Annals of Botany 112: 927– 935, 2013
doi:10.1093/aob/mct153, available online at www.aob.oxfordjournals.org
Wood anatomical correlates with theoretical conductivity and wood density
across China: evolutionary evidence of the functional differentiation
of axial and radial parenchyma
Jingming Zheng1,†,* and Hugo I. Martı́nez-Cabrera2,†
1
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, China and
2
Département des science biologiques, Université du Québec à Montréal, UQÀM, CP 8888, Succ. Centre Ville Montréal,
QC, H3C 3P8, Canada
†
The authors contributed equally to this work.
* For correspondence. E-mail [email protected]
Received: 20 March 2013 Returned for revision: 16 April 2013 Accepted: 29 April 2013 Published electronically: 31 July 2013
† Background and Aims In recent years considerable effort has focused on linking wood anatomy and key ecological
traits. Studies analysing large databases have described how these ecological traits vary as a function of wood anatomical traits related to conduction and support, but have not considered how these functions interact with cells
involved in storage of water and carbohydrates (i.e. parenchyma cells).
† Methods We analyzed, in a phylogenetic context, the functional relationship between cell types performing each of
the three xylem functions (conduction, support and storage) and wood density and theoretical conductivity using a
sample of approx. 800 tree species from China.
† Key Results Axial parenchyma and rays had distinct evolutionary correlation patterns. An evolutionary link was
found between high conduction capacity and larger amounts of axial parenchyma that is probably related to water
storage capacity and embolism repair, while larger amounts of ray tissue have evolved with increased mechanical
support and reduced hydraulic capacity. In a phylogenetic principal component analysis this association of axial parenchyma with increased conduction capacity and rays with wood density represented orthogonal axes of variation. In
multivariate space, however, the proportion of rays might be positively associated with conductance and negatively
with wood density, indicating flexibility in these axes in species with wide rays.
† Conclusions The findings suggest that parenchyma types may differ in function. The functional axes represented by
different cell types were conserved across lineages, suggesting a significant role in the ecological strategies of the
angiosperms.
Key words: Ecological strategies, evolutionary conservatism, hydraulic conductivity, parenchyma, water storage,
wood anatomy, wood density.
IN T RO DU C T IO N
Wood performs three critical functions: mechanical support of
the photosynthetic surface (Rowe and Speck, 2005); storage of
water, sugar and other nutrients (e.g. Kozlowski, 1992; Sauter
and van Cleve, 1994); and conduction of water and other substances from the soil to the photosynthetic surface (e.g. Sperry,
2003). In angiosperms, each of these functions is generally
carried out by particular cells types so that mechanical support
is primarily determined by fibres, storage by living cells such
as parenchyma, and water conduction by xylem vessels. One
cell type can, however, perform more than one function. For
example, living fibres can be the storage compartment in
woods with scanty parenchyma (Spackman and Swamy, 1949;
Carlquist, 2001; Wheeler et al., 2007) and also function as
support cells (Govindarajaru and Swamy, 1955). Because
wood carries out all these different tasks simultaneously, environmental demands that require a prominent role for a particular
function can create a trade-off and/or positive interaction with
other functional axes of variation. For example, increased mechanical support often decreases water conduction efficiency but
increases resistance to cavitation (e.g. Hacke et al., 2001;
Jacobsen et al., 2005). These inter-relationships between hydraulic and mechanical properties are, however, a matter of an
ongoing debate (Awad et al., 2012) since trade-offs may (e.g.
Gartner, 1991) or may not be recognized (e.g. Woodrum et al.,
2003; Pratt et al., 2007). Although defence and decay resistance
through compartmentalization of infection is a fourth critical
wood function, we did not include it in our analysis since it is
unclear how the anatomical traits we analysed here contribute
to this particular ecological axis.
Wood density is a key functional trait that has been the focus of
extensive research in the last few years. Since wood density describes the amount of carbon invested in support (King et al., 2005,
2006), it is related to a variety of ecological dimensions linked to
life history traits (e.g. growth rate, survival or life span). For instance, wood density is inversely related to growth rates (e.g.
Enquist et al., 1999; Roderick, 2000; Muller-Landau, 2004) and
successional position (e.g. Swaine and Whitmore, 1988). Species
with low wood density tend to be comparatively short-lived,
fast-growing pioneers, while species with high wood density
tend to be long-lived climax species (Saldarriaga et al., 1988;
# The Author 2013. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved.
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Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
Swaine and Whitmore, 1988; Wiemann and Williamson, 1988,
1989). High disturbance and turnover rates favour fast-growing
species with low wood density (ter Steege and Hammond, 2001).
Increased wood density positively influences cavitation resistance (e.g. Pratt et al., 2007) by increasing the strength of fibres
associated with vessels (Jacobsen et al., 2005). However,
increased hydraulic conductance, mainly driven by transpiration
needs, may negatively affect mechanical stability of wood (e.g.
