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TRANSCRIPT
The validity of optimal leaf traits modelled on environmental
conditions
Keith J. Bloomfield*1, I. Colin Prentice2,3, Lucas A. Cernusak4, Derek Eamus5, Belinda E. Medlyn6, Rizwana Rumman5, Ian J. Wright2, Matthias M. Boer6, Peter Cale7, James Cleverly5,8, John J.G. Egerton1, David S. Ellsworth6, Bradley J. Evans9, Lucy S. Hayes1, Michael F. Hutchinson10, Michael J. Liddell11, Craig Macfarlane12, Wayne S. Meyer13, Henrique F. Togashi2, Tim Wardlaw14, Lingling Zhu1,15 and Owen K. Atkin1,15
1Division of Plant Sciences, Research School of Biology, Building 46, The Australian National University, Canberra, ACT 2601, Australia; 2Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia; 3AXA Chair of Biosphere and Climate Impacts, Grand Challenges in Ecosystems and the Environment and Grantham Institute—Climate Change and the Environment, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK; 4Department of Marine and Tropical Biology, James Cook University, Cairns, Qld 4878, Australia; 5School of Life Sciences, University of Technology Sydney, NSW 2007, Australia; 6Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW 2751, Australia; 7Australian Landscape Trust, Renmark, SA 5341, Australia; 8TERN (Terrestrial Ecosystem Research Network, University of Technology Sydney); 9Faculty of Agriculture and Environment, Department of Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia; 10Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia; 11Centre for Tropical, Environmental, and Sustainability Sciences, James Cook University, Cairns, Qld, Australia; 12CSIRO Land and Water, Private Bag 5, Wembley, WA 6913, Australia; 13Earth and
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Environmental Sciences, University of Adelaide, Adelaide, SA 5064, Australia; 14ARC Centre for Forest Value, University of Tasmania, Hobart, TAS 7005, Australia; 15ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, Building 134, The Australian National University, Canberra, ACT 2601, Australia.
* Corresponding author: [email protected]
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Number of Figures: 6 (plus 7 in Supporting Information)
Number of Tables: 3 (plus 2 in Supporting Information)
Number of References: 69
Total Word count: 6755 (excluding Summary, References, Figures and Tables)
Summary: 200 words
Introduction: 1793 words
Materials and Methods: 1995 words
Results: 931 words
Discussion: 1946 words
Acknowledgements: 90 words
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Summary
The ratio of leaf intercellular to ambient CO2 (χ) is modulated by stomatal conductance (gs). These quantities link carbon (C) assimilation with transpiration, and along with photosynthetic capacities (Vcmax and Jmax) are required to model terrestrial C uptake. We use optimisation criteria based on the growth environment to generate predicted values of photosynthetic and water-use efficiency traits and test these against a unique dataset.
Leaf gas-exchange parameters and carbon isotope discrimination were analysed in relation to local climate across a continental network of study sites. Sun-exposed leaves of 50 species at seven sites were measured in contrasting seasons.
Values of χ predicted from growth temperature and vapour pressure deficit were closely correlated to ratios derived from C isotope (δ13C) measurements. Correlations were stronger in the growing season. Predicted values of photosynthetic traits, including carboxylation capacity (Vcmax), derived from δ13C, growth temperature and solar radiation, showed meaningful agreement with inferred values derived from gas-exchange measurements. Between-site differences in water use efficiency were, however, only weakly linked to the plant’s growth environment and did not show seasonal variation.
These results support the general hypothesis that many key parameters required by Earth system models are adaptive and predictable from plants’ growth environments.
Keywords:
aridity, photosynthesis, stable isotopes, stomatal conductance, temperature, water-use efficiency.
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Introduction
All healthy leaves ultimately fulfil the same functions of light capture and gas exchange. In C3 plants, the ratio (χ) of leaf intercellular (Ci) to ambient (Ca) partial pressure of CO2 depends on a balance between stomatal conductance (gs) and photosynthesis, which in turn depends on Ci, on leaf-level capacities for carboxylation (Vcmax) and electron transport (Jmax), and on temperature-dependent kinetic constants. We can have confidence in our ability to measure and monitor Ca, but knowledge of associated Ci - made possible by reliable estimates of χ - is crucial to understanding the leaf’s carbon economy. There are large variations in χ, gs, Vcmax and Jmax across species and environments (Schulze et al., 1994; De Kauwe et al., 2016; Wang et al., 2017b). Faced with huge global diversity in plant species (and the morphology, physiology and longevity of leaves) there is great benefit in quantifying unifying trends that may be widely applied in global-scale models. The worldwide leaf economics spectrum (LES) is a notable example (Wright et al., 2004), most importantly describing a trade-off between specific leaf area (the ratio of leaf area to dry mass) and leaf longevity. Considerable variation in the gas exchange parameters listed above is, however, unrelated to the LES. Given the fundamental importance of these parameters for modelling land-atmosphere carbon and water exchange, it is vital that tools be developed to predict variations in χ, gs, Vcmax and Jmax.
Current models of vegetation and land-surface processes, including those embedded in Earth system models (ESMs), typically prescribe values of Vcmax and Jmax for each plant functional type (PFT) and use empirical models to predict χ and gs. This approach has major limitations because (a) there is at least as much variation in Vcmax and Jmax within as among PFTs (e.g. Verheijen et al., 2013; Rogers, 2014) and (b) the empirical models require the specification of further parameters for PFTs. An alternative approach is to seek general functional relationships that allow these photosynthetic parameters to be predicted from environmental information (Reich et al., 2007; Ali et al., 2015). A promising route to
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defining such relationships is provided by optimality hypotheses, couched in quantitative terms that allow testing against field measurements (Mäkelä et al., 2002; Prentice et al., 2014; Prentice et al., 2015). There is a need to test the predictions emanating from such models against empirical data drawn from as wide as possible a range of natural settings.
To balance water loss against carbon gain, the plant must control gs
which modulates both rates. When stomata are open, the leaf-to-air vapour pressure deficit (D) provides the gradient that drives transpiration (E). Ratios of carbon assimilation (A) to E, provide one measure of the plant’s water use efficiency (WUE) and for C3 plants vary from 2 to 11 mmol C mol-1 H2O (Lambers et al., 2008), illustrating the high water cost of C gain. Cowan and Farquhar (1977) introduced an optimality hypothesis for stomatal regulation, defining a parameter λ that may be thought of as an exchange rate between water and C. Their hypothesis proposes that, over the course of a single day, optimal stomatal behaviour minimises E for a given A such that the relative sensitivities of E and A to changes in gs remain constant:
∂ E/∂ gs∂ A /∂ gs
=∂ E∂ A
=λ (1)
Cowan and Farquhar pointed out that this exchange rate must vary with ontogeny and changing environment – no single value could be appropriate at all life stages and under all conditions. Subsequent controlled-environment studies across a range of tree species have confirmed that estimated values of λ, derived from empirical models, vary with leaf temperature (T), D, irradiance, soil water content and atmospheric pressure (Lloyd, 1991; Thomas et al., 1999; Wang et al., 2017a). But as λ is not directly measurable, Eqn 1 lacks useful predictive power over time scales longer than the diurnal cycle. In consequence, it has not been adopted in models, which instead typically constrain gs
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using the empirical observations of a strong positive correlation with A (Wong et al., 1979) and a strong negative response to D.
Recent studies have attempted to combine theoretical and empirical approaches. Medlyn et al. (2011) presented a unified model of optimal stomatal conductance (gs
¿):
gs¿ ≈ g0+1.6 (1+ g1
√D ) ACa
(2)
where g0 and g1 are parameters that can be estimated using gas-exchange measurements with varying D. Eqn 2 is an approximate solution of Eqn 1 under electron-transport limitation of photosynthesis (adopted on the grounds that guard cells controlling stomatal aperture have markedly greater capacity for electron transport than for Rubisco C fixation) (Medlyn et al., 2011). The model’s slope parameter g1 is proportional to the product of λ and the photorespiratory CO2
compensation point (Γ*). The value of g1 was found empirically to vary among PFTs and to increase with growth temperature in line with the increase in Γ* (Medlyn et al., 2011; Lin et al., 2015). In a parallel result obtained by Katul et al. (2010) for Rubisco-limited photosynthesis, optimal stomatal behaviour described by Eqn 1 yields a similar response to D but a different response to Ca (see discussion by Medlyn et al., 2013).
From a modelling perspective, however, the chief difficulty with Eqn 2 is that we have no a priori derivation of the parameter values g0 and g1. Several alternatives to the Cowan-Farquhar hypothesis have been proposed, but because rates of A and E respond independently to so many variables a fully mechanistic understanding of the costs of stomatal opening is still lacking (Dewar et al., 2018). Sperry et al. (2016) and Wolf et al. (2016) considered an optimization criterion whereby an explicit carbon cost is assigned to the risk of hydraulic failure associated with water transport. Wolf et al. (2016) further critiqued the Cowan-Farquhar criterion on theoretical grounds, as its implicit water cost is
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based on the loss of water from a store shared by all plants – ignoring competition between plants for the same water violates the criterion for an evolutionarily stable strategy. Prentice et al. (2014) introduced an optimization criterion that assigns explicit carbon costs to the maintenance capacities for both water transport and carboxylation, and assumes that plants minimize the fraction of assimilation assigned to the sum of these costs. This criterion leads to an optimal value of χ (χo):
χo ≈ ξ(ξ+√ D )
withξ=√( bK1.6 a ) (3)
where K is the effective Michaelis-Menten coefficient of Rubisco (i.e. reflecting the enzyme’s twin affinities for CO2 and O2), and a and b are empirical cost factors for water transport and carboxylation respectively. Mathematically, equation (3) has a similar form to Eqn 2. If g0 in Eqn 2 is ignored, re-arrangement yields the same optimal value of χ as Eqn 3 with ξ = g1. But importantly ξ (again a parameter related to the water cost of carbon gain) now depends on K rather than Γ*, and the term λ has been replaced by the ratio b/a, which can be estimated empirically from instantaneous gas exchange measurements (as in Lin et al., 2015) or stable carbon isotope ratios (δ13C). χo increases with T because of the temperature-dependency of K (Bernacchi et al., 2001) and because the viscosity of water has an inverse relationship with T, so that the cost factor for water transport (a) decreases with T (Prentice et al., 2014). Wang et al. (2017b) showed that χo is a function of growth D, T and altitude (z) (Wang et al., 2017a) and can be represented in a linearized form around T = 25˚C as follows:
ln [ χ o/ (1− χo ) ]=1.189+0.0545 (T−25 )−0.5 ln D−0.0815 z (4)
in which the constant 1.189 (dependent on b/a) was estimated from a large global set of δ13C measurements, while the predicted coefficients of (T – 25), lnD and z in equation (4) lay within confidence intervals estimated empirically from the δ13C dataset. Under standard growth
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conditions (T = 25°C, D = 1 kPa, z = 0 m), Eqn 4 yields χo = 0.77 and this value decreases with D, increases with T, and decreases with z.
