Tree Physiology 25, 425–435
© 2005 Heron Publishing—Victoria, Canada
Diversity of leaf traits related to productivity in 31 Populus deltoides ×
Populus nigra clones
NICOLAS MARRON,1 MARC VILLAR,2 ERWIN DREYER,3 DIDIER DELAY,1 ERIC
BOUDOURESQUE,1 JEAN-MICHEL PETIT,1 FRANCIS M. DELMOTTE,1 JEAN-MARC
GUEHL3 and FRANCK BRIGNOLAS1,4
1
Laboratoire de Biologie des Ligneux et des Grandes Cultures, UPRES EA 1207, UFR-Faculté des Sciences, Université d’Orléans, rue de Chartres,
BP 6759, 45067 Orléans Cedex 02, France
2
INRA, Unité Amélioration, Génétique et Physiologie Forestières, BP 20619, Ardon, 45166 Olivet Cedex, France
3
UMR INRA-UHP, Ecologie et Ecophysiologie Forestières, INRA Nancy, 54280 Champenoux, France
4
Corresponding author ([email protected])
Received May 26, 2004; accepted October 10, 2004; published online February 1, 2005
Summary To test if some leaf parameters are predictors of
productivity in a range of Populus deltoides (Bartr.) Marsh. ×
P. nigra L. clones, we assessed leaf traits and productivity in
2-month-old rooted cuttings from 31 clones growing in 4-l pots
in a greenhouse, under conditions of controlled temperature
and optimal irrigation. We evaluated four groups of variables
describing (1) productivity (total biomass), (2) leaf growth (total leaf number increment and total leaf area increment rate),
(3) leaf structure (specific leaf area and nitrogen and carbon
contents) and (4) carbon isotope discrimination (∆), which
is negatively correlated with time-integrated water-use efficiency. High-yielding clones did not necessarily display high
leaf growth rates, but they displayed a larger total leaf area,
lower specific leaf area and lower leaf nitrogen concentration
than clones with low productivity. Total leaf area was mainly
controlled by maximal individual leaf area and total leaf area
increment rate (r = 0.51 and 0.56, respectively). Carbon isotope
discrimination did not correlate with total biomass, but it was
associated with total number of leaves and total leaf area increment rate (r = 0.39 and 0.45, respectively). Therefore, leaf area
and specific leaf area were better indicators of productivity
than leaf growth traits. The observed independence of ∆ from
biomass production provides opportunities for selecting poplar
clones combining high productivity and high water-use
efficiency.
Keywords: carbon isotope discrimination, leaf growth, leaf
plastochron index, poplar, specific leaf area, water-use efficiency.
Introduction
Poplars are among the fastest growing trees in temperate latitudes and are of considerable commercial importance (Zsuffa
et al. 1996). Large differences in productivity and in functional
and structural determinants of productivity have been observed among poplars (Ceulemans 1990). Leaf-level functional and structural components associated with high growth rates
and productivity include: internal leaf structure, leaf growth
physiology and functional traits such as photosynthetic performance and water-use efficiency (WUE).
In addition, total leaf area is closely correlated with total
biomass and can be considered as a determinant of productivity in poplar (Ceulemans 1990, Ceulemans et al. 1990, Gardner et al. 1995, Ferris et al. 2001). Two of the main factors limiting productivity during the growing season are the time necessary to reach maximal leaf area and the ability to maintain
leaf area (Loomis and Williams 1963). According to Ridge et
al. (1986), significant clonal variation exists in the three physiological components that control total leaf area of poplar trees:
individual leaf growth, rate of leaf production and duration of
leaf expansion. Clonal differences in all three variables have
been observed among and between Euramerican and Interamerican poplar clones (Ceulemans et al. 1988). Fast-growing
high-yielding clones do not necessarily have the highest rates
of leaf production, but they do have the largest leaves and the
highest rates of leaf expansion (Ceulemans 1990, Niinemets et
al. 2004).
–1
Many authors break down relative growth rate (RGR; g g DW
–1
–2
–1
day ) into net assimilation rate (NAR; g cm day ), specific
leaf area (SLA, ratio between leaf area and leaf dry mass; cm2
–1
–1
) and leaf-to-plant mass ratio (LMR; g g DW
). If correlag DW
tions between RGR and LMR are weak, the relative contribution of SLA and NAR to RGR varies (Huante et al. 1995,
Saverimuttu and Westoby 1996, Huante and Rincon 1998,
McKenna and Shipley 1999, Poorter 1999, Ryser and Wahl
2001, Taub 2002). Interspecific differences in SLA seem to be
the main determinant of RGR variability at low irradiance,
whereas interspecific differences in NAR control RGR variability at high irradiance (Poorter and Van der Werf 1998,
426
MARRON ET AL.
Shipley 2002). Specific leaf area is also correlated with leaf
traits such as leaf density and thickness (Cambridge and
Lambers 1998, Pyankov et al. 1998, Niinemets 2001).
