The Sources of Carbon and Nitrogen Supplying Leaf
Growth. Assessment of the Role of Stores with
Compartmental Models1
Fernando Alfredo Lattanzi, Hans Schnyder*, and Barry Thornton
Lehrstuhl für Grünlandlehre, Technische Universität München, D–85350 Freising-Weihenstephan,
Germany (F.A.L., H.S.); and Soil Plant Microbial Interactions Group, The Macaulay Institute,
Craigiebuckler, Aberdeen AB15 8QH, United Kingdom (B.T.)
Patterns of synthesis and breakdown of carbon (C) and nitrogen (N) stores are relatively well known. But the role of mobilized
stores as substrates for growth remains less clear. In this article, a novel approach to estimate C and N import into leaf growth
zones was coupled with steady-state labeling of photosynthesis (13CO2/12CO2) and N uptake ( 15NO32/14NO32) and
compartmental modeling of tracer fluxes. The contributions of current C assimilation/N uptake and mobilization from stores
to the substrate pool supplying leaf growth were then quantified in plants of a C3 (Lolium perenne) and C4 grass (Paspalum
dilatatum Poir.) manipulated thus to have contrasting C assimilation and N uptake rates. In all cases, leaf growth relied largely
on photoassimilates delivered either directly after fixation or short-term storage (turnover rate 5 1.6–3.3 d21). Long-term C
stores (turnover rate , 0.09 d21) were generally of limited relevance. Hence, no link was found between the role of stores and C
acquisition rate. Short-term (turnover rate 5 0.29–0.90 d21) and long-term (turnover rate , 0.04 d21) stores supplied most N
used in leaf growth. Compared to dominant (well-lit) plants, subordinate (shaded) plants relied more on mobilization from
long-term N stores to support leaf growth. These differences correlated well with the C-to-N ratio of growth substrates and
were associated with responses in N uptake. Based on this, we argue that internal regulation of N uptake acts as a main
determinant of the importance of mobilized long-term stores as a source of N for leaf growth.
Most plants store carbon (C) and nitrogen (N) either
by accumulation of assimilation/uptake overflows, or
by deposition of specific reserve or recyclable compounds (Chapin et al., 1990). Stores are continuously
built up or mobilized, hence interacting directly with
the provision and utilization of plant resources. Allocation of C and N toward, and mobilization from, storage
pools has been described in many species, and the effects
of changes in growth conditions on such patterns are
relatively well known (Millard, 1988; Chatterton et al.,
1989; Chapin et al., 1990; Volenec et al., 1996). Conversely, the role of mobilized stores in supplying plant
C and N growth demand is less clear, particularly
regarding vegetative growth of perennial species.
One reason is simply lack of information. Studies of
the role of stores require accurate identification of
substrates derived from distinct sources, which poses
methodological problems. Steady-state labeling techniques proved particularly useful in discriminating
the use of newly acquired versus already available
1
This work was supported by the Deutsche Forschungsgemeinschaft (SFB 607) and the Scottish Executive Environment and Rural
Affairs Department. F.A.L. was partially supported by an award
from the British Council and Fundación Antorchas (Argentina).
* Corresponding author; e-mail [email protected]; fax 49–
8161–71–3242.
Article, publication date, and citation information can be found at
www.plantphysiol.org/cgi/doi/10.1104/pp.104.051375.
resources in response to specific stimuli, such as
defoliation (de Visser et al., 1997). This does not,
however, provide a quantitative partitioning of the
sources supplying growth. The reason is tracers often
move through a number of pools before being incorporated into growing tissue, which diminishes
their usefulness to discriminate specific sources. Analysis of tracer time course can help infer the number
and kinetics of mixing pools (Moorby and Jarman,
1975; Rocher and Prioul, 1987). This approach, frequently applied in studies of C and N fluxes in source
systems, has received less attention in analyses of
sources supplying sinks.
Another reason hindering the understanding of the
role of stores is the absence of knowledge about its
ecophysiological determinants. Often, the relative importance of stores as a source of substrates for growth
tends to be associated with changes in mobilization
intensity. However, it may depend as much on these
as on responses of actual acquisition and demand rates
(Bausenwein et al., 2001). Further, C and N metabolisms are closely interrelated: Mobilization of organic
N implies mobilization of amino-C, C status affects
N mobilization and uptake (Thornton et al., 2002),
and N status affects C assimilation and storage and
possibly mobilization, too (Stitt and Krapp, 1999).
Such interdependent relationships, central to putative
mechanisms controlling acquisition, storage, and mobilization of both C and N (Touraine et al., 1994; Stitt
and Krapp, 1999; Farrar et al., 2000), need to be
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383
Lattanzi et al.
integrated in analyses of the role of stores in supplying
growth.
The aims of this study were to (1) develop a method
able to distinguish and quantify the sources of C and N
supplying leaf growth in grasses, and then (2) use it
to analyze the influence of plant status on the relative importance of those sources with the hypothesis
that mobilization from stores is of greater importance
for leaf growth when acquisition of C or N is more
limited. In order to do this, a previously described
method for estimating import of C and N substrates
into leaf growth zones (Fig. 1; Lattanzi et al., 2004) was
further developed to account for labeled and nonlabeled components, and coupled to steady-state labeling of C assimilation and N uptake. The time course
of tracer incorporation into imported C and N substrates was then analyzed with compartmental models, with which the size and turnover of the substrate
pool supplying leaf growth, and the contribution to it
of current assimilation/uptake and mobilization from
stores, were estimated.
The relative contribution of these sources was
evaluated in plants of a C3 (Lolium perenne) and C4
(Paspalum dilatatum Poir.) grass growing undisturbed
in mixed stands. Contrasting C3/C4 balances of the
stands were brought about by moderately high (23C)
and low (15C) temperature regimes. This exploited
the differential growth response of C3 and C4 species to
temperature to place L. perenne and P. dilatatum plants
in contrasting hierarchical positions within the stands,
and thus produce dominant and subordinate individuals with different rates of C assimilation and N uptake (Table I; see also Lattanzi et al., 2004).
Figure 1. The leaf growth zone of a grass tiller. Continuous production
of cells at basal positions and their subsequent expansion gives rise to
a flux of tissue and tissue-bound C and N out of the growth zone (EC and
EN). This export is counterbalanced by import (deposition) of C and N
substrates. The growth zone can thus be conceived as a place where
substrates are imported, transformed, and then exported as structurally
and functionally differentiated tissue. The velocity of an element
moving through the growth zone (n) increases until it equals the LER
when cell elongation ceases. Lineal density of C and N (rLC and rLN)
may increase after cell expansion ceases but becomes stable near the
end of the cell maturation zone. Consequently, EC and EN can be
estimated as LER times rLC and rLN of RPT. In turn, import rates can be
estimated by adding to EC and EN the negative or positive variation in
growth zone C and N mass.
RESULTS
Import of C and N into the Growth Zone and Tracer
Import in Briefly Labeled Plants
Sequential analysis of leaf elongation rate (LER) and
of lineal density of C and N along the immature part
of leaves showed that import of C and N into the leaf
growth zone (ÆICæ and ÆINæ; see Table II for definition of
symbols in text) was approximately constant along the
experimental period. This was true for both species
(L. perenne and P. dilatatum) and growth conditions
(mixed stands at 23C and 15C). At several times
during the experimental period, C assimilation and N
uptake were labeled over the photoperiod immediately preceding sampling (briefly labeled plants). This
demonstrated that the contributions of recent C assimilation and N uptake were also constant (Fig. 2).
Such constancy suggests a system in steady state with
respect to fluxes of C and N and the contributions of
recently assimilated C and absorbed N.
Besides their stability, a remarkable feature of the
results was the contrasting behavior of C and N
substrates. Between 48% and 64% of the C imported
over a day (i.e. 24 h) derived from C assimilated during the previous 12-h light period. Conversely, N taken
up over the same period contributed only 3% to 17%
to total daily N import.
Modeling of Tracer Time Course in Continuously
Labeled Plants
Continuous steady-state labeling was applied to
follow the saturation kinetics of label in the different
C and N pools that served as sources for leaf growth.
