Tree Physiology 32, 464–477
doi:10.1093/treephys/tps020
Research paper
Gas exchange and leaf aging in an evergreen oak: causes and
consequences for leaf carbon balance and canopy respiration
Jesús Rodríguez-Calcerrada1,4, Jean-Marc Limousin2, Nicolas K. Martin-StPaul1, Carsten Jaeger3 and
Serge Rambal1
1Center
of Functional and Evolutionary Ecology, CNRS 1919 Route de Mende, 34293, Montpellier Cedex 5, France; 2Department of Biology, University of New Mexico,
MSC03 2020, Albuquerque, New Mexico 87131-0001, USA; 3Center for Biological Systems Analysis, University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany;
4Corresponding author ([email protected]/[email protected])
Received July 8, 2011; accepted February 22, 2012; published online April 3, 2012; handling Editor João Pereira
Leaves of Mediterranean evergreens experience large variations in gas exchange rates over their life span due to aging and
seasonally changing environmental conditions. Accounting for the changing respiratory physiology of leaves over time will
help improve estimations of leaf and whole-plant carbon balances. Here we examined seasonal variations in light-saturated
net CO2 assimilation (Amax), dark respiration (Rd) and the proportional change in Rd per 10 °C change in temperature (Q10 of
Rd) in previous-year (PY) and current-year (CY) leaves of the broadleaved evergreen tree Quercus ilex L. Amax and Rd were
lower in PY than in CY leaves. Differences in nitrogen between cohorts only partly explained such differences, and rates of
Amax and Rd expressed per unit of leaf nitrogen were still significantly different between cohorts. The decline in Amax in PY
leaves did not result in the depletion of total non-structural carbohydrates, whose concentration was in fact higher in PY than
CY leaves. Leaf-level carbon balance modeled from gas exchange data was positive at all ages. Q10 of Rd did not differ significantly between leaf cohorts; however, failure to account for distinct Rd between cohorts misestimated canopy leaf respiration
by 13% across dates when scaling up leaf measurements to the canopy. In conclusion, the decline in Amax in old leaves that
are close to or exceed their mean life span does not limit the availability of carbohydrates, which are probably needed to
sustain new growth, as well as Rd and nutrient resorption during senescence. Accounting for leaf age as a source of variation
of Rd improves the estimation of foliar respiratory carbon release at the stand scale.
Keywords: carbon budget, ecosystem respiration, leaf life span, resource remobilization, soluble sugars, temperature
sensitivity.
Introduction
Leaf mitochondrial respiration plays a role in plant fitness by
providing individuals with some of the carbon skeletons and
energy necessary to grow and survive. In this process, a variable fraction of the CO2 that has been assimilated is released
back into the atmosphere, depending on environmental conditions and the developmental stage and age of leaves. Leaf
dark respiration (Rd) is generally higher in immature leaves
than in mature leaves (Shirke 2001, Armstrong et al. 2006)
and, once leaves have reached maturity, Rd tends to remain
constant or decline slightly up to the start of senescence,
when it can peak again or decrease abruptly (Arney 1947,
Collier and Thibodeau 1995, Shirke 2001, Xu and Baldocchi
2003, Kosugi and Matsuo 2006). The reduction in the rate of
Rd in aging leaves has been attributed to a declining number
of active mitochondria and a reduced activity of respiratory
enzymes (Geronimo and Beevers 1964, Gomez-Casanovas
et al. 2007), and it is associated with a reduced
capacity of energy production (Azcon-Bieto et al. 1983,
­
Priault et al. 2007).
© The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
Leaf age and respiration 465
A question that is important for knowing how age affects the
carbon balance of leaves is whether aging reduces photosynthetic CO2 uptake and respiratory CO2 release to a similar
extent (Reich et al. 2009). In mature but still non-senescent
leaves of some tree species, aging reduces mesophyll diffusion
conductance to CO2 and the biochemical capacity for CO2
assimilation, especially per unit of leaf dry mass and nitrogen,
but it seems to have little effect on respiratory rates (Wilson
et al. 2000, Niinemets et al. 2004, 2005). Metabolic changes
associated with nutrient resorption underpin the temporal variation of respiratory and photosynthetic rates once senescence
starts. Nitrogen translocation to younger leaves can reduce the
respiratory rates of older ones, but it could reduce their photosynthetic capacity to a greater extent, as most of the nitrogen
that is mobilized comes from the degradation of chloroplastic
proteins (Hörtensteiner and Feller 2002, Munné-Bosch and
Alegre 2004). The decline in photosynthesis in senescent
leaves is accompanied by a continuous phloem loading of
sucrose that ensures sap flow and nutrient resorption (Hoch
et al. 2001), although carbohydrates do not seem to drop to
concentrations limiting Rd until senescence is very advanced
(Collier and Thibodeau 1995).
Rd also depends on prevailing environmental conditions.
Over short-term rises in temperature, Rd increases exponentially; but the seasonal temperature sensitivity of Rd is often
lower than that observed over hours, a phenomenon which is
known as thermal acclimation (e.g., Atkin et al. 2005). One
important step towards modeling stand-scale foliar CO2 release
more accurately is to understand how age affects the response
of Rd to temperature over short and long timescales. To the
best of our knowledge, only Panek (2004) and Maseyk et al.
(2008) have compared the temperature response of Rd
between age cohorts of evergreen trees. They did not find any
difference between leaf cohorts in two Pinus species, yet there
are both physiological and ecological reasons to expect that
the temperature sensitivity of Rd is significantly different
between cohorts in other tree species. On the one hand, the
concentration of nitrogen and non-structural carbohydrates
can change between young and old leaves (Li et al. 2009 and
references therein), and both biochemical components influence the short- and long-term sensitivity of Rd to temperature
(Ow et al. 2010). On the other hand, from a carbon economy
point of view, since acclimation of Rd helps the leaf to accommodate to new environmental conditions and thus to extend its
life span, it is reasonable to think that young leaves exhibit a
higher degree of acclimation than older leaves that have
already paid back their construction cost.