Gartner, 1991). Zanne et al. (2010) and Zanne and Falster
(2010) described several ways in which adjustments in hydraulic
supply may alter wood structure, two of them involving adjustment
in vessel characteristics: vessel lumen fraction (i.e. cross-sectional
area occupied by open vessel spaces) and vessel composition
(i.e. size distribution). However, a relationship between vessel
characteristics and wood density cannot be assumed to be universal because while some studies have shown a trade-off between
wood density and vessel area (Jacobsen et al., 2005, 2007;
Preston et al., 2006) or vessel fraction (Preston et al., 2006),
others have shown a lack of association between these variables
(Martı́nez-Cabrera et al., 2009, 2011; Zanne et al., 2010).
Most of the studies dealing with the anatomical determinants
of wood density, especially those including large samples from
global databases, lack information on the third functional dimension, storage. As axial and radial parenchyma are associated with
stem water and nutrient storage, we expect a trade-off between
mechanical strength and storage because increased area of parenchyma in stems should be achieved at the expense of other cell
types (i.e. fibres), especially if conductance is to be maintained.
This trade-off between mechanical properties and amount of parenchyma has been empirically shown in some studies. Jacobsen
et al. (2007), for example, found that wood density and the
modulus of elasticity (MOE) were inversely related to total parenchyma area. Total parenchyma area and wood density,
however, were independent in a study including 61 shrub
species across precipitation gradients (Martı́nez-Cabrera et al.,
2009). Martı́nez-Cabrera et al. (2009) also showed that axial
and radial parenchyma have opposite correlation patterns with
wood density and several climate variables, suggesting that parenchyma types may have different functional roles. Essentially
species with low wood density from wet sites have a high proportion of rays and less axial parenchyma than species from drier
areas. This pattern was hypothesized to result from prevention
of radial water transport from other regions of the stem during
the embolism repair process that could lead to further water
loss under high water stress. Other studies have found the opposite pattern of a positive relationship between ray area and wood
density (Taylor, 1969; Woodrum et al., 2003; Rahman et al.,
2005). Besides the interaction with mechanical properties, both
types of xylem parenchyma are important for water transport
since they may serve as water reservoirs to prevent embolism formation and provide the osmotic agents (Braun, 1984) to aid embolism repair (e.g. Canny, 1997; Améglio et al., 2002; Bucci
et al., 2003). In addition, in some lineages (e.g. Rhamnaceae;
Pratt, 2007), higher total amounts of parenchyma are associated
with less negative cavitation resistance values (C50 values; water
potential at which there is 50 % loss in hydraulic conductivity),
indicating that higher xylem vulnerability is associated with increasing parenchyma. In summary, this information indicates
that storage capacity, in this case represented by xylem parenchyma, interacts with mechanical and conduction properties,
to form co-ordinated units that are modulated to meet particular
environmental demands.
Here we compiled a large database of approx. 800 tree species
from China to analyse patterns of correlated evolution between
proportions of different tissue types and functional characteristics of the stem related to support (wood density), water conduction ( potential conductivity, vessel composition and fraction)
and storage. While other analyses at global scales have focused
on vessel traits to explain variation in wood density and theoretical conductivity, we also question whether there is a clear trade-off
among anatomical traits that hypothetically represent mechanical,
conduction and storage functions, and how early these axes were
established in the history of the lineages. We acknowledge that
there is intraspecies (e.g. Gartner et al., 1997) and intra-tree (Lei
et al., 1996) radial variation in wood traits associated with ontogenetic changes that might affect the strength of trade-offs mentioned
above. Wood density, for example, reflects differences in ecological properties of species [e.g. successional position (Woodcock and
Shier, 2002); shade tolerance (Nock et al., 2009)], but there is also
radial variation that is linked to ontogenetic variation (Nock et al.,
2009). Our target in this study, however, is to determine whether
such trade-offs are generally recognized across angiosperm
lineages at the species level. We were particularly interested in incorporating axial and radial parenchyma in the study, since these
anatomical elements have not been analysed at this large scale
before.
M AT E R I A L S A N D M E T H O D S
Description of the data set
We compiled wood trait data from nearly 800 Chinese tree species
(Yang and Lu, 1993; Yang and Yang, 2001; Yang et al., 2009).
Most tree samples were taken from the Eastern Monsoonal
climate zone in China where the temperate, sub-tropical and tropical natural forests are distributed (Zhao, 1995). Wood densities
from .400 species were collected from various sources (Cheng
et al., 1992; Yang et al., 1992; Yang and Lu, 1993; Yang and
Yang, 2001, Yang et al., 2009; Zhang et al., 2010), but in all
cases density was measured using air-dried samples with 15 %
water content and following the Chinese national standard (GB
1933-1991, method for determination of the density of wood,
1991). The anatomical data we used were mainly collected from
publications by The Chinese Academy of Forestry, which hosts
the largest wood collection in China (Yang and Lu, 1993; Yang
and Yang, 2001; Yang et al., 2009) and thus the wood anatomical
traits were measured following their methods (Zeng et al., 1985).