Continuous labeling thus meant that C and N tracers entered the plants over the whole 15-d (23C) or
18-d (15C) experimental period. In this case, the proportion of tracer in imported C and N substrates
(Æ flab IC æ and Æ flab IN æ) was not stable, as in briefly labeled
plants, but characterized by rapid increases followed
by either slower increases or complete stabilization
(Fig. 3). In six out of the eight situations, a two-pool
model adequately described this biphasic pattern. The
model consisted of a substrate pool (Q1) supplying the
growth zone, which was in turn supplied by newly
acquired C or N, and by mobilization from a storage
pool (Q2). Mass transfers were governed by first-order
rate constants (k10, k12, and k21; numbers refer to donor
and receiver pools, respectively). In two cases (Æ flab IC æ
in P. dilatatum subordinate and Æ flab IN æ in L. perenne
dominant plants), the data fitted well to a one-pool
model, a reduced case of the former where k21 becomes
infinitely small (Fig. 4). Optimized and derived model
parameters are given in Table III.
Models described well the time course of Æ flab IC æ
and Æ flab IN æ; as indicated by the close agreement of
values estimated by Equation 3b and model predictions (Fig. 5). The SE of predicted values (Fig. 3), as well
as of derived parameters (Table III), was within
reasonable limits, with coefficients of variation below
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Sources of Carbon and Nitrogen Supplying Leaf Growth
Table I. Plant status at the beginning of labeling
L. perenne and P. dilatatum plants grew undisturbed in mixed stands for either 8 or 10 weeks at 23C or
15C, respectively, and thus in dominant and subordinate hierarchical positions. PPFDint indicates the
proportion of PPFD intercepted by the C3 and C4 components within each stand. C assimilation and N
uptake rates were measured over one light period (12 h). Means 6 SE (n 5 4 to 12).
Stand at 23C
Height (mm)
C mass (mg plant21)
N mass (mg plant21)
Leaf expansion duration (d)
Leaf lifespan (d)
Tillers per plant
PPFDint (%)
C assimilation (mg plant21 d21)
N uptake (mg plant21 d21)
Stand at 15C
L. perenne
Subordinate
P. dilatatum
Dominant
L. perenne
Dominant
P. dilatatum
Subordinate
485 6 12
572 6 69
51 6 4.7
571 6 14
706 6 90
46 6 7.0
349 6 4
1,103 6 108
71 6 6.0
255 6 7
81 6 9
7.5 6 0.8
13 6 1.0
35 6 4.0
15 6 0.5
11 6 0.5
24 6 2.1
8 6 0.3
16 6 1.6
43 6 6.4
29 6 0.9
23 6 5.7
71 6 17
6 6 0.3
23
25 6 10
0.89 6 0.45
77
78 6 17
1.92 6 0.96
93
90 6 12
1.56 6 0.43
7
3.7 6 1
0.02 6 0.01
20% for Q1 and close to 35% for Q2C. However, k21N had
comparatively large SE, which clearly reflected on the
SE of its derived parameter, Q2N (Table III).
Model Validation
Formally analogous models able to identify Q1 and
Q2 with biochemically and spatially defined metabolites have been evaluated by comparing predicted and
estimated values (e.g. Suc in leaves; Rocher and Prioul,
1987). In this study, the system was too complex to
expect such direct correspondences, for there are
several intermediaries with unknown spatial compartmentalization between CO2 assimilation/NO32 uptake
and subsequent import of C and N into the leaf growth
zone, mainly as Suc and amino acids (Schnyder and
Nelson, 1988; Gastal and Nelson, 1994). As an alternative, sizes of Q1 and Q2 were compared to tiller
average C and N mass. This revealed Q1C represented
between 1.1% and 1.8% of tiller C mass. Since IC was
underestimated due to unaccounted respiration, Q1C
values should have been approximately 30% higher
(Volenec and Nelson, 1984). Conversely, Q1N represented 10% to 30% of tiller N mass (Table III).
Smaller relative size of Q1C than Q1N was a common feature in all plants. But the magnitude of this
difference varied greatly. Dominant plants had relatively larger Q1C than subordinate plants. The opposite
was true for Q1N. As a result, the ratio of Q1C to Q1N
(Q1C:N) discriminated well subordinate from dominant
plants (1.0 versus 2.9 in L. perenne; 0.5 versus 1.9 in
P. dilatatum). Strictly comparable data are scarce.
Farrar (1990) estimated a pool supplying C for shoot
growth in three C3 grasses as 1.4% to 5.8% of plant C
mass, whereas Yoneyama et al. (1987) computed a pool
supplying protein synthesis in Brassica campestris
leaves equal to 14% of total plant N.
Q2 values were reasonable for C, although somewhat high in subordinate L. perenne plants. But Q2N
values were 2 to 3 times greater than the average N
mass of tillers (Table III). Clearly, despite the models’
good statistical behavior, Q2N values were unrealistic.
Knowledge of N cycling within grasses indicates that
a delay might occur between tracer incorporation (e.g.
into growing tissue) and its subsequent mobilization
(e.g. from senescing tissue; Robson and Deacon, 1978;
Millard, 1988; Thomas, 1990). To test whether such
a divergence from assumed homogeneous well-mixed
pools would affect estimated pool size, a lag time
between tracer incorporation in Q2 and subsequent
mobilization was included in the model. Optimization
yielded lag times close to 14 d for plants at 23C, and of
16.7 d for plants at 15C. These values were close to
leaf expansion durations (Table I), which agrees with
net N mobilization starting near the time a leaf is fully
expanded (Robson and Deacon, 1978). Consideration
of a delay strongly reduced the size of Q2N to 74% to
87% of tiller N mass (compare with Table III). Importantly, the lag time had only minor effects on size of Q1
(,3%) and on the contribution of mobilization from
Q2N (f; ,4%).
Taken together, these results indicate Q1 size and
half-time, and f, i.e. parameters derived from k10 and
k12, were adequately estimated. On the other hand, Q2
size and half-time were less accurately estimated,
particularly for N, and should therefore be interpreted
with more caution. The existence of a lag time between
tracer deposition and mobilization in Q2 seems worthy
of further study, but data at longer timescales are
necessary for stable model solutions.
The Age of Imported Substrates and the Sources
Supplying Them
Models were used to determine the time elapsed
between C and N tracers entering the plant and their
arrival at the growth zone. This yielded an age profile
of imported substrates, where import of nonlabeled C
or N at the end of the experimental period was
described as older than 15 (23C) or 18 (15C) d.
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Lattanzi et al.
Table II. Definitions of symbols used in text
Symbol
Description
C
N
X
A
Carbon
Nitrogen
Any element (here, carbon or nitrogen)
Molar fraction of 15N or 13C
Ævariableæ
flab, funlab
G
Denotes 24-h averages
Fraction of labeled, fraction of nonlabeled
Leaf growth zone (cell division, expansion, and
maturation zones)
Recently produced (structurally and functionally
differentiated) leaf tissue
Leaf elongation rate
Mass of X, C, or N in the growth zone
Lineal density of X, C, or N in recently produced tissue
Import (net deposition) into the growth zone of X, C, or N
Export (tissue-bound efflux) out of the growth zone
of X, C, or N
RPT
LER
GX, GC, GN
rLX, rLC, rLN
IX, IC, IN
EX, EC, EN
t
k10, k12, k21
TX, TC, TN
MX, MC, MN
DX, DC, DN
f
Q1, Q2
t0.5
Q1C:N
Time
First-order rate constants (fluxes I, D, and M,
respectively)
Incorporation of labeled X, C, or N into the system
Incorporation of nonlabeled X, C, or N into the system
(one-pool model)
Transfer from Q2 to Q1 of X, C, or N (two-pool model)
Transfer from Q1 to Q2 of X, C, or N
The relative contribution of MX to IX. Parameter directly
fitted in the one-pool model, or equal to k12/(k10 1 k12)
in the two-pool model
Compartment sizes (substrate and stores)
Half-time
Ratio of Q1C to Q1N
Additionally, in the two-pool model, the total amount
of C and N tracer derived from each day’s assimilation/uptake was further separated into the fraction
derived from mobilization from Q2 and that only
cycling through Q1 (Fig. 6).
In all treatments, imported C derived mostly from
very recent photosynthesis. A high proportion, 50% to
65%, corresponded to the C reaching the growth zone
within 12 h of entering the plant (day 1). The remaining C was chiefly imported during the following dark
and light periods (day 2). Virtually all this C cycled
only through Q1C. Thus, Q1C effectively acted as
a short-term store buffering day/night cycles.
Opposite to C results, the importance of very recent
N uptake was small: N derived from same-day uptake
(day 1) represented less than one-sixth of the total
imported N. Imported N derived in more or less
similar proportions from last 3-d (23C) to 5-d (15C)
uptake. Again, most of this recent N cycled only
through Q1 (compare with Fig. 6). With half-times
.0.8 d, stores within Q1N would have been involved in
moderating day-to-day variations in supply.
