Tree Physiology 32, 219–231
doi:10.1093/treephys/tpr141
Research paper
Temperature responses of leaf net photosynthesis: the role of
component processes
1Hawkesbury
Institute for the Environment, University of Western Sydney-Hawkesbury Campus, Locked Bag 1797, Penrith, NSW 2751, Australia; 2Department of Biological
Sciences, Macquarie University, North Ryde, NSW 2109, Australia; *Corresponding author: ([email protected])
Received October 3, 2011; accepted December 12, 2011; published online January 25, 2012; handling Editor Jörg-Peter Schnitzler
The response of photosynthesis to temperature is a central facet of plant response to climate. Such responses have been
found to be highly variable among species and among studies. Understanding this variability is key when trying to predict
the effects of rising global temperatures on plant productivity. There are three major factors affecting the response of leaf
net photosynthesis to temperature (An –T): (i) photosynthetic biochemistry, (ii) respiration and (iii) vapour pressure deficit (D)
and stomatal sensitivity to vapour pressure deficit during measurements. The overall goal of our study was to quantify the
relative contribution of each of these factors in determining the response of An to temperature. We first conducted a sensitivity analysis with a coupled photosynthesis–stomatal (An –gs) model, using ranges for parameters of each factor taken from
the literature, and quantified how these parameters affected the An –T response. Second, we applied the An –gs model to two
example sets of field data, which had different optimum temperatures (Topt) of An, to analyse which factors were most important in causing the difference. We found that each of the three factors could have an equally large effect on Topt of An. In our
comparison between two field datasets, the major cause for the difference in Topt was not the biochemical component, but
rather the differences in respiratory components and in D conditions during measurements. We concluded that shifts in An –T
responses are not always driven by acclimation of photosynthetic biochemistry, but can result from other factors. The D conditions during measurements and stomatal responses to D also need to be quantified if we are to better understand and
predict shifts in An –T with climate.
Keywords: climate warming, leaf net photosynthesis, leaf respiration, stomatal conductance, temperature response, vapour
pressure deficit
Introduction
In order to understand how climate affects the productivity
of plants, and to predict how climate warming may influence
CO2 uptake, a more detailed understanding of the controls on
leaf photosynthesis is needed (Woodward 1987, Cao and
Woodward 1998, Kirschbaum 2004). The typical response
of leaf net photosynthesis (An) to temperature (T) can be
described by a peaked surface (Fitter and Hay 2002), with low
photosynthesis at cool temperatures, increasing to a maximum rate at optimal temperatures and then decreasing again
at very high temperatures. This peaked temperature response
has been described many times in the literature for a wide
range of species (Berry and Björkman 1980, Kirschbaum and
Farquhar 1984, Battaglia et al. 1996, Fitter and Hay 2002).
In the last decade there has been a great increase in plant
and ecosystem warming experiments, many of which evaluate the response of photosynthesis to temperature (Battaglia
et al. 1996, Gunderson et al. 2000, Shaw et al. 2000,
Cunningham and Read 2002, Robakowski et al. 2002, Turnbull
et al. 2002, Niu et al. 2006, 2008, Way and Sage 2008,
© The Author 2012. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
(http://creativecommons.org/licenses/by-nc/3.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any
medium, provided the original work is properly cited.
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Yan-Shih Lin1*, Belinda E. Medlyn2 and David S. Ellsworth1
220 Lin et al.
Figure 1. ​Dependence of light-saturated net photosynthesis (An) on
temperature among different species from previous studies. Solid
black lines indicate a study on six bedding plants in Niu et al. (2008);
dotted black lines indicate a study on Agastache urticifolia, Cineraria
maritima, Petunia × hybrida and Plumbago auriculata in Niu et al.
(2006); dash-dot-dashed black line indicates a study on Picea mariana
in Way and Sage (2008); thin dotted black line indicates a study on
Quercus rubra in Gunderson et al. (2010); dash-dot-dot-dashed black
line indicates a study on Pinus cembra from Wieser et al. (2010); grey
solid lines indicate a study on Eucalyptus globulus and grey dotted
lines indicate a study on Eucalyptus nitens in Battaglia et al. (1996).
Tree Physiology Volume 32, 2012
processes is temperature dependent (Harley et al. 1992,
Leuning 2002, Medlyn et al. 2002a). The temperature dependences of these biochemical limitations have frequently been
described in the form of mathematical models based on the
Arrhenius equation for enzyme activity as a function of temperature (for Vc max), and a peaked function incorporating
effects of enzyme deactivation and conformational changes
with warming (for Jmax) (Harley et al. 1992, Medlyn et al.
2002a). The difference in temperature responses for Vc max and
Jmax occurs because the thylakoid membrane complex involved
in generating phosphorylated compounds for RuBP regeneration is more sensitive than the rate of carboxylation to high
temperatures, due to changes in whole-chain electron transport (Sage and Kubien 2007).
Differences in the An–T response among species or growth
conditions are commonly attributed to differences in the temperature dependence of these biochemical processes (Walcroft
et al. 1997, Hikosaka et al. 2006). Higher plants vary considerably in temperature sensitivities of Vc max and Jmax (Medlyn et al.
2002a, Kattge and Knorr 2007), which may potentially indicate adaptation in these responses. Changes in different
aspects of the temperature responses of Vc max and Jmax have
also been invoked to explain observed photosynthetic acclimation (June et al. 2004, Onoda et al. 2005, Hikosaka et al.
