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Characterizing spatial distributions of insect pests across Alaskan forested landscape: a case study using aspen leaf miner (Phyllocnistis populiella Chambers)Author(s): Robin M. Reich; John E. Lundquist; Vanessa A. Bravo
Source: Journal of Sustainable Forestry. 32: 527-548
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
PDF: View PDF (1.22 MB)
DescriptionInsects are ectotherms that cannot regulate their own temperature, and thus rely on and are at the disposal of the surrounding environment. In this study, long-term climatic data are used to stratify forested regions of Alaska into climatic zones based on temperature and precipitation. Temperature and precipitation are shown to be important ecological drivers in determining the distribution of aspen leaf miner (Phyllocnistis populiella Chambers) and the aspen (Populus tremuloides Michx.) host in the state of Alaska. Climatic regions based on temperatures and precipitation accounted for 83 to 97% of the variability in the probability of observing aspen and the aspen leaf miner (ALM). The frequency of observing aspen was highest throughout the central region of the state, which represents a climate with low to moderate levels of precipitation and cold to mild temperatures. The highest probability of observing aspen was in the mild-very cold region of the state. The probability of observing ALM in a given climate zone followed a pattern similar to aspen. Differences were in the colder and drier climate zones where the probability of observing ALM decreased to near zero. The derived climatic models could be used to provide a basis for the analysis of climatic impacts on the distribution of forest insects throughout the state.
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CitationReich, Robin M.; Lundquist, John E.; Bravo, Vanessa A. 2013. Characterizing spatial distributions of insect pests across Alaskan forested landscape: a case study using aspen leaf miner (Phyllocnistis populiella Chambers). Journal of Sustainable Forestry. 32: 527-548.
Keywordsbinary classification trees, climate, roadside surveys, satellite imagery, spatial error model
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