Age–age correlations for tracheid length andwood density in Pinus sylvestris

Björn Hannrup and Inger Ekberg

Abstract: The existence of strong genetic correlations between traits at an early age and at an adult age should shortenthe generation turnover of tree breeding populations and render forest tree breeding more effective. Genetic age–agecorrelations for tracheid length and wood density were estimated in Scots pine (Pinus sylvestrisL.) and the efficiencyof early selection for these traits was evaluated. Increment cores of 10-mm diameter were collected from trees of 106full-sib families in a progeny trial located in southeastern Sweden and consisting of controlled matings between 30parent trees. The additive genetic age–age correlations were consistently close to unity for all traits and ages studied.The additive genetic variance differed significantly from zero for all traits. The dominance variance was zero fortracheid length and small and insignificant for wood density. The heritabilities varied between 0.3 and 0.5. The geneticgain per year for both tracheid length and wood density was largest if selection was carried out at tree age 11, thelowest age studied, indicating that early tests for these traits will be efficient.

Résumé: L’existence de fortes corrélations génétiques entre les caractères au jeune âge et à l’âge adulte devraitréduire la rotation des générations parmi les populations d’arbres améliorés et rendre l’amélioration des arbresforestiers plus efficace. Les corrélations génétiques âge–âge pour la longueur des trachéides et la masse volumique dubois ont été estimées pour le pin sylvestre (Pinus sylvestrisL.) et l’efficacité de la sélection hâtive pour ces caractèresa été évaluée. Des carottes de sondage de 10 mm de diamètre ont été prélevées sur les arbres de 106 famillesbiparentales dans un test de descendances situé au sud-est de la Suède et consistant en des croisements contrôlés entre30 arbres parents. Les corrélations génétiques additives âge–âge étaient invariablement voisines de un pour tous lescaractères et les âges étudiés. La variance génétique additive était significativement différente de zéro pour tous lescaractères. La variance de dominance était nulle pour la longueur des trachéides et faible ainsi que non significativepour la masse volumique du bois. Les héritabilités variaient entre 0,3 et 0,5. Le gain génétique annuel pour la longueurdes trachéides et la masse volumique du bois était plus grand si la sélection était effectuée sur des arbres de 11 ans,l’âge le plus bas étudié, indiquant que des tests hâtifs pour ces caractères seront efficaces.

[Traduit par la Rédaction] Hannrup and Ekberg 1379

Reliable early tests are a powerful means to shorten thegeneration turnover in breeding populations of forest treesand thus increase the genetic gain per year. A prerequisitefor performing reliable early tests is that genetic correlationsbetween traits expressed at an early and adult age are strongand positive. Two main types of genetic correlations may bedistinguished. (i) Correlations between traits assessed on thesame plant or tree individual. These correlations can be esti-mated between the same trait at various ages, i.e., age–agecorrelations, or between different traits at the same or differ-ent ages. (ii ) Correlations between traits expressed by differ-ent but related individuals. These correlations can be basedon parent–offspring relationships or on sibling relationships.

The latter case includes retrospective early tests in whichyoung siblings of those tested in field trials are studied innurseries (Scots pine (Pinus sylvestrisL.), Mikola 1985) orin growth chambers (Pinus sylvestris, Eriksson et al. 1993).

Genetic correlations have two main causes: pleiotropy andlinkage disequilibrium (Namkoong et al. 1988). In natural,wind-pollinated populations, such asPinus sylvestris, the ex-pectation of linkage disequilibrium is low. No or weak indi-cations of linkage disequilibrium have been reported forPinus sylvestrispopulations (Rasmuson 1979; Muona andSzmidt 1985). Thus, the existence of age–age correlations inthis species strongly indicates that the same set of genes in-fluences a trait at the juvenile and the mature phase.

Only a few investigations of genetic age–age correlationsfor wood density traits and tracheid length have been pub-lished. For wood density in Norway spruce (Picea abies(L.)Karst.), strong genetic age–age correlations and weakerphenotypic correlations were reported (Hylen 1995). Simi-larly, strong genetic age–age correlations for wood density(additive genetic correlation (rA) often above 0.80) were es-timated in Sitka spruce (Picea sitchensis(Bong.) Carr.) (Lee1997), Douglas-fir (Pseudotsuga menziesii(Mirb.) Franco)(Vargas-Hernandez and Adams 1992), andPinus species(loblolly pine (Pinus taedaL.): Talbert et al. 1983; Loo et al.1984; radiata pine (Pinus radiata D. Don): Cown et al.1992). In contrast, Loo et al. (1984) could not demonstrate

Can. J. For. Res.28: 1373–1379 (1998) © 1998 NRC Canada

1373

Received February 9, 1998. Accepted July 3, 1998.