2006, Kattge and Knorr 2007). In particular, previous studies
have highlighted changes in the activation energy of Vc max, the
thermal optimum of Jmax and the ratio of Jmax to Vc max as parameters that can underlie acclimation to temperature (Hikosaka
et al. 1999, Wilson and Baldocchi 2000, June et al. 2004,
Onoda et al. 2005, Kattge and Knorr 2007).
In addition to the effects of temperature on photosynthetic
biochemistry, effects on respiratory and stomatal processes
may also be important. Net leaf photosynthesis is given by
gross photosynthesis less leaf respiration. Leaf respiration is
also a temperature-dependent process, and contributes to the
overall An–T response. It has been suggested that the ratio of
gross photosynthesis to dark respiration is lower in warmer
versus cooler conditions (Atkin and Tjoelker 2003). It has
therefore been hypothesized that increases in respiration with
temperature may become increasingly responsible for
decreased net photosynthesis under progressively higher
­temperatures (Kirschbaum 1999). Hence examination of
­mitochondrial respiration as a component of net photosynthesis is important to consider in the overall temperature response
of C3 plants.
Stomatal regulation of the internal CO2 concentration (Ci) is
the third key process determining the overall temperature
response. The role of stomatal conductance in regulating net
photosynthesis is well known (Wong et al. 1979). Stomata
may respond to temperature itself (Hendrickson et al. 2004,
Mott and Peak 2010), as well as the increase in vapour pressure deficit, D, that tends to accompany increasing t­ emperature
Downloaded from http://treephys.oxfordjournals.org/ at University of Western Sydney on September 17, 2014
Bronson and Gower 2010, Gunderson et al. 2010, Silim et al.
2010, Wieser et al. 2010). The A n –T responses are highly
variable among studies (Figure 1). This variability may be the
result of both acclimation to temperature, which involves shortto long-term changes at organismal level, and adaptation to
temperature, which involves evolutionary changes in different
environments. However, the mechanisms underlying variability
in An–T responses are not well understood, and acclimation
and adaptation are therefore not easily predictable.
There are three primary sets of processes that control the
An–T response, namely biochemical, respiratory and stomatal
processes. Much of the effort to date to understand variability
in An–T responses has focused on biochemical processes. The
two major biochemical processes thought to limit photosynthesis are the carboxylation of ribulose-1,5-bisphosphate (RuBP)
and electron transport photochemistry for the regeneration of
RuBP in the Calvin cycle (Farquhar et al. 1980). A third biochemical process, triose-phosphate utilization (TPU), can also
limit photosynthesis at high internal CO2 concentrations or with
chilling (Sharkey 1985, Sage and Kubien 2007). The biochemical model of C3 photosynthesis proposed by Farquhar et al.
(1980) has been widely applied to describe the temperature
dependence of component biochemical processes underlying
leaf photosynthesis (Long 1991, Dreyer et al. 2001, Medlyn
et al. 2002b, Kattge and Knorr 2007). In this photosynthesis
model, leaf photosynthesis is assumed to be limited by either
the maximum Rubisco carboxylation rate (Vc max) or the maximum RuBP regeneration rate (Jmax). Each of the biochemical
Temperature responses of photosynthesis 221
Materials and methods
Model
We used a coupled photosynthesis–stomatal conductance
(An–gs) model for our study. The standard biochemical model
of photosynthesis (Farquhar et al. 1980) was coupled to the
stomatal model proposed by Medlyn et al. (2011).
In the standard biochemical model of photosynthesis, the
net CO2 assimilation rate can be determined by either the
Rubisco-limited photosynthesis (Ac) or the RuBP regenerationlimited photosynthesis (Aj). We modelled these limitations to
photosynthesis following Medlyn et al. (2002a) and Kattge and
Knorr (2007). The TPU limitation was not considered in this
analysis, due to lack of parameters for the temperature
response of this limitation across species. This omission is
consistent with other studies of temperature acclimation of
photosynthesis (e.g., Medlyn et al. 2002a, 2000b, Kattge and
Knorr 2007).
Also following those studies, mesophyll conductance (gm) was
not explicitly included in our An–gs model. Although a role for gm
in temperature acclimation has been proposed (Warren and
Dreyer 2006), this component is difficult to quantify accurately
from A–Ci curves alone and is best estimated with multiple independent measures of photosynthetic performance, which were
not available for this study. In this study, therefore, Vc max and Jmax
are ‘apparent’ values, rather than actual, and their temperature
responses incorporate the temperature responses of gm.
For the temperature dependence of the Michaelis–Menten
coefficient of Rubisco and the CO2 compensation point in the
absence of mitochondrial respiration in the Farquhar model, we
used parameters proposed by Bernacchi et al. (2001) as
described in Medlyn et al. (2002a).
The temperature dependence of apparent Vc max and Jmax has
been described previously in Medlyn et al. (2002a) and
Leuning (2002). These temperature dependencies were modelled by two functions (see Medlyn et al. 2002a). The first
function is the standard Arrhenius function
 E (T − 298)  (1)
f(Tk ) = k25 exp  a k
 298RTk 
where Ea is the activation energy and k25 is the apparent Vc max
or Jmax value at 25 °C. R is the universal gas constant
(8.314 J mol−1 K−1) and Tk is the leaf temperature in K. The activation energy term Ea describes the exponential rate of rise of
enzyme activity with the increase of temperature. The second
function is a modified form of the Arrhenius function, which
yields a peaked function (Harley et al. 1992), and is given by
 E (T − 298)  1 + exp (298∆S − Hd / 298R )
f(Tk ) = k25 exp  a k
1 + exp (Tk ∆S − Hd / Tk R )
 298RTk 
(2)
where Hd is the deactivation energy and ΔS is the entropy
term. Hd describes the rate of decrease of the function above
the optimum.
