Phenotypic variation of basic wood density in Pinus ponderosa plus trees

Abstract

Phenotypic selection generally reduces the variability of both the selected trait and other associated characteristics. Some species show a negative strong relationship between growth traits and wood density. In the present study, basic wood density was assessed in a group of 23 control trees and 25 Pinus ponderosa plus trees, selected for growth and form traits in the Argentine Patagonia region. Trees were sampled in four different sites from the environmental and silvicultural point of view. We aim to study if the phenotypic variation in wood density is somehow influenced by the selection for productivity and form. The average wood density of plus trees was 0.37 g cm(-3), ranging from 0.29 to 0.46 g cm(-3). These values were not significantly different to control trees and were similar to those previously found in P. ponderosa plantations growing in the region. Wood density of the improved population, represented by plus trees did not differ from that of base population from which plus trees were selected. Significant differences were found in wood basic density among sites and age-classes. The phenotypic variation of the increment of wood density from pith to bark and among trees was different from zero. Not significant association was found between wood basic density and growth and form traits, except for stem straightness. It would be possible to identify high wood density trees at half of the rotation age.

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Phenotypic variation of basic wood density in Pinus
ponderosa plus trees
Alejandro Martinez Meier, Leonardo Gallo, Mario Pastorino, Víctor Mondino,
Philippe Rozenberg
To cite this version:
Alejandro Martinez Meier, Leonardo Gallo, Mario Pastorino, Víctor Mondino, Philippe Rozenberg.
Phenotypic variation of basic wood density in Pinus ponderosa plus trees. Bosque (Valdivia), Univer-
sidad Austral de Chile, 2011, 32 (3), pp.221-226. �10.4067/S0717-92002011000300003�. �hal-02645215�

ARTÍCULOS
Phenotypic variation of basic wood density in Pinus ponderosa plus trees
Variación fenotípica de la densidad básica de la madera de árboles plus de Pinus ponderosa
Alejandro Martinez-Meier a*, Leonardo Gallo a, Mario Pastorino a,b, Víctor Mondino c, Philippe Rozenberg d
*Corresponding author: a EEA INTA Bariloche, Grupo de Genética Ecológica y Mejoramiento Forestal, C.C. 277, (8400),
San Carlos de Bariloche, Río Negro, Argentina, tel.: 54-2944-422731, almarti@bariloche.inta.gov.ar
b Consejo Nacional de Investigaciones Cientícas y Técnicas (CONICET).
c EEA INTA Esquel, Estación Agroforestal Trevelin, Trevelin, Chubut, Argentina.
d INRA Orléans, Unité d’Amélioration, Génétique et Physiologie Forestières, Orléans, France.
SUMMARY
Phenotypic selection generally reduces the variability of both the selected trait and other associated characteristics. Some species
show a negative strong relationship between growth traits and wood density. In the present study, basic wood density was assessed in
a group of 23 control trees and 25 Pinus ponderosa plus trees, selected for growth and form traits in the Argentine Patagonia region.
Trees were sampled in four different sites from the environmental and silvicultural point of view. We aim to study if the phenotypic
variation in wood density is somehow inuenced by the selection for productivity and form. The average wood density of plus trees
was 0.37 g cm-3, ranging from 0.29 to 0.46 g cm-3. These values were not signicantly different to control trees and were similar to
those previously found in P. ponderosa plantations growing in the region. Wood density of the improved population, represented by
plus trees did not differ from that of base population from which plus trees were selected. Signicant differences were found in wood
basic density among sites and age-classes. The phenotypic variation of the increment of wood density from pith to bark and among
trees was different from zero. Not signicant association was found between wood basic density and growth and form traits, except
for stem straightness. It would be possible to identify high wood density trees at half of the rotation age.
Key words: specic wood density, repeated measurements, genetic breeding, Patagonian, Argentine.