B. Hannrup.1 Department of Forest Yield Research, SwedishUniversity of Agricultural Sciences, S-750 07 Uppsala,Sweden.I. Ekberg. Department of Forest Genetics, SwedishUniversity of Agricultural Sciences, S-750 07 Uppsala,Sweden.

1Author to whom all correspondence should be addressed.e-mail: bjorn.hannrup@sprod.slu.se

any genetic correlations for mean tracheid length at differentages ofPinus taedatrees because the genetic covarianceswere approximately zero. This is the only study so far inwhich attempts were made to estimate genetic age–age cor-relations for tracheid length. However, studies by Wardropand Dadswell (1953) and Loo et al. (1984) indicated a posi-tive phenotypic age–age correlation. Wardrop and Dadswell(1953) observed that trees with long tracheids in the firstgrowth ring also produced long tracheids through successivegrowth rings in comparison with trees in which tracheids ofthe first growth ring were shorter. Loo et al. (1984) foundstrong correlations between tracheid length of individualrings at ages 2–10 and whole-core values at age 22.

As for genetic age–age correlations, only a few geneticcorrelations for wood density have been estimated based onretrospective early tests. In the first study of this kind usingopen-pollinated families ofPseudotsuga menziesii, Adams etal. (1990) observed strong and positive genetic correlationsbetween wood density of 2-year-old progenies in the nurseryand the density of the juvenile wood in 15-year-old proge-nies tested in the field (rA = 0.65). Similar strong geneticcorrelations have been reported in two additional early testsfor wood density (Williams and Megraw 1994; Woods et al.1995).

The efficiency of early selection can be estimated as thecorrelated response of indirect selection for the mature traitfollowing selection for the juvenile trait relative to the re-sponse of direct selection for the mature trait. Another op-tion for estimating the efficiency of early selection is toinclude the age of selection at the mature and juvenilephases, which will give an estimate of the relative geneticgain per time unit (Nanson 1970). Estimates of the effi-ciency of early selection indicate that selection for wooddensity at an early age is efficient inPseudotsuga menziesii(Gonzales and Richards 1988; Vargas-Hernandez and Adams1992; Woods et al. 1995),Pinus taeda (Williams andMegraw 1994), andPicea sitchensis(Lee 1997). However,to fully exploit the advantages of early selection in a breed-ing program, both strong genetic correlations and a shorten-ing of the juvenile, nonflowering phase are prerequisites forlate-flowering species.

This is the first study in which genetic age–age correla-tions were estimated and used to investigate the genetic rela-tionship between juvenile and mature wood for tracheidlength and wood density inPinus sylvestris. For this pur-pose, increment cores were collected from trees of 106 full-sib families in a progeny trial that had reached about half therotation age. The potential of early selection for tracheidlength and wood density is evaluated based on estimationsof relative selection efficiency.

Increment-core samples were collected from a 33-year-old (agefrom seed)Pinus sylvestrisprogeny trial planted at a site in south-eastern Sweden (57°44′, 15°33′; 140 m elevation). The site is ho-mogeneous and consists of sandy soil. The progeny trial included106 full-sib families from controlled matings between 30 femaleparent trees and four male parent trees (Fig. 1). The males wererandomly sampled among the females. No selfings were included.The parents were selected plus-trees and originated from southeast-ern Sweden. The 3-year-old seedlings were randomly planted in

noncontiguous single-tree plots in 10 blocks with four seedlingsper family per block. The spacing was 1.4 × 1.4 m. A weak lowthinning was carried out at an age of 28 years. The thinning im-plied that mean values of height (at age 22) and diameter (at age27) of the thinned tree population increased 4 and 10%, respec-tively, compared with the unthinned tree population.