In this study, the Arrhenius and peaked functions were used
to model the temperature dependence of apparent Vc max and
Jmax, respectively. In order to reduce the number of parameters
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(Sage and Sharkey 1987, Leuning 1995, Day 2000). Stomatal
regulation can affect the An –T response in two major ways.
First, stomatal aperture depends on the value of D during
measurements. In some experiments, for example those using
controlled systems (such as growth chambers), D may be held
constant. In other experiments, particularly field experiments,
such control over D may not be possible, resulting in increasing D with T. Additionally, D is determined by water vapour
content of the air, which can vary significantly among seasons
and environments. In warm environments or in summer, the
water vapour content of the air tends to be higher than in cool
environments or in winter, giving a higher dewpoint and, for a
given T, a lower D. The difference in D at a given T will change
An –T response through its effect on stomatal conductance.
There is considerable variation among woody plant species in
sensitivity to D, and some studies suggest that this sensitivity
may be dependent on growth temperature (Lloyd and Farquhar
1994, Medlyn et al. 2011). As such, there may also be acclimation of stomatal sensitivity to D that affects the An –T
response measured in different seasons. Both aspects—stomatal sensitivity to D and stomatal acclimation to D—can contribute to the role of stomatal regulation in the overall A n –T
response.
Thus, there are a set of processes that can contribute to the
overall temperature response of photosynthesis. In this study,
we aimed to quantify the relative roles of photosynthetic biochemistry, stomatal control and respiration in determining the
response of leaf net photosynthesis to temperature, using a
coupled photosynthesis–stomatal (An–gs) model. Firstly, we
carried out a comprehensive sensitivity analysis. We identified
the range of each relevant parameter value from the literature
and used this parameter range to quantify the relative sensitivity of the optimum temperature (Topt) of An to each process.
Second, we applied the coupled An–gs model to photosynthetic
data from two species measured in field experiments to identify the mechanistic causes underpinning differences in the
temperature response of An. The two datasets came from a
eucalypt species growing in NSW, Australia, and a pine species
growing in North Carolina, USA. The An–T response differed
between the two species. The model was applied in a stepwise
fashion to identify which processes contributed most to the
differences in observed temperature response between the
species.
222 Lin et al.
in the model to avoid over-parameterization, the deactivation
energy (Hd) of Jmax was assumed as a constant of 200 kJ mol−1
for the model fitting (Medlyn et al. 2002a).
The day respiration rate (Rd) in the model is calculated using
a standardized rate of respiration at 25 °C (Rd25) and a temperature response equation based on the Q10 coefficient (Ryan
1991, Aber and Federer 1992, Melillo et al. 1993, Schimel
et al. 1997, Cramer et al. 1999) given by
Rd = R25Q10(T −25)/10 (3)
g  A

gs = g0 + 1 + 1  n (4)

D  Ca
where g0 and g1 are model coefficients.
This model is similar in form to the widely used empirical
models of Ball et al. (1987) and Leuning (1995), but is based
on the theory of optimal stomatal behaviour (Cowan and
Farquhar 1977, Hari et al. 1987), so that parameter values are
interpretable. Medlyn et al. (2011) found that the intercept
parameter, g0, was not significantly different from zero or was
very close to zero across various tree species; thus we
assumed that g0 = 0 in our model. The slope parameter, g1, is
related to the marginal water cost of carbon to the plant. It is
also predicted to increase with growth temperature (Medlyn
et al. 2011).
The simultaneous solution for photosynthesis and stomatal
models proposed by Leuning (1990) was adapted for use in
this study.
Sensitivity analysis to component processes
We conducted a sensitivity analysis in order to evaluate how
each temperature-dependent parameter contributes to the
overall temperature dependence of net photosynthesis rate,
based on the coupled An–gs model described above. We simulated the An–T response curve for leaf temperature from 10 to
45 °C. For our baseline, we used biochemical and stomatal
parameters of Eucalyptus crebra (see below), and held D constant at 1 kPa. We then varied parameters from biochemical,
respiratory and stomatal components one at a time, and examined the resulting change in the An–T response curve. For each
parameter, the range of values was taken from previous
Tree Physiology Volume 32, 2012
Field data
We further investigated the contribution of different processes
in determining An–T responses by applying the coupled model
to two different sets of temperature response data obtained
from field-grown evergreen trees. We used published data
from Pinus taeda L. in the Blackwood Division of Duke Forest
(Orange County, NC, USA; 35 °58′N, 79 °5′W) (Crous and
Ellsworth 2004) and unpublished data from Eucalyptus crebra
F. Muell. in the Hawkesbury Forest Experiment (HFE)
Eucalyptus common garden site (Richmond, NSW, Australia;
33 °36′S, 150 °44′E). The Blackwood Division of Duke Forest
is located in a warm and humid climatic area with a mean daily
maximum temperature of 31.5 °C in the hottest month and a
mean daily minimum temperature of −3.5 °C in the coldest
month. The common garden site is located in a sub-humid temperate climate with a mean daily maximum temperature of
29 °C in the hottest month and a mean daily minimum temperature of 3 °C in the coldest month. The long-term mean
annual rainfall is 1118 and 801 mm for Blackwood Division of
Duke Forest and HFE common garden, respectively.