In regions with marked climate seasonality, therefore, temporal changes in gas exchange parameters are due to concurrent
changes in leaf age and environmental conditions. Interactions
can occur, so that the responsiveness of leaves to shifting conditions of irradiance, soil water availability, and ­temperature vary
as a function of their age (Polle et al. 2001, Marron et al. 2002,
2008). In evergreens with episodic flushes of shoot growth, a
pertinent approach to examine the effects of age on the sensitivity of leaves to environmental changes is to compare the
physiological seasonality of leaves belonging to different age
cohorts. Here we examined the seasonal variations in Amax, Rd
and Q10 of Rd (i.e., the proportional change in Rd per 10 °C
change in temperature) in two leaf age cohorts of the broadleaved evergreen tree Quercus ilex L. The first objective was to
evaluate how and through which mechanisms leaf age affects
gas exchange and its sensitivity to seasonally changing climatic
factors, namely temperature and soil water availability. We anticipated lower values of Amax, Rd and Q10 of Rd, and a lower degree
of thermal acclimation of Rd in the old cohort. We expected that
age-related changes in these traits were associated with
changes in biochemical traits, such as nitrogen and total nonstructural carbohydrate concentration. The second objective
was to estimate the net carbon gain of leaves from a daily balance of photosynthesis and respiration, under the hypothesis
that old leaves would have a positive, but lower net carbon gain
than younger ones. Finally, our third objective was to assess the
importance of considering leaf age when modeling canopyscale leaf respiration. To this end, we compared three estimations of canopy leaf respiration, one obtained from equations of
Rd vs. temperature specific for each cohort and two others from
applying the respiratory characteristics of either current-year
(CY) or previous-year (PY) cohorts to all leaves.
Materials and methods
Study site and design
This study was conducted in an even-aged Q. ilex stand at the
Puéchabon State forest (3°35′45′E, 43°44′29′N, 270 m a.s.l.),
near Montpellier, France. The stand has long been subjected to
coppicing, with short rotation cutting cycles aimed at obtaining
firewood, charcoal and tannins. The last time the stand was
clear cut was in 1942. After 78 years of unperturbed growth,
stem density is 5100 stems ha−1, mean canopy height
is ~ 5.5 m, and current secondary growth is <1 mm year−1 for
the average diameter of trees. The canopy is fairly open with
an estimation of leaf area index (LAI) from litter fall of 1.89 m2
leaf m−2 soil (see Limousin et al. 2012 for details). This physiognomy is the result of past forest management and poor environmental conditions of the area, which has extremely rocky
soils (75% of the soil volume is stones in the 0–50 cm layer
and 90% below) and recurrent, often very intense droughts.
Minimum predawn leaf water potential in Q. ilex trees in summer varies between −2 and −5 MPa among years. Precipitation
averages 907 mm per year, and ~ 80% takes place from
autumn to spring. Mean annual temperature is 13.4 °C and the
mean of July and January are 22.9 and 5.5 °C, respectively.
Tree Physiology Online at http://www.treephys.oxfordjournals.org
466 Rodríguez-Calcerrada et al.
Predawn leaf water potential was measured over the experimental course with a pressure chamber (PMS 1000, PMS
Instruments, Corvallis, OR, USA) and used as a surrogate of
tree water availability.
Quercus ilex has synchronous flushes of shoot growth. In the
Puéchabon forest, leaves emerge in early May and only occasionally some branches develop new, shorter shoots in autumn
if weather conditions are favorable. In this study, we examined
the variation of different traits at seven dates from April 2009
to March 2010 in two leaf cohorts from the top canopy: CY
leaves (emerged in May 2009) and PY leaves (emerged in May
2008). One fully expanded leaf per cohort was selected from
the same branch in five trees at each date for measuring gas
exchange. Five to ten leaves from the same or adjacent twigs
were harvested and pooled for biochemical measurements. A
scaffold system provided access to the top of the canopy.
Irradiance at the leaf level was measured with an LI-250 Light
Meter (Li-Cor Inc., Lincoln, NE, USA) on a completely overcast
day in March 2010. Mean photon flux density relative to that
above the canopy was 84.2 ± 2.0 and 78.4 ± 2.2% for CY and
PY leaves, respectively (Pt-test = 0.087). Differences between
cohorts were small owing to the little distance between sampled leaves and the loose arrangement of neighboring shoots.
Figure 1. ​Scatter plot of leaf dark respiration (Rd) measured during the
night and day against leaf temperature in CY (filled symbols) and PY
(open symbols) leaves of Q. ilex over the course of the study (all dates
and trees pooled).
t­emperature was the highest of the day), and in the morning in
spring and summer (when air temperature was near 25 °C).
Since leaves were smaller than the 6 cm2 of the chamber, the
rates of gas exchange were corrected by the actual leaf area
measured. The ratio between the leaf area and its dry mass
was used to convert area-based gas exchange rates to massbased rates.
Leaf gas exchange measurements
Biochemical measurements
We used a portable photosynthesis system (LI-6400, Li-Cor)
equipped with a 6-cm2 chamber coupled to a light source
(6400-02B LED, Li-Cor). Measurements were made on intact,
healthy leaves at 400 µmol CO2 mol−1 air (controlled with the
6400-01 CO2 mixer), 300 µmol s−1 air flow rate, ambient temperature, ambient relative humidity, and either 1500 µmol
­photons m−2 s−1 of photosynthetically active radiation for measurements of light-saturated net CO2 assimilation (Amax) and
stomatal conductance (gs), or darkness for measurement of
dark respiration (Rd).
Rd was measured at different times of the night and day in
order to construct curves of Rd vs. temperature (Figure 1). This
methodology has been previously used for estimating the temperature sensitivity of Rd in situ (e.g., Atkin et al. 2000,
Rodríguez-Calcerrada et al. 2011). During the day, leaves were
darkened for 30 min with foil paper and then placed into the
chamber until readings of CO2 efflux were constant, typically
within the next 5 min. We ensured that leaves received full sunlight for ~ 2 h in between measurements to avoid the depletion of respiratory substrates (Azcón-Bieto 1992). Using the
same methods, we observed no diurnal variation of Rd in four
potted plants measured at constant temperature (data not
shown). Thus, we assumed that the response of Rd to diurnal
changes in temperature in our field study was a good indicator
of the short-term temperature sensitivity of Rd.
Amax was measured in the same leaf sampled for Rd at one
time of the day: at midday in autumn and winter (when air
We measured the concentration of nitrogen (N), soluble sugars
(SS) and starch. Leaves were collected 3–4 h after sunrise and
transported to the laboratory in liquid nitrogen for storage at
−80 °C until analyses. They were dried by lyophilization and
finally powdered with a ball mill. Nitrogen was measured using
an elemental analyzer (Flash EA1112 Series, Thermo Finnigan,
Milan, Italy). Soluble sugars were extracted as described by
Göttlicher et al. (2006). A volume of 25 µL supernatant from
the extract was dried in vacuo (Concentrator Plus, Eppendorf,
Hamburg, Germany) and derivatized for gas chromatographymass spectrometry (GC-MS) analysis according to Fiehn
(2006). Trimethylsilyl (TMS) derivatives of glucose, fructose
and sucrose were quantified on a GC-MS system
(7890A/5975C, Agilent Technologies, Waldbronn, Germany),
using internal and external standards (Sigma, Munich,
Germany). Starch was hydrolyzed for 60 min at 60 °C with
10 U amyloglucosidase from Aspergillus niger (Sigma, Munich,
Germany) in 20 mM sodium acetate buffer (pH 4.8), and
released glucose equivalents were quantified by GC-MS as
above.