Eqn 4 provides testable predictions of χ, and therefore of Ci. But to predict A using the standard biochemical model of C3 photosynthesis (Farquhar et al., 1980; the FvCB model) as generally employed in ESMs, we also need to know the carboxylation capacity (Vcmax) and the rate of electron transport (J). Here we introduce another long-standing optimality hypothesis, the co-limitation or co-ordination hypothesis (e.g. Chen et al., 1993; Maire et al., 2012), which states that the two limiting rates of photosynthesis tend toward equality under average daytime conditions. By equating these two co-limiting photosynthetic rates and making the simplifying assumption that J is proportional to photosynthetically active radiation (PAR) absorbed by the leaf (IL), Dong et al. (2017) derived an approximation of optimal Vcmax:
V cmax ≈ φ0 I L(Ci+K )
( Ci+2 Γ¿)(5)
where φ0 is the intrinsic quantum efficiency of photosynthesis, here taken to equal 0.085 (Collatz et al., 1991). The description of the leaf light term (IL) is given in Methods (below). Pursuing the co-ordination hypothesis further, the ratio of the rate of maximal electron transport (Jmax) to Vcmax also points to investment strategies within the leaf (Poorter & Evans, 1998) that may in turn be influenced by the environmental conditions experienced by the plant. Further derivations in Wang et al. (2017b) yield an optimal ratio:
Jmax
V cmax= 4
(C i+ K )∙
3√ (C i−Γ¿ ) ∙(C i+2 Γ¿)2
0.41(6)
where the constant 0.41 was estimated by assuming that typical values for the ratios Jmax/Vcmax (1.88) and χ (0.8) correspond with standard growth conditions (see references in Wang et al.).
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In the current study, by employing independent methods for estimating the leaf’s Ci, we set out to test the validity of these foregoing predictions. In seeking to validate the models explicitly related to stomatal behaviour (parameters g1 and χ), we focus on the estimates derived using time-averaged data (δ13C rather than instantaneous gas exchange) as the more useful test for dynamic vegetation models on similar grounds to those proposed above, arguing that a constant λ is infeasible. We present a unique Australian dataset that combines leaf gas-exchange measurements with concurrent, site-based meteorological data and foliar carbon isotope analysis. The dataset comprises 50 plant species growing at seven environmentally contrasting sites, with repeat measurements taken in contrasting seasons. We used these data to address the following questions:
1. How well do instantaneous (gas-exchange) and time-averaged (isotope) measures of optimal stomatal behaviour agree?
2. Do the predictions of the unified stomatal model represented by Eqn 2 help to reveal spatial and seasonal patterns of leaf water use efficiency?
3. Can temporal variation in the integrated Ci/Ca ratio, as indexed by δ13C measurements, be predicted from growth temperature and vapour pressure deficit as indicated by Eqn 4?
4. How well do predictions of Vcmax, and the ratio Jmax/Vcmax, derived under the co-ordination hypothesis following Eqns 5 and 6 correlate to independent gas-exchange measurements?
Methods
Study sites and climate data
Our seven study sites are a subset of TERN Ecosystem Processes 'SuperSites' (supersites.tern.org.au) (Karan et al., 2016). TERN
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Ecosystem Processes is a platform of the Terrestrial Ecosystem Research Network (TERN), Australia's land–ecosystem observatory. Site locations and key descriptors of dominant vegetation and soil type are presented in Table 1. The sites were chosen to provide a wide range of environmental conditions (Supporting Information Figure S1). Each SuperSite is equipped with a flux tower (TERN OzFlux) that records a common suite of meteorological data (Beringer et al., 2016). Our initial visits pre-dated the installation of the standard OzFlux system at three sites; in those instances we used the ANUClimate statistical model (Hutchinson et al., 2009) and data from the Australian Bureau of Meteorology’s nearest weather station (TERN eMAST). The proportion of down-welling shortwave radiation (Fsd, OzFlux) deemed as PAR was 0.47 (Britton & Dodd, 1976).
Apart from Alice Mulga, each site was visited twice. The timing of the visits was designed, within logistical constraints, to provide the widest possible seasonal contrast. The prevailing climate conditions leading up to each campaign are provided in Table 2. In testing for environmental dependencies of leaf traits, we considered a range of time-periods. In a glasshouse experiment on different Nothofagus species, Read & Farquhar (1991) found that for Australasian species the environmental parameter of the collection site most closely correlated with genetically determined C isotope discrimination (Δ) was the precipitation from the period December to March. Our study of leaf traits included seasonal contrasts (i.e. sub-annual) and so an intermediate (quarterly) time step has been adopted to represent average environmental conditions. Model iterations (e.g. Eqns 4 and 5) were run also adopting weekly and monthly time-steps, but offered neither improved explanatory power nor qualitative changes to the results (data not shown).
Leaf gas exchange
The plants measured (50 species, Supporting Information Table S1), were selected to include locally dominant species of trees and shrubs. This is a subset (combined here with C isotope analysis) of a larger
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dataset presented in Bloomfield et al. (2018). At each visit, we chose young, fully developed leaves from two sun-exposed branches. Leaf gas exchange measurements were concentrated in the morning and performed using portable photosynthesis systems (Li-Cor 6400, Li-Cor, Lincoln, NE, USA), using the 6 cm2 chamber fitted with the red-blue light source (Li-Cor 6400-02B LED). Upper canopy branches were excised using forestry shears on telescopic poles and the branches immediately placed in a bucket and the branches recut under water to re-establish the xylem water column (Domingues et al., 2010). Performing gas-exchange measurements on excised branches can affect subsequent calculations where stomatal conductance is heavily depressed. Our initial data exploration excluded such outliers (36 rows for individual leaves, 3.2% of the dataset).
For each leaf, approximately light-saturated (1500 µmol photons m-2 s-1) measures of net photosynthesis were taken at two CO2 concentrations: 400 µmol mol-1 (ppm) (A400) and 1500 µmol mol-1 (A1500). The leaf was next wrapped in aluminium foil for 30 minutes before respiration in darkness (Rdark) was measured, still at 400 ppm CO2. Air flow was held constant and a constant chamber block temperature (TBlock) was adopted for all measurements at a given site and season, set marginally (≈ 1°C) higher than expected morning air temperatures to counter the effect of transpirational cooling and ensure leaf and ambient air temperatures were similar. TBlock settings ranged from 10°C for the winter visit to Warra to 32°C for the summer visit to Calperum, reflecting the wide range of air temperatures experienced across the sites and seasons. The Li-Cor 6400 system can maintain TBlock values ±6°C away from ambient and this operating constraint precluded measuring gas exchange at a common temperature across all sites and seasons. With a constant flow rate, chamber humidity conditions varied and mean D within the chamber ranged from 0.55 kPa for the winter visit to Warra to 4.02 kPa for the summer visit to Calperum.
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In the absence of A-Ci response curves, we estimated Vcmax based on our light-saturated A400 values using the ‘one-point method’ described and validated by de Kauwe et al. (2016). Mitochondrial respiration in the light (Rday) was assumed to be equivalent to Rdark (this simplifying assumption has only a very minor effect on the estimation of Vcmax). The rate of electron transport at saturating [CO2] of 1500 ppm (Jmax) was estimated according to the equation:
Jmax=( A1500+ Rdark ) ∙(4 C i+8 Γ ¿)
(C i−Γ ¿)(7)
Our estimates of Vcmax and Jmax reflect temperature dependencies applied to K and Γ* according to Bernacchi et al. (2001). To allow comparisons of metabolic rates across sites and seasons, we calculated rates at both a standard temperature (25°C) and at the average daytime air temperature for the 90-day period leading up to the end of each field campaign (Table2). From the rate derived at a given measurement temperature, normalised Vcmax was calculated by applying the Arrhenius function (Medlyn et al., 2002) assuming an activation energy of 64.8 kJ mol-1
(Badger & Collatz, 1977).
Photosynthetic model extensions
When modeling Vcmax (Eqn 5), we adopted a simple leaf light term (IL):
I L ≈ I 0(1−e−k LAI )
LAI(8)
that estimates mean canopy light availability based on leaf area index (LAI) in the plot, where I0 is incident PAR at the top of the canopy and k is an extinction coefficient. We have assumed k = 0.5 since even distal leaves on sun-exposed branches usually experience only a fraction of full PAR incident at the horizontal plane (e.g. Kitajima et al. , 2005 ). As LAI approaches zero, the IL term’s upper limit is I0/2, but becomes a diminishing fraction of I0 as canopy density increases. Even though our sampling protocol called for sun-exposed leaves, we consider that
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adopting I0 would overestimate PAR for many mid-canopy or understory species.
The FvCB model and its subsequent implementations make the simplifying assumption for C3 plants that CO2 conductance through the leaf’s mesophyll (gm) is infinite such that the partial pressure at the site of carboxylation in the chloroplast (Cc) is equal to Ci. It is known, however, that internal resistances can lower gm and Evans & von Caemmerer (1996) reported that for many species Cc may be around 30% lower than Ci when leaves are actively photosynthesising in high light. Variations in gm are potentially important in a study such as ours that includes measurements of plants growing at environmentally contrasting sites and at unfavourable seasons. Following the approach in Ayub et al. (2011) we estimated gm based on the strong empirical correlation with Asat presented in Evans & von Caemmerer (1996), but adopting a drawdown (sub-stomatal airspace to chloroplasts: A/gm) of 50 µmol mol-1
suggested as a lower bound in a subsequent meta-analysis by Niinemets et al. (2009). Cc was then derived according to:
C c=Ci−A sat
gm=Ci−50 (9)
Photosynthetic parameters inferred from gas exchange measurements (‘one point’ estimates of Vcmax; Jmax according to Eqn 7) are presented in the analysis that follows after substituting Cc for Ci and adopting lowered Michaelis constants for Rubisco’s carboxylation and oxygenation reactions after von Caemmerer et al. (1994). Dropping the assumption of infinite gm, Wang et al. (2017b) derived a modified equation for χc with unchanged environmental dependencies, but producing a lower constant of 1.097 (replacing the 1.189 intercept term in Eqn 4) reflecting the anticipated drawdown from Ci to Cc i.e. χc < χo.