In each of these studies, the response of photosynthetic biochemistry to temperature was quantified by measuring An–Ci
responses at a range of temperatures. Field measurements of
the response of An to step changes in Ca were made using the
LI-6400 portable photosynthesis system (LI-COR, Inc., Lincoln,
NE, USA). The light source was set to 1800 µmol m−2 s−1 with
the LI-COR LED light source for both E. crebra and P. taeda,
thus allowing us to measure the potential maximum rate of An
as a function of different Ca levels at light saturation. In both
cases, measurements were begun at ambient Ca and then progressed through a series of stepwise changes in Ca to superambient, saturating Ca. The An–Ci responses were measured
on P. taeda needles in the upper canopy in December 2000
(i.e. midwinter) at four different temperature levels (10, 20, 28
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Here Q10 denotes the factor by which Rd increases for every
10 °C rise in the temperature T (°C), noting that the temperature response of Rd may not necessarily be equivalent to that of
Rdark (Atkin et al. 2000).
The photosynthesis model was coupled to the stomatal
model proposed by Medlyn et al. (2011). In this model, stomatal conductance is a function of net photosynthesis rate (An),
external air CO2 concentration (Ca) and leaf-air vapour pressure deficit (D), derived as (Medlyn et al. 2011)
­literature reviews (Table 1). Only values for woody plant species were used. The relative contribution of biochemical component processes was evaluated by altering Ea, Ha, ΔS and the
ratio of Jmax:Vcmax (J/V) at 25 °C over the ranges found in the
review by Kattge and Knorr (2007). For the respiratory component, the relative contribution was evaluated by altering the day
respiration rate at 25 °C (Rd25) over the range reported by
Reich et al. (1998), and the Q10 values over the range reported
by Tjoelker et al. (2001) (Table 1). For the stomatal component, g1 was evaluated over the range of stomatal coefficients
obtained for woody species by Medlyn et al. (2011). Several
alternative scenarios were developed for measurement D. In
two scenarios, D was held constant, as might occur if D was
controlled during measurements. The values of D selected
were 1 and 2.5 kPa. In two further scenarios, the dewpoint
was held constant (dewpoint at 8 and 24 °C), as might occur if
T was increased without attempting to control D.
Temperature responses of photosynthesis 223
Table 1. ​Parameter values used in sensitivity analysis. References for the parameter values are as follows: 1: Kattge and Knorr (2007); 2: Reich
et al. (1998); 3: Tjoelker et al. (2001); 4: Medlyn et al. (2011). The modelled range in values of Topt corresponding to the range in parameter values
is shown in the final column. The abbreviations are denoted as follows: Vc max: maximum Rubisco carboxylation rate; Ea: activation energy for Vc max;
Jmax: maximum RuBP regeneration rate; Ha: activation energy for Jmax; ΔS: entropy term; J/V: ratio of Jmax to Vc max; Rd25: leaf day respiration rate at
25 °C; Q10: factor by which the leaf day respiration rate increases for every 10° rise in the temperature; g1: stomatal model coefficient (Medlyn et al.
2011); D: leaf to air vapour pressure deficit.
Range of values
Ea
51.3
109.3
31.2
kJ mol−1
78
0.626
kJ mol−1 K−1
0.656
1.46
Unitless
2.72
0.4
μmol m−2 s−1
3.7
1.3
Unitless
2.98
3
Unitless
13.1
Constant D = 1 kPa
Constant D = 2.5 kPa
Constant dewpoint at 8 °C
Constant dewpoint at 24 °C5
kJ mol−1
Ha
ΔS
J/V2
Rd25
Q103
g1
D
1Out
Unit
Reference
1
Topt (°C)
Ac–Rd
Aj –Rd
An
21.1
27.6
27.6
27.6
34.6
27.6
21.2
36.8
32.8
30.3
26.1
29.8
25.7
25.9
29.8
27.64
25.3
22.3
24.1
21.1
28.4
26.3
26.3
26.3
26.3
26.3
26.3
29.3
24.9
28.6
24.8
24.4
29.2
26.34
23.8
19.9
24.1
1
1
1
1
2
3
4
Change in Topt
of An (°C)
26.3
26.3
26.3
26.3
26.3
26.3
29.3
24.9
28.6
24.8
24.4
29.2
26.34
23.8
19.9
24.1
7.3
0
0
0
4.4
3.8
4.8
2.5
5.4
2.2
of the simulated temperature range.
2V
c max held constant.
3Based on the temperature
range from 20–30 °C.
Topt for the sensitivity analysis.
5Simulated temperature range: 24.1–45 °C.
4Baseline
and 35 °C). Measurements on E. crebra leaves were made at
three temperature levels (15, 25 and 32 °C) in late August
2008 (i.e., late winter). In all cases, leaves were enclosed in
the leaf cuvette at the target temperature and ambient Ca level
for at least 30 min before An–Ci curve measurements were
conducted, and then allowed to equilibrate for another 30 min
at the new temperature prior to the next set of An–Ci curve
measurements. The order of temperatures used was either
progressive (for E. crebra) or random (P. taeda).
In addition to the An–Ci curve measurements, two independent sets of temperature responses of light-saturated net photosynthetic rate data were used to test the modelling results.
These measurements were made at saturated light and ambient atmospheric CO2 concentration. The light-saturated net
photosynthetic rate responses to changes in temperature were
measured on P. taeda from the Blackwood Division of Duke
Forest (Orange County, NC, USA) at a range of seasonal temperatures in December 1996 and 1997 as described in
Ellsworth (2000). A similar set of measurements was made on
E. crebra at the HFE Eucalyptus common garden site (Richmond,
NSW, Australia) at seven temperature levels (15, 20, 25, 30,
35, 40 and 45 °C) in August 2009.