Increases in leaf thickness are primarily due to additional
photosynthetically competent mesophyll cell layers, as well as
to larger cells in each mesophyll layer (Körner et al. 1989,
Niinemets 1999). Increases in leaf density are due to thicker
cell walls and to smaller and more tightly packed cells
(Garnier and Laurent 1994, Niinemets 1999). Natural variations in both leaf thickness and density are responsible for
variations in SLA among species (Abrams et al. 1994, Garnier
and Laurent 1994). In several recent broad-scale comparisons
of growth parameters in herbaceous and woody species, SLA
was positively linked to RGR (Reich et al. 1992, Cornelissen et
al. 1998, Veneklaas and Poorter 1998, Poorter and Garnier
1999, Veneklaas et al. 2002). The low SLA of slow-growing
species is often associated with long leaf life spans and high
leaf toughness (Reich et al. 1991, Wright et al. 2004) and to
high carbon (mainly in cell walls) and low nitrogen concentrations (Niinemets 1999). A negative correlation between SLA
and biomass production has sometimes been observed (Nelson
1988, Wright et al. 1994, Thumma et al. 2001) and has been
explained by the fact that plants with low SLA have a large
number of mesophyll cells per unit area or large mesophyll
cells, leading to high rates of CO2 assimilation and, consequently, high biomass production.
Productivity of fast-growing species, such as poplars, is
highly dependent on water availability (Tschaplinski and
Blake 1989, Tschaplinski et al. 1994). Inter- and intraspecific
differences in WUE, defined as the ratio between plant biomass accumulation and plant transpiration, have been reported
(Condon et al. 2002). This trait, which is estimated by carbon
isotope discrimination (∆) (Farquhar and Richards 1984), is
correlated with productivity in some species but not in others.
The lack of such a relationship suggests that inter-genotypic
variability in WUE is mainly controlled by diversity in stomatal conductance, whereas a positive relationship indicates
that WUE is controlled mainly by photosynthetic capacity
(Farquhar et al. 1989). Consequently, selection for high WUE
may result in lower or higher productivity according to the predominant factor influencing WUE in the considered species
(Condon et al. 1987). For this reason, it has been suggested
that the use of ∆ for selection should be coupled with additional and less ambiguous indicators of productivity such as
leaf structure (e.g., SLA) and leaf growth (e.g., rate of increase
in leaf number or leaf area increment rate or total leaf area)
(Ceulemans 1990, Guehl et al. 1994, Thumma et al. 2001,
Brendel et al. 2002).
To analyze clonal variability in ecophysiological traits related to productivity in poplars, we tested leaf growth and
structural traits as indicators of clonal variability in productivity, and determined if they correlated with WUE. Carbon
isotope discrimination was used as a surrogate for WUE (Farquhar and Richards 1984, Brendel et al. 2002). We compared
31 Populus deltoides (Bartr.) Marsh. × P. nigra L. clones from
various origins and contrasting productivities that were grown
in a greenhouse. Leaf structure was described by carbon and
nitrogen concentrations and SLA. We accounted for leaf position in the analysis by recording leaf plastochron index (LPI)
of each leaf (Erickson and Michelini 1957). Application of the
LPI concept provides a means both for comparing poplar
plants at different morphological stages by adjusting the plant
developmental stages to a standardized morphological time
scale, and for predicting developmental processes and events
from simple nondestructive measurements (Ceulemans and
Isebrands 1996). The use of LPI for estimating leaf age requires a constant leaf production rate and thus, non-limiting
conditions, at least for water, nutrients and light. Productivity
and leaf growth parameters included total biomass, leaf mass
ratio, root mass ratio, stem length, total leaf area, total number
of leaves, total leaf number increment rate, total leaf area increment rate, maximal leaf area, maximal leaf area increment
rate and duration of leaf expansion.
Materials and methods
Plant material and growth
The experiments were carried out with homogeneous 25-cmlong woody stem cuttings of 31 P. deltoides × P. nigra clones
from diverse origins. During February 2001, 1-month-old
rooted cuttings of each clone were repotted in 4-l pots containing a 25:25:20:20:10 (v/v) mixture of blond and brown peat,
horse manure, heather and bromide-disinfected compost (pH
5.8) (Falienor, Vivy, France). Cuttings were grown in a greenhouse in six randomized complete blocks with single replicates per block under a controlled temperature (18–20 °C) and
day length (16-h photoperiod, photosynthetic photon fluence
rate of 700 µmol m –2 s –1), with optimal irrigation regime.
To calculate LPI, leaves of each cutting were numbered
from the bottom to the top of the stem (leaf rank = a). The
young leaf just exceeding 20 mm in length was indexed as n
(Larson and Isebrands 1971). Every second day from March 7
to April 3, 2001, length and width of each leaf were measured
on all cuttings. We computed LPI as PI – a, where PI is the
plastochron index (PI = n + ((logLn – log20)/(logL n –
logL n+1))); L n is lamina length (mm) of leaf n, and L n+1 is
lamina length of leaf n+1, the first leaf measuring less than
20 mm (Erickson and Michelini 1957).
