Draft - University of Toronto T-Space · Draft 1 Abstract 2 A two-generation pedigree involving 519 Norway spruce plus-trees (at clonal archives) 3 and their open-pollinated (OP)

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Genetic analysis of wood quality traits in Norway spruce open-pollinated progenies and their parent plus-trees at

clonal archives, and the evaluation of phenotypic selection of plus-trees.

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2018-0117.R2

Manuscript Type: Article

Date Submitted by the Author: 19-Nov-2018

Complete List of Authors: Zhou, Linghua; Swedish University of Agricultural Sciences Faculty of Forest Sciences, Forest Genetics and Plant PhysiologyChen, Zhiqiang; Swedish University of Agricultural Sciences Faculty of Forest Sciences, Forest Genetics and Plant PhysiologyLundqvist, Sven-Olof; RISE Research Institutes of Sweden AB; IIC, Rosenlundsgatan 48B, SE-118 63 Stockholm, SwedenOlsson, Lars; RISE Research Institutes of Sweden ABGrahn, Thomas; RISE Research Institutes of Sweden ABKarlsson, Bo; Forestry Research Institute of SwedenWu, Harry; Swedish University of Agricultural Sciences Faculty of Forest Sciences, Forest Genetics and Plant Physiology; CSIROGarcía-Gil, María; Swedish University of Agricultural Sciences Faculty of Forest Sciences, Forest Genetics and Plant Physiology

Keyword: Solid-wood, Norway spruce, parent-offspring regression, narrow-sense heritability, repeatability

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Canadian Journal of Forest Research

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Genetic analysis of wood quality traits in

Norway spruce open-pollinated progenies and

their parent plus-trees at clonal archives, and the

evaluation of phenotypic selection of plus-trees.

Linghua Zhou1, Zhiqiang Chen1, Sven-Olof Lundqvist2,3, Lars

Olsson3, Thomas Grahn3, Bo Karlsson4, Harry X. Wu1,5, Marı́a

Rosario Garcı́a Gil1∗

1 Department of Forest Genetics and Plant Physiology, Swedish University of

Agricultural Science,Umeå, Sweden

2 IIC, Rosenlundsgatan 48B, SE-118 63 Stockholm, Sweden

3 RISE Bioeconomy, Box 5604, SE-114 86 Stockholm, Sweden

4 Skogforsk, Ekebo 2250, SE-268 90 Svalöv, SWEDEN

5 CSIRO NRCA, Black Mountain Laboratory, Canberra, ACT 2601, Australia

∗Corresponding author’s email: [email protected]

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Abstract1

A two-generation pedigree involving 519 Norway spruce plus-trees (at clonal archives)2

and their open-pollinated (OP) progenies were jointly studied with the aim to evalu-3

ate the potential of plus-tree selection based on phenotype data scored on plus-trees.4

Two wood properties (wood density and modulus of elasticity, MOE) and one fiber5

property (microfibril angle, MFA) were measured with a SilviScan instrument on6

samples from one ramet per plus-tree and 12 OP progenies per plus-tree (total 62887

trees). Three ramets per plus-tree and their OP progenies were also assessed for Pi-8

lodyn penetration depth and Hitman acoustic velocity which were used to estimate9

MOE. The narrow-sense heritabilities (h2) estimates based on parent-offspring re-10

gression were marginally higher than those based on half-sib correlation, when three11

ramets per plus-tree were included. For Silviscan data, estimates of the correlation12

between half-sib progeny-based Breeding Values (BVs) and plus-tree phenotypes,13

and repeatability estimates, were highest for wood density, followed by MOE and14

MFA. Considering that the repeatability estimates from the clonal archive trees were15

higher than any h2 estimate, selection of the best clones from clonal archives would16

be an effective alternative.17

18

Key words19

Solid-wood, Norway spruce, parent-offspring regression, heritability, repeatability20

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Introduction21

Norway spruce [Picea abies (L.) Karst.] is one of the most important conifer species22

in Europe for both economic and ecological aspects (Spiecker, 2000). Higher vol-23

ume production, vitality and log quality for straightness and branch angle has tra-24

ditionally been the main objectives of the species breeding program, while more25

recently, different aspects related to wood quality are gaining increasing attention26

(Mullin et al., 2011; Rosvall et al., 2011). For mechanical properties of wood-based27

products, wood density, microfibril angle (MFA), and modulus of elasticity (MOE)28

are considered as the most important solid-wood quality traits (Chen et al., 2015;29

