-distance dispersal
in the spread of infestations remains poorly understood. We expect that beetles should
emigrate when local resources are depleted, and therefore we hypothesized that a positively damage-dependent long-distance dispersal model would improve predictions of new mountain pine beetle infestations in previously uninfested habitat. We investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicts the occurrence of new infestations in British Columbia, using aerial survey data from 1960 to 2007. In all regions, we found that larger kernels (average dispersal distances between 5 km and 20 km) predict better than the smallest kernels (average dispersal distances of 1 or 2 km). We also found that prediction accuracy is highest when we assume that many beetles disperse from new, low severity infestations, contrary to our expectation that long-distance dispersal is positively damage-dependent. However, we suspect that differences in habitat quality and susceptibility among locations could cause new infestations to arise near to other new infestations, even if beetles disperse from old, high severity infestations. Weather and habitat gradients could also cause apparent spread patterns. Thus, we cannot conclude that the patterns we observe are caused by long-distance dispersal, or that beetles emigrate more from new, low severity infestations. Even if it is not possible to identify causal processes from analysis of infestation patterns, predicting infestation risk is useful, and we found that large, distance-weighted kernels improve predictions.

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