On March 13, 2001 (Day 72) and April 3, 2001 (Day 93), all
leaves were collected from three cuttings per clone and ordered according to their LPI. They were photocopied, dried at
75 °C for 24 h and weighed. Photocopies of the leaves were
scanned and leaf area was estimated with an image analyzer
(UTHSCSA Image Tool program developed at the University
of Texas Health Science Center, San Antonio, TX, and available from http://ddsdx.uthscsa.edu/dig/itdesc.html). Total leaf
area (TLA; cm2) was calculated by summing individual leaf
–1
) was determined as individual leaf
areas, and SLA (cm2 g DW
area per dry mass. Relationships between SLA and LPI were
established for each clone at each date. On Day 93, the length
of the main stem (L stem ) was measured, and roots and stems
were collected, dried at 75 °C for 48 h and weighed. Total cut-
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ting biomass (TCB; gDW) was calculated by summing the biomass of leaves, green stem, woody stem and roots. Leaf mass
–1
–1
ratio (LMR; g g DW
) and root mass ratio (RMR; g g DW
) were
computed as (foliage biomass)/(total biomass) and (root biomass)/(total biomass), respectively. At each date, two leaves of
each cutting, for which LPI varied between 10 and 15, were
freeze-dried and ground to powder, 1 mg of which was
combusted and analyzed for 13C and total carbon (C) and nitrogen (N) composition with a continuous flow isotope ratio mass
spectrometer (Delta S, Finnigan MAT, Bremen, Germany).
Carbon isotope composition (δ13C) was calculated relative to
the Pee Dee Belemnite standard as (Farquhar et al. 1989):
δ13C =
R sa − R sd
× 1000 ( ‰)
R sd
(1)
where Rsa and Rsd are the 13C/12C ratios of the sample and the
standard, respectively. The discrimination between atmospheric CO2 (δair was assumed to be close to –8‰) and plant
material (δplant ) was calculated as (Farquhar and Richards
1984):
∆=
δ air − δ plant
(2)
1 + δ plant / 1000
Total C and N concentrations were expressed on a dry-mass
–1
) and a leaf-area basis (CA, NA; g m –2).
(CM, NM; mg g DW
Allometric relationships between leaf dimensions (length or
width) and leaf area were estimated at Days 72 and 93 by image analysis. Leaf length and width were measured manually
six times between Days 72 and 93. The best fits between leaf
427
area and leaf dimensions were obtained with width rather than
length. The relationship was: A = aW 3 + bW 2 + cW + d, where
A is leaf area and W is leaf width. For all clones, correlations
were significant at P ≤ 0.001 (r 2 = 0.962–0.988). Total leaf
area and total leaf area increment rate (dTLA/dt; cm 2 day –1)
were computed from leaf length. For each clone, a significant,
positive linear relationship was obtained between time (days
of year) and total number of leaves (TNL) or TLA (Figure 1).
Thus, total number of leaves increment rate (dTNL/dt; day –1)
and dTLA/dt remained stable during the experiment, allowing
us to use LPI as an estimator of leaf age within each clone.
Significant trinomial functions were fitted to the relationships between SLA and LPI on Days 72 and 93 separately.
(Detailed data are available on request; see Appendix Table A1.) The maximal area reached by individual leaves
(LA max ) was estimated from the relationship between LPI and
individual leaf area (see Figure 2a). Individual leaf area maximum increment rate (Vmax ) and LPI of the first leaf with null
area increase (LPIad ) were estimated from the relationship between LPI and individual leaf area increment rates (see Figure 2b). Maximum specific leaf area (SLA max ), LPI of the leaf
with maximum SLA (LPI A ) and initial slope (S0 = (dSLA/
dLPI) 0 ) were estimated from the relationship between LPI
and SLA (see Figure 2c). The value of S0 is a simple and useful
index of dTNL/dt. As shown in Table 1, S0 is negatively linked
to dTNL/dt. Duration of leaf expansion (DLE) was computed
at both dates as (LPIad )(dt/dTNL).
Statistical analyses
Results were evaluated by linear regression and correlation
analyses and analysis of variance with the SPSS statistical
software package (SPSS, Chicago, IL). When regression anal-
Table 1. Abbreviations and descriptions of variables.