In brief, most angiosperm tree species were sampled from mature
trees with a diameter at breast height (DBH) .20 cm in natural
forests. The sampled discs were collected at a height of 1.3 m.
These discs were cut, from pith to bark, into six equal parts according to the equidistance method, and the anatomical samples were
taken from the middle of the outermost part (the closest to the
bark). The entire span of one growth ring was thin sectioned.
Wood anatomical traits were measured using an optical microscope and an attached image analyser (Q570) (Zeng et al.,
1985). The anatomical variables measured were vessel diameter
and density, and proportion of cross-sectional area occupied by
vessels, fibres, rays, axial parenchyma and cell wall. For vessel
diameter and vessel density, 100 vessels were measured in
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
earlywood and latewood of each sample. To measure tissue proportions in cross-section, images were taken using the Q570
image analyser. The percentage area of each cell type, however,
was manually determined (Yang and Yang, 2001). Percentage
areas of different cell types were determined in fields of view of
1 mm2. In ring-porous woods, the proportion of area occupied
by each cell type was determined by measuring four fields of
view, two each in earlywood and latewood. In diffuse-porous
woods, the proportional area of each cell type was calculated
from three fields of view in the beginning, middle and late
growth portions of the growth rings. The proportions analysed
are thus the mean values of these fields of view. Total cell wall
area was the total dark area in a field of view and represents the
cell wall of all cell types. Using image analysis to measure total
cell wall area in transverse sections is problematic because unstained fibre layers (e.g. S2 layers in gelatinous fibres) are often
not registered or because grey scales that are not cell walls (e.g.
gums in vessels, some substances in parenchyma cells) are recognized as such. Thus, despite the overall good quality of the sections
(15–20 mm thick), there is an important source of error in the total
cell wall area proportion we analysed.
We decided to analyse diffuse- and ring-porous woods together because the latter only represented little over 11 % of
the total number of species. However, to provide further
support for our results, we also analysed diffuse-porous species
alone since wood traits, especially those related to vessel variation, could differ between earlywood and latewood in ringporous species. As we did not find any significant difference in
our analysis between the whole data set and the partition using
only diffuse-porous species, we emphasize the results from the
whole data set in the discussion. Parallel findings by Zanne
et al. (2010) showed no divergent associations among traits in
the diffuse-porous woods and combined data sets in a global analysis including .3000 species.
It is important to highlight that ray area is a very broad parameter that can be achieved by many means and most probably hides
some functional strategies. A high proportion of rays, for
example, can be achieved by having a large number of uniseriate
rays or a few multiseriate rays (or both), with different mechanical and hydraulic implications. At a given ray cross-sectional
area, the presence of a few broad rays, especially in woody temperate angiosperms (Braun, 1970, 1984), would be translated
into a proportionally lower number of contact cells (cells
having functional connections with vessels) and a proportionally
higher number of isolation cells (which are more involved in
radial translocation; Sauter and Kloth, 1986) compared with
species with the same cross-sectional area composed of many
narrow rays. This differential proportion would conceivably influence hydraulic aspects such as embolism repair capacity or
transport of osmotically active substances during the mobilization phase in early spring (Braun, 1984), as well as differences
in mechanical properties since broad and narrow rays may
differ in this regard (Mattheck and Kubler, 1995). As we found
in preliminary analyses that the negative association of ray area
with transport efficiency traits could be the product of a large proportion of wide-rayed species with a comparatively high number
of isolation cells, we further partitioned our analyses into diffuseporous species with rays ≤5 and .5 cells wide.
To describe different aspects of water conduction, we calculated F and S vessel metrics developed by Zanne et al. (2010).
929
times vessel number per
F, the product of mean vessel size (A)
unit area (N ), measures the fraction of wood that is occupied
mm2 mm22). Increases in F should
by vessel space (F = AN;
be correlated with lower mechanical strength (Jacobsen et al.,
2005; Preston et al., 2006; Zanne et al., 2010). S is the ratio of
the same anatomical traits (S = A/N;
mm4.) and measures the
variation in vessel composition. Higher S indicates a greater contribution of large vessels to water conduction in a given area
(Zanne et al., 2010) and therefore indicates increased water capacity and increased risk of cavitation. These two metrics represent orthogonal axes of variation (Zanne et al., 2010). Using
vessel lumen fraction (F ) and the vessel composition metric
(S), we calculated potential conductivity (Ks) as
Ks / F 1·5 S0·5
For details on the derivation of this equation, see Zanne et al.
(2010). The relationship between wood density and tissue proportions was based on 408 species, while the relationship
among Ks, S and F was based on the entire data set (794
species). To analyse these relationships, we matched the anatomical traits with wood density by species name.
