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Regression Methods for Spatially Correlated Data: An Example Using Beetle Attacks in a Seed Orchard

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We present a statistical procedure for studying the simultaneous effects of observed covariates and unmeasured spatial variables on responses of interest. The procedure uses regression type analyses that can be used with existing statistical software packages. An example using the rate of twig beetle attacks on Douglas-fir trees in a seed orchard illustrates the problem and the recommended statistical procedures. Use of this procedure revealed spatial patterns in beetle attacks that were not otherwise evident. We also found that the covariates, tree vigor, cone crop, and host genotype (i.e., clone) apparently had significant effects on number of beetle attacks within a model that also included location effects. This procedure should prove useful in other situations where spatial trends are of interest in themselves and are not simply viewed as nuisance parameters to be dealt with by the proper experimental design. For. Sci. 43(1):71-77.

Keywords: Douglas-fir; Pityophthorus orarius; Poisson counts; nonparametric regression; spatial statistics; twig beetles

Document Type: Journal Article

Affiliations: Department of Environmental Science, Policy, and Management, University of California Berkeley, CA 94720

Publication date: February 1, 1997

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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