Nova Acta Leopoldina NF 114, Nr. 391, 135 –146 (2013)
Effects of Growth Conditions on Carbon Allocation
in a Perennial Grass – the Role of Stores in
Supplying Growth and Respiration
Christoph A. Lehmeier (Freising-Weihenstephan)
With 1 Figure and 1 Table
Abstract
Sustaining individual fitness in a continuously changing environment requires plants to allocate photosynthate efficiently among competing sinks. This article is concerned with the role of carbon stores as substrate for respiration
and leaf growth in Lolium perenne L., an important C3 grass in temperate humid grasslands. It is based on experiments in controlled environments. Three scenarios are compared: plants growing in ‘day/night’ cycles at high nitrogen fertilization, and plants growing in continuous light with either ‘high nitrogen’ or ‘low nitrogen’ supply. Plants
were labeled with 13CO2/12CO2, and the changes in tracer contents with time were measured in respired CO2, and in
the flux of carbon into the zones of leaf growth. The tracer time courses, analyzed with compartmental modeling,
showed a remarkable variation in the size and the turnover of stores. The adjustment of carbon storage deposition
and mobilization fluxes seems to be an important ability for achieving high growth rates and individual fitness in a
fluctuating environment.
Zusammenfassung
Um die Fitness unter ständig wechselnden Umweltbedingungen aufrechtzuerhalten müssen Pflanzen ihren Kohlenstoffgewinn aus der Photosynthese effizient auf verschiedene Senken verteilen. Dieser Artikel beschreibt die Rolle
von Kohlenstoffspeichern für die Versorgung von Respiration und Wachstum in Lolium perenne L., einem wichtigen
C3-Gras der feucht-gemäßigten Breiten, das unter verschiedenen kontrollierten Umweltbedingungen gezogen wurde:
in Tag/Nacht-Zyklen und hohem Stickstoffangebot sowie in Dauerlicht mit entweder hoher oder niedriger Stickstoffversorgung. Die Pflanzen wurden über Zeitintervalle von unterschiedlicher Länge mit 13CO2/12CO2 markiert, die
Erscheinungsraten der Markierung im respirierten CO2 sowie im Substratfluss ins Blattwachstum gemessen und mittels kompartimenteller Analyse untersucht. Die Markierungskinetiken zeigten eine ausgeprägte Variabilität in Größe
und Umwälzungsrate der Speicher für Wachstum und Respiration. Die beobachtete Dynamik scheint ein wichtiges
Mittel zu sein, Wachstumsraten und individuelle Fitness in unterschiedlichen Umweltbedingungen zu maximieren.
1.Introduction
Plants are sessile organisms. Once a seed germinates and the roots penetrate a substrate, the
seedling has to face the prevailing environment in its habitat in all weathers. The above and
below ground space of the habitat needs to be explored by shoot and root growth and exploited for available resources to ensure growth and reproductive success.
In order to achieve and maintain a ‘functional equilibrium’, a balanced acquisition of
resources (including CO2, nutrients, water and light) is required for warranting the species’
particular elemental composition and physiological functioning (Poorter and Nagel 2000,
Ågren 2008). Resource acquisition and the capacity to resist biotic and abiotic stress are
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Christoph A. Lehmeier
indispensable for the plant to stay competitive against neighbors and to sustain or increase
overall fitness (Herms and Mattson 1992, Stamp 2003, Matyssek et al. 2012).
The successful development of the plant involves the concerted functioning of many substrate-demanding processes (terminal sinks) in different organs (leaves, [pseudo]stem, roots)
that all rely on the limited amount of carbon assimilated in photosynthesis. Growth of shoot
and root as resulting in fully functional biomass, able to promote further resource acquisition (Chapin et al. 1990), can dominate carbon demand. However, respiration, a complex
process that provides energy and metabolic intermediates (Cannell and Thornley 2000,
Plaxton and Podestá 2006), can at times consume up to 80 % of gross primary production
(Amthor 1989, Van Iersel 2003). In addition, root exudation and symbiosis with mycorrhizal fungi and other micro-organisms that assist plant nutrition may account for a significant fraction of carbon consumption, too (Koide 1991, Grayston et al. 1996, Kuzyakov
and Cheng 2001, Farrar et al. 2003, Grimoldi et al. 2006). Formation of carbon-based
secondary defense compounds may further lead to a substantial demand for photosynthate
(Dixon 2001, Gayler et al. 2008).