Tree Physiology Volume 32, 2012
Short- and long-term sensitivity of Rd to temperature
To characterize the short-term temperature sensitivity of Rd of
each leaf we calculated its Q10 from the response curves of Rd
to diurnal shifts in temperature:
Q10 = e10 k
Leaf age and respiration 467
In this equation k is the slope of the linear regression between
temperature and the natural logarithm of Rd (Atkin et al. 2005).
From Q10 values, we estimated the rate of Rd at two reference
temperatures: 15 °C (R15), chosen by being recorded in all
measurement dates except August and January, and the mean
temperature of the week preceding gas exchange measurements (Ramb). The equation used to calculate R15 and Ramb was
as follows:
s­ubmodels was used to compute half hourly values of net
photosynthesis. Meteorological data needed in submodels
­
(photosynthetic active radiation, air temperature, air relative
humidity and air CO2 concentration) were taken from a nearby
eddy flux tower. Half-hourly values were finally summed up to
have the net daily carbon balance of the leaves of each cohort
at each date. A detailed description of model parameterization
and simulation is given in the Appendix.
Up-scaling of respiratory CO2 release to the whole
canopy
R x = R0Q10Tx 10
where R0 is the base rate of Rd at 0 °C (obtained from the temperature response curves), Rx is either R15 or Ramb and Tx is
either 15 °C or ambient temperature, respectively.
We calculated two indexes to quantify the degree of thermal
acclimation of Rd in response to seasonal shifts in temperature. The first index is the ratio of R15 at the coldest date to R15
at the hottest date; a value of 1 indicates no acclimation and
higher values indicate an increasing degree of acclimation.
The second index is based on the comparison of respiratory
rates at the ambient temperature of each period: the ratio
between Ramb at the coldest date and Ramb at the hottest date,
with higher values indicating an increasing degree of acclimation, and a value of 1 indicating full acclimation (Atkin et al.
2005). Coldest and hottest temperatures were reached in
January and August, respectively (Table 1). This time period
entailed a range of temperature of 26 °C but also a wide range
of soil water availability. As a consequence, we should inevitably consider these indexes as indicative of leaf respiratory
acclimation to shifts in both temperature and soil water
content.
Modeling of leaf carbon balance
We modeled net daily carbon balance of leaves from gas
exchange measurements at each date. Half hourly values of
respiratory rates were calculated from temperature response
curves, considering 50% attenuation of respiration by light
during the day time (Villar et al. 1995). Diurnal shifts in photosynthesis were estimated from coupling the Farquhar et al.
(1980) model of photosynthesis to a modified Ball et al. (1987)
model of stomatal conductance and a Fickian equation describing the diffusion of CO2 through the leaf. The analytical solution
given by Baldocchi (1994) for the coupling of the three
Daily foliar CO2 release per unit ground area was estimated for
each sampling date from base rates of Rd (R0) and Q10 values.
We considered the profile of Rd through the canopy previously
observed by Rodríguez-Calcerrada et al. (2011), where it was
verified that R0 declines with canopy depth similarly to leaf
mass per area (LMA) in Rambal et al. (1996):
R0( z) = R0 tope− kz
with z varying from 0 to the LAI of the lower canopy level, and
k being the extinction coefficient of LMA through the canopy
profile. Leaf area index of the study stand is 1.89 m2 leaf m−2
soil and was assumed to be constant across the seasons.
Foliar respiration integrated over the canopy and a time period
of 24 h (Rfol) was then calculated as
LAI
Rfol.i =
∫
0
24
∫
Ta( t ) 10
R0 i( z) Q10 i dtdz
0
As above, mitochondrial respiration occurring during the day
was taken as 50% of that occurring in darkness (Villar et al.
1995). The subindex i in parameters Rfol, R0 and Q10 indicates
that values from either CY or PY leaf cohorts were used to
estimate Rfol (i.e., RfolCY and RfolPY, respectively). These two
simulations were compared with a third one in which the
respiratory characteristics of each cohort were applied to their
respective proportion of LAI. In this case Rfol was calculated as
Rfol = αRfolPY + (1 − α)RfolCY
Table 1. ​Variation of air temperature (T, °C) and predawn leaf water potential (Ψpd, MPa; used as a surrogate of water availability for trees) in the
seven sampling dates of this study (22 April 2009, 23 June 2009, 25 July 2009, 20 August 2009, 4 November 2009, 13 January 2010 and
4 March 2010). Values of temperature correspond to the mean of the daily temperatures recorded over the week preceding sampling days.
Var.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
Air T
Ψpd
14.3
−0.37 ± 0.03
21.2
−0.73 ± 0.06
23.3
−1.98 ± 0.11
26.9
−3.33 ± 0.24
11.7
−0.70 ± 0.03
0.8
−0.54 ± 0.06
6.8
−0.42 ± 0.03
Tree Physiology Online at http://www.treephys.oxfordjournals.org
468 Rodríguez-Calcerrada et al.
where α represents the proportion of LAI attributed to PY
leaves. Assuming that the canopy was entirely composed of
two leaf age cohorts, the proportion of LAI attributed to CY
leaves was computed as 1 − α. To estimate α we first fitted a
non-linear curve to data of leaf survival vs. time obtained for
apical branches of Q. ilex in the study area. This allowed us to
know the proportion of leaves surviving to the age of each
cohort at a given date (SPY and SCY), which was used to calculate α as
α=
SPY
SPY + SCY
Since gas exchange measurements were made only on the
tree-canopy top (but were extrapolated to the whole canopy),
we also assumed that any effect of the leaf cohort on Rd did
not vary through the canopy; in other words, that the effect of
leaf age was independent of the microclimate leaves experience. Mean leaf life span was 484 ± 29 days.
Ecosystem respiration
We obtained ecosystem respiration (Reco) for the same days
we estimated canopy leaf respiration (Rfol), which served to
quantify how much of the CO2 released by the ecosystem was
attributed to the canopy foliage of Q. ilex (Rfol/Reco). Continuous
measurement of eddy covariance CO2 fluxes was taken from a
flux tower located ~100 m away from the sampled trees. Flux
data were filtered to meet quality criteria recommended by
CARBOEUROPE-IP (http://www.carboeurope.org), a European
project integrating the FLUXNET global network (http://fluxnet.
ornl.gov/fluxnet/index.cfm). For the 2009–10 period, 28% of
day time data and 48% of night-time data were discarded or
missing. These values were filled using the algorithm described
in Reichstein et al. (2005). The algorithm used to partition net
ecosystem exchange (NEE) into gross primary production and
Reco was also taken from Reichstein et al. (2005); it allows
extrapolating night-time values of respiration to the day time
using curves of seasonally varying temperature sensitivity. A
more detailed description of flux measurements and data processing is available in Allard et al. (2008).