Estimating from stable carbon isotope discriminationχ
As an alternative to instantaneous methods, χ can be inferred from the leaf’s stable isotope composition. In their uptake of atmospheric CO2,
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plants discriminate against the stable 13C isotope relative to the lighter, more abundant 12C isotope and the level of discrimination is influenced by environmental conditions (Farquhar et al., 1989). Isotopic discrimination (Δ) can thus be expressed as the 13C/12C isotope ratio (δ13C) of leaf tissue relative to that of source air. We here employ the simple model developed by Farquhar et al. (1982) whereby discrimination is given by:
∆ ( ‰ )=δ 13Ca−δ 13C l
1+δ 13Cl /1000(10)
where δ13Ca is the δ13C value of CO2 in air (assumed to be -8‰) and δ13Cl
is that of the leaf. It is then possible to derive time-averaged values of χ (weighted for photosynthetically active periods) based on the relationship with Δ according to:
χ=( ∆−a' )( b'−a' )
(11)
where a ' is the isotope fractionation arising from diffusion through the
stomata and b ' the net fractionation caused by carboxylation, with standard values for C3 plants of 4.4‰ and 27‰ respectively. Eqn 11 is an approximation and disregards a number of additional dependencies of δ13C (e.g. Ubierna & Farquhar, 2014), but works well in practice because the generally adopted value of b’ implicitly includes these effects (Cernusak et al., 2013). An extended formulation that included explicit fractionation terms for mesophyll transfer and photorespiration was presented by Seibt et al. (2008). We here extend their ‘classical’ model to include a term presented by Farquhar & Cernusak (2012) that corrects the isotopic estimates of Ci for the effects of transpiration on the diffusive flux of CO2 already captured in the LiCor formulation – the ternary factor (so called to describe the collision of molecules of CO2, water vapour and air). Solving for χ provides a revised ratio:
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χ=Δ (1−t )−a '+( b−am ) (1+ t ) A
gmCa+ (1+ t ) f Γ¿
Ca
(b+ tb−a' )
where t= E
2gs
1.6
(12)
is the ternary correction factor, b is the mean fractionation during carboxylation by Rubisco (30‰), am is fractionation during transfer through the mesophyll (1.1‰) and f is fractionation during photorespiration (8‰). Hereafter we distinguish between these two discrimination models by referring to the simple (Eqn 11) and neo-classical (Eqn 12) formulations.
δ13C methods, conducted on leaves sampled from the same branch as gas exchange measurements, are as described in Rumman et al. (2018). Briefly, after drying each leaf sample was finely ground until homogenous with a ball mill (Retsch MM300, Verder Group, Netherlands). Between one and two milligrams of ground material were sub-sampled in tin capsules for analysis of δ13C, giving three representative values per tree. All δ13C analyses were performed using a Picarro G2121-i Analyser (Picarro, Santa Clara, CA, USA) for isotopic CO2. Atropine and acetanilide were used as laboratory standard references and results were normalised with the international standards sucrose (IAEA-CH-6, δ13CVPDB = –10.45 ‰), cellulose (IAEA-CH-3, δ13CVPDB
= –24.72 ‰) and graphite (USGS24, δ13CVPDB = –16.05 ‰). Standard deviation of the residuals between the IAEA standards and the calculated values of δ13C based on best linear fit was ≈ 0.5 ‰.
Estimating g1
The value of g1-leaf was estimated by fitting the unified stomatal model (Eqn 2) to our leaf gas exchange data using the fitBB function in the plantecophys R package (Duursma, 2015); gas exchange data for input to the model were restricted here to the A400 measurements described
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above. Values of isotope-derived g1 were estimated following Medlyn et al. (2011) using χ values obtained from Eqn 12 according to
g1−isotope=( χ √ D )(1− χ )
(13)
where D relates to time-averaged daylight site values obtained from the OzFlux network.
Data analysis
All statistical analyses were conducted using the open-sourced statistical environment ‘R’ (R Development Core Team, 2017). Leaf trait and parameter observations are presented as species averages for a given site and season; sample size and standard deviations are given in Supporting Information (Table S2). The level of agreement between two independent measures of a trait, or between model predictions and independently measured trait values, was tested using Pearson correlations. Linear mixed effects models, from the nlme package (Pinheiro et al., 2018), were used to test for differences between sites and seasons for the g1 parameter and for relationships between isotope-derived χ and the environmental variables T and D; a common random effect allowed different intercepts for each plant family. Differences between sites were assigned using Tukey’s honest significant differences (HSD). Comparisons between nested models were assessed using log likelihood ratios (L). Partial residual plots showing the relationship between independent parameter estimates and model explanatory variables were represented using the visreg R package (Breheny & Burchett, 2017).
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Results
Stomatal behaviour
We found a positive, but weak correlation (r = 0.290) between our gas exchange- and isotope-derived versions of the g1 slope parameter () although for tropical forest species several of the isotope-derived values were unrealistically high (>20 kPa0.5; corresponding to Ci/Ca ratios approaching unity). Instantaneous estimates of the Ci/Ca ratio within the Li-Cor chamber were consistently lower than the isotope-derived ratios, but the effect on g1-leaf was partially offset by higher D within the chamber at the time of measurement relative to the daytime mean D of the preceding quarter (Supporting Information Figure S2). We found no seasonal differences in the species-averaged g1-isotope estimates (even when models were run for a non-tropical subset of the sites); stepwise deletions from a fully interactive Site x Season fixed term (Site + Season + Site:Season) confirmed that the parsimonious Site-only model performed just as well (L = 11.82, df = 6, p = 0.066). Wide variation among co-occurring species meant there were few differences between the study sites, but g1-isotope values for Warra were lower than for the tropical forests ().
The expected influence of environmental conditions on stomatal behaviour was only partially borne out. The g1_isotope parameter showed a weak positive relationship with time-averaged air temperatures (R2 = 0.10 Supporting Information Figure S3), but was unrelated to available indices of water availability: precipitation, moisture index or top-layer soil water fraction (e.g. for a multiple regression style model, dropping the soil water term to leave temperature as the sole explanatory variable made little difference to the model’s performance: L = 1.29, df = 1, p = 0.257). Despite the limited agreement between our two versions of the g1 parameter similar patterns were observed for g1-leaf (gas-exchange): a Site-only model was again preferred (L = 9.07, df = 6, p = 0.169); inter-site differences here suggested a progression of arid < temperate <
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tropical forests (Figure 2 inset); but relationships with environmental variables were tentative at best: T (R2 = 0.11).
Leaf Ci:Ca ratio
Estimates of χ modelled as a function of environmental parameters T and D using Eqn 4 (for this Australian dataset, we have ignored the limited site differences in altitude) showed strong correlation (r = 0.702) with corresponding ratios derived from 13C discrimination (Eqn 12) (); the correlation was stronger still when ratios for co-occurring species were expressed as a site mean (r = 0.930). The modelled ratios were, however, consistently lower and we next considered how well Eqn 4 described our Δ-derived ratios. The consistent offset from the published model meant that our isotopic data yielded a markedly higher intercept at standard conditions from that proposed by Wang et al. (2017b) - corresponding to χ=0.85 (versus 0.75); the slope of the T response did not differ significantly from the theoretical value, but the slope coefficient for D was more steeply negative (Table 3). Our result of consistently higher Δ-derived χ stems from the decision to adopt an expanded formula of 13C fractionation; a comparison of neo-classical versus simple χ-isotope confirms that, whilst very closely correlated, the ratios were always higher under the extended formulation (Supporting Information Figure S4b).
The fit of the Δ-derived ratios to the environmental data was improved when the two seasons were treated separately. The independent effects of the model’s two environmental parameters suggested a seasonal shift in relative explanatory power with T>D in the favourable season, but that ranking reversed in the off-season linked to pronounced deficits at Calperum and GWW (Table 3, ).
Photosynthetic parameters
The maximal rate of Rubisco carboxylation (Vcmax), modelled using Eqn 5 adopting Ci estimates derived from 13C discrimination, showed fair agreement with estimates inferred from gas-exchange measurements
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when expressed at the prevailing measurement temperatures (r = 0.675, a). It is noticeable, however, that for a given site and season the rates inferred from gas exchange were much more variable than the modelled values, reflecting the model’s use of environmental variables common to all co-occurring species.
The Vcmax rates shown in a reflect enzyme kinetics at the prevailing temperatures for each site and season: average daytime temperatures (Table 2) for the modelled rates and leaf temperatures close to a corresponding TBlock for the gas exchange measurements. When rates were expressed normalised to a standard temperature of 25°C, the correlation between inferred and modelled values proved weaker (r = 0.348, b). This disparity was caused, in part at least, by the respective temperature differentials from the 25°C reference: leaf temperatures in the Li-Cor chamber (inferred rates) always exceeded the daytime air temperatures (modelled rates) (Methods). The divergence between inferred and modelled normalised rates became more pronounced as the temperature differential increased (Supporting Information Figure S5). Once normalised for average canopy light conditions (IL above), our 25°C standardised gas exchange rates showed a negative relationship with growth temperature (Supporting Information Figure S6).
The modelled ratio Jmax/Vcmax (Eqn 6) similarly derived from independent isotope and environmental variables provided a good fit to the gas exchange estimate () although the modelled ratios were generally higher – both ratios estimated at the prevailing temperature. For C3 species average Jmax/Vcmax ratios of around 2.0 are widely reported (e.g. Wullschleger, 1993), but the model yielded many higher ratios (approaching 4.0 for the winter visit to Warra) and this was related to cooler growth temperatures for certain sites and seasons – consistent with both experimental evidence (Kattge & Knorr, 2007) and predictions (Wang et al., 2017b). Jmax/Vcmax ratios inferred from gas exchange measurements confirmed a strong negative dependency on leaf
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temperature, approaching unity as leaf temperatures neared 35°C (Supporting Information Figure S7).
Discussion
Theoretical optimality approaches permit prediction of photosynthetic parameters for species and ecosystems for which few or no empirical data are available, but of course such hypotheses require validation across as broad an environmental scale as possible. For diverse plant species growing at contrasting sites across Australia we show that leaf physiological parameters key to many Earth system models (ESMs) are predictable, to first order, using readily available environmental variables. Collectively, our findings support the contention that there now exists a unifying theoretical basis that ESMs can exploit to predict key leaf parameters across diverse species and biomes (Wang et al., 2017b).
The unified stomatal model
It is often assumed that estimates of the same parameter obtained using different methods should match (e.g. Quentin et al., 2015), but we found only weak agreement between our two versions of g1. Although the underlying patterns of variation were similar for both bases, the poor correlation prompts the question which is the more reliable or informative. Our gas-exchange protocol was designed primarily to provide an instantaneous estimate of photosynthetic capacity. It may be that for many of these species xylem performance was compromised by the act of cutting the branches and hence water transport to the leaves was disrupted causing a reduction in gs (Santiago & Mulkey, 2003). By contrast, g1 values derived from the isotopic composition of leaf biomass reflect the growth environment as integrated over the leaf’s development. In addition, metabolic processes post photosynthetic
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fixation create further fractionation steps that may cause Δ (hence χ and g1) derived from leaf material to differ from instantaneous estimates (Cernusak et al., 2013).