As a consequence, the plant faces a tremendous challenge: to allocate the valuable carbon
resource amongst various competing sinks and balance resource use efficiency in a continuously changing environment. For example, the circadian day/night rhythm creates an unbalance in the photosynthetic carbon supply and sink demand. In plants of the C3 and C4 photosynthetic pathway (Larcher 2003), photosynthetic carbon supply during daylight hours
mostly exceeds the concurrent carbon demands by sinks, although the sink demands at night
can still be high in the absence of photosynthetic activity. Such kinds of unbalance can be
buffered by the use of transient carbon stores (Chapin et al. 1990).
Many plants store a carbon surplus during daylight hours as carbohydrates, primarily as
transitory starch in the chloroplasts or, as in C3 cereals and grasses, as sucrose and/or fructan
(fructose-based oligo- and polysaccharides) in cell vacuoles. Stores act both as kind of an
overflow basin to prevent inhibition of photosynthesis by end product accumulation and as
a carbon source when sink demand exceeds photosynthetic supply. Therefore, stores may be
considered both as intermittent sinks and sources of carbon (Lüttge 2012).
Although the importance of carbon stores in the physiology and ecology of plants has
been acknowledged in research for decades, the understanding of regulatory source-sink coordination has progressed only recently (see reviews by Smith and Stitt 2007, Zeeman et
al. 2007, Walter et al. 2009, Graf and Smith 2011). Regarding mechanistic understanding
of the role of stores in plant performance and resource use efficiency, it is indispensable to
assess their participation in source-sink relationships and alterations in their contribution to
the supply of terminal sinks under environmental impact.
The present article first provides a methodological outline for assessing respective research questions, and then summarizes recent findings about the carbon supply for respiration and leaf growth. Growth conditions will be highlighted as affecting substrate availability, exemplifying own work conducted with perennial ryegrass (Lolium perenne L.).
Research on this important C3 forage species in grasslands of the temperate-humid climate
zone was performed within the framework of the DFG-supported Sonderforschungsbereich
607 “Growth and Parasite Defense – Competition of Resources in Economic Plants from
Forestry and Agronomy”.
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Effects of Growth Conditions on Carbon Allocation in a Perennial Grass
2. Materials and Methods
2.1 Investigating Substrate Pools with Stable Isotope Labeling Techniques
Assessing of whether two sinks like respiration and leaf growth consume the same or different substrate pools requires the characterization of the functional/biochemical identity of the
metabolic carbon pools which may serve as potential carbon sources. One common feature
of metabolic carbon pools is their turnover. That is, there is a flux of carbon through these
pools, driven by the supply of new carbon from current photosynthesis and the carbon drain
by sinks that are to be served. Pools of different functional identity turn over at different rates.
For instance, carbon in pools closely related to photosynthetic pathways is exchanged within
minutes (Bassham et al. 1954, Heber and Willenbrink 1964). Sugars in the vascular transport system of herbaceous plants show a half-life (i.e. the time it takes until 50 % of a pool is
exchanged with new carbon) of a few hours (Geiger et al. 1983, Farrar 1989). Half-lives
of one to several days were found regarding storage carbohydrates like chloroplastic starch
or vacuolar fructan (Gibon et al. 2009, Lattanzi et al. 2012). That is, information about the
half-lives of pools supplying particular processes allows to draw conclusions about biochemical and functional pool identities (e.g. currently produced assimilates vs. stores), even in the
absence of direct biochemical analysis. Labeling with the stable carbon isotope 13C is one
adequate means of gathering the required information.
