A novel optimization approach incorporating non-stomatal limitations predicts stomatal behaviour in species from six plant functional types

A model formulation that reduced carboxylation capacity to optimize water costs for carbon gain successfully predicted stomatal behaviour for species from six plant functional types, irrespective of water availability or leaf ontogeny.

Abstract

The primary function of stomata is to minimize plant water loss while maintaining CO₂ assimilation. Stomatal water loss incurs an indirect cost to photosynthesis in the form of non-stomatal limitations (NSL) via reduced carboxylation capacity (CAP) and/or mesophyll conductance (MES). Two optimal formulations for stomatal conductance (g_s) arise from the assumption of each type of NSL. In reality, both NSL could coexist, but one may prevail for a given leaf ontogenetic stage or plant functional type, depending on leaf morphology. We tested the suitability of two g_s formulations (CAP versus MES) on species from six plant functional types (C₄ crop, C₃ grass, fern, conifer, evergreen, and deciduous angiosperm trees). MES and CAP parameters (the latter proportional to the marginal water cost to carbon gain) decreased with water availability only in deciduous angiosperm trees, while there were no clear differences between leaf ontogenetic stages. Both CAP and MES formulations fit our data in most cases, particularly under low water availability. For ferns, stomata appeared to operate optimally only when subjected to water stress. Overall, the CAP formulation provided a better fit across all species, suggesting that sub-daily stomatal responses minimize NSL by reducing carboxylation capacity predominantly, regardless of leaf morphology and ontogenetic stage.

Introduction

Climate change is predicted to decrease water availability and increase drought risk in many regions around the world (Sheffield and Wood, 2007; Dai and Zhao, 2017). Water availability is one of the main factors regulating vegetation carbon and water fluxes and drought impacts on the vegetation, reducing productivity, changing species distribution, or even driving large-scale mortality events (Vicente-Serrano et al., 2013; Lobell et al., 2014; Allen et al., 2015). The impact of drought on vegetation functioning is not well represented in current dynamic global vegetation models (DGVM), which still vary greatly in their underlying assumptions (Egea et al., 2011; De Kauwe et al., 2014; Rogers et al., 2017). This is partly caused by the diversity of strategies deployed by plants to cope with drought, ranging from avoidance or migration to drought tolerance (Chaves et al., 2002; Jump and Peñuelas, 2005). Drought tolerance or avoidance requires the coordination of morphological and physiological traits (Croker et al., 1998; Brodribb and Holbrook, 2003; Bartlett et al., 2016), and one of the most important and the most immediate physiological mechanism for regulating plant water loss is stomatal closure (Flexas et al., 2006; Rodriguez-Dominguez et al., 2016; Martinez-Vilalta and Garcia-Forner, 2017). Thus, understanding stomatal regulation is fundamental to projecting the impact of increased drought risk on the climate–vegetation system.

According to optimization theory, stomata should operate to maximize photosynthetic uptake minus the cost of water loss to the plant (Cowan and Farquhar, 1977). The direct benefit of stomatal opening consists of increasing the CO₂ concentration at the sites of carboxylation, but the nature of the associated water costs remains unclear. Transpiration losses through the stomata lead to decreased leaf water potential (Ψ_leaf), which triggers a cascade of processes affecting leaf conductivity, cell turgidity, protein stability, metabolic rates, solute accumulation, and membrane and cell wall permeability (Chaves et al., 2002; Brodribb and Holbrook, 2003; Flexas et al., 2012). As a consequence, non-stomatal limitations (NSL) to photosynthesis arise in the form of reduced carboxylation capacity or CO₂ diffusivity through the mesophyll. As NSL arise, stomata should continue to operate optimally, but the sensitivities of stomatal and non-stomatal processes could be uncoupled, challenging our ability to predict stomatal behaviour. For example, changes in the capacitance and resistance pathway from the soil to the leaves can alter stomatal sensitivity to vapour pressure deficit (D_w) (Martins et al., 2016). Based on the idea that the cost of stomatal opening arises from NSL, Dewar et al. (2018) proposed two analytical formulations for stomatal conductance to CO₂ (g_sc). In these formulations, photosynthesis is maximized instantaneously and the cost of water use arises from reductions in photosynthesis resulting from decreasing Ψ_leaf. These formulations can be structurally similar to previous ones (e.g. Katul et al., 2010; Medlyn et al., 2011; Prentice et al., 2014), but their advantage is that they do not require a defined temporal framework over which stomatal behaviour is optimized. In addition, Dewar et al. (2018) provide a formulation for the elusive λ parameter (marginal water cost to carbon gain; Cowan and Farquhar, 1977) and testable predictions for this cost parameter as a function of hydraulic and photosynthetic traits. Dewar et al. (2018) hypothesized that NSL were induced by a reduction in either carboxylation capacity or mesophyll conductance (g_m). In reality, such a dichotomy is probably rare and both types of NSL coexist (Zhou et al., 2013; Drake et al., 2017). Nevertheless, we could expect a certain NSL to prevail depending on the sensitivity of g_m and the photosynthetic machinery to Ψ_leaf.