Statistical analysis
Mean values of leaf physiological and morphological variables
were compared using general linear models (GLMs). We tested
for the effects of Cohort (main effect, 1 degree of freedom
(d.f.)), Tree (random effect, 4 d.f.), Date (within-subjects effect,
5 d.f.) and Date × Cohort (interaction effect, 5 d.f.) in a mixedmodel design. When the interaction between Date and Cohort
was significant at P < 0.05, we tested for differences between
cohorts separately for each date. The analyses were made
using logarithmically transformed data for starch and Q10. Since
Tree Physiology Volume 32, 2012
only leaves from the cohort of May 2008 (PY) were present in
April 2009, this first sampling date was excluded for analyses.
Results
Over the course of the study, air temperature and soil water
availability showed the typical annual trend of Mediterraneantype climates: air temperature increased from winter to summer, whereas soil water availability for trees decreased through
summer and remained relatively constant and high for the rest
of the year (Table 1).
The seasonality in temperature and water availability was
reflected in the temporal pattern of leaf gas exchange. Net CO2
assimilation (Amax), stomatal conductance (gs) and rates of
dark respiration at a reference temperature of 15 °C (R15)
decreased through summer as temperatures increased and
soil water availability decreased, and all recovered afterwards
(Tables 2 and 3; all P < 0.001 for the Date effect). Respiration
at the mean ambient temperature of each date (Ramb) was lowest in January, when temperatures were also the lowest, and
increased in summer (Table 3; P < 0.001). The interaction
term between factors Date and Cohort specified in GLMs was
not significant for any of these variables, indicating that leaves
from PY and CY cohorts had a fairly similar response to prevailing environmental conditions along the year. Across dates, Amax
was significantly higher in CY leaves (F1, 44 = 21.83, P < 0.001)
but gs did not differ significantly between cohorts (F1, 44 = 3.59,
P = 0.064). The ratio of Amax to gs, a surrogate of leaf-level
intrinsic water use efficiency, was thus significantly higher in
CY leaves (F1, 44 = 10.18, P = 0.003; Table 2). R15 and Ramb
were also higher in CY leaves than in PY leaves (F1, 42 = 16.22,
P < 0.001 and F1, 42 = 22.88, P < 0.001, respectively; Table 3).
All the above results are for gas exchange rates expressed on
a leaf area basis. Differences in gas exchange between cohorts
were about 4% higher when values were expressed on a dry
mass basis because LMA was significantly higher in PY leaves
than in CY leaves (F1, 44 = 8.56, P = 0.005; Table 2).
Estimated daily leaf carbon balance revealed a positive net
carbon gain in all dates and in both cohorts (Table 4). Even
leaves older than the mean life span of the population (484
days) exhibited a positive carbon balance. Values were higher
in spring and autumn than in summer and winter. Leaf net
carbon gain was lowest in August, when drought-induced
reduction in net CO2 assimilation was the strongest. The
decline in the base rates of Rd during summer contributed to
keeping a positive leaf carbon balance, as demonstrated by
modeling daily carbon gain in August using the Rd and Q10
from June. In this case, net carbon gain decreased from 1.67
to 1.44 g C m−2 day−1 in CY leaves and from 0.72 to
0.52 g C m−2 day−1 in PY leaves. In winter, low carbon gain
was not due to a decline in photosynthetic capacity but
mostly to low photosynthetically active radiation as compared
Leaf age and respiration 469
Table 2. ​Age of leaves (in days) at each sampling date and mean values (±SE) of several morphological and physiological variables specified by
cohort (PY: previous-year leaves; CY: current-year leaves).
Var.
Coh.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
Age
PY
CY
PY
CY
PY
CY
PY
CY
PY
CY
PY
CY
350
412
46
230 ± 4
213 ± 7
5.6 ± 1.1
9.9 ± 1.2
0.06 ± 0.01
0.10 ± 0.02
96 ± 7
110 ± 10
2.3 ± 0.5
4.1 ± 0.7
444
78
247 ± 3
231 ± 7
4.0 ± 0.7
7.4 ± 1.0
0.04 ± 0.01
0.06 ± 0.01
100 ± 12
119 ± 5
1.4 ± 0.3
2.8 ± 0.5
470
104
233 ± 11
238 ± 6
3.2 ± 0.8
6.0 ± 0.6
0.03 ± 0.01
0.05 ± 0.01
109 ± 10
134 ± 10
1.2 ± 0.3
1.9 ± 0.2
546
180
253 ± 4
239 ± 12
12.3 ± 1.8
14.0 ± 1.2
0.17 ± 0.03
0.18 ± 0.03
76 ± 2
81 ± 4
4.7 ± 0.8
4.4 ± 0.3
616
250
242 ± 10
229 ± 3
11.5 ± 1.5
13.5 ± 1.6
0.22 ± 0.04
0.25 ± 0.04
56 ± 8
56 ± 5
4.1 ± 0.5
4.2 ± 0.5
666
300
250 ± 7
244 ± 3
7.6 ± 0.7
10.0 ± 0.7
0.11 ± 0.02
0.12 ± 0.01
80 ± 13
89 ± 8
3.0 ± 0.3
3.5 ± 0.2
LMA
Amax
gs
Amax/gs
Amax/N
268 ± 3
13.4 ± 0.9
0.15 ± 0.01
89 ± 6
3.7 ± 0.3
LMA (g m−2), leaf mass per area; Amax (μmol m−2 s−1), light-saturated net CO2 assimilation; gs (mol m−2 s−1), light saturated stomatal conductance to
water vapor; Amax/gs (μmol mol−1), intrinsic water use efficiency; Amax/N (μmol g−1N s−1), photosynthetic nitrogen use efficiency.
Table 3. ​Mean values (±SE) of respiratory parameters for previous-year leaves (PY) and current-year leaves (CY) over the course of the study.
Var.