Zobel and Jett, 1995) and, therefore, they are the focus of our study.30

The SilviScan technology was developed to measure radial variation (i.e. from31

pith to bark) of solid-wood quality traits including wood density, MFA and MOE32

(Evans, 1999; Evans and Elic, 2001; Evans, 2008),as well as fiber traits (Evans,33

1994). Its high efficiency, compared to corresponding laboratory methods con-34

tributed substantially to advance in research and development within wood biology,35

forestry and optimal use of forest resources in softwoods (Lindström et al., 1998;36

Lundgren, 2004; Kostiainen et al., 2009; McLean et al., 2010; Piispanen et al., 2013;37

Fries et al., 2014), in hardwoods (Kostiainen et al., 2014; Lundqvist et al., 2017) and38

on modelling of trait variations (Wilhelmsson et al., 2002; Lundqvist et al., 2005;39

Franceschini et al., 2012; Auty et al., 2014). SilviScan is also used to produce40

benchmark data and for validation of the more rapid and non-destructive methods41

(NDM) procedures. Examples for solid wood traits are Pilodyn penetration depth42

and Hitman acoustic velocity (Chen et al., 2015; Kennedy et al., 2013; Vikram et al.,43

2011). Pilodyn is an indirect non-destructive, low cost and easy-to-use instrument44

for estimating wood density. In Norway spruce and other conifer species, strong45

genetic correlations were observed between Pilodyn penetration depth and wood46

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density measured with SilviScan (Chen et al., 2015; Cown, 1978; Desponts et al.,47

2017; Fukatsu et al., 2011; King, 1988; Sprague et al., 1983; Yanchuk and Kiss,48

1993). Further, acoustic velocity measured with Hitman apparatus has been shown49

as an efficient indirect method related to MFA and has already been used on many50

species, such as Scots Pine (Pinus sylvestris L.) (Hong et al., 2015), white spruce51

(Picea glauca [Moench.] Voss) (Lenz et al., 2013), and Norway spruce (Chen et al.,52

2015). Models for many species were implemented in an earlier version of SilviS-53

can (Evans and Elic, 2001), followed by further improvements (Evans, 2008). An54

analogous model using the proxy measurements of acoustic velocity and Pilodyn55

penetration on standing trees was shown to be efficient for the selection for wood56

stiffness in Norway spruce (Chen et al., 2015). Pilodyn, however, measures wood57

density only in the outermost annual rings; therefore, it has also been suggested that58

it may not be reliable for ranking the whole tree in the case where the correlation59

is low between the outermost rings and inner rings (Wessels et al., 2011) or if the60

diameter of tree is wide.61

A common practice in forest tree breeding programs, which aims to guarantee62

early genetic gain, is to phenotypically select superior genotypes (plus-trees) from63

naturally regenerated mature stands (Zobel et al., 1984; Danusevicius and Lindgren,64

2002). In Sweden, selection of Norway spruce plus-trees breeding base population65

started in the 1940s (Karlsson and Rosvall, 1993). Presently, large numbers of plus-66

trees are maintained in ex-situ grafted clonal archives. These archives serve as base67

breeding populations where crossings of selected parental genotypes are conducted68

with the purpose of generating cross-pollinated progenies for the next generation69

in the breeding cycle. After establishment of the clonal archives, the plus-trees are70

genetically evaluated (ranked) for growth, straightness, branch angle and vitality71

superiority following the backward selection approach. Backward selection is an72

expensive method that starts with the establishment of open-pollinated (OP) proge-73

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nies for large numbers of families in progeny trials often tested at multiple sites.74

This is followed by the assessment of the progenies at more than one site and at tree75

age high enough for selection, and finally the estimation of breeding values (BVs)76

to identify the superior genotypes (White et al., 2007). A less expensive alterna-77

tive to backward selection is the direct selection of plus-trees in the clonal archives78

based on phenotype data directly measured on the plus-trees. This approach can be79

incorporated as a first part of a two-stage selection approach, in which plus-trees80

are selected on phenotype data for traits of high heritability, followed by a second81

part, based on clonal or progeny testing (Danusevicius and Lindgren, 2005).82

The goal of this study is to evaluate the potential of selection on phenotype data83

of outstanding plus-trees compared to backward selection based on open-pollinated84

progeny trials. For this, we conducted the following three analyses:85

1. Correlations between the plus tree BVs for wood density, MFA and MOE,86

estimated based on OP progenies and plus-tree phenotypes measured at the clonal87

archive. Where SilviScan-based data were available, correlations were estimated88

for each annual ring.89

2. Narrow-sense heritability (h2) based on parent-offspring regression and half-90

sib progeny correlation.91

3. Repeatability or the proportion of clone variation at the clonal archive to92

conduct plus-tree selection.93

Materials and methods94

Plant material95

The study was based on a two-generation pedigree involving 519 mother plus-trees96

from two different clonal archives located at Ekebo and Maltesholm in southern97

Sweden. The clonal archive at Ekebo was established in 1984 and the one at Mal-98