Leaves that are likely to experience drought during their lifespan exhibit morphological traits that contribute to maintain Ψ_leaf and physiological activity under water stress (Mediavilla and Escudero, 2003; Flexas et al., 2006); one such trait is high leaf mass per area (LMA). High-LMA leaves often have multiple layers of cells with thick walls and tortuous mesophyll interspaces (Niinemets et al., 2009; Onoda et al., 2017). These traits protect the photosynthetic machinery from dehydration (Gimeno et al., 2010; Limousin et al., 2013) but they are also associated with low mesophyll diffusivity (Niinemets et al., 2009). g_m can be the most important limitation to photosynthesis in evergreen trees and shrubs (Flexas et al., 2012; Peguero-Pina et al., 2016) and also in many ferns (Carriqui et al., 2015; Tosens et al., 2016). Ferns have more rudimentary stomata, in the sense that they are insensitive to abscisic acid, CO₂, and blue light (Brodribb and McAdam, 2011) and they appear to respond only to changes in vapour pressure deficit (Martins et al., 2016). Franks and Britton-Harper (2016) observed that stomata of some ferns can close in response to elevated CO₂, but these responses have not been tested under the framework of optimization theory.

In addition to drought tolerance and leaf morphology, longevity and construction costs can also influence stomatal behaviour. Lin et al. (2015) evaluated the convergence across plant functional types (PFTs) in their ability to operate stomata optimally, and found that PFTs with low marginal water use per unit of C gain had water-transport systems with greater construction costs, except for species with a C₄ pathway, for which optimization theory predicts the lowest marginal water cost. In addition to an associated cost to water transport, leaf construction costs could also influence stomatal regulation. Leaf construction costs depend on leaf lifespan and turnover: generally, the longer the lifespan, the slower the return of nutrient and dry mass investment (Wright et al., 2004). Leaf development is a crucial stage for all PFTs and in deciduous species, it can occur over a significant fraction of their lifespan. Yet, only mature fully expanded leaves are targeted for the vast majority of physiological measurements (with some exceptions, e.g. Field and Mooney, 1983; Medlyn et al., 2002; Locke and Ort, 2014; Macinnis-Ng et al., 2017), and hence DGVM assume constant stomatal behaviour throughout leaf ontogeny (Keenan et al., 2013). During leaf construction, respiratory costs are high, so that net photosynthesis per unit of water transpired is usually low (Rajaona et al., 2013; Jensen et al., 2015). Additionally, during development, the internal leaf anatomy experiences modifications including cell multiplication and expansion, increase in the number of chloroplasts, thickening of the cell wall, and formation of the intercellular air spaces (Niinemets et al., 2009; Kuusk et al., 2018). Collectively, these modifications should result in greater carboxylation capacity and the ability to maintain greater Ψ_leaf under drought stress, but also lower g_m (Aasamaa et al., 2005; Grassi and Magnani, 2005; Barbour et al., 2016). Hence, in developing leaves the photosynthetic costs of stomatal opening likely result from a decrease in carboxylation capacity, rather than in g_m.

Here, we tested the formulations proposed by Dewar et al. (2018) to determine when carboxylation capacity (the CAP formulation) or g_m (the MES formulation) are the predominant NSL to photosynthesis. We expected developing leaves to have a greater marginal water cost per unit of C gain than mature leaves. We also hypothesized that in developing leaves the carboxylation capacity should be more sensitive to Ψ_leaf and the CAP formulation should fit better. Regarding the effect of leaf morphological differences among species from various PFTs, we hypothesized that in leaves with lower LMA the photosynthetic machinery would be more sensitive to fluctuations in Ψ_leaf and the CAP formulation would be more suitable, while in species where g_m constitutes the main limitation to photosynthesis, such as in evergreen species with greater LMA and ferns, the MES formulation could fit better. On the other hand, in leaves with low maximum realized g_m, fluctuations in g_m would have a marginal effect and NSL should arise from limited carboxylation capacity. We address these hypotheses within the theoretical framework of Dewar et al. (2018) by comparing stomatal behaviour between mature and developing leaves of species from six PFTs (including a fern) maintained under two watering regimes.

Materials and methods

Plant material and experimental design

We selected seven species economically or ecologically relevant to the ecosystems in the south-west of France and representative of six PFTs: a fern, common bracken (Pteridium aquilinum L. Kuhn); an evergreen conifer, maritime pine (Pinus pinaster Ait.); two deciduous angiosperm temperate trees, pedunculate oak (Quercus robur L.) and silver birch (Betula pendula Roth); a C₃ grass, purple moor grass [Molinia caerulea (L.) Moench]; an evergreen angiosperm tree, cider gum (Eucalyptus gunnii Hook.f.); and a C₄ crop, maize (Zea mays L.).

Saplings of P. pinaster, Q. robur, B. pendula, and E. gunnii were grown from seeds obtained from nearby plantations in an open-air nursery at INRA Pierroton (Cestas, France; annual precipitation 977 mm, annual mean temperature 13 °C). Plants of M. caerulea and P. aquilinum were grown from tussocks (~15 cm diameter) and overwintering rhizomes, respectively, collected in February 2015 from a local forest (Le Bray experimental site; Wingate et al., 2010). Plants of Z. mays (variety DKC 5784) were grown from seeds sown in May 2015. All species were grown in 3.4 l square pots. The soil substrate consisted of a 4:2:1 (v/v) mix of bark:peat:soil (typical sandy soil from the Le Bray experimental site). A slow-release fertilizer (Osmocote^TM, Mashville, OH, USA) was added at the beginning of the experiment.