Variable
Description
Figure
Whole-plant vigor
TNL
TLA
LMR
RMR
TCB
L stem
Total number of leaves
Total leaf area (cm2)
–1
Leaf mass ratio (g gDW
) = foliage biomass/total biomass
–1
) = root biomass/total biomass
Root mass ratio (g gDW
Total cutting dry biomass (gDW)
Stem length (cm)
Figure 1
Figure 1
Leaf growth
dTNL/dt
dTLA/dt
Vmax
LPI ad
DLE
LPI A
S0
Total number of leaves increment rate (day –1)
Total leaf area increment rate (cm 2 day –1)
Maximal increase rate of individual leaf area (cm 2 day –1)
Leaf plastochron index of the first leaf with null area increase rate
Duration of leaf expansion (days) = LPI ad (dt/dTNL)
LPI of the leaf with maximal specific leaf area (SLA) along the stem
initial slope = (dSLA/dLPI)0 = (dSLA/dt)(dt/dLPI) = (dSLA/dt)(dt/dTNL)
Figure 1
Figure 1
Figure 2a
Figure 2a
C M, C A
N M, N A
LA max
SLA max
–1
Leaf carbon concentration per dry mass (mg gDW
) or leaf area (g –1 m –2)
–1
Leaf nitrogen concentration per dry mass (mg gDW) or leaf area (g –1 m –2)
Area of the largest leaf (cm 2 )
–1
Largest specific leaf area along the stem (cm 2 gDW
)
∆i
∆f
Leaf carbon isotopic discrimination measured on Day 72 (‰)
Leaf carbon isotopic discrimination measured on Day 93 (‰)
Leaf structure
Water-use efficiency
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Figure 2c
Figure 2c
Figure 2b
Figure 2c
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MARRON ET AL.
we performed multivariate analyses using principal components analysis (PCA). The basic variables were standardized
and orthogonal factors (= F1 and F2 axis) were successively
built as linear combinations of these variables to maximize the
part of the variability explained by these factors. Variables
were first represented on the plane defined by the two main
factors of the PCA; their coordinates were their linear correlation coefficients (Pearson’s coefficient) with these factors.
Figure 1. Time course of total number of leaves (䊐) and total leaf area
(䊊) for three rooted cuttings of the clone ‘Luisa_Avanzo’.
ysis indicated an effect of LPI, the latter was included in the
statistical model as a covariate. All statistical tests were considered significant at P < 0.05. Means are expressed with their
standard error (± SE).
To study inter-clonal variability of all measured parameters,
Results
Detailed results are presented for Day 93. Data from Day 72
were used to check the stability of leaf parameters between the
dates.
Clonal diversity
A range of clonal variation was recorded for most of the productivity and leaf growth parameters (Table 2). Whole-plant
biomass production ranged from 12.3 to 36.6 gDW. Highly significant clonal differences were observed in SLA. Although
NM did not differ among clones, clonal differences in NA were
observed, reflecting the large clonal differences in SLA. There
were also significant clonal differences in ∆, which varied by
over 3‰ between clones at the extremes of the range.
Stability of leaf parameters between Days 72 and 93
Clonal stability of leaf parameters was checked by comparing
plants collected on Days 72 and 93. All variables displayed
significant differences between Day 72 and 93 (Table 2, Figure 2) with the exception of ∆. Mean values of CM and CA, although significantly different, did not differ greatly, whereas
NM and NA decreased sharply (Table 2). This decrease was associated with decreased soil N availability at the end of the experiment. Between Days 72 and 93, LPIA increased significantly and SLAmax and S0 decreased (Figure 2c). Both SLAmax
and LAmax increased from the base to the top of the stem. Thus,
leaf area and thickness or density, or both, of fully expanded
leaves increased with cutting age. Values of LPIA, SLAmax and
S0 for Day 72 were positively correlated with values for Day
93, confirming that the inter-clonal differences in these variables were stable over time (Table 2). Only the variables describing leaf area (TLA, Vmax, LAmax ) and leaf C and N concentrations were not correlated between Days 72 and 93 (Table 2).
Relationships between variables
Figure 2. Relationships between leaf plastochron index (LPI), leaf
area (a), leaf area increment rate (b) and specific leaf area (SLA) (c).
These relationships were established on Day 72 (䊊) and Day 93 (䊉)
on rooted cuttings of the clone ‘Luisa_Avanzo’. Abbreviations and
symbols: LA max = maximal leaf area along the stem; LPI ad = LPI of
the first leaf with null area increase; Vmax = maximal leaf area increase
along the stem; S0 = the initial slope of the relationship for LPI = 0;
SLA max = maximum specific leaf area; and LPIA = LPI of leaf with
maximum SLA.
A general PCA was performed with clonal means of all variables from Day 93, except CA and NA because of their redundancy with CM and NM. The main plane of the PCA (F1 × F2)
explained 48.1% of the inter-clonal variability and F1 explained 28.6 % (Figure 3). Axis F3 of the PCA was analyzed
but it did not clearly differentiate clones for parameters in this
study and it explained only 12.6% of total variance (data not
shown).
The F1 axis of the PCA was clearly defined by the difference between S0 and the group of variables including LPIA,
TNL, dTNL/dt, LPI ad and L stem (Figure 3). The index S0 scaled
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429
Table 2. Minimum (Min) and maximum (Max) means of variables measured on Days 72 and 93, significant differences between clones and between Days 72 and 93 (ANOVA) and linear correlations between Days 72 and 93 (Pearson’s coefficient). Significant correlations are indicated by
asterisks: * = P ≤ 0.05; ** = P ≤ 0.01; and *** = P ≤ 0.001. See Table 1 for definition of abbreviations. Because dTNL/dt and dTLA/dt resulted
from time-repeated measurements, no inter-date differences could be computed.