Estimates of genetic correlations frequently suffer from largestandard errors. The sampling variance within a given sample sizedepends on the relationship between number of families and num-ber of individuals per family (Robertson 1959). With a sample sizeof about 1000 trees, an optimal sampling design of eight trees perfamily and 32 trees per female was used. Missing families owingto empty entries in the mating design were compensated for bysampling more trees from the remaining families of the same fe-male. This was not fully achieved, but a total of 948 trees from 106families from 10 blocks were sampled. The mean diameter andmean tree height were 105 mm and 10.8 m, respectively, at thesampling occasion.

A 10-mm increment core was taken from each tree at a 90° an-gle to the prevailing wind direction (southwest) to avoid reactionwood. Increment cores from leaning trees were taken at a 90° an-gle to the lean of the tree, but such cases were infrequent.

Measurement of wood densityOnly those increment cores (n = 834) where the pith was hit or

just missed (<4 mm) were used in the analyses of wood density.The increment cores contained on average 26 year rings and thecores were cut into four segments: (i) juvenile wood where heart-wood formation may be initiated, year rings 1–4 from the pith,(ii ) juvenile wood without heartwood formation, year rings 5–8from the pith, (iii ) transition wood, containing approximately 12year rings, and (iv) mature wood, year rings 1–6 from the bark.

This partitioning of the increment cores was made assuming thatthe relative impact of cambial age is largest in the wood closest tothe pith and the effect of the year-of-ring formation is largest in theolder wood. The wood in the two segments closest to the pith wasformed by cambium of the same age, while all wood samples fromthe segments closest to the bark were formed during the samechronological year.

The green volume of the segments was measured by the waterdisplacement method (Olesen 1971). Oven-dry mass was measuredafter 24 h at 106°C and the wood density estimated as the ratio of

© 1998 NRC Canada

1374 Can. J. For. Res. Vol. 28, 1998

Fig. 1. Schematic diagram of the mating design. The four treesthat were designated as both female and male trees areunderlined.

oven-dry mass to green volume. The area-weighted wood density(AWD) of ages 8 and 20 was calculated as a mean with wood den-sity of each segment weighted with the area it represented:

[1] AWDwd

=⋅∑

∑a

ai i

i

whereai is the cross-sectional area represented by segmenti andwdi is the wood density of segmenti.

Measurement of tracheid lengthA slightly smaller sample size was used for tracheid length (n =

810). Juvenile tracheid length was represented by the latewood ofthe fourth year ring from the pith. Mature tracheid length was rep-resented by the latewood of the third year ring from the bark,which approximately corresponds to 24 year rings from the pith.Thin slices of wood were cut from the year rings and the tracheidswere separated according to a method described by Ståhl (1988).The length of 30 unbroken tracheids from each year ring was mea-sured using a microscope connected to an image analysing system(MOP Videoplan, Carl Zeiss Gmbh, Germany). The number of un-broken tracheids varied considerable among samples. Conse-quently, it is important to measure only unbroken tracheids to get agood estimate of the mean tracheid length. To ensure this, everytracheid was examined under the microscope before it was eventu-ally measured on the screen. Another observation was that brokentracheids were far more abundant in the samples from the juvenilewood than in those from the mature wood. This is probably be-cause young tracheids have thinner cell walls, making them weakerand thus easier to break.

Statistical analysisIn the statistical analysis based on the increment-core measure-

ments, tree age was used instead of ring number from pith. It takeson average 7 years for the trees to reach breast height, and ringnumber from pith at breast height was converted to tree age simplyby adding 7 years. The following variables were included in thestatistical analysis (Fig. 2): TL11, arithmetic mean (millimetres) of30 tracheids per increment core at tree age 11; TL31, arithmeticmean (millimetres) of 30 tracheids per increment core at tree age31; WD8-11, wood density (kilograms per cubic metre) at tree ages8–11; AWD8-15, area-weighted mean of wood density (kilogramsper cubic metre) at tree ages 8–15; AWD8-27, area-weighted meanof wood density (kilograms per cubic metre) at tree ages 8–27;WD28-33: wood density (kilograms per cubic metre) at tree ages28–33.

The statistical analyses used Henderson’s (1984) mixed modelequations (MME) and variances were estimated with the EM algo-

rithm for restricted maximum likelihood (REML) estimates as per-formed in the VDIAL software, designed for diallel and factorialmating designs (Danell 1989).