RESUMEN
La selección fenotípica generalmente reduce la variabilidad de los caracteres seleccionados y la de los caracteres asociados. Algunas
especies muestran asociación negativa entre crecimiento y densidad de la madera. En este estudio, se evaluó la densidad básica de la
madera en un grupo de 23 árboles control y 25 árboles plus de Pinus ponderosa seleccionados por criterios de crecimiento y forma
en la región norpatagónica de Argentina. Las muestras provinieron de cuatro sitios con diferente clima y silvicultura. El objetivo
fue determinar si la variación fenotípica de la densidad de la madera pudo haber sido afectada por la selección para productividad y
forma. La densidad media de la madera de los arboles plus fue de 0,37 g cm-3, variando desde 0,29 hasta 0,46 g cm-3. Estos valores no
fueron signicativamente diferentes a los registrados en los árboles control y similares a los previamente registrados para P. ponderosa
creciendo en la región. La densidad de la madera de la población de mejora (árboles plus) no dirió de la población base donde fue
seleccionada. Diferencias signicativas para la densidad de la madera se pudieron establecer entre sitios y clases de edad. El incremento
de la densidad desde la médula hacia la corteza y la variación fenotípica entre árboles fue signicativamente diferente de cero. La
densidad no se encontró asociada a las características de crecimiento y forma, por las cuales los árboles plus fueron seleccionados,
excepto con la rectitud de fuste. Sería posible identicar árboles de alta densidad básica cerca de la mitad del turno de rotación.
Palabras clave: densidad básica de la madera, medidas repetidas, mejoramiento genético, Patagonia, Argentina.
INTRODUCTION
Ponderosa pine (Pinus ponderosa Dougl. ex Laws)
was introduced in the Argentinean Patagonia at the be-
ginning of the XXth century. Nevertheless, the rst plan-
tations for sawmill wood production were established in
1970. A breeding program was initiated in 1998, with the
aim of improving growth rate and form traits. Plus trees
were phenotypically selected by comparison to control
trees growing in the same microenvironmental conditions
(Ledig 1973).
The possible genetic gain in growth could have an
indirect effect on wood properties (Zobel and Jett 1995),
due to a possible unfavourable genetic correlation between
growth and wood quality. Selection for growth might thus
lead to a direct decrease of wood density. Unfavoura-
ble correlations between growth and wood quality seem
to predominate, at least in some species like douglas-r
BOSQUE 32(3): 221-226, 2011 DOI: 10.4067/S0717-92002011000300003
221

(Pseudotsuga menziesii (Mirb.) Franco) (Bastien et al.
1985), loblolly pine (Pinus taeda L.) (Paludzyszyn et al.
2005) and radiata pine (Pinus radiata D. Don) (Baltinus
et al. 2007, Wu et al. 2008). However, lack of association
between growth rate and wood density has also been re-
ported in other pines such as maritime pine (Pinus pinaster
Ait.) (Gaspar et al. 2009), and ponderosa pine (McKimmy
and King 1980).
Wood density is known to be affected by environmen-
tal conditions, including climate, resource availability,
silviculture and cambial age (Zobel and Sprague 1998).
Wood density also shows a high among-tree variation,
which is partiality controlled by genetic effects (Zobel and
Jett 1995). The aim of the present study is to know if the
phenotypic variation in wood density for ponderosa pine
trees is somehow inuenced by the selection for producti-
vity and form. If it is not the case, the breeding population
represented by the plus trees would keep original levels of
wood density phenotypic variation compared to the base
population. If this is conrmed, a reasonable phenotypic
variation is expected, and could be explored to increase
wood density by selecting trees from the rst generation
breeding program. The specic objectives of this work are:
(1) to determine the relative magnitude in wood density
across sites, trees, and rings within trees, (2) to study the
phenotypic relations between wood density and growth
and form traits and (3) to consider the feasibility of early
selection for wood density, estimating age-classes corre-
lations for this trait. The major results are compared with
other ponderosa pine studies, and some practical implica-
tions are discussed.
METHODS
Material collection. Random samples from 25 plus and
23 control trees ranging in age (at breast height) from 17
to 46 years old were collected from four different sites
(table 1). One core per tree was extracted at breast height
with a 12 mm diameter increment borer. The number of
plus trees sampled in this study represents the 29 % of
the total number of plus trees selected within the breeding
program. The four sites were very different regarding their
climatic conditions and silvicultural management. Annual
precipitations range from 2,500 mm in the western site to
670 mm in the eastern one; the number of trees per hectare
varies from 300 to 1,400.