Mobilization from Q2 supplied ,10% of imported C.
Q2C half-times, approximately 10 d, were close to leaf
expansion duration (Table I). The exceptions were
subordinate L. perenne plants, where Q2 provided 27%
Units
mm d21
mg
mg mm21
mg d21
mg d21
d
d21
mg d21
mg d21
mg d21
%
mg
d
of imported C and had a half-time twice as long and
more similar to that of Q2N (Table III). Conversely, the
contribution of mobilized N was variable but relevant
in all plants. In subordinate plants, N mobilized from
Q2 was the preponderant source, supplying threefourths of imported N. In dominant plants, it was
more important in the C4 (41%) than in the C3 (24%)
grass. Half-times of Q2N were relatively long, but these
must be interpreted with caution because they implied
unrealistically large pool sizes. Consideration of a lag
time, which could have caused such behavior, reduced
half-times to 8 (subordinate L. perenne), 10 (subordinate P. dilatatum), and 19 (dominant P. dilatatum)
d (compare with Table III). When added to the 14- to
16-d lag times, these values become closer to leaf
lifespans (Table I).
DISCUSSION
On the Approach
This article presents a novel approach for analyzing
the contribution of distinct sources in supplying C and
N substrates for growth. It involves three steps: (1)
estimating C and N import into growth zones; (2)
determining their labeled fraction under steady-state
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Sources of Carbon and Nitrogen Supplying Leaf Growth
Figure 2. Import of total (d, :) and briefly labeled (n, s) C (ÆICæ; d, s) and N (ÆINæ; :, n) substrates into the leaf growth zone.
Growth conditions as for Table I. Lines indicate average values. Error bars are 1 SE of import values.
labeling of C assimilation and N uptake; and (3)
analyzing the time course of these fractions with
compartmental models. The first is an extension of
a previously presented method, based on wellestablished knowledge of C and N fluxes within
growth zones (Lattanzi et al., 2004). Steady-state
13
CO2/12CO2 and 15N/14N labeling (de Visser et al.,
1997; Schnyder et al., 2003), as well as modeling of
tracer time courses (Moorby and Jarman, 1975; Prosser
and Farrar, 1981), are established techniques, too. New
is their concerted use to quantitatively resolve the
sources supplying growth.
Compared to prior labeling studies, this approach
imports several advancements. First, deposition of C and
N in the growth zone is solely driven by the demand of
dividing, expanding, and maturing cells (Schnyder and
Nelson, 1988; Allard and Nelson, 1991; Gastal and
Nelson, 1994). Hence, confounding effects of processes
others than these, which also affect tracer content of
leaves but bear no direct association with their growth
Figure 3. The proportion of labeled C (d, Æ flab IC æ) and N (s, Æ flab IN æ) in substrates imported into the leaf growth zone of
continuously labeled plants estimated with Equation 3b. Growth conditions as for Table I. Lines show model predictions (solid
lines) 6SE of predicted values (dotted lines). Error bars indicate 6SE of estimated values.
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Lattanzi et al.
customary to compartmental analysis are made. Some
of these are a major restriction in applying this
approach, such as that of a system in steady state;
some are of difficult validation, such as that of homogeneous, well-mixed pools. Indeed, aggregating C and
N metabolism into one or two pools is a simplification
that is almost certainly invalid. Still, we think the
approach renders a more meaningful interpretation of
tracer fluxes than assuming close correspondences
between labeled and nonlabeled substrates and the
sources supplying them. For instance, in six out of
eight modeled situations, the fraction of imported
tracer deriving from current assimilation/uptake varied from virtually 1.0, for tracer imported within 1 d
of its assimilation/uptake, to ,0.5, for tracer imported
3 d (C) or 5 to 10 d (N) after its assimilation/uptake
(Fig. 6). In these cases, assuming invariant relationships would have been misleading, revealing the
importance of analyzing time courses.
Finally, use of a modeling approach allowed the
realization that the substrate pool (Q1) acted as a shortterm store and that the ratio of C-to-N substrates
indicated plant C/N status (see below). Such insights
would have otherwise been difficult to gain.
Figure 4. Two-pool (a) and one-pool (b) models of sources supplying
leaf growth. Newly acquired (i.e. labeled) C or N (X) first enter Q1 (TX).
From there, they are either imported into the growth zone (IX) or
exchanged with Q2 (DX and MX). In solving the model, steady-state and
first-order kinetics were assumed, that is, pool sizes are constant in time
and fluxes are the product of pool size times a rate constant (k10, k12,
and k21; numbers referring to donor and receptor pools, respectively). In
the one-pool model, f is the fraction of nonlabeled C or N entering the
system, MX/(MX 1 TX), i.e. the proportional contribution of MX in
supplying Q1. In the two-pool model, f corresponds to k12/(k10 1 k12),
the average probability of an atom being exchanged through Q2 before
its import into the growth zone.
Nature and Importance of the Growth Substrate Pool, Q1
As the pool directly supplying the growth zone, Q1
is analogous to a growth substrate, i.e. compounds
readily available for tissue synthesis. A distinct feature
of Q1 was its contrasting kinetics for C and N substrates. In all cases, Q1C had a shorter half-time than
Q1N (Table III).
(e.g. carbohydrate and amino acid storage in either
growing, mature, or senescent leaves), are obviated.
The second advantage derives from explicitly addressing tracer mixing. In doing so, the assumptions
Table III. Model optimization results
Total C or N import into the growth zone (ÆIXæ) and first-order rate constants (k10, k12, and k21; numbers refer to donor and receptor pools,
respectively) fitted to two- or one-pool models describing the time course of C and N tracer incorporation into substrates supplying leaf growth (Fig. 4).
Models were run with a 0.1-d time step, and predicted values integrated to give 1-d averages. Estimated parameters and ÆICæ and ÆINæ were used to
compute pool size (Q1 and Q2), turnover rate (the ratio of atoms passing through relative to the total amount present in each pool), and half-time (t0.5;
the average time an atom will reside in a pool 5 2ln (0.5)/turnover rate), and the contribution of MX to IX [f 5 MX/(MX1TX)]. In the two-pool model,
the turnover rate of Q1 is k10 1 k12, while turnover rate of Q2 is simply k21. In the one-pool model, the turnover rate of the only pool is k10. Means 6 SE
(n 5 6). For comparison, Q1 and Q2 are shown as a proportion of tiller mass and of C assimilation and N uptake rates. Growth conditions as for Table I.
n.e., Not estimated.
Carbon
Nitrogen
L. perenne
23C
Subordinate
ÆIXæ
k10
k12
k21
mg d21
d21
d21
d21
Q1
Q2
t0.5 Q1
t0.5 Q2
f
mg
mg
d
d
%
Q1/tiller
Acquisition/Q1
Q2/tiller
P. dilatatum
15C
Dominant
23C
Dominant
L. perenne
15C
23C
Subordinate Subordinate
15C
Dominant
972
2.4
0.91
0.028
6
6
6
6
75
1,136 6 66
3,488
0.29
1.7 6 0.031
2.8
0.16
0.19 6 0.016 0.22
0.013 0.080 6 0.011 0.055
6
6
6
6
327
0.10
0.031
0.020
401
12,976
0.21
25
27
6
6
6
6
6
57
681 6 42
6,771 1,628 6 273
0.02
0.37 6 0.01
12
9 6 1.1
4.3
10 6 0.8
6
6
6
6
6
123
177 6 25
419 6 101
232 6 44
2,008
n.e.
7,879 6 6,147
n.e.
0.01 0.43 6 0.05 0.77 6 0.21 1.89 6 0.34
4.7
n.e.
20 6 13
n.e.
1.0
4 6 2.1
74 6 8.4
24 6 3.8
0.011
4.2
0.34
0.018
4.6
0.04
1,224
4,939
0.23
13
7
0.014
8.0
0.06
286 6 23
98
1.6 6 0.19 0.23
n.e.
0.66
n.e.
0.035
P. dilatatum
0.013
3.4
n.e.
6
6
6
6
6.5
85 6 5.0
0.054 0.37 6 0.066
0.24
n.e.
0.023
n.e.