Model application to data
The model was parameterized as follows. For E. crebra, the
photosynthetic parameters, apparent Vc max and Jmax, at different
temperatures were fitted to each An–Ci curve using leastsquares linear regression as described in Wullschleger (1993)
and Ellsworth (2000), respectively. The parameters for temperature responses of apparent Vc max and Jmax were then fitted
in SigmaPlot (v. 11.0, Systat Software Inc., Chicago, IL, USA)
based on Eqs. (1) and (2), respectively. For P. taeda, published
parameters for the temperature responses of apparent Vc max
and Jmax, obtained from the An–Ci data described above, were
used (Medlyn et al. 2002a). To characterize the day respiration
rate of two species, the rate of respiration at 25 °C (Rd25) was
fitted from 2008 An–Ci curves dataset for E. crebra, and taken
from Hamilton et al. (2001) for P. taeda. Since there is no study
directly quantifying the respiration Q10 value for E. crebra, we
assumed the value as 2, which is widely used in modelling the
respiration responses of temperature from leaf to ecosystem
scales (Ryan 1991, Aber and Federer 1992, Melillo et al. 1993,
Cox et al. 2000, Radtke and Robinson 2006, Atkin et al. 2008).
For the stomatal model, parameters for E. crebra were obtained
by fitting Eq. (4) to the 2009 An–T dataset described above.
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Parameter
224 Lin et al.
Biochemical components: Ea, Ha, ΔS and J/V
We first considered the effect of parameters describing temperature effects on the biochemical limitations to photosynthesis,
Respiratory components: Rd25 and Q10
An increase in Rd25 from 0.4 to 3.7 µmol m−2 s−1, and an
increase in Q10 from 1.3 to 2.98 µmol m−2 s−1, both reduce Topt
Results
Sensitivity analysis to component processes
Table 2. ​Parameter values used in field data analysis. Species are denoted as follows: EC, Eucalyptus crebra; PT, Pinus taeda. Standard deviations
are given for completeness but are not used in the analysis. Topt of An was fitted for field data using a quadratic equation.
Biochemical components
Vc max
EC
PT
Ea (SD)
kJ mol−1
62.99 (2.97)
60.88 (1.92)
Jmax
Vc max25
(SD) μmol m−2 s−1
87.68 (3.28)
57.05 (9.33)
References
Ha (SD)
kJ mol−1
31.19 (3.02)
37.87 (19.86)
Respiration components
Rd25
μmol m−2 s−1
Q10
μmol m−2 s−1
References
EC
PT
−2.47
−0.54
2
2.71
This study
Hamilton et al.
(2001)
EC
PT
Vapour pressure deficit
D
kPa
D = 0.378 × exp(0.0702 × T)
D = 0.245 × exp(0.0706 × T)
Tree Physiology Volume 32, 2012
Fitted from field data
Fitted from field data
Jmax25 (SD)
μmol m−2 s−1
170.83 (3.23)
98.5 (3.75)
ΔS
kJ mol−1 K−1
0.626
0.630
Stomatal
components
g0
mol m−2 s−1
g1
Unitless
References
0
0
4.3
4.8
This study
Medlyn et al.
(2011)
Fitted Topt of An
From field data
°C
16.2
24.5
This study
Medlyn et al.
(2002a,
2002b)
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To assess the relative contribution of biochemical components,
respiratory components and stomatal components to both
optimal temperature (Topt) and optimum rate of An, one main
parameter at a time was changed, using the published data
range across woody plant species. The results are shown in
Figure 2. The effect on Topt is shown in Table 1.
namely Ea, Ha and ΔS, and the ratio between the two major biochemical limitations, which is characterized by the ratio of Jmax to
Vc max at 25 °C. Figure 2a–d shows the effects of changes in
each of these parameters on the temperature response of An. An
increase in the activation energy of Rubisco, Ea, from 51.3 to
109.3 kJ mol−1 had a large effect on the temperature response,
increasing both Topt, from 21.1 to 28.4 °C (Table 1), and the
maximum rate of An, by 19%. On the other hand, changes in the
parameters describing the temperature dependence of RuBP
regeneration, Ha and ΔS from 31.2 to 78.0 kJ mol−1 and from
0.626 to 0.656 kJ mol−1 K−1, respectively, did not alter either Topt
or the maximum rate of An. A higher Ha, however, resulted in a
lower initial rate of An at lower temperature (<20 °C), and a
higher ΔS caused a reduction in An at higher temperature
(>30 °C). It is worth noting that whether altering Ea or Ha would
change Topt depends on which process is limiting at optimal temperature. The limiting process depends on the ratio of apparent
Jmax to Vc max (J/V). Since the baseline An–T curve in our sensitivity analysis is Ac-limited, it is not surprising that modifying Ea
causes a change in Topt whereas modifying Ha or ΔS does not.
An increase in the J/V ratio from 1.42 to 2.72 reduces the temperature at which the change in biochemical limitation occurs
(i.e., from Ac to Aj), from 26.4 to <10 °C. However, such a change
in the J/V ratio does not affect either Topt or the maximum rate of
An when other parameters are held constant (Table 1).
Parameters for P. taeda were taken from Medlyn et al. (2011),
who fitted Eq. (4) to a large dataset of stomatal conductance
measurements made at the Duke Forest site.
In this analysis, we first simulated the An–T curve for each
species based on these parameterizations, for temperatures
ranging from 10 to 45 °C. The model was driven by measured
values of D at each temperature. Then, individual sets of parameters (biochemical, respiratory and stomatal parameters, and
measurement D values) were changed from one species value
to the other species value, one set of parameters at a time, to
evaluate the relative contribution of each component to the
overall temperature dependence of An (Table 2). Our goal in
this analysis was to identify the key process(es) causing the
differences in temperature response between the two species.