Statistical analysis
To determine patterns of correlated evolution between the
wood anatomical traits and functional variables, we used phylogenetically independent contrasts (PICs). We also present the
phylogenetically uninformed (raw) correlations for the full
data set in the Supplementary Data Table S1; these were calculated using R (R Development Core Team, 2008). The phylogenetic relationships among species were reconstructed using the
program PHYLOMATIC (Webb and Donoghue, 2005), which
resulted in a polytomous tree. For the PIC analysis, we treated
polytomies as soft and reduced the degrees of freedom accordingly (Purvis and Garland, 1993; Garland and Dı́az-Uriarte,
1999). Branch lengths of the resulting tree ( prior to the actual
PIC analysis) were calculated using the branch length-adjusting
algorithm (bladj) implemented in the program PHYLOCOM
(Webb et al., 2007). To calibrate the phylogenetic tree, we
used angiosperm node ages provided in Wikstrom et al.
(2001). The resulting tree was used for the PIC analysis.
Characters not adequately standardized (i.e. the absolute
values of the standardized PICs and their standard deviations
showed a significant association) were log (continuous) or
arcsine ( proportions) transformed as suggested by Garland
et al. (1991, 1992). We used the module PDAP:PDTREE
(Midford et al., 2005) in the program Mesquite (version 2.75;
Maddison and Maddison, 2008) to calculate the PIC correlations.
To explore how axes of variation representing mechanical
strength, storage and water conduction related to each other in
a phylogenetic framework, we performed a phylogenetic principal component analysis ( pPCA; Jombart et al., 2010). A pPCA
describes overall patterns of phylogenetic signal. This variant
of pPCA has a similar methodological framework to that
used in spatial ecology (Dray et al., 2008) and detects
non-independent values of variables in relation to the phylogenetic relationship ( phylogenetic autocorrelation) between
species. This phylogenetic autocorrelation can be positive or
negative. Positive autocorrelation or global structure is the
930
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
result of global patterns of similarity in related taxa. Negative
phylogenetic autocorrelation, or local structure, results from differences among closely related species in the tips of the phylogeny. These structures are detected using Moran’s index (I).
In a pPCA analysis, the largest eigenvalues are those with large
variance and large positive Moran’s I, and correspond to
global structure that reflects divergence of traits close to the
root of the tree. The most negative eigenvalues are those with
high variance and large negative Moran’s I; these local structures
correspond to recent divergence. Phylogenetic proximities,
based on the phylogenetic distances of the tree described
above, were calculated using Abouheif’s proximity (Abouheif,
1999; Pavoine et al., 2008). The resulting matrix of phylogenetic
proximities was used to calculate phylogenetic autocorrelation
(Jombart et al., 2010). The pPCA was carried out using adephylo
(Jombart et al., 2009). We analysed the four data partitions (i.e.
full data sets, and data sets for only diffuse-porous woods, and
woods with rays ≤5 and .5 cells wide) and then compared
their results by regressing the loadings, among analyses, of
wood anatomical traits, Ks, S and F, on each of the three first
principal components.
RES ULT S
Relationship between wood density and tissue proportions
The main anatomical determinant of wood density was the
total area proportion of cell walls. Surprisingly, the area of
fibres in cross-sections was not associated with wood density.
Instead, wood density significantly increased with the proportion
of rays in cross-section and decreased with axial parenchyma
(Fig. 1, Table 1). Total parenchyma, the sum of axial and
radial parenchyma, was independent from wood density
because parenchyma types vary in opposite directions, cancelling each other out. Vessel area was also independent from
wood density (Fig. 1, Table 1). No difference between wood
density and wood anatomy traits were detected between the
full data set and that including only diffuse-porous species. In
the data set including only species with rays ≤5 cells wide, the
relationship of wood density and vessel diameter, axial parenchyma and S is no longer significant. However, some traits
such as cell wall area and ray area are more tightly correlated
with wood density in this last data partition when compared
with the full data set. In the species with rays .5 cells wide
(n ¼ 46), none of the traits was significantly associated with
wood density (Supplementary Data Table S2).
Relationship among tissue proportions
Phylogenetically independent contrast results vs. phylogenetically
uninformed analyses in the full data set
Phylogenetically informed and uninformed correlations yielded
very similar results. The few exceptions were (1) the correlation
between wood density and total parenchyma, which was not significant using PICs; wood density was only significantly correlated with (2) vessel diameter and (3) S in the PIC analysis; (4)
the proportion total parenchyma and potential conductivity
was recognized as significant in the PIC; and S was only significantly associated with (5) ray area and (6) total parenchyma area
in the phylogenetically uninformed analysis (Supplementary
Data Table S1; Table 1). Here we describe phylogenetically
informed analysis unless otherwise stated.
As expected, most of the relationships among tissue proportions were negative (Fig. 1) because the increase in one cell
type should be at the expense of others. The only positive associations were between cell wall area and proportion of fibres,
and between ray and axial parenchyma and total parenchyma
area. Only two pairs of traits were not correlated: vessel area
varied independently of axial parenchyma area, and cell wall
area and ray areas were also independent. We did not detect
any change in phylogenetic correlations, other than the reduction
of significance values with decreasing number of species, among
tissue proportions among data partitions (results not shown).