Coh.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
Q10
PY
CY
PY
CY
PY
CY
PY
CY
1.8 ± 0.1
1.9 ± 0.1
1.7 ± 0.1
0.49 ± 0.06
0.62 ± 0.03
0.72 ± 0.07
0.86 ± 0.04
0.20 ± 0.03
0.26 ± 0.03
1.6 ± 0.1
1.9 ± 0.1
0.44 ± 0.04
0.50 ± 0.04
0.65 ± 0.06
0.83 ± 0.07
0.16 ± 0.02
0.18 ± 0.01
1.9 ± 0.1
2.1 ± 0.1
0.30 ± 0.03
0.33 ± 0.02
0.64 ± 0.05
0.78 ± 0.04
0.11 ± 0.01
0.11 ± 0.01
2.7 ± 0.3
2.2 ± 0.2
0.60 ± 0.03
0.77 ± 0.06
0.43 ± 0.02
0.61 ± 0.06
0.23 ± 0.02
0.25 ± 0.03
2.4 ± 0.4
2.6 ± 0.3
0.74 ± 0.13
1.00 ± 0.06
0.21 ± 0.02
0.27 ± 0.03
0.27 ± 0.05
0.31 ± 0.02
2.4 ± 0.3
2.1 ± 0.4
0.71 ± 0.07
0.95 ± 0.12
0.37 ± 0.06
0.54 ± 0.07
0.28 ± 0.01
0.33 ± 0.04
R15
Ramb
R15/N
0.87 ± 0.07
0.84 ± 0.07
0.24 ± 0.02
Q10 (no units), temperature sensitivity of leaf dark respiration (Rd); R15 (μmol m−2 s−1), Rd at 15 °C; Ramb (μmol m−2 s−1), Rd at the mean ambient
temperature of each date; R15/N (μmol gN−1 s−1), Rd at 15 °C per unit nitrogen.
Table 4. ​Mean values (±SE) of estimated net daily carbon balance for previous-year leaves (PY) and current-year leaves (CY) over the course of
the study. Units are g C m−2 day−1.
Var.
Coh.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
Cgain
PY
CY
6.04 ± 0.71
3.55 ± 0.71
5.13 ± 0.45
1.69 ± 0.46
3.75 ± 0.61
0.72 ± 0.42
1.67 ± 0.60
4.80 ± 0.66
5.15 ± 0.18
1.74 ± 0.23
1.98 ± 0.23
2.38 ± 0.24
3.27 ± 0.25
with the remaining dates (Figure 2). Independently of seasonal variations, leaf carbon balance was always higher in CY
leaves than in PY leaves (F1, 44 = 15.91, P < 0.001 for the
main effect of Cohort and F5, 44 = 1.28, P = 0.290 for the
Date × Cohort interaction effect).
Nitrogen content per unit leaf area declined in PY leaves
after the emergence of the CY shoot. Then it was significantly
lower in PY leaves than in CY leaves (F1, 44 = 12.95, P < 0.001;
Table 5), with a non-significant trend for differences to increase
with time due to nitrogen accretion in CY leaves (F5, 44 = 1.42,
P = 0.235 for the Date × Cohort interaction effect). To assess
the extent to which differences in gas exchange between
cohorts were related to the variation of nitrogen, we expressed
rates of Amax and R15 by unit of leaf nitrogen with the result that
Amax/N and R15/N continued to be significantly different
between cohorts (Tables 2 and 3; F1, 44 = 8.03, P = 0.007 and
F1, 42 = 4.29, P = 0.044, respectively). Despite the lower rates
of Amax, the concentration of carbohydrates did not decline in
PY leaves. Actually, both soluble sugars (F1, 43 = 14.45,
P < 0.001) and starch (F1, 43 = 18.25, P < 0.001) were significantly higher in PY leaves than in CY leaves (Table 5).
Significant interactions between Date and Cohort factors
­indicated that soluble sugars were more abundant in PY leaves
than in CY leaves in spring and early summer (F5, 43 = 4.26,
Tree Physiology Online at http://www.treephys.oxfordjournals.org
470 Rodríguez-Calcerrada et al.
Figure 2. ​Diurnal variation in PAR, stomatal conductance (gs), mitochondrial respiration (R) and net carbon flux at three different dates: spring (22
April), summer (25 July) and winter (13 January). Filled symbols represent CY leaves and open symbols represent PY leaves. Data points are means
of five leaves (error bars not shown). Compared with spring time, low water availability (Table 1) limits gs and net CO2 gain in summer, whereas
acclimation to heat and drought attenuates the rise in R that would otherwise be observed at this time upon the ~13 °C increase in air temperature
that occurred from spring to summer. In winter, low PAR and air temperatures (Table 1) limit net CO2 gain.
Table 5. ​Mean values (± SE) of several biochemical variables for previous-year leaves (PY) and current-year leaves (CY) over the course of the
study.
Var.
Coh.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
N
PY
CY
PY
CY
PY
CY
3.6 ± 0.1
2.5 ± 0.2
2.5 ± 0.3
25.5 ± 2.2
17.4 ± 1.5
2.20 ± 0.28
2.69 ± 0.50
2.8 ± 0.2
2.8 ± 0.2
27.3 ± 1.9
19.0 ± 1.5
1.40 ± 0.34
0.93 ± 0.28
2.7 ± 0.1
3.1 ± 0.1
27.6 ± 3.4
23.7 ± 1.6
0.55 ± 0.03
0.59 ± 0.05
2.7 ± 0.2
3.2 ± 0.2
22.0 ± 1.6
22.9 ± 2.7
4.72 ± 0.82
1.83 ± 0.34
2.8 ± 0.1
3.2 ± 0.1
32.6 ± 2.0
33.3 ± 1.9
0.98 ± 0.19
0.58 ± 0.02
2.5 ± 0.1
2.9 ± 0.1
34.6 ± 1.9
34.8 ± 2.6
2.91 ± 0.55
1.29 ± 0.23
SS
Starch
33.8 ± 0.9
9.65 ± 0.56
N (g m−2), nitrogen content per unit leaf area; SS (g m−2), soluble sugars per unit leaf area. Starch is in g m−2 leaf.
Tree Physiology Volume 32, 2012
Leaf age and respiration 471
P = 0.003), whereas starch started to accumulate in winter
more in old PY leaves than in CY leaves (F5, 43 = 3.78,
P = 0.006). All these results are for area-based biochemical
properties. Differences between cohorts were about 6%
higher when the amount of nitrogen and non-structural
­carbohydrates were expressed per unit of leaf dry mass (data
not shown).
The short-term temperature sensitivity of Rd (Q10) differed
between the two cohorts, but large variability among trees
meant that these differences were not significantly different at
P < 0.05 (Table 3). Q10 was higher in winter than in summer
(F5, 42 = 3.98, P = 0.003), and both cohorts exhibited similar
seasonal variations. The long-term temperature sensitivity of
Rd was quantified by means of two indexes accounting for
shifts in respiratory rates between August and January
(Figure 3). Both indexes indicated that leaves acclimated to
the seasonal change in temperature (and soil water availability) that took place between these dates, but in both cases
acclimation of Rd was not significantly different between
cohorts (P > 0.10). At the canopy level (see below), we estimated that acclimation of foliar respiration to summer heat and
drought saved the release of 0.34 g C m−2 day−1 to the atmosphere. The estimation was made after modeling Rfol in August
from respiratory parameters measured in June.