From March (May for Z. mays) to September 2015, pots were kept in a glasshouse at the INRA campus of La Grande Ferrade (Villenave d’Ornon, France). Pots were watered every other day to field capacity with an automatic dripping system. In March and April 2015, to treat a fungal infection by Erysiphe sp., Q. robur plants were sprayed twice with a 0.4 g l⁻¹ solution of tebuconazole and twice with a 0.6 g l⁻¹ solution of Meptyldinocap Karathane® 3D (Merck KGaA, Darmstadt, Germany). In addition, in May 2015, an early aphid outbreak on B. pendula was controlled by spraying plants with colza oil. Climatic conditions were monitored inside the glasshouse with a temperature and humidity probe (HMP60, Vaisala, Vanta, Finland) and, in June and July, a quantum sensor (SQ-200, Apogee, Logan, UT, USA). Ten-minute averages were logged on a 21X micrologger® (Campbell Scientific, Logan, UT, USA). Mean temperature over the study period inside the glasshouse was 21.7 °C during the day and 16.6 °C at night. A shading cloth was permanently deployed from May 2015 and mean daily photosynthetic photon flux density was 121±6.4 mol m⁻² d⁻¹ (measured over 38 days in June–July).

In July 2015, we selected seven plants of each species and assigned them to a low-water-availability treatment. The concept of low water availability is complex, and attaining a homogeneous level of water stress that allows for comparison between species is not straightforward (Drake et al., 2017). Our low-water treatment consisted of cessation of watering for a number of days until the mean predawn leaf water potential (Ψ_pd) was reduced by half of the total range. We determined this range and number of days for our study species in a separate experiment. In brief, five randomly selected individuals of each species were placed in a climatically controlled chamber (MD1400, Snijders Labs, Tilburg, The Netherlands) on 1 July 2015. The temperature and relative humidity inside the chamber were set to mimic a typical summer day inside the glasshouse, with a 13/9 h light/dark cycle and a photosynthetic photon flux density of 580 µmol m⁻² s⁻¹, supplied by fluorescent lamps (BriteGro 2084, Sylvania, BioSystems, Wageningen, The Netherlands). Plants inside the chamber were watered to field capacity on the first day and then watering was withheld until all individuals died. We assessed dead individuals as plants that had no green leaves and had lost stem flexibility, and verified that none of these individuals re-sprouted following re-watering. Pots with M. caerulea tussocks were measured until at least 80% of the leaves in a pot had completely withered. After 25 days of withholding water, individuals of P. pinaster still showed no visible signs of water stress; by this point Ψ_pd had dropped from –0.32±0.04 to –1.19±0.11 MPa (Table 1) and the experiment ceased. The range of Ψ_pd for each species was determined as the difference between the mean (n=5 plants per species) maximum and minimum Ψ_pd. Measurements of Ψ_pd were made with a Scholander-type pressure chamber (SARL SAM PRECIS 2000, Gradignan, France).

Table 1.

Mean ±SE (n=5–7) predawn leaf water potential (Ψ_pd) for the study species in the trial experiment and in the August 2015 campaign under the well-watered (WW) and low water availability (LW) regimes

Species	Trial experiment		August 2015
	Max Ψpd (MPa)	Min Ψpd (MPa)	Ψpd WW (MPa)	Ψpd LW (MPa)
B. pendula	–0.56±0.1	–2.31±0.25	–0.51±0.12	–1.47±0.34
E. gunnii	–0.58±0.28	–2.03±0.3	–0.58±0.28	–0.97±0.4
M. caerulea	–0.46±0.04	–2.24±0.22	–0.48±0.05	–1.38±0.25
P. pinaster	–0.32±0.04	–1.19±0.11	–0.37±0.1	–0.54±0.08
P. aquilinum	–0.47±0.13	–1.24±0.06	–0.45±0.07	–0.89±0.25
Q. robur	–0.46±0.21	–2.44±0.2	–0.53±0.09	–0.83±0.24
Z. mays	–0.28±0.03	–1.94±0.26	–0.4±0.05	–1.42±0.29

In August 2015, there were no significant differences among species (F=1.9, P=0.11) and plants in the LW treatment had significantly lower Ψ_pd (F=15.9, P<0.01).

Morphological and physiological measurements

LMA was calculated from one mature and one young developing leaf collected from five individuals per species in mid-June 2015. Mature fully expanded leaves were sampled from the middle of the plant (in height), and young developing leaves, not fully expanded, from the upper third. For P. aquilinum, we selected a pinna (leaflet) close to the base of the frond as a mature pinna, and the most distal as a young pinna. Sampled leaves were photographed and dried at 70 °C for 48 h. Leaf area was calculated using ImageJ software for image analysis (Rasband, 2009). Leaf thickness was measured on the same leaves.

We conducted two gas-exchange campaigns, each over the course of 3 or 4 sunny and cloudless days with comparable conditions within campaigns. In the first campaign (18, 22, and 24 June 2015), we measured five well-watered individuals of each species, and during the second campaign (5, 6, 20, and 21 August 2015), we measured three or four individuals of each species and water treatment (well-watered and low-watered). On each day, we measured gas exchange starting at 08.00 h (local time) every 2–2.5 h on the same set of plants, to track net photosynthesis (A_net) and g_sc along a gradually increasing gradient of D_w (Fig. 1). Plants measured each day were kept outside in an open area next to the glasshouse, exposed to full sun, from the first until the last round of measurements. A mature fully expanded leaf, and in June also a young developing leaf, was measured on each individual at each measurement round. Gas-exchange measurements were performed with an open-flow portable photosynthesis system (IRGA, LI-6400, LI-COR, Lincoln, NE, USA) with a standard leaf chamber fluorometer head (LI-6400–40). We measured A_net and g_sc under saturating light intensity (1800 µmol m⁻² s⁻¹ for Z. mays and 1500 µmol m⁻² s⁻¹ for all other species) provided by the inbuilt LI-6400 red–blue LED lamp. The CO₂ concentration entering the cuvette was set to 390 µmol mol⁻¹ using the inbuilt CO₂ control unit, and airflow through the cuvette was set to 500 µmol s⁻¹ (300 µmol s⁻¹ when g_sc <0.03 mol m⁻² s⁻¹). Relative humidity and temperature inside the leaf cuvette were maintained as close as possible to ambient conditions during each measurement period. Measurements were logged after reaching steady state (within 3–5 min).