The following mixed (diallel) model was fitted:

[2] Y b p p f eijj k i j j jj ijj k′ ′ ′ ′= + + + +( )

whereYijj ′k is an observation of each trait of theijj ′kth tree,bi is thefixed effect of thei th block, pj andpj′ are the random effects of thefirst (j) and second (j′) parent, respectively,fjj ′ is the random inter-action effect between the parents, andeijj ′k is the random residual.The term (pj + pj′) means that individuals from the same set of par-ents appear aspj and pj′. The model assumes that the randomfactors are normally distributed with expectation zero and, conse-quently,E(yijj ′k) = bi. Furthermore, it is assumed that

Var = =p

f

e

p

f

e

I 0 0

0 I 0

0 0 I

σσ

σ

2

2

2

where σp2 is the parental variance, assuming that no maternal ef-

fects were present at the ages studied,σf2 is the family variance,

and σe2 is the residual variance.

Estimates of model effects and variances were attained by itera-tion on the following MME system:

[3]

X X X Z X Z

Z X Z Z + I Z Z

Z X Z Z Z Z + I

1 2

1 1 1 1 1 2

2 2 1 2 2 2

′ ′ ′′ ′ ′′ ′ ′

λλ

′′′

$

$

$

b

p

f

=

X Y

Z Y

Z Y1

2

whereYN×1 is the observation vector,XN×10, Z1 N × 30, andZ2 N ×106

areincidence matrices for blocks, parents, and families, respectively,and $b10 1× , $p30 1× , and$f106 1× are solutions for block, parent, and fam-ily effects, respectively. Theλ1 and λ2 are variance ratios$ $σ σe p

2 2yand $ $σ σe f

2 2y , respectively, in this case obtained by the EM algorithmat each round of the iteration (first values were guessed). The solu-tions were considered to have converged when the differencesbetween two successive estimates were <0.01% for all variancecomponents.

Additive variance was estimated as

[4a] $ $σ σA p2 24=

and dominance variance as

[4b] $ $σ σD f2 24=

Epistatic variance was assumed to be small and thus ignored. Indi-vidual tree heritabilities were calculated as

[5] $$

$ $ $

$

$h2

2

2 2 2

2

2

4

2=

+ +=

σσ σ σ

σσ

p

p f e

A

p

Estimates of the standard errors of the variance components wereobtained using the same statistical model as in VDIAL and run onPROC MIXED in the SAS software (SAS 1996). To accomplishthis, the following procedure was used: (i) the design matrix forrandom effects was created in a data step, (ii ) the TYPE = Toep(1)option in the random statement, which specifies the same variancecomponent across the parental and family effects, respectively, i.e.,σp

2 andσf2 , was used., and (iii ) two random statements, one for pa-

rental effects and one for family effects, were specified. The stan-dard errors of σA

2 and σD2 were obtained by multiplying the

standard errors ofσp2 and σf

2 by 4, respectively.

© 1998 NRC Canada

Hannrup and Ekberg 1375

WD8-11

AWD8-15

pith bark

AWD8-27

WD28-33

TL11 TL31

Fig. 2. Increment core divided into four segments representingring Nos. 1–4, 5–8, 9–20, and 21–26 from the pithcorresponding to tree ages 8–11, 12–15, 16–27, and 28–33,respectively. Tracheid length (TL) at tree ages 11 and 31, wooddensity (WD) at tree ages 8–11 and 28–33, and area-weightedwood density (AWD) at tree ages 8–15 and 8–27 are indicated.

Estimates of the covariances between the juvenile and the ma-ture traits were derived from variance estimates of each pair oftraits and their sum, i.e.:

[6] σ σ σ σxy x y x y= − −+1 2 2 2 2/ ( )

whereσx y+2 is the variance of the sum of the two traitsx andy. The

additive genetic correlation coefficient was calculated as

[7] rA AA A A= ′ ′σ σ σy( )

where A and A′ indicate a juvenile and a mature trait. The correla-tion coefficientsrE (environment) andrP (phenotype) were calcu-lated in the same way by substituting the corresponding covarianceand variance components in the formula given above. Standard er-rors of the heritabilities and the additive genetic correlations(lower boundary estimates) were calculated as in Becker (1984)and Falconer and Mackay (1996), respectively.