Cores were processed in the Laboratory of Wood Tech-
nology of the Centro de Investigación y Extensión Forestal
Andino Patagónico (CIEFAP) in order to measure basic
wood density.
Basic wood density determination. Cores were cut to ob-
tain sub-samples of at least three consecutive rings com-
patible for wood density determination methodology.
First we estimated anhydrous mass (dry weight). The sub-
samples were oven-dried at 103 °C ± 2 °C until constant
weight, which was recorded with a 0.001 g precision scale.
Saturated volume was measured through the water displa-
cement method. Sub-samples were submerged in water
and a negative pressure of 1 Mpa was applied intermit-
tently until sub-samples reached at least 150 % moisture
content following the same procedure described in Jova-
novski et al. (2002, 2005). Wood densities were computed
as the quotient between the dry weight and the volume of
each sub-sample.
Data analysis. Only plus trees were evaluated to study the
relationship between density and form traits. Whereas,
to study the phenotypic variance components and the
correlations among age-classes, we took into account the
plus and control trees.
Phenotypic variance components of wood density. Each
sub-sample was assigned to an age-class according to the
average cambial age of the existent rings on it. All the trees
were used to analyse juvenile wood. However, in order to
study the phenotypic variation of the juvenile-mature wood,
only trees from the two oldest sites (sites 1 and 2) were used.
Table 1. Plus and control trees sampling in each sites.
Árboles plus y control muestreados en cada sitio.
Description Sites
1234
Latitude S 43º 07’ 59’’ 40° 09’ 20’’ 39° 19’ 21’’ 37º 11’ 49’’
Longitude W 71º 33’ 42’’ 71° 33’ 37’’ 70° 57’ 17’’ 70º 36’ 05’’
Altitude (m) 450 750 1,180 1,680
Number of trees (plus-control)*7 - 10 5 - 5 7 - 4 6 - 4
Age (years)** 38 (7) 46 (11) 18 (3) 17 (5)
Mean annual precipitation (mm) 1,200 2,500 1,200 670
Trees ha-1 1,250 850-1,400 300-500 850
* Number of plus and control trees samples per site.
** Average and standard error (in parenthesis) of age at breast height.
BOSQUE 32(3): 221-226, 2011
Phenotypic variation of basic wood density
222

A linear mixed-effects model was used to study the
phenotypic variation of wood density. All the sub-sam-
ples measurements taken of a same tree were treated as
repeated measurements of wood density upon the same
tree. The model was computed in R (R Development Core
Team 2010) using the lmer function:
ijklklikjiijkl
ay
εαβτµ
+++++=
))((
[1]
Where,
ijkl
y
= ijklht observed sub-sample wood density value.
µ
= overall mean effect.
i
τ
= xed effect of ith age-class to which each sub-sample
belongs.
j
β
= xed effect of jth type of tree (plus or control).
k
α
= xed effect of kth site.
))(( kli
a
= denoted the measurement of wood density at
the corresponding ith age-class on the lth tree nested in the
kth site.
ijkl
ε
= random error.
Signicance of the xed and random levels was com-
puted using the likelihood ratio test (P < 0.05), comparing
the complete model with a reduced model without the fac-
tor of interest. Maximum likelihood (ML) and restricted
maximum likelihood (REML) methods were used for -
xed and random levels respectively (Faraway 2006).
Relationships between wood density and growth and
form traits. Stem straightness, branching quality (BN =
branches number, BA = branches angle, BD = branches
diameter) and crown quality (CQ) were the form traits
considered, together with diameter growth (DG), for plus
trees selection. A threshold diameter was the rst selection
criterion to identify putative plus trees in the commercial
plantation:
[2]
Where,
DT
= threshold diameter.
meanBHD
= mean breast height stand diameter.
ds
= standard deviation of the mean breast height dia-
meter.
