Tree improvement effects on tree size distributions forPicea glaucaandPicea marianain New Brunswick, Canada

Abstract

Height and diameter distributions between improved and unimproved (checklot) seedlots for white spruce [Picea glauca (Moench) Voss] and black spruce [Picea mariana (Mill.) BSP] were compared using the two-parameter Weibull function. Individual tree height at age 5Á15 years and diameter at breast height at age 15 years that were collected from two series of large-plot realized gain tests were used for this purpose. For both species, improved seedlots did not significantly (a 00.05) change the shape parameter of the distributions relative to their checklots, suggesting that an overall shape value will be adequate to predict tree size distributions of various improved seedlots. The most important finding was that seedlot influenced the scale parameter (related to the range of the distribution) significantly, but from a practical viewpoint, only those scale values of the higher rated seedlots (i.e. seedlots collected from a rogued seed orchard or superior families) were significantly higher (a 00.05) than those of the respective unimproved seedlot. The changes in scale value of the alternative improved seedlots relative to the checklot (D Scale ) were comparable in magnitude to the respective realized genetic gains, suggesting that tree improvement effects on the scale value can be adjusted by realized genetic gains. The results also suggest that site and age significantly affected the scale value; in general, D Scale became larger at earlier ages or on better sites. The findings reported here will be used in the development of yield functions for genetically improved white and black spruce in New Brunswick.

Development of improved and comprehensive growth and yield models for genetically improved stands

“…Genetics obviously has significant effects on height, diameter, and survival rate (Sharma et al 2013;Ye et al 2010), but genetic factors may affect diameter and height development disproportionally, thus affecting the stem shape and profile (Sabatia and Burkhart 2013). Genetics also can have a significant effect on height distributions and tree size distributions (Weng et al 2010;Sabatia and Burkhart 2013). Stand volume yield is affected by all these factors simultaneously.…”

Methods and applications for improving parameter prediction models for stand structures in Finland

“…In addition, it enables prediction and simulation of the future yields and the target stand states for management objectives, such as cutting regimes (Hyink and Moser 1983, McTague and Bailey 1987, Bowling et al 1989, Franklin et al 2002, Newton et al 2005. Furthermore, the effect of genetic improvement, management activities (e.g., vegetation control), or disturbances (e.g., moose browsing) on stand structure can be described through changes in diameter or height distribution (see Knowe et al 1992, Siipilehto and Heikkilä 2004, Smith 2007, Weng et al 2010. The diameter distribution of living trees is also a relevant basis for characterising stand diversity (e.g., Buongiorno et al 1994, Staudhammer and LeMay 2001, Pommerening 2002).…”