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Use of Individual Tree Mixed Models to Account for Mortality and Selective Thinning When Estimating Base Population Genetic Parameters

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The ability of individual tree mixed models to estimate unbiased genetic parameters was investigated for growth, Pilodyn penetration, and bark thickness, in an Eucalyptus urophylla progeny trial where the smaller trees have been previously thinned. A multivariate Restricted Maximum Likelihood (REML) analysis including the growth measurements of all trees prior to thinning, and later measurements of only those that remained, resulted in almost unbiased estimates of heritabilities and age-age genetic correlations for growth traits at all ages. Analysis based only on trees remaining after thinning resulted in considerably lower estimates of heritabilities and age-age genetic correlations. On the other hand, genetic parameters for Pilodyn penetration or bark thickness were not affected by the thinning. This was due to the low correlations between the thinning criteria (tree size) and the wood and bark traits. For. Sci. 44(2):246-253.

Keywords: Eucalyptus urophylla; REML; correlation; heritability; selection bias

Document Type: Journal Article

Affiliations: Cooperative Research Centre for Temperate Hardwood Forestry, and Department of Plant Science, University of Tasmania, GPO Box 252-55, Hobart 7001, Tasmania, Australia. Phone: + 613 62267615;, Fax: + 613 62262698

Publication date: May 1, 1998

More about this publication?
  • 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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