Variable
Day 72
Min
Whole-plant vigor
Leaf growth
Leaf structure
Day 93
Max
P
Min
Max
P
P
Correlation
0.24
0.031
12.28
33.43
16.0
875.3
0.39
0.117
36.61
70.93
31.5
1880.9
***
***
***
***
***
*
***
***
0.95 ***
0.15
18.28
3.73
5.00
20.26
6.5
6.0
0.40
62.99
14.85
11.10
35.45
17.0
47.1
***
***
***
***
**
***
***
***
***
**
***
***
179.99
444.6
25.73
30.8
1.44
293
***
***
***
26.3
***
–1
RMR (g gDW
)
–1
)
LMR (g gDW
TCB (gDW)
L stem (cm)
TNL
TLA (cm2)
10.0
286.2
21.7
1038.3
***
***
dTNL/dt (day –1)
dTLA/dt (cm 2 day –1)
Vmax (cm 2 day –1)
LPI ad
DLE (day)
LPIA
–1
)
S0 (cm 2 gDW
0.15
18.28
1.47
7.80
22.29
4.5
36.1
0.40
62.99
11.61
12.40
52.00
9.8
160.5
***
***
***
***
LAmax (cm2)
–1
)
CM (mg gDW
CA (g m –2)
–1
NM (mg gDW
)
–2
NA (g m )
–1
)
SLA max (cm 2 gDW
28.81
433.4
13.4
36.3
0.93
293
148.88
488.2
18.61
45.2
1.63
398
***
*
***
69.89
416.0
11.93
19.9
0.68
188
21.7
27.9
**
23.1
Water-use efficiency ∆ (‰)
negatively with TNL (r = –0.66) (Table 3). Both TNL and
dTNL/dt scaled positively with LPIad (r = 0.56 and 0.78, respectively), whereas dTNL/dt scaled negatively with DLE
(r = –0.65) because DLE was calculated as a function of LPIad
and dTNL/dt (Table 3). Both LPIA and LPI ad tended to be positively correlated (P = 0.06), indicating that LPIA, as LPI ad, reflected the rank of the first leaf with null area increase rate and
thus, young mature leaves had the highest SLA (SLA max ). Total number of leaves was linked to TLA (r = 0.44), whereas
dTNL/dt and TLA were not correlated. Carbon isotope discrimination was positively correlated with TNL and LPIA (r =
0.39 and 0.45, respectively) and was negatively correlated
with S0 (r = –0.38) (Table 3). Stem length was positively correlated with TNL, dTNL/dt, LPIA, TCB and TLA, whereas it
was negatively correlated with S0 and DLE (Table 3). Neither
TNL nor dTNL/dt correlated with TCB.
The F2 axis of the PCA was defined by a positive correlation
with TCB and TLA and a negative correlation with SLA max
and NM (Figure 3). Total leaf area was positively correlated
with maximal leaf area (LA max ) and TCB (r = 0.72 and 0.51,
respectively), whereas it was negatively correlated with
SLA max and NM (r = –0.37 and –0.48, respectively) (Table 3).
Positive scaling was found between TLA and dTLA/dt as well
as between dTLA/dt and Vmax (r = 0.56 and 0.73, respectively). Total cutting biomass was negatively correlated with
both SLA max and NM (r = –0.68 and –0.46, respectively), and
to a lesser extent with LMR (r = –0.37) (Table 3). Carbon iso-
Between dates
***
***
***
**
***
***
***
***
***
***
***
0.47 **
0.59 ***
0.49 **
0.41 *
0.47 **
0.50 **
0.65 ***
tope discrimination did not correlate with the main variables
that contributed to F2 (TLA, TCB, SLA max and NM ).
Discussion
Variations in specific leaf area
Specific leaf area increased from the top of the cutting downward, as a result of leaf expansion, up to a maximum corresponding to cessation of leaf expansion. Similar findings have
already been reported for two P. deltoides × P. nigra clones
(Marron et al. 2003). Thus, young mature leaves have the highest SLA and LPI A, as well as the highest LPI ad. The decrease in
leaf density or thickness, or both, during leaf expansion can be
explained by prolonged expansion of a fixed number of cells
as observed for P. deltoides and by simultaneous and continuous production and enlargement of cells as observed for
P. trichocarpa (Van Volkenburgh and Taylor 1996). When final leaf size was reached, SLA decreased. This may be because of thickening of the cuticle and secondary cell walls
(structural dry mass; Schumaker et al. 1997) or because of the
retention of photosynthates and mineral elements during leaf
aging (Nelson and Isebrands 1983, Marron et al. 2002); the increase in structural dry mass was probably the main factor
(Gunn et al. 1999). Specific leaf area increased slightly in the
oldest leaves, possibly reflecting remobilization of compounds from the leaf to the stem before leaf senescence. Maximal SLA decreased with cutting age; this may be an onto-
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MARRON ET AL.