We scaled Rd to the canopy over 24 h taking three different
approaches (Table 6). Applying the respiratory characteristics
(Rd and Q10 of Rd) of CY leaves to all leaves resulted in 4–10%
higher canopy leaf respiration among dates than when the Rd
and Q10 of each cohort were multiplied by their respective fraction of canopy leaf area. When respiratory characteristics of PY
leaves were applied to all leaves, the model gave 15–27%
lower values of canopy leaf respiration than the more complex
model including the specific characteristics of each cohort.
Daily estimations of canopy leaf respiration were compared
with values of ecosystem respiration (Reco) measured in the
study area for the same days (Table 6). After specifying distinct respiratory characteristics for each cohort, the foliar
­component of Reco was found to vary between 27% in early
summer to 46% in late summer; the foliar component of Reco
was 9% higher or 2% lower when respiratory characteristics of
only CY or PY leaves, respectively, were applied to all leaves.
Discussion
Leaf carbon balance and non-structural carbohydrate
pools
Figure 3. ​Acclimation of leaf dark respiration (Rd) to seasonal changes
in temperature in CY and PY leaves of Q. ilex. Upper panel: acclimation
quantified as the ratio of R15 at the coldest date (January) to R15 at the
warmest date (August). Lower panel: acclimation quantified as the
ratio of Ramb at the coldest date to Ramb at the warmest date. Data are
means ± 1 SE. Means were not significantly different between leaf age
cohorts in any case.
Whether leaves can maintain a substantially positive carbon
balance after their mean age of death has long been debated
in the literature (e.g., Kitajima et al. 1997, Ackerly 1999, Reich
et al. 2009). Based on our gas exchange data, estimated leaf
daily carbon balance was always positive, even in leaves largely
exceeding the mean life span of the studied population. Ackerly
(1999) and Reich et al. (2009) also found positive carbon balances in leaves at their mean age of death. Reich et al. (2009)
Table 6. ​Daily ecosystem respiration measured by eddy covariance (Reco) and stand-scale foliar respiration estimated by up-scaling of leaf specific
respiration rates. Three estimations are given: one in which all leaves are supposed to have the respiratory characteristics of current-year leaves
(RfolCY), another one in which all leaves are supposed to have the respiratory characteristics of previous-year leaves (RfolPY), and another one in
which the respiratory rates of each cohort are multiplied by the proportion of total LAI belonging to each cohort (Rfol). Units are g C m−2 day−1.
Monte–Carlo simulations were used to calculate standard errors.
Var.
April 09
June 09
July 09
Aug. 09
Nov. 09
Jan. 10
March 10
RfolCY
RfolPY
Rfol
Reco
1.25 ± 0.23
1.25 ± 0.23
3.14
0.94 ± 0.17
0.73 ± 0.19
0.86 ± 0.26
3.20
0.95 ± 0.16
0.75 ± 0.14
0.88 ± 0.21
2.53
0.90 ± 0.17
0.70 ± 0.15
0.84 ± 0.23
1.83
0.84 ± 0.19
0.57 ± 0.14
0.78 ± 0.24
2.61
0.48 ± 0.10
0.39 ± 0.07
0.46 ± 0.12
1.29
0.68 ± 0.24
0.49 ± 0.14
0.65 ± 0.27
1.40
Tree Physiology Online at http://www.treephys.oxfordjournals.org
472 Rodríguez-Calcerrada et al.
interpreted this surplus of carbon gain as that needed to support the respiratory maintenance costs in other parts of the
tree. In our system, the results can be further explained by the
limited shading of PY leaves by new shoots and leaves (mean
photon flux density: 78.4 ± 2.2 vs. 84.2 ± 2.0% in PY vs. CY
respectively, Pt−test = 0.087), and by the phenology of Q. ilex
where leaves from the same cohort appear simultaneously but
are shed over a long period of time. Moreover, while the carbon balance of an individual leaf can be a relevant factor in
determining its life span (Kitajima et al. 1997, Reich et al.
2009), the time of leaf death in water-limited environments is
determined by the necessity for trees to avoid high levels of
xylem embolism during the dry period (Escudero et al. 1992,
Maseda and Fernández 2006). To be in hydrological equilibrium with the environment, Q. ilex trees can only support a
limited leaf area, and the survival of leaves is driven by the
seasonality in water availability (Limousin et al. 2012). Leaves
that are shed have lower photosynthetic capacity, but also a
weaker stomatal control of water loss (Table 2, Reich and
Borchert 1988); their shedding would thus be a way to optimize canopy water use efficiency during the summer drought.
Consistently with the modeled carbon balance, there was no
sign of carbon depletion in old leaves. In fact, the concentration of starch and soluble sugars tended to be higher in PY
leaves than in CY leaves. Previous studies have reported higher
concentrations of non-structural carbohydrates in older leaves
than in younger, mature leaves (Li et al. 2009). The fact that
carbon gain and storage continue in old leaves is related to
their storage function in evergreen species (Chapin et al. 1990,
Cherbuy et al. 2001). As occurs with carbohydrates, the accumulation of nitrogen plays a role in supporting new shoot
growth, and both carbohydrates and nitrogen decreased in PY
leaves after the emergence of new ones (Table 5, Cherbuy
et al. 2001, Silla and Escudero 2003, but see Millard and
Grelet 2010). It is difficult to explain the significant interaction
between the leaf age cohort and the date of measurement for
non-structural carbohydrates, but it could be related to time
alterations in carbon source–sink relationships (Li et al. 2009).
We speculate that CY leaves could export soluble sugars to a
higher extent than PY leaves during early summer, whereas PY
leaves could accumulate starch as they age to maintain phloem
upload of sucrose during senescence, when the photosynthetic
capacity will become severely limited. Resorption of carbohydrates occurs only at advanced stages of senescence (Collier
and Thibodeau 1995, Hoch et al. 2001), which were not seen
in our leaves. Amax remained high at all dates, and nitrogen differed between cohorts due to an incipient accumulation in CY
leaves rather than to a decline of nitrogen in PY ones.
Gas exchange in CY and PY leaf cohorts
Differences in gas exchange between cohorts did not vary significantly through the year of study, and most of the variability
Tree Physiology Volume 32, 2012
associated with age was effectively captured by the two categories established: 1–10 months old for CY and 11–21 months
old for PY.
Amax of leaves older than 1 year was relatively high, but still
these leaves had significantly lower Amax than CY ones. This
result confirms earlier findings at our site where the age cohort
was demonstrated to affect Amax and the apparent
photosynthetic capacity of Q. ilex leaves (Limousin et al.