When a plant is grown under constant environmental conditions and an atmosphere with
constant isotopic signature (δ13Cair, see Farquhar et al. 1989, Coplen 2011), the δ13C of
the substrate pools as well as that of the measured processes (e.g. leaf growth or respiration)
stay constant, too. As a consequence, the δ13C of the considered processes reflect isotopic
equilibrium with δ13Cair.
When the isotopic composition of CO2 is changed to a new, different δ13Cair then the
13
δ C of the photosynthetic CO2 fixation becomes ‘labeled’. The substrate pools in the plant
are then gradually exchanged with new (labeled) carbon. This is translated into a gradual
change in the δ13C of the process supplied by such pools until a new isotopic equilibrium with
δ13Cair is reached. The higher the contribution of slowly turned over substrate to a process, the
longer it takes to reach the new equilibrium. The rate of tracer change from the old isotopic
equilibrium (δ13Cold) to the new isotopic equilibrium (δ13Cnew) can be observed by periodic
measurements of the isotopic signature of the process during labeling (δ13Csample (t)). Using a
two-member isotopic mass-balance equation, the fraction (proportion) of new, labeled carbon
in the supply of the process at different times during labeling (fnew (t)) is then obtained as:
fraction of labeled carbon (fnew (t)) = (δ13Csample (t) – δ13Cold) / (δ13Cnew – δ13Cold).[1]
By definition, fnew is 0 before the start of labeling, and it eventually approaches 1, when the
new equilibrium is reached (see Fig. 1). The rate(s) of change of fnew with time is what actually carries the information about the properties of the substrate supply system. This information can be extracted by compartmental analysis as described in the next section. For a more
detailed description of tracer techniques using stable and radioactive carbon isotopes to study
biological processes at various scales of time and complexity see Schnyder et al. (2012).
Nova Acta Leopoldina NF 114, Nr. 391, 135 –146 (2013)
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Christoph A. Lehmeier
Fraction of labeled carbon
1.0
0.8
0.6
tracer
0.4
0.2
Q1
0.0
sink
0
5
10
Q2
15
20
Duration of labeling (days)
Fig. 1 Evolution of the fraction of labeled carbon in a process with duration of labeling (black circles). The dashed
line is the prediction of the two pool compartmental model fitted to the data. See sections 2.1 and 2.2.
2.2 Analysis of Tracer Kinetics with Compartmental Models
Tracer (or labeling) kinetics (Fig. 1) can help to resolve a series of questions: How many
pools are supplying a process? How are these pools interconnected? What are their sizes and
half-lives? How much carbon do the individual pools contribute to the process? Such questions can be addressed by analyzing the tracer kinetics with compartmental models.
In a first step, the number of participating pools is explored by fitting exponential functions
to the tracer kinetics. In our example in Figure 1, the tracer time course shows the fraction of
labeled carbon increasing rapidly during the first days of labeling. At around day 3, the rate
of tracer increase slows down and continues at a slower rate until the end of measurements at
day 21. A function with two exponential terms of the form y = 1 – (a*e–*t + c*e–d*t) provided
a good fit to the tracer kinetics. This function was statistically superior to a single-term exponential function; more than two exponential terms did not provide a statistically sharpened fit
to the data, indicating that more than two pools are not necessary to simulate the given tracer
kinetics. Hence, in accordance with the principle of parsimony (Ockham’s razor) the carbon
supply system of the considered process is suggested to consist of two pools.
Exploring the interrelationship between the two pools, namely, as to whether only one or
both of them receive tracer and supply the process, is more of a challenge. Several model arrangements may exist that provide equivalent fits to the measured data, which impedes a decision purely based on statistics. However, the pool characteristics assessed by the model do
depend on its structure, and thus, it is important to consider the biologically most meaningful
pool arrangement (Schnyder et al. 2012). A priori knowledge of biochemical pathways or
cellular compartmentation is therefore essential for designing a plausible compartmental model
with valid parameter estimation. For instance, it is reasonable to assume that a store (Q2, Fig. 1)
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like a fructan pool in the cell vacuole exchanges carbon with a pool Q1 that receives carbon from
current photosynthetic assimilation, and comprises, for instance, cytosolic sugars.