A recent study by Medlyn et al. (2017) compared g1 estimates derived from three different datasets: gas exchange, leaf isotope (as here) and eddy covariance. They found that the three g1 estimates were inconsistent, but in contrast to our results values for g1-isotope were generally lower than g1-leaf for a given PFT. Our own study is heavily dominated by evergreen broad leaf trees (47 of 50 species, Supporting Information Table S1), a classification that straddles three of the PFTs adopted in the Medlyn et al. study. For those three PFTs and shrubs (also represented here), Medlyn et al. (2017) reported significant differences between g1-leaf and g1-isotope values only for the savanna plants; the authors ascribe high variability in g1-leaf for savanna trees to marked seasonal differences. Their meta-analysis, however, included measurements from savannas in the seasonally dry tropics where monthly rainfall can vary by hundreds of mm (see for example Figure 1 in Eamus et al., 2000). The g1 parameter is inversely proportional to the plant’s intrinsic water use efficiency (defined as A/gs) and we expected to find inter-site differences that followed a gradient of moisture availability. Instead differences emerged only between the temperate forest at Warra and the moist tropical forests in FNQ. In a global study of 314 species at 56 sites, Lin et al. (2015) found no difference in g1
among C3 plants from temperate or tropical biomes, but did find important differences among PFTs: tropical moist forest trees at 3.37, shrubs at 4.22 and evergreen savanna trees at 7.18. Compared to the Lin et al. study, our tropical forest results are roughly double, whilst our g1 values for the arid sites, derived in part from evergreen savanna trees, are much lower. Those lower g1 estimates reported here () arguably conform better to expectations of higher WUE in the dry conditions of those arid sites.
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Stomatal behaviour must, in part, depend on environmental conditions, but we found no evidence of a seasonal effect on values of g1. Lack of a seasonal response might be expected for moist tropical forests where the T and D differentials of wet versus dry seasons are much less pronounced than the summer versus winter contrasts of mid-latitude sites (Table 2) (Buchmann et al., 1997). Surprisingly our results showed no relationship between g1 and indices of water availability. In another Australian study of five evergreen eucalypt species growing at three sites along a rainfall gradient in the Northern Territory, Eamus et al. (2000) found no seasonal differences in transpiration rates despite marked seasonality in rainfall, aridity and leaf water potential. The authors propose that for these savanna trees, water use is determined by the limiting dry season conditions.
Our results showed poor consistency between the gas-exchange and isotope methods of estimating optimal stomatal behaviour. We found that the g1 parameter of WUE was weakly influenced by air temperature over broad scales, but locally was at once widely variable among co-occurring species and conserved across changing seasons.
Predicting the ratio of leaf internal to ambient [CO2]
A large body of work from the 1980s onwards has shown that 13C discrimination is a reliable proxy for time-averaged χ (see Methods). Our results lend support to the model presented by Wang et al. (2017b) and show that isotope-derived χ is closely correlated to the corresponding value predicted from climate variables of growth T and D. In contrast to the original study, however, we found that the isotope-derived ratios were consistently higher than those produced by the model. Departing from the original study, we have applied an extended formulation of 13C fractionation (Eqn 12); the ternary and photorespiration corrections here are relatively minor and act as partial offsets and so the principal disparity stems from our treatment of mesophyll conductance. Current ESMs, and the standard implementation of the FvCB model, ignore the limitation placed on photosynthesis by gm. In this study we adopted a
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single empirical correlation for estimating the drawdown from Ci to Cc
(Eqn 9), but this is inevitably an oversimplification. Despite difficulties in measurement, gm is known to vary among species and in response to environmental conditions (Flexas et al., 2008; von Caemmerer & Evans, 2015). Importantly, 13C discrimination methods can be combined with gas exchange measurements to provide estimates of gm (Cernusak et al., 2013). This is an important area for future research, both empirical and theoretical, on the control of photosynthetic traits although debate remains as to the necessity of incorporating gm estimates into wider models (e.g. Bahar et al., 2018).
Fitting the Wang et al. model to our data gave a coefficient of determination very similar to the original study (R2 of 0.41 versus 0.39), but the fit was improved if the two seasons were treated separately (Table 3). One might expect seasonal contrasts to be captured by a stomatal model that specifies T and D as explanatory variables, but we found that their relative importance altered seasonally. In the favourable growing season, χ was more strongly influenced by variation in daytime T, but in the unfavourable season the primacy shifted to D. Greater influence for D under drier conditions is supported by a seasonal comparison of ecosystem WUE at the Alice Mulga site, where the response to D was more pronounced in the dry (winter) than the wet season linked to contrasting levels and variability of soil moisture content in the two seasons (Eamus et al., 2013).
Offset notwithstanding, the implication of our results is that we now have a means of estimating Ci for modelling purposes. For this Australian study, where none of the sites sat higher than 750 m above sea level, we have ignored altitude effects on χ that are included in the original formulation (Eqn 4). Further studies could usefully test the model along a gradient encompassing a wider range of altitudes.
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Photosynthetic parameters from first principles
Whilst our predictions of key parameters provided generally good agreement with gas exchange values, several discrepancies emerged. For a given site and season, the species-averaged Vcmax rates inferred from our gas-exchange measurements showed wide variation. Such variability amongst co-occurring species is to be expected (e.g. Bloomfield et al., 2018) and likely reflects factors such as canopy position, and therefore light environment, and VPD. Our model performed less successfully when rates were normalised to the standard reference temperature of 25°C. The weaker correlation here may be partially explained by the underlying differentials in temperature offset from the common reference (see above): leaf temperature versus daytime averages. One effect of standardising the rates to a common reference temperature was to narrow their range: inferred and modelled. Despite this contraction, the spectrum of site averages indicated higher rates for the species growing in arid conditions. When our inferred Vcmax25 rates were modelled as a function of the light, T and χ variables underpinning Eqn 5, the residuals showed no discernible pattern with leaf mass per unit area (data not shown); suggesting that variation in leaf morphology (at a coarse scale) provided little explanatory power beyond these environmental variables.
What might be missing from our Vcmax model? In adopting a universal extinction coefficient (recognising that not all leaves display perpendicular to the sun’s rays) and a leaf PAR estimate averaged for each plot (seasonal LAI values were adopted where available), our Vcmax
model assumed a uniform light environment for all leaves (Eqn 8). One of the major difficulties is that we know very little about the light environments for these species. That quandary extends to our gas-exchange measurements, which employed a single saturating light setting across the study, and is likely one of the key reasons for the wide within-site variation in inferred Vcmax values. It has, however, been demonstrated that within-canopy gradients of photosynthetic capacity
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and light are not strictly proportional (e.g. Lloyd et al., 2010) e.g. leaves in poor light appear to have superfluous capacity thus breaching the optimality hypothesis. From our own results it is not apparent that the model performed differently for those sites (Daintree and Cumberland Plain) where we sampled tree branches from the gondola of a crane and where we might have expected the light-averaging step to produce an underestimate. In considering why optimal canopies are not observed in nature, Niinemets (2012) proposed a series of additional constraints or mechanisms including leaf construction, acclimation and light-competition.
Even more challenging is the possibility that rates of photosynthesis are driven by the plant’s requirements for photosynthate (buffered by existing stores) rather than what is possible or optimal. The demands - spanning respiration, reproduction, defence, exudates – represent a complex network of plant processes intimately connected to the surrounding biotic and abiotic environment and subject to stochastic fluctuation. The different timescales operating for our two Vcmax
estimates create the likelihood of quite distinct underlying phenology and sink demand.
Changes or differences in growth temperature are expected to require adjustment in the plant’s photosynthetic apparatus, often manifested as variation in the Jmax/Vcmax ratio. Accordingly, we found elevated ratios related to the cooler seasons and sites reflecting the ratio’s negative dependence on temperature (). Our gas exchange data suggested a strong negative linear relationship with leaf temperature (Supporting Information Figure S7), but with a steeper slope (-0.08 versus -0.035 °C-1) than that reported by Kattge & Knorr (2007). Our model (Eqn 6) predicted Jmax/Vcmax ratios that were consistently higher than those inferred from the gas exchange measurements and that coincides with the elevated χisotope values described above: the modelled Jmax/Vcmax ratio increasing with Ci.
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Concluding remarks
We have used our unique dataset to test predictions of leaf photosynthetic traits across a range of climates and plant species. Demonstrating the validity of these predictions has application for the ESM community. Current ESMs are still hampered by uncertainty, much of which relates to incomplete or inaccurate representations of the fundamental processes that govern carbon fixation at the leaf, canopy and ecosystem levels. The path forward will involve sustained efforts both to formulate explicit, unifying theories about the control of these processes and to test and refine them with the help of field measurements. This research represents a step along that path.
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Acknowledgments
TERN (http://www.tern.org.au) is supported by the Australian Government through the National Collaborative Infrastructure Strategy (NCRIS). This work was specifically facilitated through a Supplemental Grant to TERN’s eMAST and SuperSites facilities. The support of the Australian Research Council to OKA (DP130101252 and CE140100008) and DE (DP140101150) is also acknowledged. This work is a contribution to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment. KJB was hosted during the writing of this manuscript by the University of Exeter.
Author contributions
ICP, OKA and KJB designed the study. DE, DSE, MMB, PC, JC, MJL, CM, WSM, TW and IJW co-ordinated site access, species identification and logistical support. KJB, JJGE, LSH, HFT and LZ conducted fieldwork. BJE, MFH and MJL helped to compile the climate data. RR conducted the isotope analysis and LAC proposed the extended discrimination model (Eqn 12). BEM guided calculation of the g1 parameter. KJB led the analysis of the data and wrote the manuscript with input from all authors.
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References
Ali AA, Xu CG, Rogers A, McDowell NG, Medlyn BE, Fisher RA, Wullschleger SD, Reich PB, Vrugt JA, Bauerle WL, et al. 2015. Global-scale environmental control of plant photosynthetic capacity. Ecological Applications 25(8): 2349-2365.
Ayub G, Smith RA, Tissue DT, Atkin OK. 2011. Impacts of drought on leaf respiration in darkness and light in Eucalyptus saligna exposed to industrial-age atmospheric CO2 and growth temperature. New Phytologist 190(4): 1003-1018.
Badger MR, Collatz GJ. 1977. Studies on the kinetic mechanism of ribulose-1, 5-bisphosphate carboxylase and oxygenase reactions, with particular reference to the effect of temperature on kinetic parameters. Carnegie Institute of Washington Yearbook 76: 355-361.
Bahar NHA, Hayes L, Scafaro AP, Atkin OK, Evans JR. 2018. Mesophyll conductance does not contribute to greater photosynthetic rate per unit nitrogen in temperate compared with tropical evergreen wet-forest tree leaves. New Phytologist 218(2): 492-505.
Beringer J, Hutley LB, McHugh I, Arndt SK, Campbell D, Cleugh HA, Cleverly J, Resco de Dios V, Eamus D, Evans B, et al. 2016. An introduction to the Australian and New Zealand flux tower network - OzFlux. Biogeosciences Discuss. 2016: 1-52.
Bernacchi CJ, Singsaas EL, Pimentel C, Portis AR, Long SP. 2001. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant Cell and Environment 24(2): 253-259.
Bloomfield KJ, Cernusak LA, Eamus D, Ellsworth DS, Prentice IC, Wright IJ, Boer M, Bradford MG, Cale P, Cleverly J, et al. 2018. A continental-scale assessment of variability in leaf traits: within species, across sites and between seasons. Functional Ecology 32(6): 1492-1506.
Breheny P, Burchett W. 2017. Visualization of regression models using visreg. The R Journal 9(2): 56-71.