The relative selection efficiency (RSE) expressing the relativegenetic gain per time unit was calculated as

[8] RSE /A j j m m m j= r i h t i h t

whererA is the additive genetic correlation,i is the selection inten-sity, h is the square root of the heritability,t is the tree age at selec-tion, and “j” and “m” are the indices for the juvenile and maturetrait, respectively. The same selection intensity for the juvenile andmature trait was used in the calculations.

Mean tracheid lengths and wood densities increased withage (Table 1).

Estimates of biological and statistical variance compo-nents and heritabilities for the different traits of tracheidlength and wood density are given in Table 2. For all traitsthe additive variance components differed significantly fromzero, as is evident from their 95% confidence intervals. Nodominance effects could be shown for the two tracheidlength traits, as the dominance variance components werezero. For the wood density traits the dominance variancecomponents were small and did not differ significantly fromzero according to the 95% confidence intervals. The her-itabilities were high and exceeded 0.30 for both tracheidlength and wood density traits. For tracheid length theheritability was highest at tree age 31 (TL31). The her-itability of wood density traits was nearly the same at ages11, 15, and 33 whereas a slight increase was observed at treeage 27 (AWD8-27).

The additive genetic age–age correlations were high andhigher than the phenotypic correlations for both tracheidlength and wood density traits (Table 3). The environmentalcorrelations were close to zero (Table 3) except for the cor-relation between AWD at tree age 27 (AWD8-27) and wooddensity at ages 28–33 (WD28-33).

For tracheid length the genetic gain per unit time wastwice as high when selection was carried out at tree age 11than when it was carried out at tree age 31 (Table 4). Thecorresponding gain in selection for wood density at tree age11 or 15 was two to three times higher than with selection atage 33 (Table 4). A lower gain for wood density was ob-served when a late selection at age 27 was made.

Age-dependent changes in mean tracheid length andmean wood density

Increasing tracheid length and mean wood density withage (Table 1) is a general trend observed in pines (tracheidlength: Sanio 1872; Hartig 1895 (both cited in Larson 1994);wood density: Zobel and van Buijtenen 1989). Our esti-mated mean tracheid length at tree age 11 corresponds wellto a study of radial and longitudinal variation in onePinussylvestristree (Atmer and Thörnqvist 1982). Taking into ac-count the slightly different age at sampling, the meantracheid length at tree age 31 is in accordance with the ear-lier studies ofP. sylvestristrees originating from the sameregion in Sweden (Ståhl 1988; Persson et al. 1995). Similarwood density and increase in wood density from pith to barkas in this study were reported for a progeny trial ofPinussylvestrislocated close to the present trial, both trials withthe same site index and tree age (Persson et al. 1995).

Variance componentsNo significant dominance variance could be demonstrated

for any trait studied (Table 2), indicating that progeny test-ing for identification of trees with good general combiningability can rely on open-pollinated progenies. Our resultsalso indicate that the probability of finding families showinga high specific combining ability is very limited. Further-more the additive genetic variance for wood density was2.5–6.5 times larger than the dominance variance, ratios thatare larger to much larger than corresponding ratios for, e.g.,height growth observed in other studies (see review by Die-ters et al. 1995). However, the used mating design with fewfull-sib families per half-sib family is not optimal for esti-mating the dominance variance (Namkoong and Roberds1974), and the dominance variances were consequently esti-mated with large standard errors.

The heritability also depends on the uniformity of the en-vironmental conditions at the site of the trial because the en-vironmental variance is included in the denominator of theheritability expression. In pines, heritability estimates fortracheid length range from 0 to 0.97 (Nicholls et al. 1964;Matziris and Zobel 1973; Stonecypher et al. 1973; Okwu-agwa and Guries 1981; Wiselogel and Tauer 1982; Loo et al.1984; Cown et al. 1992). However, most of the estimates arein the range of 0.3–0.5. In their review, Zobel and vanBuijtenen (1989) stated that tracheid length is under moder-ate genetic control, which is in accordance with the present

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1376 Can. J. For. Res. Vol. 28, 1998

TraitRing(s) atbreast heigth

Tree age(years) n Mean

TL11 4 11 810 1.79 mmTL31 24 31 810 3.11 mmWD8-11 1–4 8–11 834 369 kg/m3

AWD8-15 1–8 8–15 834 384 kg/m3

AWD8-27 1–20 8–27 834 416 kg/m3

WD28-33 21–26 28–33 834 494 kg/m3

Table 1. Year ring number from the pith at breast height, treeage, sample size (n), and mean value for tracheid length (TL) attree ages 11 and 31, wood density (WD) at tree ages 8–11 and28–33, and area-weighted wood density (AWD) at tree ages8–15 and 8–27.