Having made such a decision about the model, the further analysis of pool characteristics
is straightforward. The fraction of tracer in the pools Q1 and Q2 (Fig. 1) is given by:
dQ1/dt = Tracer + k21*Q2 – k10*Q1 – k12*Q2[2a]
dQ2/dt = k12*Q1 – k21*Q2[2b]
The rate constant k12 governs the flux from pool Q1 to Q2 and k21 that from Q2 to Q1. The flux
out of Q1, that is either the respiration rate or the carbon flux into the leaf growth zone, is governed by k10. It is assumed that the system is in a steady-state, that is, pools sizes and fluxes do
not change with time, and so, the flux into the system (tracer) equals either the respiration rate
or the carbon flux out of the leaf growth zone into newly formed tissue. The differential equations [2] may then be either implemented in commercially available software (e.g. Lattanzi
et al. 2005) or in a custom-made computer program (e.g. Lehmeier et al. 2008). The rate constants are optimized to find the best fit to the tracer time course (the measured values; Fig. 1).
The optimized rate constants are used to calculate the pool sizes as
Q1 = Tracer/k10[3a]
Q2 = Tracer/k10 * k12/k21[3b]
and the pool half-lives (t1/2) as
t1/2(Q1) = ln(2)/(k10+k12)[4a]
t1/2(Q2) = ln(2)/(k21).[4b]
The probabilities that tracer moves either directly through Q1 without visiting Q2, or that it is
first deposited and remobilized in the store Q2 are interpreted as the fractional contribution of
a pool in the supply of the sink and defined as
Contribution of Q1 = (k10/k10+k12)[5a]
Contribution of Q2 = (k12/k10+k12).[5b]
The analysis of a tracer time course with compartmental models as exemplified here is based
on several assumptions which are stated and discussed in Lattanzi et al. (2005) and Leh­
meier et al. (2008). As shown above, a major one is that the system is in a physiological/
metabolic steady-state and tracer is supplied with constant isotopic composition.
2.3 Steady-state Growth Conditions in Controlled Environments
Perennial ryegrass was sown individually in plastic pots, which were filled with quartz sand, and
distributed in growth chambers at a density of 378 plants m–2. We applied three different treatments (see Lehmeier et al. 2008, 2010a, b): plants were either grown under continuous 24-hour
light exposure (275 µmol photons m–2 s–1) with high or low nitrogen fertilization (“High N” and
“Low N”, respectively), or with alternating 16-hour light (425 µmol photons m–2 s–1) and 8-hour
dark periods (“Day/night”). Every three hours, all plants received a modified Hoagland nutrient
solution that contained either 1 mm NO3 – (“Low N”) or 7.5 mm NO3 – (“High N” and “Day/
night”). All other experimental conditions were the same in the three treatments, including constant air temperature of 20 °C, relative air humidity of 85 % and ambient-air CO2 concentration
of 360 µL L–1. Total daily irradiance was 24 mol photons m–2 in all treatments.
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Christoph A. Lehmeier
The growth chambers were part of a 13CO2/12CO2 gas exchange facility (Schnyder et al.
2003). CO2-free air was mixed with commercially available CO2 of given δ13C from gas
cylinders and supplied to the chambers. The isotopic composition of the CO2 in the growth
chambers was monitored with a continuous-flow isotope-ratio mass spectrometer (CFIRMS), and was kept constant by adjusting the rate of air flow through the chambers. Within
each treatment, half of the plants were grown under 13C-enriched CO2, the other half under
13C-depleted CO . Other growth conditions were identical.
2
Once closed stands were established, labeling was performed by switching the δ13C of the
CO2 supplied to the plants (from constant 13C-enriched CO2 to constant 13C-depleted CO2 or
vice versa), while all other environmental factors remained unchanged.