The search of plus trees was oriented to the more vi-
gorous trees, with straight and cylindrical stems, reduced
dsmeanBHDDT +=
and cylindrical crowns, thin short branches and with an-
gles of insertion near to 90º. Each plus tree was evaluated
with respect to the average of the control trees according
to a score given to each selection criterion. A positive or
negative value was assigned following the superiority or
inferiority of the plus tree based on an assigned conside-
ration to each variable. Thus for example, for the diame-
ter growth, an extra point was assigned to each plus tree
by every 10 % of superiority with respect to the average
diameters of the control trees. For further details of the
selection methodology, as on the selection criteria and plus
trees evaluation see Martinez-Meier et al. (2005).
The mean wood density of each tree (stem wood den-
sity at breast height) was computed. As our data do not
show a bivariate normal distribution, Kendall rank corre-
lation and its p-value on a two-sided test were computed
to analyse the association between the wood density and
the relative performance of plus tree in growth and form
traits. Kendall library was used in R (Development Core
Team 2010).
Correlation among age classes. Pearson’s correlation
coefcients were computed between wood density values
at age 20 and wood density at ages 25, 30, 35 and 40 years
considering the plus and control trees from sites 1 and 2.
Pearson correlation coefcients were estimated using the
cor.test function in R (R Development Core Team 2010).
RESULTS
Average wood density for juvenile wood was very
similar to juvenile-mature wood, averaging 0.37 and
0.36 g cm-3, and ranging from 0.29 to 0.46 g cm-3 in plus
and control trees respectively.
The xed effect of type of tree was not signicant for
juvenile wood (P = 0.6086) and juvenile-mature wood
(P = 0.5613) analyses. Age-class and site were signicant
for juvenile wood (P = 0.0016 and P < 0.001, respectively)
and for juvenile-mature wood (P < 0.001 and P = 0.0261,
respectively) (table 2).
In the juvenile wood analysis, the estimated wood den-
sity of plus trees (reference levels in the model) increased
0.008 g cm-3 for each increment of age-class. Site 4 was the
site with the highest average density (0.41 g cm-3), while
site 1, reference level in the model, was a site with lower
average density. These sites are climatically contrasting
and differ strongly in their latitude (site 4 northern site,
and site 1 southern site). A more pronounced increment of
wood density for each increment of age-class was estima-
ted for the juvenile-mature wood analysis.
The random effects variances were different from zero
and explain a signicant proportion of the wood density
phenotypic variance in juvenile wood and juvenile-mature
wood (table 2). The variation due to wood density increa-
ses by age-class was relatively small in contrast with the
variation among trees that was quite large.
BOSQUE 32(3): 221-226, 2011
Phenotypic variation of basic wood density
223

Table 2. Estimated average xed effects and their standard error, and variance components (expressed as standard deviation) of ran-
dom effects for juvenile wood and juvenile-mature wood.
Valor promedio estimado por el modelo de los efectos jos, su correspondiente desvío estándar y los componentes de varianza de los efectos
aleatorios (expresados en valores de desvío estándar) para madera juvenil y madera juvenil-madura.
Classication level Juvenile wood Juvenile-mature wood
Fixed effects
overall mean 0.338 (0.009) 0.361 (0.009)
age-class 0.008 (0.026) ** 0.014 (0.001) ***
Type - 0.004 (0.008) ns 0.006 (0.011) ns
Site 2
Site 3
Site 4
0.019 (0.012) ***
0.014 (0.011) ***
0.074 (0.012) *** 0.024 (0.011) **
Random effects
increment age-class|tree:site 0.0488 *** 0.0331 ***
increment age-class 0.0133 *** 0.0059 ***
Residual 0.0181 0.0195
Signicant levels: *** P < 0.001; ** P < 0.05 and P > 0.001; ns P > 0.05.
Table 3. Tau Kendall’s rank coefcients and two-sided associa-
ted P-values between wood density and growth and form traits
considered for the selection of ponderosa pine plus-trees (n = 25).