Fig. 1.

Mean ±SE (n=3–5) vapour pressure deficit (D_w), stomatal conductance to CO₂ (g_sc), and photosynthesis (A_net) measured in mature (Mat) and developing (Dev) leaves and in well-watered (WW) and low-watered (LW) plants during the day. Circles and triangles depict measurements from the June 2015 and August 2015 campaigns, respectively. Different shades correspond to measurements on WW versus LW plants or to measurements on mature versus developing leaves. Note the change in scale for A_net in the Z. mays panel.

In August, we measured predawn leaf (B. pendula, Q. robur, M. caerulea, Z. mays), needle (P. pinaster), branch (E. gunnii), or pinna (P. aquilinum) water potential (Ψ_pd) on all water-stressed plants (n=7 per species) and on the well-watered plants selected for gas exchange measurements in this campaign (n=3 or 4 per species, except for well-watered E. gunnii, where Ψ_pd was assumed to be the maximal Ψ_pd from the trial experiment).

Data analyses and model fitting

We tested the effects of leaf age, species, and water stress on optimal stomatal behaviour according to the formulations of Dewar et al. (2018). When reduced carboxylation capacity (CAP) underlies the costs of stomatal opening, Dewar et al. (2018) predict that g_sc (stomatal conductance to CO₂ in mol m⁻² s⁻¹) should follow:

$${\displaystyle \begin{array}{l}{\displaystyle g_{sc}=g_0+(1+\frac{\xi }{\sqrt{D_w}})\frac{A_{net}}{C_a-{\mathrm{\Gamma }}^{*}}}\end{array}}$$ (1)

where D_w (mmol mol⁻¹) is the leaf-to-air vapour pressure deficit, A_net (µmol m⁻² s⁻¹) is net photosynthesis, C_a (µmol mol⁻¹) is the ambient CO₂ concentration, Γ* (µmol mol⁻¹) is the CO₂ photorespiratory compensation point according to Bernacchi et al. (2001), and ξ (mmol^0.5 mol^−0.5) is a fitting parameter proportional to the square root of λ (Cowan and Farquhar, 1977) and equivalent to the g₁ parameter in Medlyn et al. (2011). The intercept g₀ (mol m⁻² s⁻¹) is another fitting parameter that was added to the model to represent suboptimal water losses through leaf cuticles or leaky stomata (Medlyn et al., 2011). Parameters ξ and g₀ were estimated from non-linear least-square regressions using the ‘nls’ function in R (R Development Core Team, 2017). The non-linear nature of the CAP formulation does not allow a straightforward comparison among species and between treatments. Therefore, to assess differences in ξ among species, between leaf ages (June campaign) and between watering treatments (August campaign), we compared the 95% confidence intervals (CI) for the fitted parameter (ξ; Gimeno et al., 2016). Our estimates of ξ (in mmol^0.5 mol^−0.5 and calculated for g_sc) can be related to the g₁ estimates of Lin et al. (2015, in kPa^0.5 and calculated for g_s to water) according to $g_1=\xi /1.6\sqrt{P}$, where P is atmospheric pressure and the factor 1.6 accounts for the ratio of the molecular diffusion coefficients for CO₂ and water.

In addition, according to the CAP formulation, Dewar et al. (2018) also predict that parameter ξ should vary according to:

$${\displaystyle \begin{array}{l}{\displaystyle \xi \sqrt{V_{cmax0}}=\sqrt{\frac{K_{sl}\left|{\mathrm{\Psi }}_c\right|k_m+{\mathrm{\Gamma }}^{*}}{1.6}}}\end{array}}$$ (2)

where V_{c max0} is the maximum carboxylation capacity in the absence of NSL, k_m is the Michaelis constant for CO₂ (710 μmol mol⁻¹; Bernacchi et al., 2001), and Ψ_c is the critical leaf water potential when photosynthetic capacity is zero (–2 MPa; Dewar et al., 2018). Noting that Dewar et al. (2018) neglect mitochondrial respiration, V_cmax=0 at Ψ_leaf=Ψ_c also translates into A_net=0. We compared our estimates of ξ $\sqrt{{\widehat{V}}_{cmax0}}$ with the predicted values according to equation 2 for a range of predawn Ψ (Ψ_pd, from 0 to –2 MPa). To do so, we estimated temperature-corrected V_cmax0 (${\widehat{V}}_{cmax0}$) with the one-point method (De Kauwe et al., 2016) from mid-morning gas-exchange measurements (see Supplementary Protocol S1 at JXB online). The soil-to-leaf hydraulic conductance (K_sl) was estimated from equations 5b and 5c in Dewar et al. (2018) using a range of fixed values of the root-to-leaf hydraulic conductance (K_rl=2, 5, 12, and 50 mmol m⁻² s⁻¹ MPa⁻¹, Dewar et al., 2018), and a soil-to-root conductance estimated from retention curve parameters typical of an organic soil (Ogée and Brunet, 2001) and a bulk density of 0.25 g cm⁻³.