2.4 Respiration and Leaf Growth Measurements
Plants remained in the presence of the labeling CO2 for intervals of different length, ranging
from 1 h until almost 1 month. At the end of the target duration of labeling, plants were taken
out of the growth chambers and either used for respiration measurements or harvested for leaf
growth analysis.
Shoot and root respiration of individual plants was monitored for 5 – 8 h in the dark using
a custom-made automated gas-exchange measurement system as described by Lötscher et
al. (2004) and Lehmeier et al. (2008). The system served to determine the rates of shoot
and root respiration (as mg C h–1) as well as the δ13C of shoot- and root-respired CO2, and
to calculate the rate and δ13C composition of whole-plant respiration. Thereafter, the plants
were weighed and frozen, and the contents of total carbon and nitrogen, and of water-soluble
carbohydrates (including sucrose and fructan) were analyzed in the freeze-dried shoot and
root biomass as described by Lehmeier et al. (2010a).
For leaf growth analysis, plants were dissected and the leaf growth zones at the bases
of individual tillers were harvested as small, functionally distinct segments as outlined by
Lattanzi et al. (2005) and Wild (2010). Freeze-dried tissue samples were combusted in an
elemental analyzer, and carbon content as well as δ13C of the biomass were assessed with the
CF-IRMS, yielding rate and δ13C of the carbon influx into the leaf growth zone as described
by Lattanzi et al. (2004, 2005).
The same protocol of respiration and leaf growth measurements was followed for
non-labeled control plants that experienced the same δ13C exposure throughout growth until sampling. Analysis of these plants provided the end-members, δ13Cold and δ13Cnew, of the
two-member mixing equation (see Equation [1]), which served to partition the fractions of
old and new carbon in shoot and root respiration as well as in the carbon influx into the leaf
growth zone. In that way, the kinetics of tracer incorporation into respiration and leaf growth
during the labeling period were obtained.
3. Results and Discussion
All plants remained in vegetative growth throughout the entire experimental periods in all
treatments (High N, Low N and Day/night, see Section 2.3). Measurements of leaf elongation of individual tillers and the analysis of carbon contents in shoot and root biomass with
time showed that growth proceeded at constant specific rates. Also, specific shoot and root
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Effects of Growth Conditions on Carbon Allocation in a Perennial Grass
respiration rates did not change during the experiments. Thus, the ratio of photosynthate
incorporated into new biomass to the carbon costs for new biomass synthesis plus biomass
maintenance costs were also constant (Lehmeier et al. 2008, 2010a, b, Wild 2010). Such
a performance underlines that the particular growth chamber conditions in each treatment
provided steady-state conditions for plant growth with constant allocation patterns. That is,
the only parameters that changed significantly with time were the fractions of tracer in the
carbon supply of the sinks.
Tracer kinetics of plant respiration of all three treatments, and of the substrate flux into the
leaf growth zones in the High N treatment showed a similar pattern: respiration and leaf growth
was supplied by carbon from two sources, namely from current photosynthetic assimilation and
from carbon that first underwent storage before it arrived at the sinks. The mechanistic features
of source pools serving respiration and growth were extracted by compartmental analysis of the
tracer kinetics using the two-pool model as shown in Figure 1 (see Section 2.2).
3.1 Day-length Effects on Carbon Stores for Respiration
The only difference in growth conditions for plants in the “Day/night” and the “High N” treatment was how the (same) amount of daily irradiance was distributed over the day: either it
was provided during a 16 h photoperiod (with high irradiance) or it was supplied continuously
(with low irradiance, Section 2.3). The difference in light regime had little influence on the
growth performance of plants (Lehmeier et al. 2010b). Specific growth rates were about 8 %
per day, and the photosynthetic carbon use efficiencies [calculated as growth rate/(growth rate
+ respiration rate)] were high and similar (approximately 0.7) in both light regimes. There
were, however, fundamental effects on the turnover and possibly also on the biochemical
identity of the storage pool supplying respiration.