study. The environmental variance made up a larger propor-tion of the total variance for tracheid length at tree age 11than at age 31, which resulted in a lower heritability at theyounger age (Table 2). If a larger number of broken tra-cheids were included by mistake at age 11, this would haveincreased the proportion of environmental to total variance.Another reason for this higher proportion might be a truehigher environmental impact caused by establishment ef-fects. The heritability of about 0.4 for wood density obtainedin this study (Table 2) is in agreement with the moderate tohigh heritabilities often found in pines (see review by Zobeland Jett 1995).

Age–age correlationsThe high genetic age–age correlation close to unity for

tracheid length between ages 11 and 31 observed in thisstudy (Table 3) is exactly opposite to what was found inPinus taeda, the only study avaliable until now, where a zerogenetic age–age correlation was found (Loo et al. 1984).However, they found strong phenotypic correlations (rP =0.55–0.84) between tracheid length at different ages andwhole-core values. It has been suggested that phenotypiccorrelations could replace genetic correlations (Cheverud1988; Roff 1995), particularly when small sample sizes areused, as the estimation errors of the genetic correlations insuch cases are large. Loo et al. (1984) used a sample size of15 families compared with 30 families used in the presentstudy. Apart from a fundamental biological difference be-tween the two species the different sample sizes might be anexplanation of the opposite results with respect to geneticage–age correlations of tracheid length.

The high genetic age–age correlations for wood densityfound in this study (Table 3) are in accordance with earlierstudies onPinus taeda(Talbert et al. 1983; Loo et al. 1984),Pinus radiata(Cown et al. 1992),Picea abies(Hylen 1995),Picea sitchensis(Lee 1997), andPseudotsuga menziesii(Vargas-Hernandez and Adams 1992). Also, in retrospectiveearly tests, high genetic correlations were found inPseudo-tsuga menziesii(Adams et al. 1990; Woods et al. 1995) andPinus taeda(Williams and Megraw 1994).

A potential source of error is the thinning at age 28, assampling for tracheid length and wood density was doneonly among remaining stems. With regard to height mea-sured at age 22 and diameter at age 27 the heritabilities inthe thinned and the unthinned populations attained the samemagnitude, and correlations close to unity were observed be-tween breeding values of the thinned and unthinned popula-tions (data not shown). This indicates that the thinning didnot bias the parental ranking. We have no reason to believethat the thinning should affect tracheid length and wood den-sity differently. Thus the effect of the thinning on the age–age correlations is negligible.

The environmental age–age correlations are close to zerofor both tracheid length and wood density except for the cor-relation between AWD at ages 8–27 and wood density atages 28–33 (Table 3). This latter correlation indicates the ex-istence of autocorrelation, which may affect the estimate ofthe genetic correlation. To minimize the influence of auto-correlation caused by part–whole relationships, we haveconsistently correlated the wood density at younger ageswith the wood density at ages 28–33 and not at ages 8–33.Furthermore, it has been shown inPinus sylvestristhat the

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Hannrup and Ekberg 1377

Biological variance components Statistical variancecomponents95% CI 95% CI

Trait σA2 Lower Upper σD

2 Lower Upper σE2 σP

2 h2 SE h2 σp2 σf

2 σe2

TL11 0.0064 0.0018 0.011 0 — — 0.015 0.021 0.31 0.09 0.0016 0 0.018TL31 0.026 0.0097 0.041 0 — — 0.028 0.055 0.48 0.14 0.0066 0 0.041WD8-11 371 115 627 80 –65 225 473 925 0.40 0.12 93 20 719AWD8-15 208 64 353 86 –4 176 223 517 0.40 0.12 52 21 391AWD8-27 263 94 432 41 –34 117 214 519 0.50 0.14 66 10 377WD28-33 378 93 661 83 –94 263 583 1044 0.36 0.11 94 21 834

Note: See Table 1 for explanation of traits and their units.σA2 , additive genetic variance;σD

2 , dominance genetic variance;σE2, environmental variance;

σP2, phenotypic variance;σp

2, parental variance;σf2, family variance;σe

2, error variance.