­
2010). This age-related decline in photosynthesis is relatively
independent of leaf nitrogen content in Mediterranean evergreen oaks (Niinemets et al. 2004, 2005, 2006). Indeed,
Amax/N and Amax/gs were also lower in PY leaves than in CY
leaves, which suggests that differences in Amax between
cohorts were mostly unrelated to nitrogen concentration and
stomatal conductance. There are two explanations of this finding. The first one is related to an age-dependent variation of
mesophyll conductance. Niinemets et al. (2005) found an
increasing resistance to CO2 diffusion through the mesophyll
into the chloroplasts as leaves of Q. ilex aged, and argued that
this was an effect of the increased lignification of leaves.
However, the age effect on mesophyll conductance seen by
Niinemets et al. (2005) was not verified at our site in a previous study on the same species (Limousin et al. 2010). The
second explanation is that a greater fraction of nitrogen is
associated with cell walls in older and more lignified leaves, so
that less nitrogen is available for photosynthetic proteins
(Onoda et al. 2004, Niinemets et al. 2005).
Previous-year leaves exhibited lower rates of Rd than CY
ones. This difference was not linked to growth, as all leaves
were fully developed at the time of sampling, nor to the availability of carbohydrates, which was higher for PY leaves (see
above). On the contrary, the lower nitrogen concentration and
possibly the increasing allocation of nitrogen towards structural
components in PY leaves (Onoda et al. 2004, Niinemets et al.
2005) were likely related to a lower amount of r­espiratory proteins as compared with CY ones. This may imply that the old
cohort of leaves suffers a decline in respiratory capacity. A
reduction in the number of mitochondria could also underpin a
decline in the respiratory capacity, and it would be consistent
with differences in nitrogen concentration. Concomitant reductions in rates of Rd and the total or metabolically active number
of mitochondria have been observed in some species as leaves
develop and age (Armstrong et al. 2006, Gomez-Casanovas
et al. 2007). However, we should also consider potential differences in energy demand between cohorts for discussing differences in Rd. Lower energy demand in the older cohort of leaves
might arise from a lower turnover and repair of photosynthetic
and respiratory proteins (Ryan et al. 1996), and lower phloem
upload of sucrose during early summer (Giaquinta 1983, see
above). The hypothesis of an adenylate restriction of Rd in old
leaves was challenged by the inconsistent and non-significant
differences in Q10 between cohorts (Atkin et al. 2005).
Leaf age and respiration 473
Rd participates in the protection of leaves from oxidative
damage by reducing PS II over-excitation and helping repair
degraded proteins (Atkin et al. 2000). Since several components of the antioxidant system have been found to decline in
old, non-senescent leaves of trees (Polle et al. 2001), the
reduced Rd rate of PY leaves could lead to an increased
probability of old leaves suffering photodamage when they
­
experience stressful conditions (Kar et al. 1993, Hoch et al.
2001). However, an increased electron partitioning towards
the alternative respiratory pathway could occur in PY leaves in
relation to high levels of carbohydrates (see González-Meler
et al. 2001), something that was not addressed in our study,
but that should be considered in the context of protection from
oxidative damage in aging leaves.
The importance of considering leaf age when modeling
canopy respiration
Here we took into account the seasonality in Rd and the Q10 of
Rd when modeling whole-canopy CO2 release and, further,
incorporated the leaf age cohort as a parameter of variation in
Rd and Q10. Considering the respiratory characteristics of CY
and PY mature leaves allows capturing part of the variability
linked to age in the evergreen Q. ilex and improves the
­estimation of whole-canopy respiration relative to more simple
models assuming similar characteristics in all leaves. The
­
importance of including leaf age in the model comes from the
difference in the base rates of Rd between cohorts because,
as observed in previous works, the short-term temperature
sensitivity of Rd did not vary significantly (Panek 2004, Maseyk
et al. 2008). The concentrations of nitrogen and soluble sugars, two factors implicated in the temperature sensitivity of Rd
(Atkin et al. 2005), differed significantly between cohorts, but
the Q10 and acclimation of Rd did not. Contrary to the hypothesis that internal redistribution of resources would result in a
higher ­acclimation of Rd to the environment in younger than
older leaves that had already surpassed their mean life span, it
seems more likely that acclimation of Rd does not vary between
leaf cohorts because it has a minor metabolic cost and the
same physiological triggers for all leaves.
The difference in canopy leaf respiration of up to 27% that
arises from considering or not the particular Rd of each cohort
is important in terms of CO2 release (Table 6). Furthermore,
such difference is relevant at the ecosystem level, as leaves
accounted for 37% of ecosystem respiration. This value is
higher than previous estimations for Q. ilex forests (Reichstein
et al. 2002, Misson et al. 2010), but is close to the 36%
reported by Kosugi et al. (2005) for a Japanese evergreen
broadleaved forest, and the 35% reported by Maseyk et al.
(2008) in a Pinus halepensis Mill. forest for the sum of expanding and mature leaves. The foliar component of Reco varied
between 27% in early summer to 46% in late summer, suggesting that acclimation of leaf Rd to summer heat and drought
contributes to but is not the main factor for the typical summer
decline of Reco in Q. ilex forests. Allard et al. (2008) estimated
the mean NEE to be 254 g C m−2 year−1 in our study forest
stand, even though it typically became a source of CO2 by late
summer. Our data show that acclimation of the canopy leaf
respiration to heat and drought can save as much as
32 g C m−2 over the summer (i.e., 13% of annual NEE) and
contribute to enlarging the period over which the ecosystem
acts as a net carbon sink.
Taken together, these results underscore the need for predictive models of ecosystem respiration to incorporate leaf age
variation in Rd, for example, by including an age cohort scaling
factor for species with a seasonal flush type shoot growth pattern. More research is required though on the effects of age on
leaf physiology and responsiveness to abiotic factors in order
to improve up-scaling accuracy. The effect of age would also
be indirectly taken into account when up-scaling leaf nitrogen
vs. respiration relationships to the whole canopy, although we
have seen here that differences in Rd between age cohorts are
only partly explained by nitrogen.
Acknowledgments
We thank Jean Marc Ourcival and Alain Rocheteau for technical
assistance and María del Carmen del Rey for nitrogen analysis.
We acknowledge critical comments from two anonymous
reviewers.
Funding
J.R.-C. was partially supported by a postdoctoral fellowship
from the Spanish Ministry of Science and Innovation.
N.K.M.-S. was partially supported by a Ph.D. grant from the
French National Agency for Environment and Energy
Management (ADEME). This work was performed within the
framework of projects MIND (EVK2-CT-2002-000158) and
DROUGHT+ (ANR-06-VULN-003-01), and the EU IMECC I3
initiative (Infrastructure for Measurements of the European
Carbon Cycle 026188) and CARBO-Extreme GA no. 226701
(FP7/2007-2013).
