Genetic Performance and Maximizing Genetic Gain Through Direct and Indirect Selection in Cherrybark Oak

In 1987, an open-pollinated test of cherrybark oak (Quercus pagodae Raf.) was established on a loess site in Carlisle County, Kentucky. The test contained 37 half-sib families representing eight provenances from Louisiana, Mississippi, Tennessee, and Virginia. Height measurements were taken at ages one, three, five, ten, and fifteen, and diameter at ages five, ten, and fifteen. Significant differences existed among provenances and among families within provenances. Seed sources from the west-central Mississippi area performed better for both diameter and height, yet no overall geographic trend was apparent. The top three families were all from the Warren Co., Mississippi source while two of the top three diameter families were from Washington Co., MS and the third was from Warren Co., Mississippi. Survival among the eight provenances was constant from age one to ten. A drop in survival was shown between ages 10 and 15, probably a result of inter-tree competition. Height and diameter growth between ages five and 10 was nearly double that prior to age five and between ages 10 and 15. Family heritabilities for height and diameter were calculated for each measurement year. Family heritabilities for diameter ranged from 0.55 to 0.70 while height ranged from 0.50 to 0.70. Strong age-age correlations for height, diameter, and volume were found indicating good trait predictability from early measurements. Genetic gain equations were used to identify the optimum selection age and trait for maximizing age 15 volume. Early selection of families within provenances should yield gains in height, diameter, and volume.

Optimum selection age for wood density in loblolly pine

Genetic and phenotypic parameters for core wood density of Pinus taeda L. were estimated for ages ranging from 5 to 25 years at two sites in southern United States. Heritability estimates on an individual-tree basis for core density were lower than expected (0.20–0.31). Age–age genetic correlations were higher than phenotypic correlations, particularly those involving young ages. Age–age genetic correlations were high, being greater than 0.75. Age–age genetic correlations had a moderately linear relationship, while age–age phenotypic correlations had a strong linear relationship with natural logarithm of age ratio. Optimum selection age for core density was estimated to be 5 years when calculations were based on both genetic and phenotypic correlations. However, age 5 was the youngest examined in this study and optimum selection age may be younger than 5. Generally, the optimum selection age was robust to changes in breeding phase and assumptions concerning age-related variation in heritability estimates. Early selection for core density would result in a correlated increase in earlywood density but little progress in latewood density or latewood proportion at maturity.

Optimum breeding generation interval considering buildup of relatedness

Models taking genetic gain, relatedness, delay at generation turnover, and breeding population size into account have been developed to optimize selection age and generation interval. Relatedness (expressed as group coancestry) and average breeding value for the breeding population are merged into a joint index ("group merit"). The negative impact of group coancestry (like potential inbreeding depression) is expressed in a scale compatible with breeding value. Group merit measures the desirable characteristic of a breeding population. Annual increase of group merit is the criterion for comparing alternatives. Optimum generation interval is when annual group merit increase is highest. Generally the optimum selection age becomes higher when increase in relatedness is considered. We quantify the influence of relatedness penalty, early-mature genetic correlation, breeding population size, and delay at generation turnover on optimum selection age. A reasonable large population counteracts the increase of relatedness and, thus, favors early selection. Early selection can have a negative impact if a small early selection gain does not compensate for the buildup of relatedness at generation turnover. Conditions for this to occur are quantified. Early selection requires sufficient high juvenile-mature correlation to have a positive effect; this requirement can be reduced by using a large breeding population. The methods developed were applied to a number of situations relevant to forest tree improvement.

A probability distribution model for age–age correlations and its application in early selection

Bootstrap-generated distributions of age–age correlations of stem volume in a red pine (Pinusresinosa Ait.) spacing trial supported the contention of the β distribution as a realistic model for age–age correlations in small populations where normal approximations are inappropriate. The model describes age–age correlations as a repeatability of tree performance. The orthonormal decomposition of the total variance into among- and within-tree variance components was derived from Fourier-transformed time series of the trait values. Applications for early selections include (i) finding the optimum selection age for various numbers of selections made and (ii) predictions of age–age correlations.