When decreased mesophyll conductance (MES) underlies the cost of stomatal opening, Dewar et al. (2018) predict:

$${\displaystyle \begin{array}{l}{\displaystyle g_{sc}=g_0+\sqrt{\frac{E_{max}{A}_{net}}{1.6D_w(C_a-{\mathrm{\Gamma }}^{*})}}}\end{array}}$$ (3)

where

$${\displaystyle \begin{array}{l}{\displaystyle E_{max}=K_{sl}(\mathrm{\Psi }-{\mathrm{\Psi }}_c)}\end{array}}$$ (4)

and Ψ_c represents here the critical leaf water potential that leads also to A_net=0 but through a reduction of g_m rather than V_cmax. We estimated E_max from the square of the slope of the linear relationship between g_sc and $\sqrt{\frac{A_{net}}{1.6D_w(C_a-{\mathrm{\Gamma }}^{*})}}$ (hereafter the MES index) for each species, leaf age, and watering treatment. We used analyses of homogeneity of slopes to test for the effects of species, leaf age (June campaign), and water stress (August campaign) on $\sqrt{E_{\max }}$. Additionally, we compared our estimates of E_max with the predicted value according to equation 4 for the same range of Ψ_pd (0 to –2 MPa and assuming Ψ_s=Ψ_pd) and K_sl, calculated from four K_rl (2, 5, 12, and 50 mmol m⁻² s⁻¹ MPa⁻¹; Dewar et al., 2018) as explained above.

We fitted equations 1 and 3 to our gas-exchange data and used the likelihood ratio test, the Akaike Information Criterion (AIC, with ΔAIC>10 indicating an improvement in the model fit), and root mean square error (RMSE) from the relationship between observed and predicted g_sc to compare between formulations for each species, leaf age, and watering treatment. We assessed differences among species and between leaf ages on morphological parameters (LMA and leaf thickness, June campaign) and among species and between watering treatments on Ψ_pd (August campaign) with two-way ANOVA. We tested for the overall effect of species and leaf age (June campaign) or watering treatment (August campaign) on A_net and g_sc with linear mixed models including plant and round of measurements as random factors (Zuur et al., 2009). We tested for the effect of leaf age (June campaign) and watering (August campaign) on ξ and E_max with one-way ANOVA. Finally, we explored the relationship between ξ and E_max and LMA and leaf thickness, for different species and leaf ages (only the June campaign), using linear regression. All analyses were performed in R v3.4.3 (R Development Core Team, 2017).

Results

The seven study species exhibited morphological trait values representative of their corresponding PFT (F=60.1 for LMA and 38.6 for thickness, P<0.001; Supplementary Fig. S4). In all species, LMA and leaf thickness were significantly lower in developing leaves than mature leaves (F=11.1, P=0.002 for LMA and F=16.2, P<0.001 for leaf thickness; Supplementary Fig. S4).

Over the course of the gas-exchange campaigns, D_w increased gradually from 8 to 42 mmol mol⁻¹. For all species and in both campaigns, we measured A_net and g_sc under a D_w range of at least 12–35 mmol mol⁻¹ (Fig. 1). We measured maximum A_net and g_sc on the first round of measurements, when D_w was minimal. In general, as D_w increased, A_net, g_sc, and estimated carboxylation capacity (${\widehat{V}}_{cmax}$) decreased from mid-morning to midday measurements (Supplementary Fig. S1). The ratio of intercellular to ambient CO₂ concentrations (C_i/C_a) was between 0.6 and 0.8 for the C₃ species and remained relatively constant within species along an increasing D_w gradient in the June campaign (Supplementary Fig. S2). We measured the lowest C_i/C_a in the C₄ crop (Z. mays) under well-watered conditions (Supplementary Fig. S2). In August, under low water availability, C_i/C_a gradually increased with D_w in P. pinaster and Z. mays (Supplementary Fig. S2). There were significant differences among species (P<0.05, except for g_sc in June, P=0.07) in g_sc and A_net: the C₄ crop (Z. mays) had significantly higher A_net and lower g_sc than the other species (Fig. 1). In June, developing leaves had significantly higher g_sc and lower A_net than mature fully expanded leaves, although the leaf age effect was species specific, with maximum differences in the C₄ species for A_net and in the fern for g_sc (Fig. 1). In addition, developing leaves had lower ${\widehat{V}}_{cmax0}$ (Supplementary Fig. S1). The low-water treatment reduced Ψ_leaf, g_sc, A_net, and ${\widehat{V}}_{cmax0}$ (Table 1, Fig. 1, Supplementary Fig. S1). Again, the treatment effect was species specific: the reduction in both g_sc and A_net under low-water treatment was least pronounced in the two evergreen trees (Fig. 1), to the point that Ψ_leaf of P. pinaster was only marginally affected (Table 1).

