Detecting spatial hot spots in landscape ecology

Trisalyn A. Nelson, Barry Boots

Research output: Contribution to journalArticlepeer-review

138 Scopus citations

Abstract

Hot spots are typically locations of abundant phenomena. In ecology, hot spots are often detected with a spatially global threshold, where a value for a given observation is compared with all values in a data set. When spatial relationships are important, spatially local definitions - those that compare the value for a given observation with locations in the vicinity, or the neighbourhood of the observation - provide a more explicit consideration of space. Here we outline spatial methods for hot spot detection: kernel estimation and local measures of spatial autocorrelation. To demonstrate these approaches, hot spots are detected in landscape level data on the magnitude of mountain pine beetle infestations. Using kernel estimators, we explore how selection of the neighbourhood size (τ) and hot spot threshold impact hot spot detection. We found that as τ increases, hot spots are larger and fewer; as the hot spot threshold increases, hot spots become larger and more plentiful and hot spots will reflect coarser scale spatial processes. The impact of spatial neighbourhood definitions on the delineation of hot spots identified with local measures of spatial autocorrelation was also investigated. In general, the larger the spatial neighbourhood used for analysis, the larger the area, or greater the number of areas, identified as hot spots.

Original languageEnglish (US)
Pages (from-to)556-566
Number of pages11
JournalEcography
Volume31
Issue number5
DOIs
StatePublished - Oct 1 2008
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

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