Figure 3. Distribution of the 20 variables (see Table 1 for description of variables) (a) and projection of the 31 clones (b) in the factorial plane F1 ×
F2 of the principal components analysis defined at Day 93. Axes F1 and F2 are linear combinations of the 18 variables and were built to maximize
the part of the data variability that they explained. Clone abbreviations: 2000v = ‘2000_verde’; AF = ‘Agathe_F’; Bcl = ‘Boccalari’; Bg =
‘Branagesi’; Bt = ‘Brenta’; CB = ‘Cappa_Bigliona’; Cc = ‘Carpaccio’; Cm = ‘Cima’; Ds = ‘Dorskamp’; E28 = ‘Eco_28’; Fv = ‘Flevo’; Gv =
‘Gaver’; Gy = ‘Ghoy’; H5 = H-523-9; I2 = ‘I214’; I4 = ‘I45-51’; Kp = ‘Kopecky’; Ks = ‘Koster’; Lb = ‘Lambro’; LA = ‘Luisa_Avanzo’; Ml =
‘Mella’; MC = ‘Mellone_Carlo’; N10 = NL-1070; N31 = NL-3149; N39 = NL-3972; N40 = NL-4040; Pn = ‘Pannonia’; Rb = ‘Robusta’; SM =
‘San_Martino’; Sg = ‘Soligo’; and Tp = ‘Triplo.’
genetic process paralleling the increase in LAmax or a plastic
response to increases in irradiance during the experiment
(Abrams et al. 1994, Niinemets 2001). Specific leaf area of the
largest, fully expanded leaf (SLA max ) displayed large clonal
variability and was a much better index of clonal diversity in
SLA than the mean value of SLA computed from whole cuttings.
Relationships between leaf traits and productivity
Although buds flushed synchronously in the poplar clones,
large clonal differences were observed for TLA, LA max and
leaf growth parameters such as dTNL/dt, dTLA/dt, Vmax and
DLE. Total leaf area was closely correlated with total biomass,
as found in earlier studies (Ceulemans 1990). Total leaf area
was positively correlated with LAmax but less closely with Vmax
and LPIad. However, TLA did not correlate with dTLN/dt.
Thus, high-yielding hybrid clones did not necessarily have the
highest leaf production rates, but they displayed the largest total leaf area (cf. Ceulemans 1990). Based on the tight correlation between TLA and LAmax, we conclude that LA max could
be an estimator of clonal variation in TLA. The high dTLA/dt
was partly due to a high Vmax, as indicated by the positive correlation between dTLA/dt and Vmax.
Total leaf number increment rate was negatively correlated
with DLE. Thus, clones with the highest rate of leaf production were also clones with the shortest time for leaf expansion.
We found a positive correlation between dTNL/dt and LPIad.
Such a concomitance between LPI at leaf maturity and leaf
production rate has previously been detected for Populus
(Ceulemans et al. 1988, Ceulemans 1990). Although dTNL/dt
was negatively correlated with DLE, clones with high
dTNL/dt also had the largest number of expanding leaves, as
indicated by the positive correlation between LPI ad and
dTNL/dt. We conclude that clonal diversity in the number of
expanding leaves on a stem is a result of variability in
dTNL/dt rather than duration of leaf expansion.
Variations in S0 can be caused by variations in SLA max or
LPI A. The orthogonal position of SLA max relative to S0 and
LPI A, as well as the correlation between LPI A and S0, showed
that S0 was under the control of LPI A rather than SLA max.
Thus, low S0 was mainly due to high LPI A (number of expanding leaves). Moreover, LPIA was positively correlated with
TNL or dTNL/dt. Thus, the F1 axis of the PCA allowed us to
discriminate clones, not only for their number of expanding
leaves, but also for TNL and dTNL/dt.
Total biomass scaled negatively with LMR and with
SLA max. Therefore, we can hypothesize that inter-clonal variability in biomass production (and probably relative growth
rate) was mainly under the control of LAR rather than of NAR.