Temperature responses of photosynthesis 225
significantly. The change in Rd25 has the largest effect on Topt,
causing a decrease from 29.3 to 24.9 °C, while the change in
Q10 causes a reduction in Topt from 28.6 to 24.8 °C (Table 2).
However, the major effect of higher Rd25 or Q10 on the temperature response is a large reduction in An at higher temperature (>35 °C) (Figure 2e and f).
Stomatal components: g1 and D
The stomatal parameter g1 also has a large effect on the temperature response curve. An increase in g1 from 3.0 to 13.1
caused an increase in Topt from 24.4 to 29.2 °C, and the maximum rate of An increased from 14.8 to 22.7 µmol m−2 s−1.
In contrast, increasing D tends to lower both Topt and the
maximum rate of An. Consequently, assuming a temperaturedependent D in the model analysis markedly lowers Topt of An
compared with the Topt when D is held constant. The Topt of An
when D is held constant at 1 or 2.5 kPa is 26.3 and 23.8 °C,
respectively. In contrast, if D is assumed to vary with T, with a
dewpoint at 8 or 24 °C, Topt is reduced to 19.9 or 24.1 °C,
respectively.
Tree Physiology Online at http://www.treephys.oxfordjournals.org
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Figure 2. ​Simulated temperature dependency of light-saturated leaf photosynthesis (An) under different ranges of (i) biochemical components: Ea,
Ha, J/V, ΔS (a, b, c, d); (ii) respiration components: Rd25, Q10 (e, f); (iii) stomatal components: g1 (g); and (iv) vapour pressure deficit: D (h).
Parameter ranges for sensitivity analysis are given in the figure and were chosen based on previous studies on woody plant species (Reich et al.
1998, Kattge and Knorr 2007, Medlyn et al. 2011). Photosynthetic and stomatal parameters for the model simulation baseline are given in Table 2
(for E. crebra) with constant D = 1 kPa. Black lines denote the limited processes for An, and grey lines denote the non-limited processes for An.
226 Lin et al.
Overall, each of the component processes was equally
important to the shift of Topt of An. While varying respiratory
components (Rd25 and Q10) changed Topt of An to around 3.8–
4.4 °C in the sensitivity analysis, varying D and Ea altered Topt
of An up to 6.4 and 7.3 °C, respectively (Table 1).
Field data example
Tree Physiology Volume 32, 2012
Figure 3. ​Dependence of light-saturated net photosynthesis (An) on
temperature, according to model simulations and field data.
Relationships are shown for Eucalyptus crebra (a) and Pinus taeda
(b). Solid lines indicate the model results derived from photosynthetic,
stomatal and vapour pressure deficit parameters for each species
given in Table 2. Open circles and triangles indicate field data. Other
lines indicate simulation results when one set of component parameters is changed to that for the other species (red: vapour pressure
deficit; green: biochemical parameters; yellow: respiratory parameters;
blue: stomatal conductance parameter).
1.3 °C increase for E. crebra and a 2.2 °C reduction for P. taeda.
However, the parameters for the biochemical component for
the two datasets were very similar, so there was ­virtually no
difference in An–T responses when swapping the biochemical
component.
Discussion
Photosynthetic responses to temperature are central to plant
carbon balance, particularly in different climates or under different thermal regimes. However, An –T responses are very
variable among studies (Leuning 2002, Medlyn et al. 2002a,
Katul et al. 2003, Kattge and Knorr 2007) and among species (Figure 1). Understanding and quantifying this variability
is crucial when trying to predict the effects of rising global
temperatures on plant productivity. However, as yet, we have
a poor quantitative understanding of the mechanisms causing
this variability (Dreyer et al. 2001, Medlyn et al. 2002a,
Hikosaka et al. 2007). There has been some progress made
in understanding how the biochemical component of photosynthesis acclimates to growth temperature (Medlyn et al.
2002a; Hikosaka et al. 2006; Kattge and Knorr 2007).
Downloaded from http://treephys.oxfordjournals.org/ at University of Western Sydney on September 17, 2014
We now examine an example comparison between two sets of
field data for E. crebra and P. taeda. Responses of An to temperature were measured in field conditions and are shown in
Figure 3. The Topt of An was estimated from these data by quadratic fitting and differed considerably between the species:
values obtained were 16.2 and 24.5 °C for E. crebra and
P. taeda, respectively.
As shown in Table 2, there were several differences between
the species that could have contributed to the difference in
Topt. There were small differences in the parameters for the
biochemical (Ea, Ha and ΔS) and stomatal components (g1).
There was a large difference in the respiratory component:
E. crebra had nearly five times higher Rd25 than P. taeda. In
addition, the measurement D differed between the experiments. Both were field datasets in which D was not well
­controlled: the dewpoint was approximately constant and D
increased with warming temperature. The dewpoints of the
two datasets were approximately 8 and 16 °C for E. crebra and
P. taeda, respectively (Table 2). This difference in dewpoint
resulted in a higher D of approximately 0.75 kPa across the
temperature range for E. crebra.
We applied the An–gs model to these two datasets to analyse why Topt differed, and to quantify the role of each of the
different components.
First, the Topt of An was 20.9 and 23.2 °C according to simulated An–T curves for E. crebra and P. taeda, respectively
(Table 3; Figure 3). Simulated Topt values were thus lower for
E. crebra, as were measured Topt values. For both E. crebra and
P. taeda, we changed each parameter, one set at a time, to the
value for the other species and observed the effect on simulated Topt (Table 3, Figure 3).