Relationship of Ks, F and S with tissue proportions
TA B L E 1. Phylogenetically independent contrast correlations
between functional traits and wood anatomy in the full data set
Vessel density
Vessel diameter
Vessel area
Cell wall area
Fibre area
Ray area
Axial
parenchyma area
Total
parenchyma area
Ks
S
F
Wood density
(n ¼ 404)
Ks
(n ¼ 793)
S
(n ¼ 793)
F
(n ¼ 793)
– 0.036
– 0.14**
– 0.08
0.3****
0.04
0.13**
– 0.11**
–0.3****
0.82****
0.39****
–0.24****
–0.25****
–0.15****
0.15****
–0.93****
0.93****
–0.12****
–0.05
0.06*
–0.01
0.16****
0.42****
0.3****
0.64****
– 0.3****
– 0.4****
– 0.2****
0.05
0.06
– 0.15**
– 0.1*
– 0.12***
–0.087*
–
0.59****
0.67****
0.06
–
–
–0.07*
****P , 0.0001; ***P , 0.001; **P , 0.01; *P , 0.05
n ¼ number of species used in the PIC correlation analysis.
– 0.18****
–
–
–
Ks increased with vessel and axial parenchyma area and
decreased with total parenchyma, ray, fibre and cell wall areas
(Table 1). Ks was negatively associated with wood density
(Table 1). Vessel composition metric S was positively associated
Wood density
Positive correlation
Negative correlation
Vessel area
Total parenchyma area
Wall area
Axial parenchyma area
Fibre area
Ray area
F I G . 1. Diagram showing significant (P , 0.05) PIC correlations among wood
density and cell type areas in cross-section. Lines indicate significant relationship
between variables. Positive and negative correlations are as indicated in the key.
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
vessels (higher S), lower area occupied by vessels, higher potential conductance and larger amount of axial parenchyma than
species in the left side of the pPCA plot (Fig. 2). Lineages with
highly efficient water conduction and high amount of axial
parenchyma include Fabaceae (Fabales), Moraceae (Rosales),
Bombacoideae (Malvales) and Scrophulariaceae (Lamiales),
while more inefficient water transport and lower amounts of
parenchyma are present in Cercidiphyllaceae, Hamamelidaceae
(Saxifragales), Rosaceae, Rhamnaceae, Ulmaceae (Rosales),
Salicaceae (Malpighiales), Ericaceae and Theaceae (Ericales)
(Fig. 2C). In the second pPCA axis, from top to bottom in
Fig. 2A, species decrease in wood density and proportion of
cell wall area, and increase in conduction capacity (Ks), proportion of axial parenchyma and vessel fraction (Fig. 2A). This axis
describes a trade-off between mechanical support and conduction efficiency. Among the families with low wood density,
high water conduction and high axial parenchyma proportions
are Schrophulariaceae and Bignoniaceae (both in Lamiales),
Fabaceae, Moraceae, and Rutaceae and Anacardiaceae (in
Sapindales). Rosaceae, Fagaceae, Rubiaceae and Salicaceae,
among others, have higher wood density, lower conduction capacity and lower amount of parenchyma. In the third pPCA, wood
density and proportions of rays and fibres have the highest loadings; this axis could be interpreted as a radial mechanical strength
axis (see discussion below). Myrtaceae (Myrtales), Fagaceae,
Clusiaceae (Malpighiales), Salicaceae and Ulmaceae are
among the families with high wood density and ray area,
while, among others, Araliaceae, Lauraceae and Moraceae
showed lower values of both of these anatomical variables
(Fig. 2C).
with fibre and axial parenchyma areas and negatively associated
with vessel area. Vessel fraction F was positively associated with
vessel area and negatively with total parenchyma and ray, fibre
and wall area, and varied independently of axial parenchyma
(Table 1). As expected, both S and F were positively related
to potential conductivity, and their relationship with wood
density was negative, although less significant (Table 1). We
detected only one difference between the full data set and the partition including only diffuse-porous woods (Supplementary Data
Table S2): the relationship between Ks and proportion of total
parenchyma ( – 0.087 vs. – 0.07) became non-significant. In the
data set including rays ≤5 cells wide, the proportion of fibres
was no longer associated with Ks, but it was tightly associated
with S (Supplementary Data Table S2). In species with ray
width .5 cells, fibre, ray, axial parenchyma and total parenchyma areas were no longer associated with Ks, and fibre area
is not correlated with S (Supplementary Data Table S2).