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tesholm in 1985-1987. At the time of establishment, on average 10 ramets were99

grafted for each plus- tree and planted with a spacing of 3m× 0.5m. At the time of100

sampling, spacing had been increased through thinning two times, leaving the ma-101

jority of the genotypes with first seven and then only three ramets remaining. For102

their corresponding 519 open-pollinated (OP) families, more progenies per fam-103

ily were planted at each progeny trial. Data from two progeny trials were used:104

S21F9021146 aka F1146 (Höreda, Eksjö, Sweden) and S21F9021147 aka F1147105

(Erikstorp, Tollarp, Sweden), both established in 1990 with spacing 1.4m × 1.4m.106

The same OP families are present in both progeny trials. Increment cores from the107

progenies of the OP families were sampled in 2010 and from the ramets at the clonal108

archive in 2017.109

Silvicultural activities110

Mild precommercial thinnings were conducted in Höreda and Erikstorp in 2008 at111

age of 18 and in 2010 at age of 20. At this first thinning, only strongly suppressed112

trees which were judged not to reach commercial dimensions were cut down. Most113

of these were less than 50 mm diameter at breast height (DBH), and their removal114

was assumed not to have affected the growth or properties of remaining trees. The115

second thinning was performed in the year of sampling and can only have influenced116

the outermost growth ring, for which data were excluded for other reasons, see117

below. The clone archive at Ekebo and Maltesholm were topped in the autumn of118

2007 at age 23 when a large seed crop was harvested. The 15-20% uppermost part119

of the trees were removed. Thinnings of the Ekebo clonal archive and parts of the120

Maltesholm archive were carried out the first time in the late 1990s and the last time121

in the autumn of 2009 at age 25.122

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Phenotypic measurements123

The radial variations in wood density, MFA and MOE had been assessed already in124

a previous studies (Chen et al., 2014). Increment cores of up to 12 progenies per125

OP family (six from each progeny trial) had been analyzed from pith to bark with126

SilviScan, followed by the calculation of area-weighted averages, representing the127

trait averages of all wood formed in the stem cross-sections at each cambial age.128

In the current study, analogous Silviscan data were generated also for one ramet129

from each clone from the parental 519 plus-trees at the clonal archives. Pilodyn130

6J Forest and Hitman ST300 instruments were used on the standing trees to assess131

penetration depth and acoustic velocity, of the same ramets. These measurements132

were used to estimate MOE(ind), using the formula:133

134

MOE(ind) = (1/P ilo)× 10, 000× AV 2135

136

where Pilo is the Pilodyn penetration depth (mm) and AV is the velocity of the wave137

through the material (km/s). AV has a high inverse correlation with MFA and the138

inverse of Pilo has a high correlation with wood density (Chen et al., 2015).139

When data for more than one ramet was available, the average was used for140

further Pearson correlation analysis. The evaluations were based on data from ring141

3 to ring 16. The two rings closest to the pith were removed from the evaluations142

as the rings here may be so curved that the X-ray beam used on measurement will143

pass through wood of adjacent rings. Yet, values for rings number 1 and 2 are kept144

in Fig. 1 to illustrate the described problematic. Data on rings larger than 16 of145

the progeny trials were excluded to avoid problems of representability given that146

the slow-growing trees did not reach the highest cambial ages (Lundqvist et al.,147

2018). The number of rings per tree varied from 10 to 18. Further, data for the148

outermost ring of each tree was excluded from the evaluations as they may not be149