0.12
0.14
2.3
23C
Dominant
15C
Subordinate
278
0.43
0.30
0.010
6
6
6
6
26.4
0.034
0.051
0.013
645
19,485
0.95
69
41
6
6
6
6
6
79
369 6 89
25,853 4,037 6 27,807
0.08
2.39 6 0.57
91
35 6 238
4.5
76 6 6.9
0.10
0.23
n.e.
388
0.11
0.37
3.4
26
0.071
0.22
0.020
6
6
6
6
2.6
0.015
0.067
0.14
0.29
0.01
3.2
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Sources of Carbon and Nitrogen Supplying Leaf Growth
Figure 5. Comparison between estimated (Eq. 3b) and predicted
(model) values of the fraction of labeled C (Æ flab IC æ; d, n, :, ;)
and N (Æ flab IN æ; s, h, n, ,) into the growth zone for continuously
labeled L. perenne (d, s, n, h) and P. dilatatum plants (:, n, ;, ,)
at 23C (d, s, :, n) or 15C (n, h, ;, ,). Growth conditions as for
Table I. The line indicates the y 5 x relationship.
At first glance, such a difference might be thought to
be related to a greater proximity, either physical or
metabolic, of CO2 fixation to subsequent import of Suc
into the growth zone than that of NO32 uptake from
amino acid import. Short-term labeling studies do
show that photoassimilates can reach the leaf growth
zone within 10 to 20 min, whereas it would take at
least 1 h for newly absorbed N to do the same
(compare with Cooper and Clarkson, 1989; Allard
and Nelson, 1991; Thorpe and Minchin, 1991; Hayashi
et al., 1997). But this cannot be a significant determinant of observed 0.5- to 2-d shorter half-times of Q1C
than Q1N. The nature of such differences must therefore be related to kinetics of storage components
included within Q1. Indeed, analysis of Figure 6
revealed that Q1 acted not only as a gateway for
substrates in transit to the growth zone, but also as
a short-term store (see ‘‘Results’’). Q1C represented
,2% of tiller C mass and was small compared to C
gain, while Q1N constituted 10% to 30% of tiller N mass
and represented several days of N uptake (Table III),
further supporting the idea of a greater storage component within Q1N than Q1C.
We are not aware of any other comparative study of
C and N growth substrate kinetics with which these
results could be compared. Compartmental analyses
of source photosynthetic leaves have consistently indicated a cytoplasmic/apoplastic (Suc) transport pool,
and a vacuolar (Suc)/chloroplastic (starch) storage
pool with half-times ,2 h and 12 to 25 h, respectively
(Rocher and Prioul, 1987; Farrar, 1990, and refs.
therein). Conversely, source metabolism of N is more
tortuous, as nitrate and amino acids are subject to
storage in both roots and shoot. For root nitrate, halftimes ,15 min and 4 to 7 h are typical for transport
and vacuolar pools (Devienne et al., 1994, and refs.
therein). Amino acids often account for a substantial
part of soluble N, but there are hardly any data on
their kinetics. A study in B. campestris suggests halftimes ,35 min, and 3 to 20 h for transport and storage
pools (Yoneyama et al., 1987). Further, xylem/phloemcycling amino acids, a general feature in C3 and C4
species (Touraine et al., 1994; Engels and Kirkby, 2001),
could act as a labile reserve (Cooper and Clarkson,
1989).
Contrasting these values with Q1 half-times (Table
III) leads to three conclusions. First, it corroborates that
Figure 6. Age of substrates imported into the leaf growth zone. Import of C and N (ÆICæ and ÆINæ) is shown as a function of the
number of days elapsed between assimilation/uptake and subsequent deposition in the growth zone. For each day, the white part
of the bar indicates substrates that cycled only through Q1, while the dark part refers to substrates exchanged with Q2 prior to
import (see Fig. 4). Growth conditions as for Table I.
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Lattanzi et al.
Table IV. Estimated contribution of carbohydrates (CHO-C) and amino-C to imported C
Amino-C import was estimated as N import times an assumed C:N ratio of 2.6 (w/w) for imported N
compounds (see Schnyder and de Visser, 1999; Amiard et al., 2004). Estimation of Q2-derived amino-C
further assumed that there was a strict association between unlabeled C and unlabeled N within organic N
compounds. Growth conditions as for Table I. n.e., Not estimated.
L. perenne
Import of
P. dilatatum
23C
Subordinate
15C
Dominant
23C
Dominant
15C
Subordinate
1,136
221
0.19
3,488
723
0.21
286
68
0.24
117
53
251
298
Total C
Total amino-C
Amino-C/total C
mg d21
mg d21
972
255
0.26
Q2C total C
Q2C amino-C
mg d21
mg d21
265
188
Q2C amino-C/Q2C C
0.71
0.45
Q2C amino-C/total C
Q2C CHO-C/total C
0.19
0.08
0.05
0.06
Q1C and Q1N included storage components; neither Q1C
nor Q1N labeling kinetics were as fast as expected if
accounted for solely by transport pools. Second, differences of 0.5 to 2 d between Q1C and Q1N half-times can
be explained only by differences in the kinetics of C
and N storage (not transport) pools. Thus, Q1C halftimes ,0.5 d resemble a flux-weighted average of
transport and storage pools in source leaves. But Q1N
half-times $0.9 d suggest N tracer entered storage
pools more than once before reaching the growth zone.
Third, the more straightforward metabolism of C,
compared to that of N, makes a more important
storage component within N than C growth substrates
highly probable, independent of species or growth
conditions. Inherently different kinetics of C and N
growth substrates may be a consequence of contrasting buffering requirements. Whereas C acquisition has
an abrupt diurnal cycle, N uptake is likely to experience more drastic changes at timescales of days due to
the pulsed and patchy nature of external N availability
Figure 7. Correlation between the ratio of C-to-N growth substrates
(Q1C:N) and the relative contribution of long-term stores (Q2N) to N
import into the leaf growth zone, f[MX/(TX 1 MX)]. Data are from
L. perenne (d, n) and P. dilatatum (:, ;) plants growing in mixed
stands at 23C or 15C, and thus in dominant (d, :) or subordinate (n,
;) positions. Error bars indicate 6SE. *, P , 0.01.
1.2
n.e.
n.e.
11
51
4.6
n.e.
n.e.
(Chapin et al., 1990). Plants deprived from N are able
to maintain unaltered growth rates for several days. By
using supply interruption cycles, Macduff and Bakken
(2003) estimated this buffer capacity to be between 0.5
to 3 d, which compares favorably with Q1N half-times
(Table III).
The Role of Mobilized Stores in Supplying Leaf Growth
Stores were an integral part of the supply of C and N
substrates for leaf growth in all plants, providing about
one-half of C and .80% of N imported into the leaf
growth zone (Fig. 6). We hypothesized the importance
of mobilized stores in supplying leaf growth increases
whenever resource acquisition becomes limited. This
was not the case for C. Subordinate plants had low C
assimilation rates, closely related to their reduced light
capture (Table I). But C older than 2 to 3 d contributed
little to C import, corroborating the notion that leaf
growth relies on recent assimilates (Anderson and
Dale, 1983, but see below), and stores within Q1C were
important in supplying leaf growth in both dominant
(well-lit) and subordinate (shaded) plants (Fig. 6). This
substantiates results from experiments with Arabidopsis (Arabidopsis thaliana) starchless mutants, which
showed that the relationship between daily starch
turnover and plant growth is not affected by light
intensity (Schulze et al., 1991). Although the actual
contribution of C stores to growth was not assessed in
that study, its consideration together with present
results strongly suggests that the importance of shortterm C stores in supplying growth is closely associated
with the need of buffering day/night cycles and independent of C assimilation rates. Similar responses of
P. dilatatum and L. perenne suggest analogous mechanisms in starch-storing (C4) and Suc/fructan-storing
(C3) grasses.
In exception to this general pattern, mobilization
from Q2C provided 27% of imported C in subordinate
L. perenne plants (Table III). In vegetative grasses, there
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Sources of Carbon and Nitrogen Supplying Leaf Growth
Figure 8. The relationship between labeling and sampling times for
briefly (A) and continuously (B) labeled plants. Arrows indicate times of
plant transfers, dotted vertical lines indicate sampling times (harvests
1–6), and gray shades show the resulting periods over which assimilated C and absorbed N were labeled. All harvests were done
immediately after lights went off. Black and white bars in the time
axis indicate dark and light periods, and the white asterisk indicates the
time when the 15N enrichment of the nutrient solution watering
cabinets I and II was changed.
are two potential sources of old C: carbohydrate stores
in sheath bases and stems (Chatterton et al., 1989; Thom
et al., 1989) and amino-C derived from protein turnover.