Phylogenetic principal component analysis
We did not detect negative Moran’s I values; therefore, local
structures representing divergence in trait values among
closely related species were not obvious in our sample
(Fig. 2B). The first three principal components (first three
global structures) explained 93 % of the trait variation. In the
first phylogenetic principal component ( pPCA1), traits with
higher loadings were the proportion of vessels, axial parenchyma
and S, and, with somewhat lower loadings, Ks (Fig. 2A). This axis
describes hydraulic efficiency and storage capacity, with species
occupying the right side having comparatively fewer larger
A
931
C
Wood density
Wall area
Lauraceae
Magnoliaceae
Annonaceae
Sapotaceae
Theaceae
Ericaceae
Araliaceae
Boraginaceae
Scrophulariaceae
Rubiaceae
Nyssaceae
Fibre area
PC2
Ray area
S
Euphorbiaceae
Salicaceae
Vessel area
F
K
Axial parenchyma
area
Fabaceae
Rosaceae
Ulmaceae
Moraceae
B
Phylogenetic autocorrelation (I)
PC1
Fagaceae
Juglandaceae
Betulaceae
Myrtaceae
Malvaceae
Rutaceae
Meliaceae
Anacardiaceae
Sapindaceae
l1
l8
l9
0
l6
l7
0·5
l4
l5
1·0
l3
1·5
Variance
l2
2·0
2·5
PC1 PC2 PC3
3·0
–4
–2 2
4
F I G . 2. pPCA of anatomical, wood density and potential conduction data. (A) pPCA plot of the first and second principal component (global structures). (B)
Eigenvalue decomposition showing phylogenetic autocorrelation (Moran’s I) as a function of variance for each one of the eigenvalues (l ). (C) Phylogenetic tree
used in this study and the three first global structures. Positive and negative scores are indicated by black and white circles, respectively; symbol size is proportional
to absolute scores values.
932
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
The results of the pPCA were highly congruent among all four
data partitions when judged by the significant determination
coefficients of trait loadings among analyses (Supplementary
Data Table S3 and Fig. S1). This resemblance was especially
high in the first pPCA axis and lowest, but still very significant,
in the third pPCA axis (Supplementary Data Table S3). As
expected, the highest resemblance was between the full data
set and the data set of only diffuse-porous species (R 2 . 0.95,
P , 0.0001 for loadings in the three first three PCA axes;
Supplementary Data Table S3). The pPCA including only
species with rays .5 cells wide had a larger deviation in loadings
compared with the full data set, most probably due to the less tight
association among variables as exhibited by the PIC analysis
results. This analysis (rays .5 cells wide) essentially showed a
trade-off between wood density and space occupied by vessels
(F and vessel area) in the first global structure, and an orthogonal
trade-off between wood density, conduction capacity and storage
capacity (amount of both axial and radial parenchyma) in the
second pPCA axis (Supplementary Data Fig. S1). In this
second pPCA axis, conduction capacity is directly related to
the proportion of both parenchyma types.
DISCUSSION
Xylem parenchyma has long been recognized as the storage compartment of wood (e.g. Sauter and van Cleve, 1994; Wheeler et al.,
2007), and both parenchyma types, axial and radial, have also been
repeatedly implicated in embolism repair (e.g. Tyree et al., 1999;
Ameglio et al., 2001; Salleo et al., 2004). Living parenchyma also
produces secondary metabolites that serve as a defence mechanism against pathogens (Wheeler et al., 2007). Here we present evidence that different parenchyma types have contrasting patterns of
correlated evolution with wood density and water conduction
properties, suggesting some degree of functional differentiation
between them. Axial parenchyma was negatively related to
wood density and positively associated with potential conductivity and the vessel composition metric S. Rays, on the other hand,
were positively associated with wood density and negatively associated with potential conductivity and vessel fraction F. This
pattern indicates that ray proportion varies directly with increased
support (wood density), while axial parenchyma is directly related
to increased conduction capacity.
Rays have been related to radial transport of Münch-water
between phloem and xylem (Van Bel, 1990; Milburn, 1996;
Höltta et al., 2006) and storage of water, sugar and other nutrients
(Sauter and van Cleve, 1994), but their mechanical significance
has been explored only recently (Mattheck and Kubler, 1995;
Burgert et al., 1999; Burgert and Eckstein, 2001). Burgert
et al. (1999) experimentally demonstrated that under controlled
radial tensile loads, the direction of growth of radial tissue shifted
parallel to the applied force, suggesting that rays are mechanically functional. Additionally, microtensile experiments showed
that radial strength of isolated rays was three times higher than
radial strength of wood as a whole; most probably, this difference
would be even greater when compared with axial tissue alone
(Burgert and Eckstein, 2001). Rays also influence the radial
MOE since there is a direct relationship between this mechanical
parameter and the proportion of ray area in cross-section (Burgert
and Eckstein, 2001).
The importance of rays in radial mechanical strength described
above, together with our result showing that the proportion of rays
is larger in denser woods, indicate that a high prevalence of ray parenchyma appears to contribute to stem radial mechanical stability.
However, the relationship between mechanical strength and ray
proportion is probably more complex than the one we depicted
above. We have assumed that wood density is linked to mechanical
strength and stiffness [MOE and modulus of rupture (MOR)] as
has been shown in some studies (e.g. Pratt et al., 2007), but mechanical strength at the tissue level is not necessarily good for whole
tree support efficiency (Larjavaara and Muller-Landau, 2010). In
addition, in one of the few studies that measure anisotropy (mechanical response of wood along and across the grain) in wood properties, Guitard and El Amri (1987) found that while a higher
proportion of rays increases the radial MOE it also limits the longitudinal MOE for a given wood density, and longitudinal properties are the most important mechanical determinants in stems.