This may be due at least partly to the low irradiance in the
greenhouse, because SLA is the main determinant of RGR at
low irradiance, whereas NAR becomes the main determinant
at high irradiance (Poorter and Van der Werf 1998, Shipley
2002). Similarly, interspecific variations in SLA are important
in determining variations in RGR when growth temperature is
TREE PHYSIOLOGY VOLUME 25, 2005
TREE PHYSIOLOGY ONLINE at http://heronpublishing.com
ns
ns
ns
0.39
*
ns
RMR
TCB
Lstem
TNL
S0
ns
ns
ns
NA
LAmax
SLAmax
NM
CA
ns
ns
ns
–0.64
***
0.51
**
ns
ns
ns
ns
ns
ns
ns
DLE
ns
ns
–0.44
*
0.45
*
–0.38
*
–0.37
*
–0.52
**
ns
LPIad
ns
ns
ns
ns
Vmax
ns
ns
0.51
**
ns
ns
ns
ns
ns
ns
ns
ns
dTLA/dt
–0.37
*
ns
–0.68
***
ns
ns
ns
0.40
*
ns
LPIA
CA
CM
0.62
***
0.50
**
ns
–0.52
**
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
–0.48
**
ns
–0.46
**
ns
ns
ns
ns
0.39
*
–0.69
***
ns
ns
ns
ns
ns
ns
ns
ns
*
0.44
*
ns
ns
ns
0.43
*
ns
ns
ns
ns
ns
ns
ns
ns
ns
–0.36
ns
0.36
*
–0.69
***
ns
ns
ns
ns
–0.38
*
–0.68
***
–0.66
***
–0.44
*
ns
ns
–0.47
**
ns
ns
0.54
**
ns
0.49
**
0.70
***
0.41
*
ns
0.41
*
ns
LPIA
S0
NΜ
SLAmax LAmax
NΑ
Leaf growth
Leaf structure
0.45
*
ns
dTNL/dt
TLA
ns
∆
LMR
Leaf structure CM
Leaf growth
Whole-plant
vigor
Variable
ns
ns
–0.65
**
ns
–0.38
*
–0.64
***
ns
ns
ns
ns
DLE
0.78
***
0.45
*
0.39
*
0.56
**
ns
ns
ns
ns
ns
LPIad
0.73
***
ns
ns
ns
ns
ns
ns
ns
Vmax
0.37
*
0.56
***
ns
ns
ns
ns
ns
0.45
*
0.80
***
ns
ns
ns
ns
0.72
***
0.56
**
0.44
*
ns
ns
dTLA/dt dTNL/dt TLA
0.67
***
ns
ns
ns
TLN
0.56
**
ns
*
ns
Lstem
Whole-plant vigor
–0.37
*
ns
TCB
0.38
RMR
Table 3. Linear correlations (Pearson’s coefficient) between vigor variables (TNL, TLA, LAR, RMR, TCB, L stem ), leaf growth variables (dTNL/dt, dTLA/dt, Vmax, LPIad, DLE, LPIA, S0), leaf structure
variables (CM, CA, NM, NA, LAmax, SLAmax ) and ∆. Linear correlations are measured on Day 93. Level of significance is indicated by asterisks: ns = nonsignificant; * = P ≤ 0.05; ** = P ≤ 0.01; and *** =
P ≤ 0.001. See Table 1 for definition of abbreviations.
LEAF TRAITS AND PRODUCTIVITY IN P. DELTOIDES × P. NIGRA
431
432
MARRON ET AL.
above 20 °C, whereas NAR plays a major role below 20 °C
(Loveys et al. 2002).
Low SLA max was associated with high CA. At the same time,
clones with the highest SLA displayed the highest NM. This relationship holds for many species: high NM and SLA characterize leaves with short life spans because these traits increase
leaf vulnerability to herbivory and physical hazards (Wright et
al. 2004). Area-based nitrogen (NA ) was negatively linked to
LPI A and positively linked to S0. Moreover, LPIA was positively correlated with TNL and dTNL/dt. Thus, leaves with
high NA characterized clones that presented a limited number
of expanding leaves and a low dTNL/dt. Total biomass was
negatively linked to SLA max and NM. Thus, dense or thick
leaves characterized high-yielding poplar clones. Although
leaf structure estimated as SLA was not correlated with the
leaf growth parameters, SLA was negatively correlated with
total leaf area and total biomass, indicating that high-yielding
P. deltoides × P. nigra hybrids are characterized by low SLA
and high TLA.
Clonal differences in ∆ were significant and remained stable
between dates (Table 2). Carbon isotope discrimination was
associated with LPIA and to lesser extent with S0. Although ∆
and leaf growth parameters scaled negatively, WUE did not
depend on structural leaf traits such as density or thickness. No
correlation was observed between total biomass and ∆, supporting the independence of productivity from WUE. The relationship between ∆ and productivity varies widely among
herbaceous and woody species. Positive relationships were reported for tomatoes (Martin and Thorstenson 1988), alfalfa
(Ray et al. 1999), common bean (Zacharisen et al. 1999) and
Stylosanthes (Thumma et al. 2001) as well as for pine
(Prasolova et al. 2003) and P. trichocarpa × P. deltoides
hybrids (Rae et al. 2004), whereas negative relationships were
reported for sunflower (Virgona and Farquhar 1996), peanut
(Hubick et al. 1986, Wright et al. 1988) and wheat (Ehdaie et
al. 1993). From a practical point of view, the independence
of ∆ from productivity in poplar indicates that it should be possible to select clones combining high productivity and high
water-use efficiency, which would be a considerable advantage for poplar cultivation in moderately drought-prone areas.
In conclusion, we tested whether an array of leaf structural,
leaf growth and functional parameters are indicators of productivity in P. deltoides × P. nigra hybrid poplars. We found
that the highest-yielding clones did not necessarily display the
highest leaf growth parameters, but did have higher TLA and
lower SLA than clones with lower vigor. No correlation was
observed between ∆ and total biomass, suggesting that it may
be possible to select for clones displaying both high productivity and high WUE. However, the stability of these relationships needs to be tested (1) with older individuals of the same
clones grown under natural conditions, (2) with clones born
from different cross-breeds (such as P. trichocarpa × P. deltoides hybrids), (3) during longer periods and (4) by studying
relationships between ∆ and intrinsic WUE (i.e., the CO2 assimilation rate/stomatal conductance ratio).