Coeciente de correlación de rangos (tau) y su valor de pro-
babilidad P asociada entre la densidad de la madera y los criterios de
selección por crecimiento y forma de los árboles plus de pino ponderosa
(n = 25).
Selection criteria Tau P
Growth diameter 0.14 0.36
Stem straightness -0.30 0.05
Branches number 0.18 0.27
Branches angle 0.14 0.39
Branches diameter -0.22 0.16
Quality of crown 0.06 0.71
A moderate signicant association (P = 0.05) between
wood density and form trait was found for stem straight-
ness (tau = -0.30). All the other associations were not sig-
nicant (table 3).
Phenotypic correlations for wood density between 20
year age-class and the following ones (25, 30, 35 and 40)
were signicant (P < 0.001), reaching r = 0.85 between the
20 and 25 year age-classes, and decreasing for the rela-
tions with older age-classes (r = 0.78, r = 0.69, r = 0.58
between 20 and 30, between 20 and 35 and between 20 and
40 year age-class respectively).
DISCUSSION
Average wood density in the selected trees is similar
to those in the control trees. In addition, wood density in
the plus trees is similar to those reported by other authors
(Jovanoski et al. 2002) for unselected dominant and co-
dominant trees growing in the region. Nevertheless, the
wood density variation is lower than that observed by
Jovanoski et al. (2002). Jovanoski et al. (2002) attribute
the exceptional high density values (0.78 g cm-3) found
in their work to compression-wood. The probability of
nding high density values due to compression-wood
in the superior trees, selected for growth and form, is,
however, very low.
Wood density values reported in this study are also si-
milar to those registered by other authors for unselected
ponderosa pine trees (Burdon and Low 1991, Burdon et
al. 1991, Koch and Fins 2000), although Markstrom et al.
(1983) found higher average wood density values using
samples from managed and non-managed stands in their
natural range.
Signicant differences among sites for wood density
properties are reported for many species (Zobel and Spra-
gue 1998, Larson et al. 2001, Jokela et al. 2004, Gundogan
et al. 2005). The ponderosa’s pine plantation area in the
Patagonian region is characterized by a strong west-east
precipitation gradient. Site 4 (included only in juvenile
wood analysis), the site with higher juvenile wood density,
represents the extreme north and east area. It is the only
site with low annual precipitations. Site 3 (another juvenile
wood site), is a site with lower wood density. In this site,
there are a low number of trees per hectare (300 – 500 trees
ha-1) and precipitation over 1,200 mm, while in the other
sites there are from 850 to 1,400 trees ha-1. Low number
of tree by hectare and high precipitation could be the com-
bination to produce low wood density in ponderosa pine.
Environmental and silvicultural practices are conside-
red to be relevant factors for wood density determination.
Generally these factors affect ring density via the effects in
ring width (Guilley et al. 2004). Nevertheless, wood den-
sity can follow a complex pattern with important changes
principally in juvenile wood (Zobel and Sprague 1998).
These changes are related to ontogenetic effects, explained
BOSQUE 32(3): 221-226, 2011
Phenotypic variation of basic wood density
224

by the cambial age, environmental and silvicultural effects
and the genotype effects. Microdensity proles analysed in
more than 500 ponderosa pine trees, constituting the base
material for future work, show that wood density in the
juvenile wood portion is strongly affected by earlywood
components. High wood density values in Site 4 could be
related to tracheids with narrow lumens and/or thick cell
walls. This is a hypothesis that has to be conrmed due to
the signicant differences found for wood density among
sites. It is possible to interpret that the high density values
in the juvenile wood portion found in the more xeric geo-
graphic region could be related to different hydraulic pro-
perties (Alder et al. 1996, Hacke et al. 2001, Domec and
Gartner 2003), which in turn could be related to different
safety margins helping trees to prevent xylem embolism
caused by the more adverse climate conditions during the
growing season.
The age-class xed effect is signicant and consistent
with the expected cambial age effect (juvenile wood and
juvenile-mature wood) mentioned by different authors
about many forest species (Zobel and Jett 1995) and spe-
cically by Jovanoski et al. (2002) for ponderosa pine
in the region. Wood density is one of the characteristics
that better denes wood quality (Zobel and Jett 1995).