Compared to dominant L. perenne plants, the higher
contribution of old C observed in subordinate plants
seems strictly associated with differences in mobilized
amino-C (Table IV). This is also suggested by a longer
half-time of Q2C in subordinate L. perenne, close to that
of Q2N and to the leaf lifespan. Hence, when long-term
N stores contribute greatly to N import, old amino-C
could become an important source of C for leaf growth
in C3 grasses. Interestingly, Thornton et al. (2004) found
an important contribution of old C to root exudates,
which includes a substantial amount of amino-C. Better
understanding of C supply to growth processes clearly
requires more information on amino-C fluxes.
Notably, this flux of old C was absent in subordinate
P. dilatatum plants, even though long-term N stores
supplied an equally significant part of imported N.
In fact, calculations in the C4 grass indicated more
Q2-derived amino-C than the total amount of Q2derived C, and thus an unrealistic, higher than 100%,
contribution of amino-C (Table IV). The reason for this
difference between L. perenne and P. dilatatum is not
clear. Varying the assumed C-to-N ratio affected estimated values only slightly. Hence, the fault probably
resides in assuming a strict relationship between unlabeled C and unlabeled N within organic N compounds.
Stores provided most of imported N in all treatments. However, this similarity hid contrasting responses. In dominant plants, stores within Q2N were
important, particularly in L. perenne (Fig. 6; Table III).
The comparatively low importance of mobilization
from Q2N plus the short half-time of Q1N determine
that, in fact, N import in these plants depended
strongly on continuous uptake. Conversely, leaf
growth in subordinate plants of both species became
largely independent of external N, as 75% of imported
N derived from Q2N. However, there is no indication
that dominant and subordinate plants would have
used qualitatively different N sources. In all plants, the
lag time between incorporation and mobilization of N
tracer in Q2N, and the close relation between the halftime of this pool and leaf lifespan, suggest that
mobilization of long-term N stores was associated
with leaf turnover. This agrees with long-term N stores
in grasses being largely accounted for by export of
amino acids from senescing leaves (Millard, 1988;
Feller and Fischer, 1994; Bausenwein et al., 2001).
Interestingly, Q1C:N was closely and negatively correlated with the fractional contribution of Q2N to IN,
accommodating differences between plants growing in
contrasting hierarchical positions, and also between L.
perenne and P. dilatatum dominant plants (Fig. 7). This
indicates that the importance of long-term stores in
supplying N for leaf growth was related to the relative
abundance/scarcity of C and N substrates. There is
now convincing evidence for nitrate uptake being
controlled by plant N demand through the regulatory
activity of C and N metabolites, indicators of plant C/N
status (Touraine et al., 1994; Stitt and Krapp, 1999). A
possible explanation for Figure 7 is that such a mechanism is also a determinant of the importance of long-term
N stores for leaf growth. Support for this possibility
is lent by the fact that the partitioning of N uptake
between dominant and subordinate plants within
each stand was fairly similar to that of intercepted
photosynthetic photon flux density (PPFD), and hence
of C assimilation (compare with Table I), which suggests that uptake rates of subordinate plants were
limited by their lower C/higher N plant status (Gastal
and Saugier, 1989). Crucially, these differences were not
caused by variations in N external availability because
all plants had access to full nutrient supply.
In suggesting a causal link between the importance of
long-term stores and the internal capacity for N acquisition, this explanation is consistent with our working
hypothesis. A corollary of plant C/N status controlling
Figure 9. Comparison between values of the fraction of labeled C or N
imported into the growth zone (Æ flab IX æ) measured in briefly labeled
plants and estimated using Equation 3b. The line indicates the y 5 x
relationship.
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Lattanzi et al.
both N acquisition and the importance of long-term
stores is that, whenever possible, a plant will acquire
and use new N rather than stores (and increase its
growth rate). This might not be so in all grasses. Farrar
(1990) suggested the inherent difference between slowand fast-growing species is their use of C stores. A
differential use of nutrient stores seems a plausible
possibility worthy of experimental testing.
CONCLUSIONS
A novel approach to study the sources of C and N
supplying leaf growth is presented. Compared to previous analyses, it imports a more mechanistic definition
of the process studied, an increased accuracy with
which it is measured, and a more meaningful interpretation of tracer fluxes. By using such an approach, we
were able to show that stores were a critical part of the
supply of C and N substrates for leaf growth in both
L. perenne (C3) and P. dilatatum (C4). Long-term carbohydrate stores were of little relevance for leaf growth in
these undisturbed plants, and short-term C stores had
an important role in buffering light/dark cycles in all
plants. Hence, no evidence was found of a causal link
between C acquisition and the importance of C stores
in supplying leaf growth. Long-term N stores were important in supplying leaf growth in all situations, but
particularly in plants where growth was more C than N
limited and N uptake capacity was lower. It is proposed
that a common mechanism regulates N acquisition and
use of N stores.
MATERIALS AND METHODS
In the following, we will first describe the labeling facility employed, along
with the plant material and growth conditions used to generate stands with
contrasting C3/C4 balances. Next, we will detail the two labeling strategies
(and associated sampling schemes) used, and formulas to calculate import of
total and labeled C and N into the growth zone. Last, we will present the
models used to describe tracer kinetics, the assumptions made in solving
them, and their (partial) verification.
13
CO2/12CO2 Labeling Facility
The equipment and principles involved have been detailed elsewhere
(Schnyder et al., 2003). In brief, four growth cabinets (E15; Conviron,
Winnipeg, Manitoba, Canada) were operated as open gas exchange systems.
Air supply was generated by mixing CO2-free air with CO2 of known d (the
deviation of the 13C-to-12C ratio in CO2 from that of the international standard,
Vienna Pee Dee Belemnite [VPDB]). Periodic adjustments of airflow and CO2
concentration in the inlet maintained a constant CO2 concentration of 300 mL
L21 within the growth cabinet. Two growth cabinets received 13C-enriched
CO2 (Messer Griesheim, Frankfurt; d 2 2.9&, cabinets I and II), and two
13
C-depleted CO2 (Messer Griesheim; d 2 47.0&, cabinets III and IV).
Plant Material and Growth Conditions
Plant material and growth conditions have also been detailed previously
(Lattanzi et al., 2004). Briefly, mixed Lolium perenne/Paspalum dilatatum stands
were constructed by arranging 178 pots (i.d. 50 mm, length 350 mm) with one
seedling of either L. perenne or P. dilatatum into plastic boxes (0.43 m2). Two
such stands were allocated to each of the four growth cabinets.
Plants grew under a 12-h photoperiod, with a PPFD of 550 mmol photons
m22 s21 at canopy height, provided by cool-white fluorescent lamps. Vapor
pressure deficit was controlled at 0.5/0.3 kPa (light/dark) in all growth
cabinets. An automated irrigation system watered the stands four times a day,
flooding the boxes for 30 min with modified one-half-strength Hoagland
solution (105 mg N L21 supplied only as nitrate). Stands were periodically
flushed with distilled water to prevent salt accumulation.
Canopy air temperature was set at 25C/23C (light/dark) in two growth
cabinets (cabinets II and III), and at 15C/14C (light/dark) in the other two
cabinets (cabinets I and IV). These temperature regimes resulted in daily
average sand temperatures (15 mm below surface) of 23C and 15C,
respectively.
After 8 weeks of growth at 23C, P. dilatatum individuals were larger and
taller than their L. perenne neighbors. The opposite occurred in mixed stands at
15C (Table I). Within each mixture, larger individuals will be referred to as
dominant plants and the others as subordinate plants.
Partitioning of Intercepted PPFD
On day 0, after either eight (23C) or 10 (15C) weeks of uninterrupted
growth, PPFD and plant leaf areas were recorded at 15-cm increments from
top to the bottom of the stands. Intercepted PPFD was then partitioned
between the C3 and C4 grass by weighting the fraction of PPFD intercepted at
each canopy stratum by the proportional contribution of each species to the
leaf area present in that stratum.
Leaf Turnover
Leaf expansion duration and leaf lifespan were estimated from biweekly
measurements of leaf appearance rate, number of growing leaves, and
number of green leaves in a set of 12 tillers per treatment (Davies, 1993).