Britton CM, Dodd JD. 1976. Relationships of photosynthetically active radiation and shortwave irradiance. Agricultural Meteorology 17(1): 1-7.
Buchmann N, Guehl JM, Barigah TS, Ehleringer JR. 1997. Interseasonal comparison of CO2 concentrations, isotopic composition, and carbon dynamics in an Amazonian rainforest (French Guiana). Oecologia 110(1): 120-131.
Cernusak LA, Ubierna N, Winter K, Holtum JAM, Marshall JD, Farquhar GD. 2013. Environmental and physiological
30
763
764765766767
768769770771
772773774775776
777778779780781
782783784785
786787788
789790791792793
794795
796797798
799800801802
803804
determinants of carbon isotope discrimination in terrestrial plants. New Phytologist 200(4): 950-965.
Chen JL, Reynolds JF, Harley PC, Tenhunen JD. 1993. Coordination theory of leaf nitrogen distribution in a canopy. Oecologia 93(1): 63-69.
Collatz GJ, Ball JT, Grivet C, Berry JA. 1991. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology 54(2-4): 107-136.
Cowan IR, Farquhar GD 1977. Stomatal function in relation to leaf metabolism and environment. In: Jennings DH ed. Integration of Activity in Higher Plants. Cambridge: Cambridge University Press, 471-505.
De Kauwe MG, Lin YS, Wright IJ, Medlyn BE, Crous KY, Ellsworth DS, Maire V, Prentice IC, Atkin OK, Rogers A, et al. 2016. A test of the 'one-point method' for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytologist 210(3): 1130-1144.
Dewar R, Mauranen A, Mäkelä A, Hölttä T, Medlyn B, Vesala T. 2018. New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis. New Phytologist 217(2): 571-585.
Domingues TF, Meir P, Feldpausch TR, Saiz G, Veenendaal EM, Schrodt F, Bird M, Djagbletey G, Hien F, Compaore H, et al. 2010. Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. Plant Cell and Environment 33(6): 959-980.
Dong N, Prentice IC, Evans BJ, Caddy-Retalic S, Lowe AJ, Wright IJ. 2017. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14(2): 481-495.
Duursma RA. 2015. plantecophys - an R package for analysing and modelling leaf gas exchange data. Plos One 10(11).
Eamus D, Cleverly J, Boulain N, Grant N, Faux R, Villalobos-Vega R. 2013. Carbon and water fluxes in an arid-zone Acacia savanna woodland: An analyses of seasonal patterns and responses to rainfall events. Agricultural and Forest Meteorology 182: 225-238.
Eamus D, O'Grady AP, Hutley L. 2000. Dry season conditions determine wet season water use in the wet-dry tropical savannas of northern Australia. Tree Physiology 20(18): 1219-1226.
Evans JR, von Caemmerer S. 1996. Carbon dioxide diffusion inside leaves. Plant Physiology 110(2): 339-346.
Farquhar GD, Cernusak LA. 2012. Ternary effects on the gas exchange of isotopologues of carbon dioxide. Plant Cell and
31
805806
807808809
810811812813
814815816817
818819820821822
823824825826827
828829830831832
833834835
836837
838839840841
842843844
845846
847848
Environment 35(7): 1221-1231.Farquhar GD, Ehleringer JR, Hubick KT. 1989. Carbon isotope
discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology 40: 503-537.
Farquhar GD, O'Leary MH, Berry JA. 1982. On the relationship between carbon isotope discrimination and the inter-cellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9(2): 121-137.
Farquhar GD, von Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 78-90.
Flexas J, Ribas-Carbo M, Diaz-Espejo A, Galmes J, Medrano H. 2008. Mesophyll conductance to CO2: current knowledge and future prospects. Plant Cell and Environment 31(5): 602-621.
Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, Papadopol P. 2009. Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology 48(4): 725-741.
Karan M, Liddell M, Prober SM, Arndt S, Beringer J, Boer M, Cleverly J, Eamus D, Grace P, Van Gorsel E, et al. 2016. The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory. Science of the Total Environment 568: 1263-1274.
Kattge J, Knorr W. 2007. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant Cell and Environment 30(9): 1176-1190.
Katul G, Manzoni S, Palmroth S, Oren R. 2010. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration. Annals of Botany 105(3): 431-442.
Kitajima K, Mulkey SS, Wright SJ. 2005. Variation in crown light utilization characteristics among tropical canopy trees. Annals of Botany 95(3): 535-547.
Lambers H, Chapin FS, Pons TL. 2008. Plant Physiological Ecology: Springer New York.
Lin YS, Medlyn BE, Duursma RA, Prentice IC, Wang H, Baig S, Eamus D, de Dios VR, Mitchell P, Ellsworth DS, et al. 2015. Optimal stomatal behaviour around the world. Nature Climate Change 5(5): 459-464.
Lloyd J. 1991. Modeling stomatal responses to environment in Macadamia integrifolia. Australian Journal of Plant Physiology 18(6): 649-660.
32
849
850851852
853854855856
857858859
860861862
863864865866867
868869870871872
873874875
876877878879
880881882
883884
885886887888
889890891
Lloyd J, Patiño S, Paiva RQ, Nardoto GB, Quesada CA, Santos AJB, Baker TR, Brand WA, Hilke I, Gielmann H, et al. 2010. Optimisation of photosynthetic carbon gain and within-canopy gradients of associated foliar traits for Amazon forest trees. Biogeosciences 7(6): 1833-1859.
Maire V, Martre P, Kattge J, Gastal F, Esser G, Fontaine S, Soussana JF. 2012. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. Plos One 7(6).
Mäkelä A, Givnish TJ, Berninger F, Buckley TN, Farquhar GD, Hari P. 2002. Challenges and opportunities of the optimality approach in plant ecology. Silva Fennica 36(3): 605-614.
Medlyn BE, De Kauwe MG, Lin Y-S, Knauer J, Duursma RA, Williams CA, Arneth A, Clement R, Isaac P, Limousin J-M, et al. 2017. How do leaf and ecosystem measures of water-use efficiency compare? New Phytologist 216(3): 758-770.
Medlyn BE, Dreyer E, Ellsworth D, Forstreuter M, Harley PC, Kirschbaum MUF, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, et al. 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell and Environment 25(9): 1167-1179.
Medlyn BE, Duursma RA, De Kauwe MG, Prentice IC. 2013. The optimal stomatal response to atmospheric CO2 concentration: Alternative solutions, alternative interpretations. Agricultural and Forest Meteorology 182: 200-203.
Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, de Angelis P, Freeman M, Wingate L. 2011. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology 17(6): 2134-2144.
Niinemets U. 2012. Optimization of foliage photosynthetic capacity in tree canopies: towards identifying missing constraints. Tree Physiology 32(5): 505-509.
Niinemets U, Diaz-Espejo A, Flexas J, Galmes J, Warren CR. 2009. Role of mesophyll diffusion conductance in constraining potential photosynthetic productivity in the field. Journal of Experimental Botany 60(8): 2249-2270.
Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team 2018. nlme: linear and nonlinear mixed effects models. https://CRAN.R-project.org/package=nlme.
Poorter H, Evans JR. 1998. Photosynthetic nitrogen-use efficiency of species that differ inherently in specific leaf area. Oecologia 116(1-2): 26-37.
Prentice IC, Dong N, Gleason SM, Maire V, Wright IJ. 2014. Balancing the costs of carbon gain and water transport: testing a
33
892893894895896
897898899
900901902
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907908909910911
912913914915
916917918919920
921922923
924925926927
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934935
new theoretical framework for plant functional ecology. Ecology Letters 17(1): 82-91.
Prentice IC, Liang X, Medlyn BE, Wang YP. 2015. Reliable, robust and realistic: the three Rs of next-generation land-surface modelling. Atmospheric Chemistry and Physics 15(10): 5987-6005.
Quentin AG, Pinkard EA, Ryan MG, Tissue DT, Baggett LS, Adams HD, Maillard P, Marchand J, Landhausser SM, Lacointe A, et al. 2015. Non-structural carbohydrates in woody plants compared among laboratories. Tree Physiology 35(11): 1146-1165.
R Development Core Team 2017. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Read J, Farquhar G. 1991. Comparative studies in Nothofagus (Fagaceae). 1. Leaf carbon isotope discrimination. Functional Ecology 5(5): 684-695.
Reich PB, Wright IJ, Lusk CH. 2007. Predicting leaf physiology from simple plant and climate attributes: A global GLOPNET analysis. Ecological Applications 17(7): 1982-1988.
Rogers A. 2014. The use and misuse of Vc,max in Earth system models. Photosynthesis Research 119(1-2): 15-29.
Rumman R, Atkin OK, Bloomfield KJ, Eamus D. 2018. Variation in bulk-leaf 13C discrimination, leaf traits and water-use efficiency-trait relationships along a continental-scale climate gradient in Australia. Global Change Biology 24(3): 1186-1200.
Santiago LS, Mulkey SS. 2003. A test of gas exchange measurements on excised canopy branches of ten tropical tree species. Photosynthetica 41(3): 343-347.
Schulze ED, Kelliher FM, Körner C, Lloyd J, Leuning R. 1994. Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate and plant nitrogen nutrition: a global ecology scaling exercise. Annual Review of Ecology and Systematics 25: 629-660.
Seibt U, Rajabi A, Griffiths H, Berry JA. 2008. Carbon isotopes and water use efficiency: sense and sensitivity. Oecologia 155(3): 441-454.
Sperry JS, Wang YJ, Wolfe BT, Mackay DS, Anderegg WRL, McDowell NG, Pockman WT. 2016. Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits. New Phytologist 212(3): 577-589.
Thomas DS, Eamus D, Bell D. 1999. Optimization theory of stomatal behaviour - II. Stomatal responses of several tree species of north Australia to changes in light, soil and atmospheric water content and temperature. Journal of Experimental Botany 50(332): 393-400.
34
936937
938939940
941942943944
945946947
948949950
951952953
954955
956957958959
960961962
963964965966967
968969970
971972973974
975976977978979
Ubierna N, Farquhar GD. 2014. Advances in measurements and models of photosynthetic carbon isotope discrimination in C3
plants. Plant Cell and Environment 37(7): 1494-1498.Verheijen LM, Brovkin V, Aerts R, Bonisch G, Cornelissen JHC,
Kattge J, Reich PB, Wright IJ, van Bodegom PM. 2013. Impacts of trait variation through observed trait-climate relationships on performance of an Earth system model: a conceptual analysis. Biogeosciences 10(8): 5497-5515.
von Caemmerer S, Evans JR. 2015. Temperature responses of mesophyll conductance differ greatly between species. Plant Cell and Environment 38(4): 629-637.
von Caemmerer S, Evans JR, Hudson GS, Andrews TJ. 1994. The kinetics of ribulose-1,5-bisphosphate carboxylase/oxygenase in vivo inferred from measurements of photosynthesis in leaves of transgenic tobacco. Planta 195(1): 88-97.