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full formed, and to avoid problems of data distortion due to damage of the ring150

during the increment core extraction.151

The genetic parameters were calculated based on averages for stem cross-sections152

at different cambial ages (ring numbers) using R (v3.3.3).153

Breeding value (BV) of mothers based on progeny tests154

The linear mixed model used for the estimation of parental BV and variance com-155

ponents was expressed in matrix form:156

157

y = Xb+ Zu+ e158

159

where y is a vector of measured data, b is a vector of fixed effects with its design160

matrix X , u is a vector of random effects with it design matrix Z, e is a vector161

of residuals. Fixed and random effect solutions are obtained by solving the mixed162

model equation (White and Hodge, 2013):163

X′X X′ZZ′X Z′Z+Iα

b̂û

=X ′yZ ′y

where b is the fixed effects including site, block within site and provenance, u is the164

random effect which is the family. I is the identity matrix with dimensions equal165

to the number of mothers, α is a ratio of residual variance and genetic variance166

explained by the random family effect.167

The estimation of BV (u), variance and covariance components were done using168

lme4 package (Bates et al., 2015) in R (v3.3.3).169

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Pearson correlation170

For all wood properties, measured with SilviScan and indirect methods, Pearson171

correlation was calculated between the plus-trees BVs and plus-trees phenotypes172

data. In the case of SilviScan-based analysis only one ramet was available, whereas173

in the case of Pilodyn, Velocity and MOE(ind) two or three ramets were available174

depending on the open-pollinated family.175

Narrow-sense heritability (h2)176

Two methods for calculating heritability were estimated. The first method is one177

based on half-sib family progeny analysis and the linear mixed model was fitted as178

follows:179

180

yijklm = µ+ Si +Bj(i) + Pk + Fl(k) + SFil(k) + eijklm181

182

where yijklm is the phenotypic individual observation, µ is the general mean and183

Si, Bj(i) and Pk are the fix effects of the ith site, the jth block within site and the184

kth provenance, respectively. Fl(k) is the random effects of the lth family within kth185

provenance, SFil(k) is the random interactive effect of the ith site and the lth family186

within kth provenance and eijklm is the random residual effect.187

Narrow sense heritability was estimated for each trait as188

189

ĥ2 =σ̂2Aσ̂2P

=4×σ̂2F

σ̂2F+σ̂2SF+σ̂

2e

190

191

where σ̂2A, σ̂2P , σ̂

2F , σ̂

2SF , σ̂

2e are estimation of additive genetic variance, phenotypic192

variance, family variance, family site interaction variance and residual variance re-193

spectively.194

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The second one was based on parental-offspring regression.195

We used a linear regression to model the mother-offspring pairs for each trait196

value:197

y = β0 + β1X198

199

where y is the phenotype value for the offspring and X is the phenotype value for200

a mother. Since genetic covariance between parents and offspring is equal to 12σ2A201

(Falconer and Mackay, 1996), we can get202

203

β1 =Cov(X,Y )V arX

=12σ2Aσ2P

204

205

The individual tree narrow-sense heritability is206

207

h2 =σ2Aσ2P

208

209

So from the slope of the regression, the estimation of the h2 can be obtained210

from211

212

ĥ2 = 2β̂1213

214

The standard error of heritability is estimated by 2/√N ((Falconer and Mackay,215

1996)),where N is the number of families.216

This way, the parent-offspring based heritability was computed for SilviScan217

data for each annual ring, and for Pilodyn penetration depth, Hitman acoustic veloc-218

ity and MOE(ind). To allow comparison between the estimates based on Silviscan219

and those based on indirect measurements, all the heritabilities were computed only220

on the 162 families for which three ramets were available in the clonal archives.221

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In our study, the heritabilities for Silviscan data were calculated for each cambial222

age from the area-weighted averages representing stem cross-sections.223

Repeatability224

Repeatability indicates the proportion of total variation in a trait that is due to dif-225

ferences between clones (Falconer and Mackay, 1996). The individual repeatability226

R was calculated as (Falconer and Mackay, 1996; Lynch and Walsh, 1998):227

228

R = σ2c

σ2c+σ2e

229

230

where σ2c is the estimated clone variance and σ2e is the residual variance.231

Progeny size effect on heritability232

In order to investigate the effect of progeny size on the estimation of heritability233

based on a parent-offspring regression, we used a subset of progeny trees where234

each family had exactly six progenies in each of the two trails. In total, 180 families235

and 2160 progeny trees were included in the analysis. From this subset, one to six236

progenies were randomly selected per family, from each site. Heritability being es-237

timated using parent-offspring regression. The process was bootstrapped 500 times,238

the means and standard errors of the heritability were then estimated for compari-239

son. The most prominent consequence of increasing the number of open-pollinated240

progenies is the decrease in the standard errors (i.e., more precise estimation of her-241

itability) (Fig. 4). When a progeny size of four trees was selected, parent-offspring242

heritability stabilizes for MOE(ind) and peaked for Velocity, while it for the Pilo-243

dyn trait reached a maximum value at progeny size 6. Based on these results, all the244

genetic parameters involving progeny data were estimated using the highest number245