Labeling Strategies and Sampling Times
Plants were sampled in a series of six harvests at day 0, 1, 2, 4, 8, and 15
(23C), or at day 0, 1, 2, 5, 12, and 18 (15C). There were two labeling strategies
associated with these harvests: Assimilated C and absorbed N were labeled
either briefly (last 12 h, i.e. the entire photoperiod prior to sampling) or
continuously (since day 0) before sampling (Fig. 8). In both strategies, C
labeling was performed by swapping individual plants between growth
cabinets, which resulted in the exposure of plants grown under 13C-enriched
CO2 (d 2 2.9&) to 13C-depleted CO2 (d 2 47.0&), and vice versa. In the case of
N, the 15N enrichment of nutrient solution watering cabinets I and II was
increased from 0.37 atom% (natural abundance) to 1.1 atom% immediately
after the first harvest (Fig. 8).
In the case of briefly labeled plants, four plants per species and temperature regime were swapped during the dark period preceding the labeling
photoperiod. Previously, plants were flushed with 0.5 L of distilled water.
Importantly, labeling commenced only once lights went on. For C, the reason
is obvious. For N, this was so because the first irrigation event after the plant
transfer was scheduled immediately before the start of the light period.
In the case of continuously labeled plants, 16 plants per species and
temperature regime, different from briefly labeled ones, were swapped during
the dark period after the first harvest: eight plants per species transferred from
cabinet II to cabinet III, plus eight from cabinet III to cabinet II (23C
treatment), and the same for cabinets I and IV (15C treatment; Fig. 8).
Previously, stands were flooded with distilled water three successive times.
Sample Collection and Analysis
At each harvest, 10 plants per treatment (five per growth cabinet) were
sampled from the central part of the stands: Four were continuously labeled
plants, two were briefly labeled plants, and four were nonlabeled (control)
plants. In each plant, the growth zone and an immediately adjacent piece of
recently produced tissue (RPT) were dissected out of two to three growing
leaves whose length was approximately one-half that of the youngest fully
expanded leaf present in the tiller (Fig. 1; for details, see Lattanzi et al., 2004).
The rest of the plant, including root and shoot, was pooled into one sample.
After dissection, samples were heated to 100C for 1 h to inactivate metabolism irreversibly, then dried during 48 h at 65C. This procedure might alter
some labile metabolites, but it causes no measurable loss of total C or N (Cone
392
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Sources of Carbon and Nitrogen Supplying Leaf Growth
et al., 1996), which is the subject of this work. Once dry, samples were weighed
and then milled. C and N content and 13C:12C and 15N:14N isotope ratios were
determined on 1-mg dry-matter aliquots using a CHN elemental analyzer
(NA1500; Carlo Erba Strumentazione, Milan) interfaced to a continuousflow isotope mass ratio spectrometer (Delta plus; Finnigan MAT, Bremen,
Germany). From isotope ratios, molar fractions were calculated (atom%, A).
Standards were run every 10 samples (SD 5 0.25& for d; SD 5 0.0017%
for 15N atom%).
Calculation of the Proportion of Tracer in a Sample
The proportion of C or N (X) tracer in a sample is directly proportional to
the content of 13C or 15N in it. Therefore, the amount of 13C or 15N in each
sample (Aspl X) was expressed as a lineal function of the fractions of labeled
and unlabeled C or N (flab X and funlab X), and the 13C or 15N content of
analogous samples from control plants (Alab X and Aunlab X),
Aspl X 5 flab X Alab X 1 funlab X Aunlab X :
ð1aÞ
For example, for a plant swapped from cabinet II to cabinet III, Aunlab C and
Alab C correspond to the amount of 13C of samples taken from control plants
continuously grown under CO2 with d 2 2.9& and CO2 with d 2 47.0&,
respectively. Aunlab C and Alab C were determined, for each harvest date, in two
plants per species and temperature regime. Aunlab N was determined, for each
harvest date, in four plants per species and temperature regime sampled from
cabinets III and IV, i.e. irrigated with nonenriched nutrient solution. Alab N was
not experimentally determined but assumed to be equal to 1.1%, i.e. the 15N
enrichment of the labeling solution. This implies that eventual discrimination
effects were ignored. Given the enrichment used, the error introduced in the
measurement of flab N was small: ,1.0% for a 20& discrimination.
Since funlab X 5 1 2 flab X, Equation 1a can be solved for flab X:
flab X 5 ðAspl X 2 Aunlab X Þ=ðAlab X 2 Aunlab X Þ:
Tissue-bound C and N export out of the growth zone (EX) was estimated
over 1-d intervals (ti21 2 ti) as the product of leaf elongation rate (ÆLERæ, where
Æ æ denotes 24-h averages) and the density of C and N per unit length in RPT
(ÆrLXæ). Import of substrates into the growth zone (ÆIXæ) was then estimated by
adding the negative or positive variation in mass of the growth zone over that
time interval ðGX at ti 2 GX at ti21 Þ: Respiration was ignored, thus underestimating actual C import by the amount lost through respiration. This measure of
import is analogous to a net deposition rate (Allard and Nelson, 1991; Maurice
et al., 1997; Schäufele and Schnyder, 2001). Essential prerequisites for the
validity of these calculations are the specific definition of the growth stage of
sampled leaves and the precise location and size of the piece of RPT where
ÆrLXæ is determined (for details and validation, see Lattanzi et al., 2004).
This method was further developed to estimate the labeled and nonlabeled
components of C and N fluxes. The amount of labeled C or N imported into
the growth zone (Ilab X) was assessed for 1-d intervals as the export of labeled C
or N (Elab X) plus the variation in mass of labeled C or N within the growth
zone (Glab X) over the same time interval:
ð1bÞ
The amount of labeled C or N was then calculated as the C or N mass of the
sample times the corresponding flab C or flab N value.
Estimation of C Assimilation and N Uptake Rates
C assimilation and N uptake were estimated as the total amount of C and N
tracer found in whole briefly labeled plants. For C, this is a measure related to
daytime C gain, although respiration of nonlabeled C is unaccounted for. In
the case of N, this measure is close to a net balance between N influx and efflux
over the 12-h light period.
ÆIlab X æ 5 ÆElab X æ 1 ðGlab X at ti 2 Glab X at ti21 Þ:
ð2Þ
The fraction of labeled C or N in import ðflab IX 5 Ilab X =IX Þ thus results:
Æ flab IX æ 5 ÆElab X æ=ÆIX æ 1 ðGlab X at ti 2 Glab X at ti21 Þ=ÆIX æ:
ð3aÞ
Since IC and IN, as well as GC and GN, were constant in time (at a
1-d timescale; Lattanzi et al., 2004), then EX 5 IX, and GX at ti 5 GX at ti21 : Hence,
flab IX can be directly estimated as:
Æ flab IX æ 5 Æ flab EX æ 1 ð flab GX at ti 2 flab GX at ti21 Þ GX =ÆIX æ:
ð3bÞ
Equation 3b explicitly shows that estimation of flab IX over a 1-d time
interval (ti21 – ti) required estimation of (1) flab EX over the same time interval;
(2) flab GX at the lower (ti21) and upper (ti) ends of the time interval; and (3) GX/
ÆIXæ. The former was estimated as the definite integral of nonlinear functions
fitted to the
R i time course of the fraction of labeled X in samples of RPT
(Æ flab EX æ 5 i21 flab RPTX ). Similarly, nonlinear functions fitted to the time
course of the flab GX were used to estimated values at ti21 and ti. Values of
GX/ÆIXæ were taken from Lattanzi et al. (2004).
The five specific 1-d intervals over which Æ flab IX æ was estimated were
defined by the six harvest dates as follows: days 0 to 1, 1 to 2, 3 to 4, 7 to 8, and 14
to 15, and days 0 to 1, 1 to 2, 4 to 5, 11 to 12, and 17 to 18 for the 23C and 15C
temperature regimes, respectively. To test the accuracy of the method, Æ flab IC æ
and Æ flab IN æ values estimated through Equation 3b were compared with those
directly measured in sets of briefly labeled plants. The regression of estimated
versus observed values did not differ from the y 5 x line (P . 0.10; Fig. 9).
