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Variable Selection in Dendroclimatology: An Example Using Simulated Tree-Ring Series

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Regression analysis is often used to select important climatic variables from a group of candidate variables in dendroclimatology. Model R² and F statistics are commonly used as a measure of the importance of these variables. The reliability of these selections in the presence of substantial unexplained variance has not been previously considered (with the exception of some climatic reconstruction studies). In this study multiple regression analysis with stepwise (forward inclusion and backward elimination), best subsets, and principal components was conducted on simulated tree-ring index series constructed from linear combinations of temperature and precipitation data with varying amounts of white noise. Regression techniques increasingly failed to select correct and/or complete sets of variables with increasing proportions of introduced noise, even with a limited number of candidate variables and a known relationship between predictor and dependent variables. R² estimates were inflated with increasing noise for all techniques used. The results imply that climatic regression models may be incorrectly specified when large proportions of unexplained variability are present. There are implications for the analysis of data in other areas of forestry in which variable selection is attempted with datasets containing large amounts of unexplained variability. For. Sci. 35(2):294-302.
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Keywords: Regression analysis; tree growth; variance

Document Type: Journal Article

Affiliations: Research Ecologist, Pacific Southwest Forest and Range Experiment Station, USDA Forest Service, 4955 Canyon Crest Dr., Riverside, CA 92507

Publication date: 1989-06-01

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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.
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    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

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    June 1, 2016 to Feb. 28, 2017

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