We found that both the CAP and the MES formulations fitted our data for most species, leaf ages, and watering treatments, with a few exceptions. Neither the CAP nor the MES formulation fitted the fern (P. aquilinum) data for either mature or developing leaves in the June campaign, or for well-watered ferns in the August campaign (Supplementary Fig. S3). Similarly, neither the CAP nor the MES formulation fitted the measurements on well-watered plants of M. caerulea (the C₃ grass) in the August campaign (Supplementary Fig. S3). In addition, the CAP formulation did not fit the measurements on the C₄ species Z. mays for developing leaves in June and under either watering treatment in August (Supplementary Fig. S3). For the other species, leaf ages, and watering treatments, both the CAP and MES formulations fitted our observations. When both the CAP and MES formulations could explain our data, the AIC value and the RMSE, as well as the likelihood ratio test (in most cases), indicated that the CAP formulation provided a better fit than that of the MES formulation, with the exception of developing leaves in P. pinaster and Q. robur (Table 2). Observed and predicted g_sc values were more closely related for the CAP formulation (lower RMSE), regardless of leaf age and watering regime (Fig. 2). For the CAP formulation, there was a significant difference between mature and developing leaves, with a smaller RMSE for mature leaves compared with developing leaves (Table 2). There were no significant differences in the fit between low-watered and well-watered plants (Fig. 2).

Table 2.

Estimated intercept (g₀) and slope parameters (±SE), RMSE, and AIC for the two formulations for stomatal conductance, CAP (ξ and g₁) and MES (E_max), for all the study species in mature and developing leaves (June 2015) and for well-watered and low watered plants (August 2015), and results of the model intercomparison

Species	Campaign	Leaf age	Treatment	CAP					MES				χ2
				g 0 (mmol m−2 s )	ξ (mmol0.5 mol−0.5)	g 1 (kPa0.5)	RMSE (%)	AIC	g 0 (mmol m−2 s )	E max (mmol m−2 s−1)	RMSE (%)	AIC
Z. mays	June 2015	Mature	Well-watered	1.3±11.4	2±0.7	0.4±0.1	1.57	–120	9.4±18.5	5.1±1.9	1.89	–111	8.44
B. pendula				4.1±7.7	7.7±1.9	1.5±0.4	1.94	–90	–4.3±14.4	6.4±3.2	2.30	–72	<0.01
Q. robur				1.5±4.7	6.2±1	1.2±0.2	1.21	–108	–17.2±10.4	7.7±2.4	1.52	–86	<0.01
E. gunnii				3.2±7.8	5.7±1.1	1.1±0.2	1.76	–99	–19.5±13.7	7.9±2.5	2.11	–92	7.31
M. caerulea				–0.1±7.4	9.8±0.9	2±0.2	1.11	–92	–31.4±12.9	15±3.5	1.83	–77	15.91
P. pinaster				3.1±10.2	6.9±1.3	1.4±0.3	1.62	–81	–20.1±20.6	9.3±3.7	2.23	–70	10.39
P. aquilinum				17.3±6.9	1.3±1.5	0.3±0.3	0.99	–103	21.7±12.6	0.6±0.7	1.30	–93	9.26
Z. mays	June 2015	Developing	Well-watered	16±4.8	0.9±0.4	0.2±0.1	0.95	–143	–4.7±11.1	5.4±1.4	1.40	–125	17.5
B. pendula				–16.8±13.4	14.8±2.9	2.9±0.6	2.64	–74	–32.2 ± 21	17±7.2	3.16	–67	6.45
Q. robur				21±5.1	4.7±1.7	0.9±0.4	0.96	–123	14.1 ± 6.6	2.8±1.2	0.90	–126	2.79
E. gunnii				8.4±12	5.3±1.9	1.1±0.4	1.97	–84	–0.7±19.1	5±2.9	2.23	–80	4.53
M. caerulea				3.1±9.1	16.1±2.2	3.2±0.4	2.02	–83	–19.7±15.3	17.5±5.3	2.55	–75	8.36
P. pinaster				–4.2±13.2	10.5±2.1	2.1±0.4	2.38	–68	–29.1±16.8	13.3±4	2.27	–70	1.53
P. aquilinum				90.6±39.9	–1±12.8	–0.2±2.6	9.66	–23	59.3±54.9	4.6±11.9	9.39	–24	0.92
Z. mays	August 2015	Mature	Well-watered	7.8 ±8	0.9±0.9	0.2±0.2	1.23	–48	4.7±17.8	2.3±1.7	2.06	–38	9.2
B. pendula				–0.5±8.5	9±1.7	1.8±0.4	1.76	–57	–26.9±16	11.1±3.9	1.81	–44	<0.01
Q. robur				–6.5±4.6	8.7±0.9	1.7±0.2	1.06	–107	–26.0±10.3	9±2.4	1.69	–90	16.75
E. gunnii				8.1±3.3	3.8±0.9	0.8±0.2	0.85	–81	–10.7±8	4±1.4	0.95	–56	<0.01
M. caerulea				39.8±31.2	2±15.5	0.4±3.1	4.70	–43	14.9±51.7	5.9±16	4.58	–44	0.77
P. pinaster				7.5±3.4	4.1±0.8	0.8±0.2	0.80	–103	0.8±5.7	2.5±0.7	0.93	–91	<0.01
P. aquilinum				24.2±15.7	3.4±3.5	0.7±0.7	2.23	–37	10.8±25.9	3.9±4.4	2.42	–35	1.47
Z. mays	August 2015	Mature	Low-watered	4.3±0.7	–1.1±0.4	–0.2±0.1	0.20	–138	0.8±2.3	0.5±0.2	0.27	–89	<0.01
B. pendula				4.7±1	2.2±0.4	0.4±0.1	0.32	–133	–2.6±2.6	1.6±0.4	0.37	–82	<0.01
Q. robur				5.3±2	2.6±0.6	0.5±0.1	0.50	–172	–0.1±3.1	1.4±0.3	0.56	–167	5.12
E. gunnii				8.2±2	4.2±0.8	0.8±0.2	0.66	–109	–7.7±4.7	3.3±0.9	0.52	–58	<0.01
M. caerulea				12.2±2	3.7±0.9	0.7±0.2	0.53	–155	5.5±3	2.2±0.6	0.55	–153	1.39
P. pinaster				0.2±2.4	4.±0.5	0.8±0.1	0.70	–115	–9.4±5.1	2.7±0.7	1.09	–99	15.19
P. aquilinum				2.8±1.1	2±0.7	0.4±0.2	0.28	–101	–2.6±2.6	1.2±0.4	0.29	–71	<0.01

Significant (P<0.05) results (χ²) and coefficients significantly different from 0 are in bold.