Estimation of Import of Labeled and Nonlabeled
C and N into the Leaf Growth Zone
Modeling of Time Course of C and N Tracer Imported
into the Growth Zone
Briefly Labeled Plants
We propose a two-pool model to describe the time course of the incorporation of tracer into the growth zone, which is formally similar to that
used in compartmental analyses of C export from source leaves (e.g. Moorby
and Jarman, 1975; Rocher and Prioul, 1987). Newly acquired (i.e. labeled) C
and N first enter Q1. From there, C and N can be either imported into the
growth zone or exchanged with Q2 (Fig. 4a). Assuming first-order kinetics—
that is, fluxes are the product of pool size times a rate constant (k10, k12, and k21;
numbers referring to donor and receptor pools)—the rate of change of each
compartment with respect to time (t) is given by:
The amount of labeled C or N imported into the growth zone was
estimated as the sum of labeled C or N present in the growth zone plus labeled
C or N present in the piece of RPT (Fig. 1). Clearly, this assumes all imported
tracer would still be present in these leaf segments at the end of the 12-h
labeling period. This was likely true. On one side, deposition of C and N
outside the growth zone is very small relative to deposition within the growth
zone (Allard and Nelson, 1991; Gastal and Nelson, 1994; Maurice et al., 1997).
On the other, the sampled piece of RPT corresponded to tissue exported
during the last 16 to 19 h (Lattanzi et al., 2004). Therefore, all imported tracer
would have been initially deposited within the growth zone, and any tracer
exported out of the growth zone by tissue-bound efflux during the 12-h
labeling period would still be present in the piece of RPT.
ð4aÞ
dQ2 =dt 5 k12 Q1 2 k21 Q2 :
ð4bÞ
Assuming the system is in steady state, that is, dQ1/dt 5 dQ2/dt 5 0, pool
sizes are given by
Continuously Labeled Plants
The simple estimation of import of labeled C and N in briefly labeled
plants could not be extended to continuously labeled plants. This is because,
after 16 to 19 h, part of the imported tracer would have left the ‘‘growth zone 1
RPT’’ segment due to tissue-bound efflux (Fig. 1). A new approach was
therefore taken based on a previously presented method to estimate fluxes of
C and N through leaf growth zones (Lattanzi et al., 2004).
dQ1 =dt 5 T 1 k21 Q2 2 ðk10 1 k12 ÞQ1
Q1 5 T=k10
ð5aÞ
Q2 5 ðT=k10 Þðk12 =k21 Þ
ð5bÞ
T5I
ð6aÞ
M 5 k21 Q2 5 Iðk12 =k10 Þ 5 k12 Q1 5 D;
ð6bÞ
and fluxes by
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Lattanzi et al.
where I is the measured import rate, and k10, k12, and k21 are fitted parameters.
In some cases, a simpler one-pool model was proposed in which labeled
and nonlabeled C and N enter Q1 and from there are imported into the growth
zone (Fig. 4b). Hence, the one-pool model is the special case of the former,
where Q2 becomes infinitely large and k21 infinitely small.
Assuming first-order kinetics,
dQ1 =dt 5 T 1 M 2 k10 Q1 :
ð7Þ
Then, under the steady-state assumption,
Q1 5 T=k10
ð8Þ
T5If
ð9aÞ
M 5 Ið12fÞ;
ð9bÞ
where I is the measured import rate, and k10 and f are fitted parameters. Note
that f equals k12/(k10 1 k12) in the two-pool model.
Models were implemented in MODELMAKER (version 4.0; Cherwell
Scientific, Oxford, UK). Differential equations were solved using the fourthorder Runge-Kutta numerical method, with a step size of 0.01 d. Predicted
flab IC and flab IN were integrated over 1-d intervals, thus producing data
comparable to measured values (i.e. Æ flab IC æ and Æ flab IN æ). A built-in optimization function was used to estimate the values of fitted parameters by
iterative minimization of the sum of squared differences between measured
and predicted Æ flab IC æ and Æ flab IN æ:
Verification of Assumptions
The models hold the assumptions customary of compartmental analyses.
Two are explicit: (1) The system is steady and (2) fluxes obey first-order
kinetics. Two are implicit: (3) Pools are homogeneous and well mixed and (4)
13
C/12C and 15N/14N isotopic discrimination in exchanges can be neglected.
Regarding Assumption 1, previously presented data from this same
experiment (Lattanzi et al., 2004) and from other studies (Allard and Nelson,
1991; Gastal and Nelson, 1994) show that growth of the most rapidly
elongating leaf had indeed manifested steady-state dynamics when considered at 1-d intervals. Leaf elongation proceeded at a steady rate, and
variations in rLC and rLN and in GC and GN were minor, hence EC and EN and IC
and IN were virtually constant (Lattanzi et al., 2004). Further, import of C and
N assimilated/absorbed over the 12-h light period, i.e. import of labeled C and
N in briefly labeled plants, was approximately constant. Therefore, not only
total C and N import but also their source composition appeared stable (Fig.
2). Steady state is here defined at a 1-d timescale, and diurnal cycles
(Yoneyama et al., 1987; Schnyder and Nelson, 1988) were not considered by
the model.
Eventual effects of noncontinuous incorporation of tracer, arising from
diurnal cycles in C assimilation/N uptake, were assessed assuming that
import of C and N into the growth zone (IX) was constant over the day, but
tracer import (TX) occurred only over the 12-h light periods. Optimized
solutions indicated consistent reductions of about 25% in size and half-time of
Q1C in all modeled situations. However, these were unstable, probably due to
a limited data set. Including a similar diel cycle in N uptake had no effect upon
Q1N parameters. Since general responses were unchanged and because of
a lack of quantitative support for eventual day/night changes in C and N
import rates, we chose to retain the previous optimized values, observing that
Q1C size and half-time would be overestimated.
Assumption 2 is, in a strict sense, probably false. However, support for its
practical validity has been found repeatedly (Prosser and Farrar, 1981;
Yoneyama et al., 1987; for discussion, see Farrar, 1990).
Assumption 3 is perhaps the model’s most drastic simplification. Probably,
neither Q1 nor Q2 are homogeneous and well-mixed pools, but comprise a set
of biochemically and/or spatially distinct compartments. Further compartmentalization, however, did not improve goodness of fit of the model,
although this may reflect a limited number of data points or the short time
span of the experiment.
Assumption 4 was most likely true because fractionation during C and N
transport and conversion are small compared to the 13C and 15N enrichments
used.
Error Estimation and Statistics
The SE associated with the determination of the total and proportional
amount of labeled and nonlabeled C or N in the flux imported into the growth
zone was estimated by Gaussian error propagation. Models were assessed by,
and selection of alternative models based on, ANOVA. Ideally, partitioning
the residual mean square into lack of fit and pure error terms would provide
an objective basis for choosing between alternative models. In this case,
however, lack of true time replicated Æflabæ values prevented this. Hence, the
two-pool model was preferred over the one-pool model whenever it reduced
residual mean square by at least one-half. Importantly, in this case, lack of time
replication does not imply repeated measures because variables were estimated on different experimental units (i.e. plants) at different times.
In nonlinear regression, the SE of a fitted parameter is of very limited value
in assessing its significance. This is because the usually non-normal distribution of errors renders strongly asymmetric confidence intervals. Thus, SE of
fitted parameters in the one- and two-pool models (i.e. rate constants and f)
are given only for informative purposes. Rather, their usefulness resides in
their use, along with the correlation matrix, to compute the SE of predicted Æflabæ
values and of derived quantities biologically meaningful, such as Q1 and Q2
sizes and half-times, and of the contribution of MX to IX [f 5 MX/(TX 1 MX);
see Ross, 1981 for a discussion]. Note that SE of Q1 and Q2 sizes also involved SE
of total C and N import.
ACKNOWLEDGMENTS
We thank Dr. Warren Williams (AgResearch, Palmerston North, New
Zealand) for providing seeds of Paspalum dilatatum. The staff at the Lehrstuhl
für Grünlandlehre (Technische Universität, Munich) provided invaluable
assistance, particularly Rudi Schäufele and Wolfgang Feneis.
Received August 9, 2004; returned for revision October 13, 2004; accepted
October 26, 2004.