Signicant phenotypic variance is found among and
within trees that is modelled by the intercept and slope,
assuming that sub-samples taken from the same trees are
repeated measurements of wood density for the corres-
ponding tree. As many references state the high levels
of heritability for wood density (Cornelius 1994, Zobel
and Jett 1995), it is possible to assume that phenotypic
wood density variation could be partially explained by
genotypic differences among trees. This variation, which
is not decreased by the growth and shape selection, will
be the potential phenotypic variation to exploit in future
selections within the planted progeny trials to improve
wood properties.
When plus trees are to be used in seed-orchard they
should, if possible, be exceptional in as many characteris-
tics as possible (Isaac 1955). This exceptionality is descri-
bed by the selection criteria, which take into account the
superiority of a plus tree in relation to the control trees.
Unfavourable correlations between the traits considered
for selection and other non assessed traits may lead to
reduced performance of the selected trees regarding the
former traits. Results presented here indicate that there is
no relationship between wood density and growth and/or
form traits, except in the case of stem straightness. Ne-
vertheless, the negative association between wood densi-
ty and stem straightness is moderate (tau = -0.30) and it
could be just indicating the presence of compression-wood
in those selected trees of low stem straightness values. The
selection intensity used to select the plus trees with respect
to the control trees could be the reason for nding no rela-
tionship between wood density and growth. Nevertheless,
it is important to highlight that the relations described in
this work are phenotypic correlations. So, it is not possible
to deduce the size and the sign of the underlying gene-
tic and environmental correlation (Falconer and Mackay
1996, White et al. 2007).
The high to moderate signicant age-age phenotypic
correlations between the 20 year reference age-class and
all the following age-classes observed in this work indi-
cate that it is possible to identify high density trees at mid
ages that will have intermediate or high density at nal
rotation. McKimmy and King (1980) nd that early selec-
tion of high density trees can improve mature tree density.
Wu et al. (2007) and Apiolaza (2009) use this early selec-
tion criterion for screening the best individuals for wood
quality.
CONCLUSIONS
Wood density and its phenotypic variation in selected
plus trees were similar to those registered in unselected
trees. Selection for growth and form appeared, thus, to
not alter the density values and their phenotypic varia-
tion. This is important since the criteria used to select the
plus trees (growth, branching and crown quality and stem
straightness) could otherwise have indirect consequences
in nal-products, whereas the maintenance of the original
phenotypic variation in wood density allows to include this
trait as a selection criterion in further breeding activities.
Results also indicate that it is possible to identify high
density trees at mid rotation ages with a high probability
that these trees will maintain this characteristic up to har-
vest rotation age. Nevertheless, the results of this study are
preliminary and have to be conrmed with new studies,
using more rened tools as X-ray microdensity proles.
In our breeding program, no information is available yet
about the genetic variation of wood characters and the
genetic correlations between wood and growth. Whether
the observed weak or null phenotypic correlations are in-
dicators of no genetic correlations remains to be explored.
The progeny trails planted in the region will provide this
information in the future, allowing us to select desirable
genotypes to produce advantageous ponderosa pine wood
density properties in the region.
ACKNOWLEDGEMENTS
Research was supported by INTA (Instituto Nacio-
nal de Tecnología Agronómica)-SAGPyA (Secretaría de
Agricultura, Ganadería, Pesca y Alimentos): Programa de
Producción de Material de Propagación Mejorado, Subre-
gión Andino Patagónica para Coníferas y otras especies,
Proyecto Forestal de Desarrollo – BIRF, Nº 3948-AR. We
would like to thank CIEFAP, particularly Alejandro Jova-
noski and Darío Lopez who helped us to assess ponde-
rosa pine wood density. Finally, we also thank the three
reviewers whose comments helped to improve the manus-
cript.
BOSQUE 32(3): 221-226, 2011
Phenotypic variation of basic wood density
225

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Recibido: 04.03.11
Aceptado: 13.06.11
BOSQUE 32(3): 221-226, 2011
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