Wang H, Prentice IC, Davis TW, Keenan TF, Wright IJ, Peng CH. 2017a. Photosynthetic responses to altitude: an explanation based on optimality principles. New Phytologist 213(3): 976-982.
Wang H, Prentice IC, Keenan TF, Davis TW, Wright IJ, Cornwell WK, Evans BJ, Peng CH. 2017b. Towards a universal model for carbon dioxide uptake by plants. Nature Plants 3(9): 734-741.
Wolf A, Anderegg WRL, Pacala SW. 2016. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proceedings of the National Academy of Sciences of the United States of America 113(46): E7222-E7230.
Wong SC, Cowan IR, Farquhar GD. 1979. Stomatal conductance correlates with photosynthetic capacity. Nature 282(5737): 424-426.
Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, et al. 2004. The worldwide leaf economics spectrum. Nature 428(6985): 821-827.
Wullschleger SD. 1993. Biochemical limitations to carbon assimilation in C3 plants - a retrospective analysis of A/Ci curves from 109 species. Journal of Experimental Botany 44(262): 907-920.
35
980981982
983984985986987
988989990
991992993994
995996997
998999
1000
1001100210031004
100510061007
1008100910101011
101210131014
1015
1016
1017
Tables
Table 1 Site location and descriptors. The sites are listed in order of increasing absolute latitude.
Site Lat Long Biome VegetationLeaf area
indexCanopy height Soil type Soil texture MAT
Annual Precip
Moisture index
° S ° E ratio m Sand : Silt : Clay °C mm ratio Daintree 16.103 145.447 Tropical moist forest Closed forest 2.65 25.0 Acidic, dystrophic, brown Dermosol 37 : 36 : 27 24.3 3,671 3.36 Robson Creek 17.117 145.630 Tropical moist forest Closed forest 3.19 28.0 Acidic, dystrophic, brown Dermosol 61 : 09 : 30 20.4 1,813 1.65 Alice Mulga 22.400 133.250 Tropical savanna Low, open, arid woodland 0.34 6.5 Eutrophic, red Kandosol 74 : 11 : 15 22.5 357 0.22 Great Western Woodlands 30.191 120.654 Mediterranean woodland Semi-arid woodland 1.07 18.0 Kandosol 51 : 17 : 32 18.9 291 0.31 Cumberland Plain 33.619 150.738 Temperate forest Dry woodland 1.20 23.0 Grey podsol 33 : 47 : 20 17.7 788 0.83 Calperum Mallee 34.003 140.588 Mediterranean woodland Sparse, mallee woodland 0.88 3.0 Tenosol (Calcisol) 90 : 01 : 09 17.4 268 0.32 Warra 43.095 146.654 Temperate forest Tall, wet forest 5.84 55.0 Kurosolic, redoxic Hydrosol 24 : 48 : 28 9.8 1,591 4.41
An Australian map with the site locations indicated is included in Supporting Information (Figure S1). Leaf area index is the ratio of projected leaf to ground surface area. Climate indices are
annual long-term averages of interpolated data obtained from the Terrestrial Ecosystem Research Network (TERN) eMAST facility. Moisture index is shown as the ratio of precipitation to
potential evapotranspiration. Additional details for each site may be found on the TERN SuperSite website (www.supersites.net.au).
36
1018
1019
1020
1021
10221023
1024
Table 2 Climate conditions for each campaign (Site_Season) for the 90-day period ending on the final day of fieldwork.
Site Visit Season Source Ta Precip Fn VPD Sws Fsd °C mm J s-1 m-2 kPa fraction J s-1 m-2
Calperum Mallee Summer, 2013 Unfavourable OzFlux 28.1 44 305.8 3.07 0.030 537.2 Calperum Mallee Winter, 2013 Favourable OzFlux 14.1 97 155.4 0.67 0.044 226.9 Great Western Woodlands Summer, 2013 Unfavourable OzFlux 26.0 161 315.4 2.32 0.214 467.3 Great Western Woodlands Winter, 2013 Favourable OzFlux 14.8 53 249.6 0.87 0.165 345.1 Alice Mulga Winter, 2014 Unfavourable OzFlux 18.4 26 382.2 1.59 0.038 592.1 Cumberland Plain Summer, 2014 Favourable OzFlux 25.6 183 345.6 1.59 0.054 455.0 Cumberland Plain Winter, 2014 Unfavourable OzFlux 16.6 32 205.9 0.83 0.059 305.4 Warra Summer, 2012 Favourable ANUClimate 16.2 229 142.7 0.45 NA NAWarra Winter, 2013 Unfavourable OzFlux 8.8 254 47.9 0.36 0.164 166.4 Robson Creek Dry, 2012 Unfavourable ANUClimate 17.7 211 149.3 0.33 NA NARobson Creek Wet, 2014 Favourable OzFlux 24.9 856 241.4 0.52 0.315 318.8 Daintree Dry, 2010 Unfavourable ANUClimate 23.1 477 182.2 0.56 NA NADaintree Wet, 2014 Favourable OzFlux 25.9 2,194 161.6 0.72 0.315 223.6
Air temperature (Ta), precipitation, net radiation (Fn), vapour pressure deficit (VPD), soil water fraction (Sws, top layer),
short-wave down-welling radiation (Fsd). Rainfall figures are totals, other values are means; temperature, radiation and
VPD figures relate to daylight hours only. In most cases the climate data have been obtained from instruments centred on
flux towers located at each site and managed under the OzFlux network (www.ozflux.org.au). In three cases our fieldwork
campaigns were made before the flux towers were fully operational and in these instances we have used interpolated 24 hour
data obtained from ANUClimate (www.emast.org.au/our-infrastructure/observations/anuclimate_data/). As we prepared the
manuscript, daily radiation data for the periods of interest were not available within ANUClimate and so those radiation data
have been obtained for the nearest automated weather station operated by the Australian Bureau of Meteorology
(www.bom.gov.au). The designation of Favourable and Unfavourable seasons is based on a local assessment of growing
conditions. NA: not available.
1025
1026
1027
102810291030103110321033103410351036
1037
Table 3 Linear model summaries, following Wang et al. (2017b) and Eqn 4: In each case the response variable is the logit-
transformed isotope-derived ratio of leaf internal to ambient CO2 partial pressure (χ).
Predictor Estimate Std error df t value p value t testTheoretical
value
Intercept 1.7101 0.119 50 14.425 < 0.0001 < 0.0001 1.0970Δ T 0.0735 0.016 50 4.719 < 0.0001 0.2278 0.0545ln D -0.8901 0.144 50 -6.165 < 0.0001 0.0094 -0.5000
Marginal R 2 0.439
Estimate Std error df t value p value t test Estimate Std error df t value p value t test
Intercept 1.7349 0.186 24 9.308 < 0.0001 0.0022 1.6617 0.137 16 12.122 < 0.0001 0.0008Δ T 0.1204 0.022 16 5.568 < 0.0001 0.0077 0.0356 0.018 16 1.929 0.0717 NAln D -0.9866 0.288 16 -3.431 0.0034 0.1100 -0.8192 0.158 16 -5.186 < 0.0001 0.0604
Marginal R 2 0.498 0.483
Both seasons combined
Favourable season Unfavourable season
The explanatory variables are the difference in the mean air temperature for the preceding quarter from 25°C (ΔT, °C) and
the natural logarithm of the mean vapour pressure deficit for the preceding quarter (lnD, kPa). The theoretical values are
partial derivatives for each predictor, evaluated at ‘standard’ conditions (T=25°C, D=1 kPa). The t-test result is the
probability of no difference between the fitted estimate and the theoretical value (null hypothesis). Separate model iterations
were run for the complete data-set and then for each of the two seasons. NA: not applicable.
38
1038
1039
1040
1041
1042104310441045
1046
Figures
Figure 1 Relationship between two estimates of the g1 parameter (Eqn 2) derived from: instantaneous gas exchange
measurements (g1-leaf) and leaf isotope discrimination (g1-isotope). The points represent seasonal means for individual plant
species and sites are differentiated by colour. The dashed line represents the ideal 1:1 fit. The x-axis has been restricted to
exclude eight species (1 Warra, 5 Robson Ck and 2 Daintree) with g1-isotope greater than 20 kPa0.5.
39
1047
1048
1049
105010511052
1053
1054
Figure 2 Boxplot showing the g1 values estimated from leaf isotope discrimination grouped by site and including both
seasons. The y-axis has been restricted to exclude eight species (1 Warra, 5 Robson Ck and 2 Daintree) with g1-isotope greater
than 20 kPa0.5. The boxes indicate the interquartile range and median values. Whiskers extend to the largest or smallest
observation that fall within 1.5 times the box size; any observations outside these values are shown as individual points.
Boxes which share the same letter denote site means that were not significantly different (Tukey’s honest significant
differences). The number of species included for each site is indicated. The inset plot shows the equivalent analysis for the
parameter derived from instantaneous gas exchange measurements (g1-leaf, notice that the scales of the axes differ).
40
1055
1056
105710581059106010611062
1063
1064
Figure 3 Values of χ (the ratio of leaf internal to ambient CO2 partial pressure) obtained from 13C isotope discrimination,
Eqn 12 plotted against corresponding predicted values of χ based on environmental variables of temperature (T) and vapour
pressure deficit (D) obtained from the OzFlux network, Eqn 4. Both estimates have been modified to account for mesophyll
conductance. The large coloured dots represent the site means (combining all species and seasons) and the whiskers give the
standard deviation. The smaller dots represent underlying seasonal means for individual plant species (three to five plants
per species). The dashed line represents the ideal 1:1 fit. The Pearson correlation indicated relates to the underlying species’
means (smaller dots); the corresponding correlation based on site means (large dots) is 0.93.
41
1065
1066
106710681069107010711072
1073
1074
Figure 4 Partial residual plots showing the logit-transformed ratio of leaf internal to external CO2 partial pressure (χ) derived
from stable carbon isotope discrimination data as a function of time averaged environmental variables: air temperature and
vapour pressure deficit (D). Separate panels are shown for the contrasting visits: favourable season (plots (a) and (b)),
unfavourable season (plots (c) and (d)). Model coefficients and summaries appear in Table 3.
42
1075
1076
107710781079
1080
1081
Figure 5 Goodness of fit plot for the carboxylation (Vcmax) model Eqn 5 using Ci data derived from carbon isotope
discrimination plotted against estimates inferred from independent gas exchange measurements. Panel (a) presents rates at
the prevailing measurement temperature; panel (b) shows the corresponding plot with rates standardised to a common
reference temperature of 25°C. The large coloured symbols represent the Site_Season means (combining all species) and the
whiskers give the standard deviation; the smaller dots represent underlying seasonal means for individual plant species (three
to five plants per species). The dashed line represents the ideal 1:1 fit. The Pearson correlations indicated relate to the
underlying species’ means (smaller dots).