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of progeny size.246

247

Results248

Traits curve for progenies and plus-trees249

Average values for ring width, diameter at breast height (DBH), wood density, MOE250

and MFA were plotted against each annual ring for progenies and plus-trees (Fig.1).251

The ring numbrs larger than 27 for the clonal archive and ring numbers larger than252

16 for progeny trees were excluded as it was based on very few trees.253

Ring width and wood density curves showed clear discrepancies between the254

trees at the clonal archive and those at the progeny trials. In the progeny test, the255

average widths of the rings decreased steeply until about ring number 10, after256

which it became rather stable, until the overrepresentation of fast-growing trees257

became visible at above ring number 15 (Lundqvist et al., 2018), which in the figure258

is indicated with a black vertical line. The density average was high closest to the259

pith, then low on stable level until ring number 10, after which it started to increase260

steeply until the fast-growing trees became overrepresented. In contrasts to the261

progeny trial, the ring width means of the clonal archives started low and increased262

steadily until rings number 10 to 12, presumingly at the time the archive was first263

thinned from dense to low spacious compared to the progeny trials. Then, the means264

started to decrease with age. These trees were topped at age 23 years, which should265

approximately correspond to ring number 18 as indicated with a grey vertival line.266

At higher ages, ring width experienced a sharp drop, which can be interpreted as a267

physiological response of the trees to the removal of the upper canopy. From this we268

concluded that data at higher ages of the clonal archive may not represent the natural269

development of trees are not be fully comparable with the expected response at the270

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progeny trials at older ages. At ages deemed representative, the wood density curve271

for the clonal archive mirrored the changes in ring width, which is not surprising272

considering that growth and density are negatively correlated (Chen et al., 2014). In273

reference to DBH, we observed that the trees at the clonal archive were thinner from274

pith up to ring number 14. After this ring, due to steaily wider rings, they became275

thicker than those at the progeny trial.276

The curves representing change in MFA change with annual ring were very sim-277

ilar for the trees at the progeny trial and clonal archive. In both types of plantation278

MFA decreased sharply and stabilized towards the bark. The slight increases of the279

averages for the last rings shown may reflect over-representation of fast-growing280

trees. As expected, the decrease in MFA is accompanied by an increase in MOE,281

due to the strong negative correlation between thetraits, also shown based on the282

same data in (Chen et al., 2014). It is also expected that the progeny trial MOE283

reached higher values than those at the clonal archive, MOE shows positive corre-284

lation with wood density, which is higher for these trees in rings larger than10. In285

contrast to ring width and wood density, MFA and MOE curves did not reveal an286

effect of tree topping.287

Breeding value and phenotypic value correlation288

Per ring correlations between half-sib progeny-based BVs and plus-trees pheno-289

types for the SilviScan data are presented in Fig. 2. For wood density, correlation290

estimates increased steadily from low levels at the pith to about 0.4 at rings number291

12 to 15. For MFA, the estimates reached a plateau of about 0.17 at rings number292

4 to 7 and then decreased gradually. The estimates for MOE were in between: an293

initial increase was followed by a plateau, with a decreasing tendency only near the294

bark, possibly an effect of the increasing over-representation of fast-growing trees295

at these ring numbers.296

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The estimated correlations between half-sib progeny-based BVs and plus-trees297

phenotypes were 0.29, 0.13 and 0.23 for Pilodyn penetration depth, Hitman acous-298

tic velocity and MOE(ind), respectively. When using three plus-tree ramets, the299

correlation increased to 0.32, 0.15 and 0.28, respectively. These values were in300

concordance with the Silviscan-based estimates of correlation where the highest301

values were reached for density, followed by MOE and MFA in this order.302

Heritability estimates on progeny and parent-offspring regression303

Narrow-sense heritability (h2) estimations based on parent-offspring regression at304

each annual ring using SilviScan data are presented in in Fig. 3. The h2 estimates for305

wood density increased from pith to bark, for MFA they remained on the same level306

across all annual rings. In the case of MOE an initial increase of the h2 estimates307

was followed by a plateau.308

The h2 estimations of the whole stem cross-sections based on half-sib progeny309

correlation and parent-offspring regression are presented in Table 1. Based on310

progeny correlation, they were 0.43, 0.29 and 0.38 for wood density, MFA and311

MOE, respectively. In the case of mean parent-offspring, the h2 estimates (based on312

one ramet) were 0.35, 0.15 and 0.28 for wood density, MFA and MOE, respectively.313