Table 2. Estimates of biological variance components, heritability, and statistical variance components for tracheid length (TL), wooddensity (WD), and area-weighted wood density (AWD) traits at various ages.

Age–age correlation rA rE rP

TL11–TL31 0.92 (0.03) 0 0.36WD8-11–WD28-33 0.88 (0.05) –0.12 0.31AWD8-15–WD28-33 0.92 (0.03) 0.03 0.43AWD8-27–WD28-33 1.0 (0.00) 0.43 0.68

Note: See Table 1 for explanation of traits.

Table 3. Additive genetic correlation (rA) (standard error inparentheses), environmental correlation (rE), and phenotypiccorrelation (rP) for tracheid length (TL), wood density (WD),and area-weighted wood density (AWD) traits at various ages.

Age–age correlation tj tm RSE

TL11–TL31 11 31 2.08WD8-11–WD28-33 11 33 2.78AWD8-15–WD28-33 15 33 2.13AWD8-27–WD28-33 27 33 1.44

Note: See Table 1 for explanation of the traits.

Table 4. Age of selection for the juvenile (tj) and mature (tm)trait and relative selection efficiency (RSE, expressed as geneticgain per time unit) of an indirect selection at the juvenile age formature tracheid length (TL) and wood density (WD).

wood density increases rapidly to a tree age of about 25years (Atmer and Thörnqvist 1982; Persson et al. 1995) andstabilizes thereafter at this level. This means that the woodformed at ages 28–33 will be similar to the wood formedfrom these ages up to rotation age.

One hypothesis for the high genetic age–age correlationsfor tracheid length and wood density is that these traits areinfluenced by a lower number of genes than growth traits,which generally show lower genetic age–age correlations(Hodge and White 1992; Kremer 1992; Dieters et al. 1995;Xie and Ying 1996). Further genetic studies using DNAtechniques may reveal whether this hypothesis should be re-jected or not. Development of these new experimental tech-niques expands the possibilities of performing such studies.So far, studies of quantitative trait loci (QTL) for wood den-sity in pines have not revealed single QTLs with very largeeffects (Groover et al. 1994; Knott et al. 1997).

Relative selection efficiencySelection for tracheid length and wood density was shown

to give a higher genetic gain per time unit at age 11 than atlater ages (Table 4). The higher efficiency of early selectioncan be explained by the earlier age at selection, the high ge-netic age–age correlations, and the similarity in heritabilitiesbetween the ages. As the tracheid length was measured onlyat ages 11 and 33, the optimum selection age for this traitcannot be determined in this study. The relative selection ef-ficiency for wood density decreased with the age of selec-tion, and this indicates that the optimum age of selection isbelow age 11. To determine the optimum selection age, thesampling of the increment cores should be made at a heightbelow 1.3 m. InPseudotsuga menziesii, it was shown thatsampling can be done at a height of 0.7 m with the same re-sult as at 1.3 m (Loo-Dinkins and Gonzales 1991). Trans-lated to Pinus sylvestris, this would mean that selectioncould be made at a tree age of 8–9 years. In Sweden the se-lection for height in thePinus sylvestrisbreeding program iscurrently performed at an age of 15 years (G. Jansson, TheForestry Research Institute of Sweden, personal communica-tion). Our results indicate that it should be possible to selectfor tracheid length and wood density at an earlier age thanfor height. A prerequisite for including trees of this age in abreeding program is that early flowering can be induced.Another option to further decrease selection age for thesetraits would be to apply retrospective early tests studyingyoung siblings of those tested in field trials.

Our results indicate that strong genetic age–age correla-tions exist between juvenile and mature wood for tracheidlength and wood density inPinus sylvestris. The geneticgain per year for tracheid length and wood density was twoto three times larger when selection was carried out at age11 rather than at age 33. This suggests that early tests forthese traits should increase the efficiency of thePinussylvestristree breeding program.

The additive genetic variance was large for all traitswhereas the nonadditive genetic variance was zero fortracheid length traits and did not differ significantly fromzero for wood density traits. This suggests that progeny test-

ing for identification of trees with good general combiningability for these traits can rely on open-pollinated progenies.The high heritabilities varying between 0.3 and 0.5 indicatethat the relative impact of the environment on tracheidlength and wood density traits was low.

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