This sensitivity analysis shows that the difference in the respiration rate between the two species was the most important
factor contributing to the difference in An–T response between
the two species. Changing the respiration rate changed Topt of
An by 1.9 °C lower and 1.1 °C higher for P. taeda and E. crebra,
respectively. This result is not surprising, since Rd25 for E.
­crebra is nearly five times higher than that for P. taeda.
The relative contributions of g1 and D were also important in
An–T responses, despite the fact that there are only relatively
small differences in the two parameters. The small change in
g1 from 4.3 to 4.8 changed Topt of An by 0.7 °C, from 20.9 to
21.6 °C in E. crebra and from 23.2 to 22.5 °C in P. taeda.
Swapping the different D conditions between the two datasets
also resulted in a large shift in Topt of An, for both species—a
Temperature responses of photosynthesis 227
Table 3. ​Sensitivity analysis of the response of An and its components for two sets of field data. For both E. crebra and P. taeda, each parameter
was changed, one set at a time, to the value for the other species (Table 2), and the effect on simulated Topt was observed.
Species
Simulated from the An –gs model
Sensitivity analysis
Topt
Changed
parameters
Ac (°C)
Aj (°C)
An (°C)
EC
22.6
25.0
20.9
PT
24.1
29.8
23.2
However, as demonstrated in our study, there are three major
components affecting the A n –T response, each of which may
significantly affect the An –T response (Tables 1 and 2; Figures
2 and 3). Thus we need to understand and quantify the role
of each of these components to be able to predict A n –T
responses.
Vapour pressure deficit (D)
A key point demonstrated in our sensitivity analysis is that
differences in measurement D alone can change the Topt
(Table 1; Figure 2h), without any acclimation of plant function.
As measurement D increases, stomata close, causing a
reduction in Ci, which results in a decrease in temperature
optimum (Sage and Kubien 2007). This effect of measurement D has important implications for the interpretation of
shifts in A n –T responses. For example, in warmer climates or
warmer seasons, dewpoint is generally higher, meaning that
at a given temperature, D is lower. Many examples of changes
in Topt with season or climate may be at least partially due to
this shift in dewpoint and associated D, rather than any acclimation to growth temperature. For example, Baldocchi et al.
(2001) found that the temperature optimum for canopy CO2
uptake increases with mean summer temperature (their
Figure 9), and interpreted this change as an adaptive
response to climate. However, increasing dewpoint with
increasing summer temperature could also explain this
response.
Similarly, shifts in Topt observed in warming experiments do
not necessarily indicate plant acclimation to temperature. The
effect of changes in D on temperature optima needs to be first
quantified and factored out, before it can be concluded that
acclimation has occurred. Different experiments take different
approaches to the control of D. In some studies D is fully controlled and may be held constant (e.g., using a climate chamber
to achieve the desired ambient air condition) (Dreyer et al.
2001, Cunningham and Read 2006), but such studies typically
Ac (°C)
Aj (°C)
An (°C)
21.9
22.6
23.4
24.1
24.9
24.1
23.4
21.8
26.8
25.0
25.9
26.6
28.3
29.8
29.1
28.1
20.2
22.0
21.6
22.2
23.9
21.3
22.5
21.0
Change in
Topt of An (°C)
−0.7
+1.1
+0.7
+1.3
+0.7
−1.9
−0.7
−2.2
only allow small plants to be studied, and often do not track
realistic diel or seasonal temperature variations. In other studies, temperature is manipulated while holding air water vapour
content constant, which is effectively the same as holding
dewpoint constant. As shown in Figure 2h, these two
approaches would result in markedly different values for Topt of
An. Thus, values of Topt from these two types of study cannot
easily be directly compared.
Current climate change scenarios suggest that relative
humidity is likely to remain constant in future (CSIRO 2007,
Amthor et al. 2010), which would result in a different D–T relationship from either of these two types of study. To predict Topt
under such conditions, we either need experiments that
attempt to maintain relative humidity constant, or else we need
to quantify responses to T and D separately so that these can
be combined to make predictions for a range of possible future
conditions. The latter approach is clearly more flexible.
It is very difficult to control D with experimental warming
especially in the field, particularly under a wide range of
desired temperatures. Nevertheless, it is crucial to know the
D–T relationship in a given set of measurements if we are to be
able to correctly interpret measured photosynthetic responses
to temperature. Unfortunately, many warming experiments fail
to adequately monitor or report D (Saxe et al. 2001, Aronson
and McNulty 2009). If warming experiments are to inform
models of plant temperature responses, D must be routinely
monitored and reported.
Sensitivity of stomatal conductance to D
Stomatal conductance, through its control of intercellular CO2
concentration (Ci), is clearly a key element mediating the overall temperature response of An (Katul et al. 2000). It appears
that gs is more sensitive to the change of D with rising temperature than to temperature itself, because the Ci:Ca ratio
tends to remain constant if D is held constant independent of
temperature (von Caemmerer and Farquhar 1981, Ehleringer
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Ea, Ha, ΔS
Rd25, Q10
g1
D
Ea, Ha, ΔS
Rd25, Q10
g1
D
Topt
228 Lin et al.
Respiratory processes
Day respiration rate (Rd) is generally a small fraction of An, and
therefore at most physiological temperatures it would not be an
important control over net photosynthesis. However, in the case
of species with low photosynthetic rate, such as spruce species,
much of the differences in An–T responses can be attributed to
changes in Rd (Way and Sage 2008). In addition, certain conditions could be expected to increase the ratio of Rd to An and
therefore increase the importance of the temperature response
of Rd. The temperature response of day respiration rate (Rd) is
commonly described by a temperature coefficient model (Q10;
Eq. (3)) with a constant Q10 value of 2.0 to e­stimate the
t­ emperature-dependent Rd (Ryan 1991, Aber and Federer 1992,
Melillo et al. 1993, Schimel et al. 1997, Cramer et al. 1999).