Acknowledgments
Financial support was provided by the Conseil Régional, Région Centre, France, and by INRA through the Transversal Action ECOGENE.
N. Marron was supported by a Ph.D. grant from the Conseil Régional,
Région Centre, France. The authors thank R. Bénardeau, G. Moreau,
P. Priault, S. Ribert and G. Vidal for technical assistance and C. Bréchet for the nitrogen, carbon and carbon isotope discrimination analyses. We also thank AFOCEL, Cemagref and IDF for their help with
the choice of clones.
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435
Appendix
–1
) and leaf plastochron index (LPI) established for Days 72 and 93. The
Table A1. Nonlinear regressions between specific leaf area (SLA; cm2 gDW
3
2
established relationship is: SLA = aLPI + bLPI + cLPI + d. For each clone and date, the fraction of explained variance (r 2) is significant at P ≤
0.001. Nonsignificant parameters are in brackets.
Clone
‘2000_verde’
‘Agathe_F’
‘Boccalari’
‘Branagesi’
‘Brenta’
‘Cappa_Bigliona’
‘Carpaccio’
‘Cima’
‘Dorskamp’
‘Eco_28’
‘Flevo’
‘Gaver’
‘Ghoy’
H-523-9
‘I214’
‘I45-51’
‘Kopecky’
‘Koster’
‘Lambro’
‘Luisa_Avanzo’
‘Mella’
‘Mellone_Carlo’
NL-1070
NL-3149
NL-3972
NL-4040
‘Pannonia’
‘Robusta’
‘San_Martino’
‘Soligo’
‘Triplo’
Day 72
Day 93
2
a
b
c
d
r
0.10
0.07
0.13
0.10
0.12
0.24
0.07
0.11
0.28
0.55
0.10
0.24
0.13
0.20
0.20
1.23
0.20
(0.16)
0.40
0.13
0.07
0.15
0.13
0.16
0.18
0.10
0.10
0.07
0.27
0.31
0.31
–4.02
–3.68
–4.75
–3.58
–5.16
–8.27
–3.51
–4.43
–9.14
–13.99
–3.95
–8.44
–5.30
–6.67
–6.49
–26.56
–7.53
(–5.13)
–8.76
–5.12
–3.03
–6.12
–5.04
–5.82
–6.06
–4.08
–3.98
–2.93
–8.22
–9.20
–9.18
44.4
48.4
46.2
38.6
59.8
78.0
47.2
48.7
80.7
98.8
44.1
78.1
55.7
58.8
58.9
160.5
76.2
(38.8)
57.7
55
38.5
67.6
52.8
55.8
55.1
48.0
47.8
36.1
71.0
75
75.7
159
170
216
175
130
136
130
178
155
129
154
176
202
221
181
(96)
137
287
208
161
140
113
133
178
185
131
121
182
133
152
136
0.79
0.59
0.61
0.33
0.81
0.66
0.80
0.88
0.82
0.76
0.65
0.81
0.23
0.62
0.75
0.87
0.81
0.66
0.71
0.89
0.61
0.76
0.77
0.60
0.54
0.83
0.82
0.58
0.84
0.74
0.78
a
b
c
d
r2
0.01
(0.01)
0.02
0.01
0.04
0.027
0.02
0.04
0.03
0.14
(0.01)
0.05
0.03
0.03
(0.01)
0.04
0.06
0.05
0.03
0.04
0.04
0.08
0.02
0.03
0.05
0.01
0.02
0.02
0.19
0.13
0.08
–0.38
(–0.43)
–0.98
–0.72
–2.19
–1.59
–1.18
–2.14
–1.42
–4.44
(–0.27)
–2.79
–1.80
–1.50
(–0.33)
–2.02
–2.76
–2.63
–1.63
–2.00
–1.97
–3.32
–1.01
–1.77
–2.11
–0.87
–0.91
–0.96
–5.40
–4.38
–2.93
6.0
10.4
18.6
16.5
31.9
28.2
23.3
29.9
22.6
41.1
8.0
39.9
33.9
25.5
9.0
29.8
37.1
37.7
25.1
30.7
31.5
40.5
14.0
26.7
23.6
18.9
16.7
18.2
47.1
42.7
33.2
160
175
128
128
101
113
98
118
141
122
193
119
94
153
172
74
88
102
92
92
86
104
160
96
143
109
127
139
103
95
99
0.37
0.41
0.79
0.80
0.82
0.75
0.89
0.64
0.61
0.69
0.54
0.64
0.87
0.59
0.38
0.84
0.82
0.78
0.75
0.86
0.87
0.56
0.55
0.81
0.24
0.69
0.60
0.56
0.62
0.48
0.48
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