Fig. 2.

Relationship between predicted and observed stomatal conductance to CO₂ (g_sc) according to the CAP (A and C) and MES (B and D) formulations for mature and developing leaves (A and B) and under well-watered and low-watered conditions (C and D) for the study species for which the CAP and MES fits were significant. The dashed grey line depicts the 1:1 line. The slope of each linear relationship is indicated on each plot (with its corresponding 95% CI).

Comparison of estimated ξ (CAP formulation) values with their 95% CI showed that there were some differences among species. Deciduous angiosperm trees had the highest ξ values and the C₄ crop (maize) the lowest (Table 2). Under well-watered conditions and in mature leaves, estimated ξ values for the four tree species were similar (Fig. 3A). We did not find clear differences in ξ values between mature and developing leaves in any species (Fig. 3A), although in M. caerulea ξ tended to be higher in developing than mature leaves. The low-water treatment decreased ξ in the deciduous angiosperm trees (oak and birch; Fig. 3B). For the MES formulation, analyses of homogeneity of slopes showed similar results to the comparison of the 95% CI for ξ values. Species differed significantly in their slopes ($\sqrt{E_{\max }}$) in both measurement campaigns (Table 3). Deciduous angiosperm trees had the highest slope, and the C₄ crop the lowest, whereas all tree species had comparable slopes for mature leaves under well-watered conditions (Table 2). Leaf age did not have a significant effect on E_max and the water-stress treatment decreased E_max in angiosperm deciduous trees (Tables 2 and 3), although the relationship between g_sc and the MES index did not appear to be linear for the majority of plants under the low-water treatment (Supplementary Fig. S2).

Fig. 3.

Parameter estimate (±95% CI) for the CAP formulation (ξ) fitted for (A) mature and developing leaves (June 2015 campaign, only well-watered plants) and (B) well-watered and low-watered plants (August 2015 campaign, only mature leaves), for all study species. Non-overlapping CIs indicate significant differences (P<0.05) among species, leaf ages, or watering treatments. Grey symbols indicate species for which the CAP fit was non-significant.

Table 3.

Results of the analyses of homogeneity of slopes to test for the effects of species, leaf age (mature versus developing), and watering treatment on optimization behaviour according to equation 4 (MES index is √A/1.6D(C_a-Γ^*))

Campaign	Effect	F	P
June	MES	205.8	<0.001
	MES×age	1.7	0.195
	MES×species.	3.7	0.002
	MES×age×species	0.6	0.742
August	MES	579.2	<0.001
	MES×treatment	32.2	<0.001
	MES×species	5.3	<0.001
	MES×treatment×species.	1.1	0.341

Significant effects (P<0.05) are indicated in bold.

Analyses of homogeneity of slopes for E_max and comparisons of the 95% CIs for ξ values were consistent with results of one-way ANOVAs. Under low water availability, E_max decreased (Fig. 4, F=8, P=0.015), but the species mean ξ values did not decrease significantly (Fig. 3, F=2.2, P=0.16). Leaf age did not have a significant effect on E_max (F=0.5, P=0.511) or ξ (F=0.4, P=0.561). We did not find any significant correlation between ξ and E_max with LMA or leaf thickness (F≤3 and P≥0.1 for all analyses; Supplementary Fig. S5).

Fig. 4.

(A) Parameter estimate for the CAP model (ξ) multiplied by the square root of temperature-corrected estimated maximum carboxylation capacity (${\widehat{V}}_{cmax0}$, only for the six C₃ species) and (B) parameter estimate for the MES model (E_max), both plotted against mean predawn leaf water potential (Ψ_pd). Error bars indicate ±SE. Parameters ξ and E_max were fitted to mature leaves measured in the August campaign in well-watered and low-watered plants. Grey symbols without vertical error bars indicate species for which the CAP or MES fit were non-significant (P>0.05). The lines indicate the model predictions for different values of root-to-leaf xylem conductivity (K_rl, mmol m⁻² s⁻¹ MPa⁻¹) from equation 2 (A) and equation 4 (B).

When we plotted $\xi \sqrt{{\widehat{V}}_{cmax0}}$ against Ψ_pd for well-watered and low-watered plants (Fig. 4A), we found that estimates for deciduous angiosperm trees varied greatly between watering treatments and fell close to the values predicted by the CAP formulation (equation 2) for high K_rl (50 mmol m⁻² s⁻¹ MPa⁻¹). In contrast, for all other C₃ species, $\xi \sqrt{{\widehat{V}}_{cmax0}}$ did not appear to decrease notably from well-watered to low-watered plants, and our estimates fell within the CAP model predictions for lower K_rl (2–12 mmol m⁻² s⁻¹ MPa⁻¹). The plot of E_max against Ψ_pd showed a similar pattern (Fig. 4B), but with a larger spread. Our E_max estimates for deciduous angiosperm trees fell closer to the theoretical estimates (equation 4) predicted for higher K_rl.