The h2 values estimated by progeny correlation were 0.31, 0.20 and 0.28 for Pilo-314

dyn, Velocity and MOE(ind), respectively. Moreover, based on parent-offspring315

regression the h2 values ranged from 0.27 to 0.41, 0.13 to 0.29 and 0.13 to 0.30316

for Pilodyn, Velocity and MOE(ind), respectively. With respect to the indirect mea-317

surements of wood quality, these results indicate that h2 estimation based on parent-318

offspring regression were only marginally higher than those based on half-sib cor-319

relation, even when three ramets per plus-tree were included in the analyzes. Based320

on data collected with indirect methods, the progeny-based h2 estimates were higher321

than parent-offspring regression h2 estimates for one ramet. Instead, the progeny-322

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based h2 estimates were marginally lower than the h2 estimates obtained for parent-323

offspring regression for three ramets. Based on Silviscan data, the progeny-based h2324

estimates were higher than the h2 estimates obtained for parent-offspring regression325

for one ramet. To allow comparison, all the h2 estimates in Table 1 were computed326

only on the 162 families for which three ramets were available in the clonal archive.327

Repeatability estimates were higher than any h2 estimate.328

Discussion329

In this study, we evaluated the potential of ranking and selection for better solid-330

wood quality traits of outstanding phenotypes (plus-trees) as an alternative to back-331

ward selection based on breeding value (BV) estimates on half-sib progenies. The332

evaluation was based on multiple genetic parameters: correlation between half-sib333

progeny BVs and plus-tree phenotype data, repeatability, and narrow-sense heri-334

tability (h2) based on parent-offspring regression as compared to half-sib correla-335

tion.336

The h2 estimates for wood density, MFA and MOE measured with SilviScan337

from increment cores were 0.43, 0.29 and 0.38, and 0.35, 0.15 and 0.28 based on338

progeny correlation and parent-offspring regression, respectively. When using in-339

direct measurements directly on standing trees, the h2 estimates based on progeny340

correlation were 0.31, 0.20 and 0.28 for Pilodyn, Velocity and MOE(ind), respec-341

tively. Moreover, based on parent-offspring regression the values ranged from 0.27342

to 0.41, 0.13 to 0.29 and 0.13 to 0.30 for Pilodyn, Velocity and MOE(ind), respec-343

tively. Our h2 values estimated by progeny correlation were in the range of those344

previously reported for wood properties in Pinus taeda L. (Isik et al., 2011),Pinus345

pinaster Ait. (Louzada, 2003; Gaspar et al., 2008), Pinus contorta Douglas (Hay-346

atgheibi et al., 2017), Norway spruce (Hylen, 1997, 1999; Hannrup et al., 2004;347

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Hallingbäck et al., 2008), Picea glauca (Moench) Voss. (Lenz et al., 2010) and348

British Columbia’s interior spruce (Ivkovich et al., 2002). Similarly, our h2 esti-349

mates based on parent-offspring regression also agree with previously reported val-350

ues for wood properties in Norway spruce (Steffenrem et al., 2016), P.Pinus taeda351

(Loo et al., 1984; Williams and Megraw, 1994) and Picea glehnii (Tanabe et al.,352

2015).353

Our repeatability estimates for the indirect measurements based on the analysis354

of three ramets per plus-tree were 0.52, 0.30 and 0.45 for Pilodyn, Velocity and355

MOE(ind), respectively. Previously reported repeatability estimates for wood qual-356

ity and growth in Norway spruce (Rosner et al., 2007; Gräns et al., 2009; Steffenrem357

et al., 2016) and Picea glehnii (F Schmidt) Mast. (Tanabe et al., 2015) are also in358

concordance with our estimates, while other studies have reported either higher val-359

ues MFA and MOE in P.Pinus radiata D. Don, (Lindström et al., 1998) or lower360

values for MOE in Picea sitchensis (Bong.) Carr., (Hansen and Roulund, 1997).361

Interpretation of the discrepancies between progeny and plus-tree362

for ring width and wood properties363

The observed discrepancies in developments across annual rings between the trees364

at the progeny trials and the clonal archive for ring width, wood density and MOE365

could be attributed to a difference in spacing, including thinning of the clonal366

archive. During the first years, the trees of the clonal archive are only 0.5m apart367

from their next neighbours and under strong competition compared to the trees in368

the progeny trials. This is presumed to explain their thinner annual rings and higher369

wood density at these ages. The thinning performed at two occasions even out this370

difference in competition, and widths and densities become similar. Thinning re-371

sult in more favorable growth conditions for the clonal archive trees regarding both372