While the pool of current assimilates (Q1) was turned over at similar, rapid rates (halflife 5 h for Day/night and 6 h for High N; Tab. 1), the half-life of the store (Q2) in Day/night
was almost 4 times faster than the store in the High N treatment with continuous light. The
store’s half-life of 13 h in Day/night is close to half-lives reported for vacuolar storage sucrose (Farrar 1989), while the half-life of 48 h in the continuous light treatment is closer
to half-lives for vacuolar fructan in leaf blades of perennial ryegrass (Lattanzi et al. 2012).
Furthermore, plants in the Day/night treatment had a much lower fructan to sucrose ratio in
total plant biomass (3:1 vs 10:1 in continuous light; Lehmeier et al. 2010b). These findings
indicate that sucrose was a more abundant biochemical substrate in the respiratory store of
Day/night plants, whereas plants in continuous light drew on a greater proportion of stored
fructan for respiration.
3.2 Nitrogen Deficiency Effects on Carbon Stores for Respiration
The carbon mass of plants in the Low N treatment increased only at 4 % per day, that is, at
half the rate of the plants in the High N and Day/night treatments. The specific respiration
rate of Low N plants, however, was only reduced by about one third (Lehmeier et al. 2010a).
As a result, the photosynthetic carbon use efficiency was reduced to 0.6. At least part of this
lower efficiency was probably due to a lower shoot to root ratio of 3:1 as compared to 4:1 in
the other treatments, and to higher specific costs of nitrate assimilation under conditions of
nitrogen deficiency (Cannell and Thornley 2000, Lehmeier et al. 2010a).
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Christoph A. Lehmeier
While the storage pool supplying respiration comprised only 13 % of total carbon in the biomass of High N plants, the proportion was 20 % in nitrogen-deficient plants (Lehmeier et al.
2012). Also, the half-life of the store increased from 48 h at High N to almost two weeks at
Low N (Tab. 1). A half-life of almost two weeks is much slower than the half-lives reported
for storage carbohydrates in leaf blades of grasses (Farrar 1989). Rather, such slow turnover suggests that carbohydrates, mainly fructans, in basal leaf sheaths posed a significant
fraction of substrate in the respiratory store of Low N plants (Borland and Farrar 1988).
Leaf sheaths of grasses are known to be important storage organs under conditions of limited
nitrogen availability (Pollock and Cairns 1991). Hence, growth conditions may not only
affect the biochemical identity of the stored substrate for respiration (see 3.1) but also the
spatial location amongst functionally distinct parts of the plant.
Tab. 1 Half-lives and fractional contributions of substrate pools (Q1, Q2, see Fig. 1) supplying respiration and leaf
growth of Lolium perenne plants grown either in continuous light with High or Low nitrogen (N) supply, or in Day/
night cycles with high N supply. Total daily irradiance and all other growth conditions were the same in the three
scenarios (see Sections 2.2 and 2.3). Data are adopted from Lehmeier et al. (2010a, b) and Wild (2010).
Leaf growth
Respiration
Continuous light
Day/night
High N
High N
Low N
High N
Half-life (h)
Q1
Q2
1
68
6
48
20
288
5
13
Fractional contribution (%)
Q1
Q2
Unclassified old carbon
69
31
0
44
53
3
67
33
0
36
56
8
3.3 Shoot and Root Respiration are Supplied by the Same Substrate Pools
In all three treatments, the tracer kinetics of CO2 respired from whole shoot and root systems, and consequently, the model structures and half-lives of pools serving shoot and root
respiration were nearly identical (Lehmeier et al. 2008, 2010a, b). Furthermore, as it is typical for grasses (Sullivan and Sprague 1943, Davidson and Milthorpe 1966), more than
90 % of all water-soluble carbohydrates – as the most likely major constituents of respiratory
substrate – were located in the shoot biomass. Consequently, most of the storage-derived
carbon supplying root respiration must have resided in the shoot biomass (Lehmeier et al.