Paradoxally, Woodrum et al. (2003) have found among Acer
species a high correlation between percentage of ray parenchyma
and axial bending MOE, and discussed this coincidental relationship, opposed to physics, in connection with the hard maple
ecology. A comprehensive study of how longitudinal and radial
MOE and MOR interact simultaneously to determine stem
support efficiency remains to be carried out.
Martı́nez-Cabrera et al. (2009) suggested a possible functional
divergence between axial and ray parenchyma because they
showed opposite correlation patterns with wood density and
climate. They found higher proportions of rays and lower axial parenchyma in species with low wood density from wet sites, while
the opposite was true for dry areas. Because rays can be involved
in embolism repair (Tyree et al., 1999), these authors suggested
that the lower proportion of rays (and low contact between rays
and vessels) associated with high-density xylem in species from
arid regions could be related to a detrimental effect of embolism
repair involving Münch-water in extremely dry sites. Since rays
transport water across sections in stems (Hölttä et al., 2006),
water used to repair embolized vessels must come from other
stem regions. Because of this, when drought is prolonged, embolism repair could be detrimental to plants as stem water lost through
transpiration would occur in the absence of tissue rehydration due
to environmental water deficit (Martı́nez-Cabrera et al., 2009).
Here, however, we found, as has been found in other studies
(e.g. Taylor, 1969; Rahman et al., 2005), the opposite pattern: a
higher ray area occurs in species with high wood density. The
difference between the results presented here and those of
Martı́nez-Cabrera et al. (2009) might be explained because they
analysed shrubs from drier areas while we included only trees
from relatively mesic sites. It is unlikely that the disadvantage of
ray tissue in very dry environments described above is relevant
to our samples from wetter sites. Alternatively, the difference
between the two studies in the relationship between wood
density and the proportion of ray parenchyma may be a function
of the differences in growth forms. Wood of trees and shrubs has
been shown to differ in several other traits (e.g. Wheeler et al.,
2007; Jacobsen et al., 2012), including evolutionary integration
of wood density, height and vessel anatomy (Martı́nez-Cabrera
et al., 2011).
Another interesting aspect of our results is that ray proportion
is negatively related to Ks despite being involved in embolism
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
repair (Table 1). This suggests that conduction efficiency and potential for repair (given by the large amount of ray tissue) are
either negatively related or, most probably, that the mechanical
function of rays (given by its relationship to wood density) is
driving the negative association between ray proportion and
water conduction efficiency at least up to a certain limit (only
in narrowly rayed species). In our sub-set of species with rays
.5 cells wide, wood density and ray proportion vary in the opposite direction in two different pPCA component axes, indicating that the mechanical link between these two traits is probably
lost when rays are very wide. Moreover, in this same data partition, ray proportion increases, in the multivariate space, with
conduction capacity, suggesting that perhaps the function of
the rays varies with their size. That is, a high ray area in narrowly
rayed species is associated with increased mechanical strength,
while a high ray area in wide-rayed species is more tightly associated with traits conferring increased conductance. This result is
exactly the opposite to our expectation of narrow rays being more
involved in hydraulic aspects than wide rays (Sauter and Kloth,
1986) because of their higher number of contact cells. We
should mention, however, that the relationships of rays to wood
density and Ks are not longer recognized for the wide-rayed
group in the PIC correlations.
An alternative explanation to the negative relationship
between wood density and axial parenchyma area and axial parenchyma and ray areas could be, again, related to anisotropy
without having to involve the ray’s role in hydraulics. That is,
as axial parenchyma weakens longitudinal strength, increases
in ray area would avoid diminished mechanical strength and
maintain high storage capacity when axial parenchyma area
decreases. This alternative hypothesis and the one above relating
the variation patterns we found here to the hydraulic role of rays
remain to be tested.
Our analysis also indicates that axial parenchyma, and presumably storage and embolism repair function, is directly correlated with conduction efficiency and negatively with wood
density. Wood density is often inversely related to relatively
small declines in stem water potential and stem water storage
(Borchert, 1994). This relationship emerges because the withdrawal of water from intracellular storage compartments
lessens the decline in water potential during periods of reduced
water availability (Holbrook et al., 1995). Presumably, axial parenchyma provides the water storage capacity associated with low
wood density. In temperate and tropical trees, capacitance during
dehydration (water storage capacity) and the amount and distribution of axial parenchyma are positively correlated. In a
recent study, deciduous hardwood species with imperforate
tracheary elements enclosing vessels had low water storage capacitance, whereas stem succulent species with abundant paratracheal axial parenchyma had high capacitance (Borchert and
Pockman, 2005). Correspondingly, deciduous hardwoods had
higher wood density than succulents. These differences observed
also corresponded to two main seasonal drought strategies:
drought tolerance vs. drought avoidance. Drought tolerators
have small amounts of axial parenchyma and are highly tolerant
to low water potentials, while in drought avoiders, with large
amounts of paratracheal axial parenchyma, the probability of
cavitation is low despite high water loss (Borchert et al., 1994;
Goldstein et al., 1998). Species with extensive paratracheal
axial parenchyma, often drought avoiders, have low cavitation
933
even though up to 20 % of water in stem water is transpired
daily (Machado and Tyree, 1994; Goldstein et al., 1998). This relationship between the amount of axial parenchyma and cavitation suggests that it is part of a suite of traits that are involved in
the trade-off between conduction efficiency and cavitation resistance, in which axial parenchyma is either buffering cavitation or
involved in embolism repair (e.g. Tyree et al., 1999). The positive relationship between axial parenchyma area and hydraulic
efficiency we showed here suggests that axial parenchyma
might be linked to efficient conduction with storage/repair capacity in relatively mesic trees.