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access to light and other resources, which is presumed to explain that trees at these373

ages instead have wider annual rings and lower wood densities. After topping of374

the trees, it is harder to related to the developments of growth and patterns.375

Less spacing between trees is known to result in stronger competition for re-376

sources. Under tight spacing lower diameter is primarily the result of competition377

for light (Turner et al., 2009). Trees tend to grow taller at the expense of diameter in378

their attempt to outcompete the neighbour trees in search of light. Multiple studies379

in conifer species have reported effects of plantation density on growth (diameter380

and slenderness) and wood and fiber properties. Wider spacing at planting has been381

reported to be associated with higher tree diameter and lower MOE in Scots pine382

(Persson et al., 1995) and a number of coniferous species (Chuang and Wang, 2001;383

Zhang et al., 2002; Clark et al., 2008; Lasserre et al., 2008, 2009; Schimleck et al.,384

2018). Ring width and wood density are negatively correlated, and MOE negatively385

correlated with wood density and MFA, respectively (Loo et al., 1984; Hodge and386

Purnell, 1993; Zhang and Morgenstern, 1995; Waghorn et al., 2007; Gaspar et al.,387

2008; Lasserre et al., 2009; Chen et al., 2014). The effect of spacing on growth and388

wood properties together with their well-documented correlations strengthen our in-389

terpretation above regarding thinner rings and higher density for the clonal archive390

trees in the first rings, and the adverse later on. It also supports our interpretation of391

the higher MOE, and lower MFA, at these latter ages for the progeny trees.392

While narrow spacing could account for the results we have obtained, it is also393

possible that additional factors have contributed to the discrepancies between the394

two types of plantation: Abiotic factors such as rainfall, temperature or soil proper-395

ties. However, a previous study conducted on the same data from the progeny trials,396

both treated with similar silvicultural activities, revealed low G × E interaction397

(Chen et al., 2014), which indicates that climatic conditions or soil properties are398

not factors behind the differences. quality may not be the most determinant factors399

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explaining our results, at least, in southern Sweden where all three plantations are400

located.401

Potential for selection of Norway spruce plus-trees on phenotype402

data at clonal archives403

In operational breeding, selection of plus-trees as gene donors to the next generation404

is usually conducted through evaluation of their OP progenies grown in common-405

garden experiments (progeny trials), a breeding design known as a backward selec-406

tion. This is a method that involves multiple actions such as seedling production,407

seedling establishment (often in multiple sites), and assessment and evaluation of408

multiple tree properties when the trees in the trial have grown at least 10 rings at409

breast height. The high demands in time and costs of this approach motivates eval-410

uation of alternatives, such as plus-trees selection based on phenotype data assessed411

at the clonal archive.412

Phenotypic selection of plus-trees is a common practice to establish the founda-413

tions of a breeding program, while providing early genetic gains (Zobel et al., 1984).414

Furthermore, two-stage selection strategies where plus-trees are first selected based415

on phenotype followed by a second stage based on clonal or progeny test of plus-416

trees have previously been proposed in conifers (Danusevicius and Lindgren, 2002,417

2004). Danusevidous and Lindren concluded that when heritability is high, phe-418

notypic selection is a superior breeding strategy and a two-stage strategy based on419

progeny testing improves by the first stage of phenotypic selection.420

Considering that repeatability and h2 estimates are similar we suggest that se-421

lection of MFA at the clonal archive would be an effective alternative. However,422

given the low values of correlation between plantations, h2 and repeatability it is423

expected a lower efficiency in tree improvement for MFA than for other traits with424

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higher h2, such as density (Chen et al., 2014). This conclusion can be made exten-425

sive to both progeny-based or plus-tree phenotype-based selection. The heritability426

of MOE using three ramets based on progeny-parental regression (0.30) is higher427

than using half-correlation (0.28). However, considering that clonal repeatability428

for MOE (0.45) is higher than any h2 estimate, we suggest that it would be more429

cost- and time-effective to select clonal archive trees based on MOE scored with430

indirect measurements. Previously, MFA and MOE have been reported to have low431

and moderate heritabilities, respectively, in Norway spruce (Hannrup et al., 2004;432