2008). These findings dictate the conclusion that also the control of assimilate supply to root
respiration resided in the shoot. Consequently, the supply of substrate to root symbionts and
soil microbes feeding on plant root exudates seems to be largely influenced and controlled by
allocation changes in the shoot organs of the plant (see Grayston et al. 1996, Kuzyakov and
Cheng 2001, Farrar et al. 2003, Thornton et al. 2004).
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3.4 Differences in Substrate Supply to Leaf Growth and Respiration
The substrate pools supplying leaf growth were characterized for the same population of plants
that served to assess the substrate pools for respiration at High N. The two substrate supply
systems showed considerable differences. The pool of current assimilates supplying leaf growth
(Q1) was exchanged much faster with new carbon than the corresponding pool supplying respiration (half-life 1 h versus 6 h, Tab. 1). Indeed, both half-lives are close to those reported for
sucrose, the major transport sugar in the vascular system of herbaceous plants (Geiger et al.
1983, Farrar 1989). However, it is reasonable to assume that growth was mainly supported
by sucrose that rapidly passed through the cytoplasm, apoplast, and sieve tubes and companion
cells of the phloem in actively photosynthesizing and exporting leaves before arriving at the
basal growth zones of a tiller (Geiger et al. 1983, Wild 2010). Conversely, respiration was
measured at the whole-shoot level and thus integrated all shoot tissues, including mature, exporting leaves as well as growing and senescing organs. Possibly, the turnover of the sucrose
pool differs between functionally and developmentally distinct tissues.
About 50 % of all respired carbon cycled through the store before it arrived at the centers
of respiration and it was, most likely, mainly present in carbohydrates. In contrast, the fractional contribution of the store to leaf growth was only 30 % (Tab. 1). Combined labeling
with 13C and 15N (Lattanzi et al. 2005, Wild 2010) suggested that the store supplying leaf
growth included both carbohydrate-carbon and amino-carbon derived from protein turnover,
and that each of the two components supplied only about 15 % to total carbon flux into the
leaf growth zone (Wild 2010). This indicated that leaf growth of the grasses under steadystate conditions in continuous light relied to a much lesser degree on storage carbohydrates
than did respiration.
4.Conclusions
The combined approach of 13CO2/12CO2 labeling techniques and compartmental analysis of
tracer time courses enabled the identification of major substrate pools supplying leaf growth
and respiration of perennial ryegrass. The partitioning into functionally distinct components
(current assimilates versus stores), and the assessment of their response to environmental
conditions proved to be a meaningful way to clarify key controls of carbon allocation.
Experimental alteration of growth conditions unveiled great functional plasticity in plants
to adjust the use of stores, which appears to be an important capacity to support high growth
rates and individual fitness in a fluctuating environment (Smith and Stitt 2007, Walter et
al. 2009, Graf and Smith 2011; and references therein).
The concerted use of labeling with stable isotopes and analysis of tracer kinetics can be
employed at different scales of biological integration, for instance, to study resource fluxes on
the level of single leaves or the ecosystem scale (Gamnitzer et al. 2009, Epron et al. 2011,
Lattanzi et al. 2012). The proven effectiveness of such an approach may stimulate further
research to identify the substrate sources supplying other sinks like the synthesis of carbon-based secondary defense compounds. Such new insights into source-sink relationships
of plants can help to advance new concepts about the controls of carbon allocation between
growth, storage and defense and to quantitatively balance related costs and trade-offs in plant
carbon allocation (Lüttge 2012, Matyssek 2012).
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Christoph A. Lehmeier
Acknowledgements
I would like to thank Hans Schnyder, Jarad Mellard, Jim Hagengruber and Rainer Matyssek for helpful comments and Kyungjin Min for valuable support during the preparation of this manuscript.
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Dr. Christoph A. Lehmeier
Lehrstuhl für Grünlandlehre
Department für Pflanzenwissenschaften
TU München
85350 Freising-Weihenstephan
Germany
Department of Ecology and Evolutionary Biology
The University of Kansas
66047 Lawrence
USA
Phone: 01 785 8641500
Fax: 01 785 8641534
E-Mail:[email protected]
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