LITERATURE CITED
Allard G, Nelson CJ (1991) Photosynthate partitioning in basal zones of tall
fescue leaf blades. Plant Physiol 95: 663–668
Amiard V, Morvan-Bertrand A, Cliquet JB, Billard JP, Huault C,
Sandström J, Prud’homme MP (2004) Carbohydrate and amino-acid
composition in phloem sap of Lolium perenne L. before and after
defoliation. Can J Bot 82: 1594–1601
Anderson LS, Dale JE (1983) The sources of carbon for developing leaves of
barley. J Exp Bot 34: 405–414
Bausenwein U, Millard P, Raven JA (2001) Remobilized old-leaf nitrogen
predominates for spring growth in two temperate grasses. New Phytol
152: 283–290
Chapin FS, Schulze ED, Mooney HA (1990) The ecology and economics of
storage in plants. Annu Rev Ecol Syst 21: 423–447
Chatterton NJ, Harrison PA, Bennett JH, Asay KH (1989) Carbohydrate
partitioning in 185 accessions of Graminae grown under warm and cool
temperatures. J Plant Physiol 134: 169–179
Cone JW, van Gelder AH, Marvin HPJ (1996) Influence of drying method
and ageing on chemical and physical properties and in vitro degradation
characteristics of grass and maize samples. J Agric Sci 126: 7–14
Cooper HD, Clarkson DT (1989) Cycling of amino-acids and other
nutrients between shoots and roots in cereals—a possible mechanism
integrating shoot and root in the regulation of nutrient uptake. J Exp Bot
40: 753–762
Davies A (1993) Tissue turnover in the sward. In A Davies, RD Baker, A
Grant, AS Laidlaw, eds, Sward Measurement Handbook, Ed 2. The
British Grassland Society, Reading, UK, pp 183–216
de Visser R, Vianden H, Schnyder H (1997) Kinetics and relative significance of remobilized and current C and N incorporation in leaf and root
growth zones of Lolium perenne after defoliation: assessment by 13C and
15
N steady-state labelling. Plant Cell Environ 20: 37–46
Devienne F, Mary B, Lamaze T (1994) Nitrate transport in intact wheat
roots. 2. Long-term effects of NO3-concentration in the nutrient solution
on NO3 unidirectional fluxes and distribution within the tissues. J Exp
Bot 45: 677–684
Engels C, Kirkby EA (2001) Cycling of nitrogen and potassium between
shoot and roots in maize as affected by shoot and root growth. J Plant
Nutr Soil Sci 164: 183–191
Farrar JF (1990) The carbon balance of fast-growing and slow-growing
394
Plant Physiol. Vol. 137, 2005
Downloaded from on July 31, 2017 - Published by www.plantphysiol.org
Copyright © 2005 American Society of Plant Biologists. All rights reserved.
Sources of Carbon and Nitrogen Supplying Leaf Growth
species. In H Lambers, ML Cambridge, H Konings, TL Pons, eds, Causes
and Consequences of Variation in Growth Rate and Productivity of
Higher Plants. SPB Academic Publishing, The Hague, The Netherlands,
pp 241–256
Farrar JF, Pollock C, Gallagher J (2000) Sucrose and the integration of
metabolism in vascular plants. Plant Sci 154: 1–11
Feller U, Fischer A (1994) Nitrogen metabolism in senescing leaves. Crit
Rev Plant Sci 13: 241–273
Gastal F, Nelson CJ (1994) Nitrogen use within the growing leaf blade of
tall fescue. Plant Physiol 105: 191–197
Gastal F, Saugier B (1989) Relationships between nitrogen uptake and
carbon assimilation in whole plants of tall fescue. Plant Cell Environ 12:
407–418
Hayashi H, Okada Y, Mano H, Kume T, Matsuhashi S, Ishioka NS,
Uchida H, Chino M (1997) Detection and characterization of nitrogen
circulation through the sieve tubes and xylem vessels of rice plants.
Plant Soil 196: 233–237
Lattanzi FA, Schnyder H, Thornton B (2004) Defoliation effects on carbon
and nitrogen substrate import and tissue-bound efflux in leaf growth
zones of grasses. Plant Cell Environ 27: 347–356
Macduff JH, Bakken AK (2003) Diurnal variation in uptake and xylem
contents of inorganic and assimilated N under continuous and interrupted N supply to Phleum pratense and Festuca pratensis. J Exp Bot 54:
431–444
Maurice I, Gastal F, Durand JL (1997) Generation of form and associated
mass deposition during leaf development in grasses: a kinematic
approach for non-steady growth. Ann Bot (Lond) 80: 673–683
Millard P (1988) The accumulation and storage of nitrogen by herbaceous
plants. Plant Cell Environ 11: 1–8
Moorby J, Jarman PD (1975) The use of compartmental analysis in the
study of the movement of carbon through leaves. Planta 122: 155–168
Prosser J, Farrar JF (1981) A compartmental model of carbon allocation in
the vegetative barley plant. Plant Cell Environ 4: 303–307
Robson MJ, Deacon MJ (1978) Nitrogen deficiency in small closed
communities of S24 ryegrass. 2. Changes in the weight and chemical
composition of single leaves during their growth and death. Ann Bot
(Lond) 42: 1199–1213
Rocher JP, Prioul JL (1987) Compartmental analysis of assimilate export in
a mature maize leaf. Plant Physiol Biochem 25: 531–540
Ross GJS (1981) The use of non-linear regression methods in crop
modelling. In DA Rose, DA Charles-Edwards, eds, Mathematics and
Plant Physiology, Vol 17. Academic Press, London, pp 269–282
Schäufele R, Schnyder H (2001) Carbon and nitrogen deposition in
expanding tissue elements of perennial ryegrass (Lolium perenne L.)
leaves during non-steady growth after defoliation. Plant Cell Environ
24: 407–417
Schnyder H, de Visser R (1999) Fluxes of reserve-derived and currently
assimilated carbon and nitrogen in perennial ryegrass recovering from
defoliation. The regrowing tiller and its components functionally
distinct zones. Plant Physiol 119: 1423–1435
Schnyder H, Nelson CJ (1988) Diurnal growth of tall fescue leaf
blades. 2. Dry matter partitioning and carbohydrate metabolism in
the elongation zone and adjacent expanded tissue. Plant Physiol 86:
1077–1083
Schnyder H, Schäufele R, Lötscher M, Gebbing T (2003) Direct measurement of mesocosm-scale natural abundance 13CO2/12CO2 gas exchange,
13
C discrimination, and labelling of CO2 exchange flux components in
controlled environments. Plant Cell Environ 26: 1863–1874
Schulze W, Stitt M, Schulze ED, Neuhaus HE, Fichtner K (1991) A
quantification of the significance of assimilatory starch for growth of
Arabidopsis thaliana L Heynh. Plant Physiol 95: 890–895
Stitt M, Krapp A (1999) The interaction between elevated carbon dioxide
and nitrogen nutrition: the physiological and molecular background.
Plant Cell Environ 22: 583–621
Thom ER, Sheath GW, Bryant AM (1989) Seasonal variations in total
nonstructural carbohydrate and major element levels in perennial
ryegrass and paspalum in a mixed pasture. NZJ Agric Res 32: 157–165
Thomas H (1990) Leaf development in Lolium temulentum: protein metabolism during development and senescence of attached and excised leaf
tissue. J Plant Physiol 136: 45–50
Thornton B, Paterson E, Kingston-Smith AH, Bollard AL, Pratt S, Sim A
(2002) Reduced atmospheric CO2 inhibits nitrogen mobilization in
Festuca rubra. Physiol Plant 116: 62–72
Thornton B, Paterson E, Midwood AJ, Sim A, Pratt S (2004) Contribution
of current carbon assimilation in supplying root exudates of Lolium
perenne measured using steady-state 13C labelling. Physiol Plant 120:
434–441
Thorpe MR, Minchin PEH (1991) Continuous monitoring of fluxes of
photoassimilate in leaves and whole plants. J Exp Bot 42: 461–468
Touraine B, Clarkson DT, Muller B (1994) Regulation of nitrate uptake at
the whole plant level. In J Roy, E Garnier, eds, A Whole Plant Perspective
on Carbon Nitrogen Interactions. SPB Academic Publishing, The Hague,
The Netherlands, pp 11–30
Volenec JJ, Nelson CJ (1984) Carbohydrate metabolism in leaf meristems of
tall fescue. 2. Relationship to leaf elongation rates modified by nitrogen
fertilization. Plant Physiol 74: 595–600
Volenec JJ, Ourry A, Joern BC (1996) A role for nitrogen reserves in forage
regrowth and stress tolerance. Physiol Plant 97: 185–193
Yoneyama T, Yamada H, Yamagata M, Kouchi H (1987) Nitrate reduction
and partitioning of nitrogen in Komatsuna (Brassica campestris L. var.
rapa) plants: compartmental analysis in combination with 15N tracer
experiments. Plant Cell Physiol 28: 679–696
Plant Physiol. Vol. 137, 2005
395
Downloaded from on July 31, 2017 - Published by www.plantphysiol.org
Copyright © 2005 American Society of Plant Biologists. All rights reserved.