The lack of more clearly defined axes of variation in our pPCA
analysis could be due to several reasons, the most important
being that the anatomical traits we measured have high functional complexity and, in many cases, are important in performing
more than one function. For instance, the multiple functions of
some cell types can lower our power to detect the axes of variation clearly. For example, the cytoplasmatic contents in
septate (living) fibres (e.g. Vestal and Vestal, 1940; Spackman
and Swamy, 1949) together with their high incidence in woods
with scanty parenchyma (Spackman and Swamy, 1949;
Wheeler et al., 2007) suggest that living fibres have a dual function of storage and mechanical support (Govindarajaru and
Swamy, 1955; Wheeler et al., 2007). A similar complexity is
represented by rays and axial parenchyma which have links to
the three aspects: wood mechanics, storage and hydraulics.
Another source of error in our analyses is introduced by inaccuracies of the image analysis in measuring total cell wall area; this
is likely to be behind the low proportion in wood density
explained by cell wall area (9 %).
The predominance of global structures in the pPCA indicates a
high phylogenetic signal in the three PCA axes. Trait divergence
among closely related taxa was not large in our study, as we did
not detect local structures. Jombart et al. (2010) suggested that
low eigenvalues of local structures can still be interpreted
because Moran’s I distribution is asymmetric (negative values
often have a smaller range of variation than positive values)
and pPCA would more easily detect extreme autocorrelation
associated with global structures than the less extreme negative
values of local structures (Jombart et al., 2010). Interestingly,
most of the anatomical traits that had high values in the three
first local structures are related to water conduction, indicating
that water conduction characteristics tend to vary among
closely related taxa more than traits associated with mechanical
support. However, as none of the eigenvalues was negative
(below the dashed line in Fig. 2B), the evolution of these water
conduction traits is not clearly divergent.
Conclusions
The pPCA provided parallel information to the PIC analysis.
The first pPCA represents a water conduction and storage (and
possibly repair) axis. In this component, species varied from hydraulically efficient with high storage capacity to hydraulically
inefficient with low storage capacity. The second global component integrates conduction capacity, storage and support, and
thus represents the trade-off between conduction efficiency,
wood density and cavitation resistance seen in many studies
(e.g. Hacke et al., 2001, 2006). The third global structures represent the radial mechanical strength axis, with wood density and
934
Zheng & Martı́nez-Cabrera — Functional differentiation of axial and radial parenchyma
proportion of rays varying together. However, as mentioned
earlier, the importance of some traits in more than one component indicates a greater complexity of variation and is probably
the reason why we did not detect very clear axes of variation (especially in the second pPCA axis). Given the nature of our study,
the axis co-ordination presented here should be taken as a starting
point for other studies incorporating wood biomechanics, hydraulics and anatomy, but with greater focus on species variation
in particular environments. Although much remains to be investigated regarding the role of anatomical elements with multiple
functions (e.g. living fibres, contact and isolation cells in parenchyma), the fact that these functional axes of variation and their
anatomical determinants were very conserved across the Chinese
lineages we analysed is significant. These functional axes were
possibly established very early in the history of the angiosperms,
indicating their significance in the functional strategies of the
group.
S UP P L E M E NTA RY DATA
Supplementary data are available online at www.aob.oxfordjournals.org and consist of the following. Table S1: phylogenetically uninformed Pearson correlation coefficients among
variables. Table S2: PIC correlations between functional traits
and wood anatomy. Table S3: results of the regression of trait
loadings of the phylogenetic principal component analysis for
the three data partitions. Fig. S1: pPCA plots for all data
partitions.
AC KN OW LED GEMEN T S
We thank Professor Cynthia Jones and John Silander for their
valuable suggestions on the draft and their help with the
English language of this manuscript, and Dr Xiangping Wang
for his help in data collection and discussion. We also acknowledge the helpful comments of two anonymous reviewers. This
work was supported by the China Ministry of Science and
Technology under Contract (2011CB403201) to J.Z. H.I.M.C.
thanks CONACYT and FQRNT for support.
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