Lenz et al., 2010; Chen et al., 2014), while higher heritabilities have been reported433

in Scots pine for MOE (Hong et al., 2015) and for MOE and MFA in P. taeda (Isik434

et al., 2011). Similar to the other wood properties, repeatability for wood density435

is higher than correlation and h2, selection of trees at the clonal archive based on436

indirect measurements of this trait will be efficient. Considering that h2 increases437

towards the bark it is expected that a higher response to selection at older ages.438

Other studies also support our observation of higher heritability for wood density439

than for MFA and MOE (Lenz et al., 2010; Isik et al., 2011; Chen et al., 2014).440

Conclusion441

Our study resulted in the following conclusions:442

• Narrow spacing at the clonal archive could account the discrepancies between443

the progeny trial and clonal archive for ring width and wood density traits.444

• Narrow-sense heritabilities (h2) estimated from parent-offspring regression445

using a single ramet were lower than based on half-sib correlation. Based446

on indirect measurements, parent-offspring h2 estimates using three ramets447

were higher than based on half-sib correlation, indicating that multiple copies448

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of ramets are critical in estimating reliable genetic parameters and making449

selection in archive.450

• Wood density or its surrogate trait Pilodyn measurement had the highest h2451

among the three wood quality traits, whether it is based on SilviScan data452

using increment cores or indirect measurements on standing trees, and based453

on parent-offspring regression or half-sib correlation, followed by MOE and454

MFA.455

• Backward selection, whether based on offspring data alone or a combination456

of offspring and clonal archive data would be most effective for wood density,457

and least effective for MFA, while MOE in the middle.458

• Based on higher repeatability estimates as compared to the h2 estimates se-459

lection of the best clones would be highly cost- and time-effective from clonal460

archives.461

• The observed discrepancies between both types of plantation for growth,462

wood, and fiber properties could be mostly explained by the tighter tree spac-463

ing at the clonal archive.464

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Acknowledgments465

We acknowledge Skogforsk for support on the collection of data in both the clonal466

archive and progeny trials, and also Åke Hansson, Thomas Trost and Fredrik Adås,467

Innventia, now RISE Bioeconomy, for the excellent work with the Silviscan wood468

analyses. We also acknowledge Bio4Energy and the Swedish Foundation for Strate-469

gic Research (SSF, grant number RBP14-0040) ), funding from Vinnova (the Swedish470

Governmental Agency for Innovation Systems) and KAW (The Knut and Alice Wal-471

lenberg Foundation) for support to conduct this study.472

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Tables and Figures686

Table 1: Heritability and repeatability estimates based on measurements of wooddensity, MFA and MOE from SilviScan, and Pilodyn penetration depth and Hitmanacoustic velocity. To allow comparison, all the heritability estimates were basedonly on the 162 families for which three ramets were available in the clonal archive.In bold are marked those heritability values that are statistically significantly differ-ent from zero.

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2

3

4

5

0 10 20Ring

Rin

g w

idth

(m

m)

motherprogeny

0

50

100

150

0 10 20Ring

Ste

m d

iam

eter

(m

m)

425

450

475

500

0 10 20Ring

Woo

d de

nsity

(kg

m3 )

10

15

20

25

30

0 10 20Ring

MFA

(o)

5.0

7.5

10.0

12.5

15.0

0 10 20Ring

MO

E(G

Pa)

Figure 1: Mean values generated with SilviScan data from the open-pollinated pro-genies and from the clonal archive.

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0.0

0.2

0.4

0.6

4 8 12 16Ring

Cor

rela

tion

DensityMFAMOE

Figure 2: Correlations of SilviScan data for each annual ring between plus-treesbreeding values (BVs) estimated from the progeny and phenotypic values from theplus-trees for area-weighted.

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0.0

0.2

0.4

0.6

4 8 12 16Ring

Her

itabi

lity

DensityMFAMOE

Figure 3: Heritability estimates using parents-offspring regression of area-weightvalues calculated from SilviScan data across annual ring.

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0.3

0.4

0.5

0.6

2 4 6 8 10 12

Number of progenies

Her

itabi

lity

Trait

MOE(ind)

Pilodyn

Velocity

Figure 4: Heritability estimation by parent-offspring regression based on differentnumber of progenies for Pilodyn, Velocity and MOE(ind). The number of rametsper mother clone varied among plus-trees from one to three.

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Documents

Draft - University of Toronto T-Space · Draft 1 Abstract 2 A two-generation pedigree involving 519 Norway spruce plus-trees (at clonal archives) 3 and their open-pollinated (OP)