Abstract
Epidemic populations of mountain pine beetle highlight the need to understand landscape scale spatial patterns of infestation. The observed infestation patterns were explored using a randomization procedure conditioned on the probability of forest risk to beetle attack. Four randomization algorithms reflecting different representations of the data and beetle processes were investigated. Local test statistics computed from raster representations of surfaces of kernel density estimates of infestation intensity were used to identify locations where infestation values were significantly higher than expected by chance (hot spots). The investigation of landscape characteristics associated with hot spots suggests factors that may contribute to high observed infestations.
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Acknowledgements
This project was funded by the Government of Canada through the Mountain Pine Beetle Initiative, a 6 year, $40 million Program administered by Natural Resources Canada, Canadian Forest Service. Publication does not necessarily signify that the contents of this report reflect the views or policies of Natural Resources Canada—Canadian Forest Service. The authors are grateful for the constructive comments of two anonymous reviewers that helped enhance the final presentation.
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Nelson, T., Boots, B. Identifying insect infestation hot spots: an approach using conditional spatial randomization. J Geograph Syst 7, 291–311 (2005). https://doi.org/10.1007/s10109-005-0005-6
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DOI: https://doi.org/10.1007/s10109-005-0005-6