43
1082
1083
108410851086108710881089
1090
1091
Figure 6 Goodness of fit plot for the modelled ratio of electron transport to carboxylation capacity (Jmax/Vcmax) from Eqn 6
using Ci data derived from carbon isotope discrimination plotted against independent gas exchange estimates – both ratios
expressed at the prevailing temperature. The large coloured symbols represent the Site_Season means (combining all
species) and the whiskers give the standard deviation. The smaller dots represent underlying seasonal means for individual
plant species (three to five plants per species). The dashed line represents the ideal 1:1 fit. The Pearson correlation indicated
relates to the underlying species’ means (smaller dots); the corresponding correlation based on Site_Season means (large
symbols) is 0.86.
44
1092
1093
109410951096109710981099
1100
1101
1102
Supporting information
Table S1 Plant species listed by site with indication of assigned plant functional type (PFT). PFT categories are broad leafed tree (BlT) and shrub (S); see footnotes below table.
Site Species Family PFTAlice Mulga Acacia aneura Mimosaceae BlT 1
Alice Mulga Corymbia terminalis Myrtaceae BlTAlice Mulga Eucalyptus camaldulensis Myrtaceae BlTCalperum mallee Eucalyptus dumosa Myrtaceae BlTCalperum mallee Eucalyptus socialis Myrtaceae BlTCumberland Plain Eucalyptus amplifolia Myrtaceae BlTCumberland Plain Eucalyptus fibrosa Myrtaceae BlTCumberland Plain Eucalyptus moluccana Myrtaceae BlTCumberland Plain Eucalyptus tereticornis Myrtaceae BlTDaintree Alstonia scholaris Apocynaceae BlTDaintree Argyrodendron peralatum Sterculiaceae BlTDaintree Cardwellia sublimis Proteaceae BlT 2
Daintree Castanospermum australe Fabaceae BlTDaintree Dysoxylum papuanum Meliaceae BlTDaintree Elaeocarpus grandis Elaeocarpaceae BlTDaintree Ficus variegata Moraceae BlTDaintree Gillbeea whypallana Cunoniaceae BlTDaintree Rockinghamia angustifolia Euphorbiaceae BlTDaintree Synima cordierorum Sapindaceae BlTDaintree Syzygium sayeri Myrtaceae BlTDaintree Xanthophyllum octandrum Xanthophyllaceae BlTDaintree Endiandra leptodendron Lauracea BlTRobson Creek Alphitonia whitei Rhamnaceae BlTRobson Creek Alstonia muelleriana Apocynaceae BlTRobson Creek Cardwellia sublimis Proteaceae BlT 2
Robson Creek Ceratopetalum succirubrum Cunoniaceae BlTRobson Creek Daphnandra repandula Monimiaceae BlTRobson Creek Ficus leptoclada Moraceae BlTRobson Creek Gillbeea adenopetala Cunoniaceae BlTRobson Creek Litsea leefeana Lauracea BlTRobson Creek Prunus turneriana Rosaceae BlTRobson Creek Doryphora aromatica Monimiaceae BlTRobson Creek Flindersia bourjotiana Rutaceae BlTRobson Creek Polyscias elegans Araliaceae BlTGreat Western Woodlands Eucalyptus clelandii Myrtaceae BlTGreat Western Woodlands Eucalyptus salmonophloia Myrtaceae BlTGreat Western Woodlands Eucalyptus transcontinentalis Myrtaceae BlT
45
1103
11041105
1106
46
1107
Table S1 contd.
Site Species Family PFTWarra Acacia melanoxylon Mimosaceae BlTWarra Anopterus glandulosus Escalloniaceae SWarra Atherosperma moschatum Monimiaceae BlTWarra Eucalyptus obliqua Myrtaceae BlTWarra Eucryphia lucida Eucryphiaceae BlTWarra Leptospermum lanigerum Myrtaceae BlTWarra Melaleuca squarrosa Myrtaceae BlTWarra Notelaea ligustrina Oleaceae BlTWarra Nothofagus cunninghamii Fagaceae BlTWarra Phyllocladus aspleniifolius Podocarpaceae BlT 3
Warra Pittosporum bicolor Pittosporaceae BlTWarra Pomaderris apetala Rhamnaceae SWarra Tasmannia lanceolata Winteraceae S
1: Arguably better described as needle leafed tree, but an angiosperm rather than gymnosperm; displays phyllodes rather than true leaves
2: Measured at both Daintree and Robson Creek
3: This is a gymnosperm with cladodes rather than true leaves; tree to height 20-30 m
47
1108
1109
11101111
1112
1113
1114
Table S2 Leaf gas exchange traits by site and season and species: the number of branches measured (n, typically two per plant), standard deviation (sd). Light saturated net photosynthesis (A400_a, µmol m-2 s-1); stomatal conductance (gs, mol H2O m-2 s-1); the ratio of the leaf intercellular (Ci) to the ambient (Ca) partial pressure of CO2; leaf dark respiration (Rdark_a, µmol m-2 s-1) and leaf temperature in the measuring chamber (Tleaf, °C). All traits relate to conditions of chamber [CO2] 400 ppm.
g s Ci/Ca ratio R dark, a T leafSite Season Species n Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd)Calperum Summer Eucalyptus dumosa 8 10.38 (5.08) 0.099 (0.06) 0.47 (0.08) 1.94 (0.43) 33.3 (2.5)Calperum Summer Eucalyptus socialis 8 7.49 (4.63) 0.071 (0.05) 0.47 (0.07) 2.69 (0.98) 35.9 (2.6)Calperum Winter Eucalyptus dumosa 8 11.63 (1.57) 0.105 (0.01) 0.51 (0.05) 0.79 (0.19) 18.8 (2.4)Calperum Winter Eucalyptus socialis 8 14.99 (3.15) 0.203 (0.07) 0.65 (0.05) 0.84 (0.21) 17.7 (1.2)GWW Summer Eucalyptus clelandii 8 9.1 (2.48) 0.081 (0.03) 0.47 (0.07) 2.7 (0.68) 29.3 (2.9)GWW Summer Eucalyptus salmonophloia 10 5.62 (2.7) 0.06 (0.02) 0.57 (0.16) 2.53 (0.67) 28.5 (2.6)GWW Summer Eucalyptus transcontinentalis 10 5.7 (2.51) 0.047 (0.02) 0.42 (0.08) 2.72 (1.2) 30.3 (2.3)GWW Winter Eucalyptus clelandii 10 7.39 (3.32) 0.073 (0.02) 0.56 (0.12) 1.56 (1.05) 22.1 (0.9)GWW Winter Eucalyptus salmonophloia 8 6.46 (1.71) 0.053 (0.02) 0.45 (0.1) 1.41 (0.75) 21.3 (2.3)GWW Winter Eucalyptus transcontinentalis 10 6.87 (3.17) 0.055 (0.03) 0.46 (0.05) 1.25 (0.44) 20.7 (1.6)Alice Winter Acacia aneura 19 1.9 (0.86) 0.012 (0.01) 0.4 (0.2) 0.77 (0.63) 24.1 (2.7)Alice Winter Corymbia terminalis 8 1.62 (2.25) 0.016 (0.01) 0.59 (0.22) 1.21 (0.66) 25.4 (0.6)Alice Winter Eucalyptus camaldulensis 6 4.59 (1.9) 0.033 (0.01) 0.42 (0.09) 2.38 (0.6) 27 (0.5)Cumberland Summer Eucalyptus amplifolia 8 12.04 (5.49) 0.143 (0.09) 0.58 (0.06) 4.02 (1.31) 33.9 (0.6)Cumberland Summer Eucalyptus fibrosa 10 6.6 (3.2) 0.066 (0.03) 0.54 (0.08) 1.62 (0.81) 31 (2.4)Cumberland Summer Eucalyptus moluccana 10 10.51 (2.78) 0.137 (0.05) 0.63 (0.05) 1.83 (0.62) 30.4 (0.5)Cumberland Summer Eucalyptus tereticornis 9 12.43 (4.67) 0.198 (0.14) 0.63 (0.11) 3.96 (0.98) 34 (0.8)Cumberland Winter Eucalyptus amplifolia 10 11.36 (3.6) 0.127 (0.09) 0.54 (0.18) 1.55 (0.54) 21 (1.1)Cumberland Winter Eucalyptus fibrosa 8 3.06 (1.57) 0.039 (0.01) 0.67 (0.11) 1.49 (0.72) 22.2 (2.5)Cumberland Winter Eucalyptus moluccana 10 3.43 (1.57) 0.042 (0.01) 0.64 (0.12) 1.43 (0.53) 21.6 (3.5)Cumberland Winter Eucalyptus tereticornis 9 11.48 (3.06) 0.164 (0.12) 0.63 (0.15) 1.78 (0.37) 21.1 (1.1)
A 400, a
48
111511161117
1118
1119
1120
Table S2 contd.
g s Ci/Ca ratio R dark, a T leafSite Season Species n Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd)Warra Summer Acacia melanoxylon 5 10.35 (3.81) 0.127 (0.05) 0.61 (0.08) 1.08 (0.48) 22.4 (2)Warra Summer Anopterus glandulosus 4 3.83 (2.33) 0.047 (0.04) 0.56 (0.14) 0.24 (0.05) 21 (0.2)Warra Summer Atherosperma moschatum 4 3.12 (2.04) 0.025 (0.02) 0.5 (0.14) 0.74 (0.38) 23.1 (1.1)Warra Summer Eucalyptus obliqua 5 9.81 (2.1) 0.092 (0.02) 0.53 (0.06) 1.24 (0.62) 20.5 (0.7)Warra Summer Eucryphia lucida 4 2.48 (1.61) 0.022 (0.01) 0.53 (0.13) 0.46 (0.18) 23.5 (0.9)Warra Summer Leptospermum lanigerum 3 8.79 (0.46) 0.15 (0.04) 0.72 (0.05) 1.45 (0.24) 18.5 (0.2)Warra Summer Melaleuca squarrosa 3 7.96 (3.18) 0.137 (0.07) 0.71 (0.08) 1.02 (0.29) 20.8 (0.4)Warra Summer Notelaea ligustrina 4 7.65 (2.19) 0.081 (0.04) 0.56 (0.1) 1.32 (0.51) 23 (2)Warra Summer Nothofagus cunninghamii 4 3.91 (2.38) 0.035 (0.02) 0.51 (0.03) 0.97 (0.33) 20.7 (3.1)Warra Summer Phyllocladus aspleniifolius 2 6.46 (1.48) 0.058 (0.02) 0.51 (0.02) 1.56 (0.35) 22.7 (0.6)Warra Summer Pittosporum bicolor 4 9.12 (2.88) 0.164 (0.06) 0.74 (0.03) 0.87 (0.14) 22 (0.6)Warra Summer Pomaderris apetala 3 2.23 (1.86) 0.029 (0.02) 0.69 (0.15) 0.53 (0.05) 21.5 (0.6)Warra Summer Tasmannia lanceolata 4 7.51 (1.92) 0.104 (0.03) 0.67 (0.03) 0.69 (0.23) 19.5 (0.6)Warra Winter Acacia melanoxylon 8 5.51 (1.51) 0.093 (0.04) 0.73 (0.05) 0.21 (0.05) 11 (1.3)Warra Winter Atherosperma moschatum 10 4.59 (1.22) 0.055 (0.02) 0.64 (0.04) 0.18 (0.09) 10.5 (0.9)Warra Winter Eucalyptus obliqua 10 5.93 (2.25) 0.071 (0.04) 0.6 (0.09) 0.51 (0.12) 10.5 (1.1)
A 400, a
49
1121
1122
1123
1124
Table S2 contd.
