Identifying insect infestation hot spots: An approach using conditional spatial randomization

Trisalyn Nelson, Barry Boots

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)291-311
Number of pages21
JournalJournal of Geographical Systems
Volume7
Issue number3-4
DOIs
StatePublished - Dec 2005
Externally publishedYes

Fingerprint

beetle
insect
statistics
raster
Values
mountain
test

Keywords

  • Conditional spatial randomization
  • Kernel density estimation
  • Local statistics
  • Mountain pine beetle

ASJC Scopus subject areas

  • Geography, Planning and Development

Cite this

Identifying insect infestation hot spots : An approach using conditional spatial randomization. / Nelson, Trisalyn; Boots, Barry.

In: Journal of Geographical Systems, Vol. 7, No. 3-4, 12.2005, p. 291-311.

Research output: Contribution to journalArticle

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