However, long-term (weeks to months) temperature acclimation
of Rd reported by previous studies (Atkin et al. 2000, Tjoelker
et al. 2001, Ow et al. 2008, Tjoelker et al. 2009) suggests that
a temperature-dependent Q10 value might better estimate the Rd
at higher temperature. Despite potentially overestimating Rd at
higher temperature by using a constant Q10 value in our study,
our results demonstrated that the offset contribution of Rd to An,
Tree Physiology Volume 32, 2012
as well as to Topt of An are equally important compared with
other process components considered in the study (Table 1;
Figure 2e and f). Thus the role of respiratory component should
also be stressed when analysing An–T responses.
Biochemical processes
The key biochemical component processes of photosynthesis
are (i) carboxylation of RuBP, characterized by the maximum
Rubisco activity Vc max, and (ii) electron transport and RuBP
regeneration, characterized by the maximum electron transport rate Jmax. Some progress has been made in quantifying
temperature acclimation and adaptation of these processes.
In most species, the dependence of apparent Vc max on temperature can be described by an Arrhenius function, because
it increases near-exponentially over a wide range of temperatures and does not deactivate until very high, near-lethal temperatures (>50 °C; Eq. (2)) (Leuning 2002, Medlyn et al.,
2002a, Salvucci and Crafts-Brandner 2004). Previous studies
suggest that the activation energy (Ea) of apparent Vc max
increases with growth temperature (Medlyn et al. 2002a,
Hikosaka et al. 2006, Hikosaka et al. 2007, Kattge and Knorr
2007). This change in Ea with growth temperature has been
ascribed to changes in the mesophyll CO2 conductance gm
(Makino et al. 1994, Law and Crafts-Brandner 1999, Bernacchi
et al. 2002, Salvucci and Crafts-Brandner 2004, Warren and
Dreyer 2006). The increase of Ea for apparent Vc max is likely to
result in the increase of Topt of An in some species, as shown
in the sensitivity analysis (Figure 2a). Hikosaka et al. (2006)
suggests an increase in Topt by 0.54 °C per 1 kJ mol−1 increase
in Ea for Plantago asiatica; however, we found a much smaller
fraction increase in Topt in tree species (0.13 °C per 1 kJ mol−1
increase in Ea) based on our An –gs model (Figure 2a).
Different Ha or J/V might change the temperature at which
the limiting step shifting occurred. Aj may be the limiting step
for leaf photosynthesis under certain conditions, such as under
elevated CO2 or measuring at temperature lower than growth
temperature (Sage 1990, Onoda et al. 2005, Hikosaka et al.
2006). However, this does not change the Topt of An nor the
optimum rate of An (Figure 2b–d). Given that Aj is limiting at
much higher Ci in plants (Kirschbaum and Farquhar 1984) and
that Aj-limited photosynthesis may occur at temperatures lower
than Topt of An (Onoda et al. 2005), Ac may often be the limiting
step for light-saturated An at Topt at ambient CO2 (Figure 2a–d).
Conclusions
Using a coupled photosynthesis–stomatal conductance model,
we investigated the role of the three key components involved
in determining the temperature responses of photosynthesis:
photosynthetic biochemistry, respiration and stomatal sensitivity to D. We found that each of the three processes was quantitatively important, suggesting that each needs to be quantified
Downloaded from http://treephys.oxfordjournals.org/ at University of Western Sydney on September 17, 2014
and Cerling 1995, Leuning 1995). However, stomatal conductance may also acclimate to changes in growth temperature.
While previous studies have discussed the mechanisms
involved in temperature acclimation of the photosynthetic biochemistry (Medlyn et al. 2002a, Hikosaka et al. 2006, Kattge
and Knorr 2007), very little attention has been paid to the
question of whether stomatal conductance also acclimates to
environmental conditions. As shown in Figure 2g, changes in
the stomatal conductance–photosynthesis relationship (g1
parameter) can have a major impact on the An–T relationship.
We found that a small difference in this parameter between
Pinus taeda and Eucalyptus crebra could have caused a 0.7 °C
difference in Topt (Table 3).
There are theoretical reasons for expecting acclimation in g1
in response to temperature (Medlyn et al. 2011). The very limited amount of data available thus far suggests that acclimation
of the stomatal sensitivity to D may occur. For example, Lloyd
and Farquhar (1994) found that, at a given D, the Ci:Ca ratio
was lower in cool environments than in warm temperate environments. Consistent with this observation, Medlyn et al.
(2011) observed that the g1 parameter appeared to increase
with growth temperature. Leuning (1990) also reported an
increase in the slope of the Ball–Berry stomatal conductance–
photosynthesis relationship between spring and summer in
field-grown Eucalyptus grandis. These observed changes all act
in the direction of causing higher optimal temperatures for
photosynthesis. Further experimental studies are needed to
investigate whether acclimation of stomatal conductance and
the Ci:Ca ratio occurs, and to what extent such acclimation can
explain changes in the Topt of An.
Temperature responses of photosynthesis 229
Funding
We acknowledge funding by the Australian Government’s
Department of Climate Change and the Department of
Agriculture, Fisheries and Forestry for the Hawkesbury Forest
Experiment. The senior author was partially supported by a UWS
International Postgraduate Research Scholarship with UWS
International Award (UWSIPRS/UWSIA) and a National Climate
Change Adaptation Research Facility (NCCARF-PIARN) scholarship from The Primary Industries Adaptation Research Network.
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