g s Ci/Ca ratio R dark, a T leafSite Season Species n Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd)Robson Ck Dry Alphitonia whitei 4 8.78 (2.54) 0.161 (0.08) 0.72 (0.06) 0.73 (0.31) 24.6 (0.8)Robson Ck Dry Alstonia muelleriana 3 6.9 (3.37) 0.104 (0.05) 0.71 (0.03) 1.14 (0.44) 24.7 (0.5)Robson Ck Dry Cardwellia sublimis 4 7.69 (3.43) 0.154 (0.13) 0.67 (0.16) 0.67 (0.54) 24.7 (0.7)Robson Ck Dry Ceratopetalum succirubrum 3 3.91 (1.73) 0.059 (0.03) 0.69 (0.08) 0.64 (0.12) 24 (0.5)Robson Ck Dry Daphnandra repandula 4 4 (0.88) 0.049 (0.02) 0.63 (0.13) 0.81 (0.65) 25.1 (0.5)Robson Ck Dry Ficus leptoclada 4 4.12 (2.25) 0.102 (0.06) 0.79 (0.12) 1.14 (0.24) 24.3 (1.1)Robson Ck Dry Gillbeea adenopetala 5 6.2 (2.84) 0.111 (0.06) 0.72 (0.11) 0.81 (0.35) 24.9 (1)Robson Ck Dry Litsea leefeana 4 5.54 (1.49) 0.069 (0.03) 0.62 (0.08) 0.89 (0.54) 24.7 (0.3)Robson Ck Dry Prunus turneriana 5 9.55 (4.48) 0.164 (0.12) 0.67 (0.1) 0.65 (0.15) 24.6 (1)Robson Ck Wet Alphitonia whitei 10 7.29 (2.4) 0.156 (0.12) 0.72 (0.15) 0.77 (0.4) 27.9 (1.3)Robson Ck Wet Alstonia muelleriana 7 7.77 (3.9) 0.129 (0.08) 0.7 (0.06) 1.04 (0.42) 27.8 (1)Robson Ck Wet Cardwellia sublimis 9 7.09 (3.3) 0.128 (0.07) 0.71 (0.09) 0.82 (0.39) 28.5 (0.9)Robson Ck Wet Ceratopetalum succirubrum 7 3.67 (0.88) 0.042 (0.01) 0.61 (0.08) 0.55 (0.06) 29 (0.5)Robson Ck Wet Daphnandra repandula 10 5.72 (1.62) 0.091 (0.04) 0.69 (0.07) 0.9 (0.21) 28.7 (1.1)Robson Ck Wet Doryphora aromatica 3 3.23 (0.59) 0.045 (0.01) 0.68 (0.02) 0.48 (0.23) 27.7 (0.5)Robson Ck Wet Ficus leptoclada 10 6.52 (3.29) 0.109 (0.05) 0.73 (0.06) 1.03 (0.23) 28.4 (0.7)Robson Ck Wet Flindersia bourjotiana 9 8.44 (2.64) 0.13 (0.06) 0.68 (0.08) 0.74 (0.27) 27.7 (0.5)Robson Ck Wet Gillbeea adenopetala 10 7.78 (3.16) 0.156 (0.06) 0.75 (0.07) 0.49 (0.13) 28.5 (0.4)Robson Ck Wet Litsea leefeana 10 5.59 (2.5) 0.109 (0.09) 0.71 (0.11) 1.19 (0.39) 28.5 (1.6)Robson Ck Wet Polyscias elegans 10 8.53 (3.21) 0.136 (0.07) 0.69 (0.08) 1 (0.27) 27.9 (1)Robson Ck Wet Prunus turneriana 10 8.89 (4.25) 0.146 (0.1) 0.68 (0.08) 0.96 (0.37) 27.5 (0.6)
A 400, a
50
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Table S2 cont.
g s Ci/Ca ratio R dark, a T leafSite Season Species n Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd)Daintree Dry Alstonia scholaris 4 12.64 (2.29) 0.188 (0.03) 0.68 (0.01) 1.24 (0.45) 31.4 (0.6)Daintree Dry Argyrodendron peralatum 4 15.08 (0.69) 0.265 (0.02) 0.72 (0.02) 1.44 (0.24) 30.6 (0.1)Daintree Dry Cardwellia sublimis 4 12.34 (1.48) 0.271 (0.05) 0.77 (0.01) 0.99 (0.38) 31.1 (0.5)Daintree Dry Castanospermum australe 4 13.31 (1.21) 0.194 (0.03) 0.66 (0.05) 1.29 (0.47) 29.2 (0.4)Daintree Dry Dysoxylum papuanum 4 13.62 (0.94) 0.282 (0.07) 0.75 (0.05) 1.78 (0.34) 30.8 (0.8)Daintree Dry Elaeocarpus grandis 4 18.16 (0.74) 0.472 (0.04) 0.79 (0.03) 1.15 (0.27) 29.4 (0.2)Daintree Dry Ficus variegata 2 9.59 (0.3) 0.174 (0.06) 0.73 (0.08) 1.33 (0.63) 31.2 (0.6)Daintree Dry Gillbeea whypallana 4 11.85 (4.15) 0.22 (0.17) 0.68 (0.1) 1.25 (0.35) 30.5 (0.8)Daintree Dry Rockinghamia angustifolia 4 5.61 (1.52) 0.167 (0.04) 0.84 (0.05) 0.62 (0.26) 30.2 (0.3)Daintree Dry Synima cordierorum 4 10.99 (0.94) 0.187 (0.04) 0.71 (0.04) 1.21 (0.39) 31.7 (0.4)Daintree Dry Syzygium sayeri 4 11.09 (0.63) 0.131 (0.01) 0.6 (0.03) 1.15 (0.32) 31.3 (0.4)Daintree Dry Xanthophyllum octandrum 3 9.12 (2.77) 0.154 (0.08) 0.68 (0.14) 0.78 (0.24) 30.9 (0.4)Daintree Wet Alstonia scholaris 8 9.34 (3.39) 0.125 (0.05) 0.65 (0.09) 1.62 (0.36) 32.3 (0.6)Daintree Wet Argyrodendron peralatum 7 5.6 (2.71) 0.061 (0.03) 0.57 (0.08) 1 (0.32) 32.2 (0.7)Daintree Wet Cardwellia sublimis 8 9.7 (2.48) 0.202 (0.13) 0.71 (0.1) 1.19 (0.24) 32.2 (0.7)Daintree Wet Castanospermum australe 8 6.82 (3.92) 0.082 (0.06) 0.56 (0.08) 1.46 (0.46) 32.3 (0.7)Daintree Wet Dysoxylum papuanum 8 9.56 (2.96) 0.161 (0.07) 0.7 (0.08) 1.94 (0.55) 32.2 (0.7)Daintree Wet Elaeocarpus grandis 8 15.83 (2.8) 0.298 (0.12) 0.7 (0.08) 1.25 (0.47) 31.8 (0.7)Daintree Wet Endiandra leptodendron 8 8.11 (0.92) 0.16 (0.04) 0.75 (0.05) 1 (0.41) 31.8 (0.4)Daintree Wet Ficus variegata 6 14.79 (2.98) 0.272 (0.09) 0.72 (0.04) 1.38 (0.2) 31.8 (0.1)Daintree Wet Gillbeea whypallana 8 12.76 (1.7) 0.335 (0.12) 0.79 (0.04) 0.87 (0.3) 31.2 (0.4)Daintree Wet Rockinghamia angustifolia 8 5.87 (2.34) 0.109 (0.04) 0.76 (0.06) 0.92 (0.28) 32.2 (0.4)Daintree Wet Synima cordierorum 8 6.92 (2.87) 0.097 (0.04) 0.68 (0.06) 1.37 (0.52) 32 (0.7)Daintree Wet Xanthophyllum octandrum 8 11.26 (2.1) 0.173 (0.05) 0.68 (0.07) 1.05 (0.16) 31.2 (0.7)
A 400, a
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Figure S1 Map and key climate indicators for each of the study sites: Alice Mulga (AMU), Calperum Mallee (CAL), Cumberland Plain (CBLP), Daintree (DRO), Great Western Woodlands (GWW), Robson Creek (RCR) and Warra (WAR). Climate variables are interpolated long-term monthly averages (1970-2012) generated by a spatial model: TERN Ecosystem Modelling and Scaling Infrastructure Facility. Precipitation (monthly total, mm); air temperature (°C); net radiation (MJ m -2 day-1); vapour pressure deficit (kPa). Precipitation axes vary for the seven sites.
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Figure S2 Scatterplots comparing instantaneous and time-averaged estimates of (a) the leaf’s Ci/Ca ratio and (b) vapour pressure deficit (kPa). Instantaneous values were obtained during gas exchange measurements with ambient [CO2] in the Li-Cor chamber set to 400 µmol mol-1. The isotope-derived values of χ follow the neo-classical fractionation (Eqn 12). Each point is the seasonal mean for a single species (three to five plants per species) and sites are differentiated by colour. The dashed line represents the ideal 1:1 fit.
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Figure S3 Estimated g1 values derived from instantaneous gas exchange measurements as a function of mean air temperature (°C). The points represent seasonal means for individual plant species (three to five plants per species); sites are differentiated by colour and seasons by symbol.
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Figure S4 Comparison of (a) the g1_istope parameter and (b) the Ci/Ca ratio calculated according to two fractionation formulae: a simple, linear formula (Simple; Eqn 11) and an extended formula (Neo-classical; Eqn 12) that attempts to explicitly account for mesophyll resistance and molecular ternary effects.
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Figure S5 Comparison of leaf temperatures in the LiCor gas chamber with long-term daytime air temperatures. The open symbols show corresponding 24-hour averages for the three early visits that preceded the inception of the OzFlux network.
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Figure S6 The relationship between long-term daytime air temperatures and Vcmax rates inferred from gas exchange measurements standardised to 25 °C and normalised for mean canopy light conditions. Each point is the seasonal mean for a single species (three to five plants per species); sites are differentiated by colour and seasons by symbol. Open symbols denote 24-hour (rather than daytime) temperatures for the three early visits that preceded the inception of the OzFlux network (Methods). The solid line is the fitted linear regression. The relationship is stronger and its slope steeper if data are restricted to daytime averages (r2=0.34) or to ex-tropical sites (r2=0.40).
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Figure S7 Temperature dependence of the Jmax/Vcmax ratios inferred from gas exchange measurements. Each point is the seasonal mean for a single species (three to five plants per species) and sites are differentiated by colour. [Many more species were sampled for the summer visit to Warra (n=13) than the winter visit (n=3).] The solid line is the fitted linear regression.
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