Conflict of interest
None declared.
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Appendix: Description of the coupled leaf
photosynthesis–stomatal conductance model
used to estimate net daily carbon balance
We estimated net photosynthesis at 30 min intervals making
use of the analytical solution of Baldocchi (1994) for coupled
equations of (i) photosynthesis, (ii) stomatal conductance and
(iii) internal CO2 concentration described on mechanistic and
empirical models of Farquhar et al. (1980) and Ball et al.
(1987).
(i) The model of Farquhar et al. (1980) assumes that net
photosynthesis is the result of subtracting the rate of mitochondrial respiration under light conditions (Rlight) to the minimum value between the rate of carboxylation when ribulose
bisphosphate (RuBP) regeneration is limiting (Wj) and the rate
of carboxylation when RuBP carboxylase/oxygenase (Rubisco)
is limiting (Wc):
A = min(Wc ,Wj) − Rlight (A.1)
When the amount and activity of Rubisco limits carboxylation:
W c = Vc max
C i −Γ*
C i + K c(1 + O i / K o ) (A.2)
where Vcmax is the maximum rate of carboxylation, Ci is the
internal concentration of CO2, Oi is the internal concentration
of O2, Γ* is the CO2 compensation point in the absence of
Tree Physiology Online at http://www.treephys.oxfordjournals.org
476 Rodríguez-Calcerrada et al.
mitochondrial respiration, and Kc and Ko are the Michaelis–
Menten coefficients for carboxylation and oxygenation by
Rubisco, respectively. To estimate photosynthesis at different
times of the day, Baldocchi (1994) proposed an analytical
solution for a coupled photosynthesis and conductance model,
which requires that only Ci be unknown in the previous equation. Therefore, we calculated Vcmax from our punctual measurements of gas exchange using the one-point method
explained by Kosugi et al. (2003). The method assumes that
photosynthesis is limited by substrate and not by the regeneration of RuBP, which is the case at the 1500 µmol photons
m−2 s−1 of photosynthetically active radiation (PAR) used in our
measurements. Under these conditions:
Amax = Vc max
C i −Γ*
− Rlight (A.3)
C i + K c(1 + O i / K o )
Amax and Ci were measured values for each leaf, Oi was fixed at
20 kPa and values of Kc, Ko and Γ* at the temperature at which
Amax was measured were calculated from Bernacchi et al.
(2001). Rlight was estimated at the same temperature as Amax
from temperature response curves of Rd, considering a 50%
inhibition of the rates measured in darkness in illuminated
leaves (Villar et al. 1995). Vcmax was obtained after rearranging
the previous equation. Comparison of Vcmax obtained by the
one point method with values derived from A/Ci curves previously performed on Q. ilex trees from the same population (see
Limousin et al. 2010) showed a good correlation (intercept = −0.51, P = 0.4; slope = 0.90, r 2 = 0.92, P < 0.001).
When the regeneration of RuBP by electron transport limits
carboxylation:
Wj = J
C i −Γ*
(A.4)
4C i +8Γ *
where J is the effective electron transport that depends on PAR
as (Medlyn et al. 2002)
J=
1
(ϕPARJmax − (ϕPARJmax ) − 4θϕPARJmax (A.5)
2θ
In this equation θ is the curvature of the relationship between
electron transport rate and PAR, φ is the quantum yield after
accounting for leaf absorbance and Jmax is the maximum
potential rate of electron transport. The parameter θ was set
to 0.9 according to Medlyn et al. (2002) and φ was set to
0.24 based on the apparent quantum efficiency of photosynthesis (α) given by Tenhunen et al. (1990) and the relationship between α and φ given by Korzukhin et al. (2004). To
calculate Jmax we considered a constant Jmax to Vcmax ratio of
2.1 as reported previously for our site (Limousin et al. 2010),
Tree Physiology Volume 32, 2012
and used Vcmax values estimated by the one-point method
(see above).
(ii) The empirical model of Ball et al. (1987) is widely used
to estimate stomatal conductance (gs). It takes the following
form:
gs = g0 + m
ARH
(A.6)
Cs
In this equation g0 and m are the intercept and slope and represent, respectively, the cuticular resistance to water vapor and
an empirical fitted coefficient; RH is the air relative humidity;
and Cs is the concentration of CO2 at the leaf surface. Cs was
calculated as
Cs = Ca −
A
gb (A.7)
where Ca is the concentration of CO2 in the air and gb is the
boundary layer conductance to CO2, which was set at 4 m s−1
and modified according to the air temperature.
The coefficient m has been found to vary in some
Mediterranean species during summer in response to decreasing soil water potential (e.g., Sala and Tenhunen 1996), but not
in others (Xu and Baldocchi 2003). Here, we used a modified
version of the Ball et al. (1987) model, which takes into account
the effect of predawn leaf water potential (Ψpd) on stomatal
conductance regulation through a linear effect of Ψpd on m.
This decision was based on the observation that m—calculated as the slope of Eq. (A.6) using values of Amax, RH, gs and
Cs measured with the LI-6400—decreased with decreasing
Ψpd across dates. Thus at each date we computed gs as
gs = g0 + (a + bΨpd )
A RH
Ca − ( A gb ) (A.8)
with g0, and parameters a and b linking m to Ψpd, being determined independently for each cohort using least-squares nonlinear regression.
(iii) The internal concentration of CO2 (Ci) was computed
considering that CO2 diffusion through the leaf follows Fick’s
law:
Ci = C s −
A
gs (A.9)
Once we had a system of coupled equations, (A.1), (A.8) and
(A.9), where the only unknowns were A, gs and Ci, we estimated these three variables using the analytical solution of
Baldocchi (1994). Meteorological variables at half-hourly intervals were taken from a nearby eddy flux tower. Values of PAR
Leaf age and respiration 477
and air temperature were fed into Eq. (A.1). Values of air CO2
concentration and relative humidity were incorporated into Eq.
(A.8). Additional equations were included to account for the
temperature sensitivity of Jmax and Vcmax (Harley et al. 1992)
and of Kc, Ko and Γ* (Bernacchi et al. 2001). Parameters g0, a
and b in Eq. (A.8) were considered invariable through the day.
Rlight in Eq. (A.1) was estimated as a function of temperature
using Q10 values of Rd, and considering a 50% inhibition of
respiratory rates by light (Villar et al. 1995) during the day (i.e.,
when PAR > 10 µmol m−2 s−1). Half hourly values of net photosynthesis were finally integrated over the day time and converted into g C m−2 day−1. Leaf carbon balance was finally
estimated from the yield of CO2 resulting from photosynthesis
and respiration over 24 h.
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