Discussion

In this study we compared two analytical solutions to predict g_sc based on optimization theory, assuming that the water costs of stomata opening originate from NSL to photosynthesis caused by either a reduction in carboxylation capacity (the CAP formulation) or in g_m (the MES formulation; Dewar et al., 2018). We expected the CAP formulation to fit better for developing leaves and for PFTs with low-LMA leaves. Our results showed that the CAP formulation fitted better to our measurements not only for low-LMA leaves, but for all species, leaf ages, and watering regimes.

One single formulation to predict stomatal behaviour across plant functional types

We had hypothesized that the suitability of the two formulations proposed by Dewar et al. (2018) would vary across species from PFTs depending on leaf morphology. Here, the variation in LMA observed among species from contrasting PFTs was consistent with previous classifications (Poorter et al., 2009). Anatomical traits encompassing low g_m are usually associated with high LMA, and thus the limitation to photosynthesis imposed by g_m should increase with LMA (Flexas et al., 2008; Niinemets et al., 2009). We expected a better fit of the MES formulation for evergreen species (which have high LMA; Veromann-Jurgenson et al., 2017). However, this was not the case. Instead, the CAP formulation provided a better fit for all species, including the conifer and evergreen angiosperm trees, especially under conditions of reduced water availability (Fig. 4). This result is consistent with the midday depression observed in carboxylation capacity for all species, leaf ages, and watering levels (Supplementary Fig. S1). A better fit of the CAP over the MES formulation does not imply that g_m is not limiting photosynthesis, but rather that decreased carboxylation capacity in response to diurnal changes in Ψ_leaf can impose a stronger constraint on carbon gain over the timescale at which stomata operate (Franks et al., 2017). This result supports our alternative expectation that in evergreen leaves, with low maximum g_m (Niinemets et al., 2009; Flexas et al., 2012), marginal reductions in g_m in response to daily oscillations in Ψ_leaf, are unlikely to impose further NSL to photosynthesis over short (sub-daily) timescales. Nevertheless, the extent of our results is limited by the lack of a quantitative analysis of the relative limitations to photosynthesis (Grassi and Magnani, 2005).

Our estimates of ξ (parameter of the CAP formulation) for our study species were generally lower than but comparable to those of Lin et al. (2015) for their corresponding PFT. In agreement with previous classifications, we found that ξ was lowest for the C₄ species (maize) and higher for the deciduous and herbaceous angiosperm species (Lin et al., 2015; Miner et al., 2017). In addition, our ξ estimates for tree saplings and potted plants were comparable to previously reported values for congeneric and closely related species grown under controlled conditions (Héroult et al., 2013; Zhou et al., 2013, 2014). For the crop with a C₄ pathway (maize), we found that the CAP formulation provided a better fit (especially for mature leaves) in the June campaign, but in the August campaign it did not provide a reasonable fit. Our measurements of A_net and g_sc in this latter campaign were lower than those from the June campaign and some were nearing the limit of detection of our porometer (Fig. 1). It is possible that during this second campaign, maize plants were close to the senescent stage (Jordan-Meille and Pellerin, 2004), and a coordinated age-related decline in both g_sc and g_m could have influenced our measurements (Barbour et al., 2016).

As far as we know, this is the first study to specifically test for optimal stomatal behaviour in a fern species. We found that, unless subjected to reduced water availability, stomata in the fern P. aquilinum did not operate according to optimization theory. The main NSL to photosynthesis in many ferns is g_m (Carriqui et al., 2015; Tosens et al., 2016), so we expected the MES formulation to provide the best fit, but this was not the case. We suggest that in ferns, although g_m often imposes the main limitation to photosynthesis (Carriqui et al., 2015), at the sub-daily timescale at which stomata operate, marginal reductions in g_m with Ψ_leaf are unlikely to incur further costs on C gain. Additionally, although carboxylation capacity can co-limit photosynthesis in some ferns (Gago et al., 2013), it is unlikely that rudimentary fern stomata are capable of exhibiting a coordinated response to minimize damage to the photosynthetic machinery (Brodribb and McAdam, 2011). Instead, collectively, our results could be interpreted as further supporting the passive hydraulic control of stomata in ferns (despite our coarse characterization of the sensitivity of g_sc to D_w; Fig. 1), where changes in D_w and leaf capacitance would underlie stomatal control (Brodribb and McAdam, 2011; Martins et al., 2016).

DGVM need measurable physiological traits for distinct PFTs to provide reliable estimates of vegetation–atmospheric fluxes (Miner et al., 2017). Our results support that the parameter ξ (from the CAP formulation in Dewar et al., 2018, and equivalent to g₁ in Medlyn et al., 2011) is a good candidate trait to be incorporated into DGVM to predict the coupling of carbon and water fluxes under future atmospheric conditions, in combination with other fundamental physiological traits. Parameter ξ (or g₁) captures well the variability across PFTs (Fig. 3) and is commonly associated with other traits, so that it could contribute to defining common plant strategies within PFTs. For example, species with high ξ should exhibit low V_cmax0 (Lin et al., 2015; Hasper et al., 2017; Medlyn et al., 2017), as well as high |Ψ_c| and low K_rl, and hence high resistance to cavitation. Yet, we should be cautious regarding the generalization of a static ξ value for each PFT, particularly under decreasing water availability (Zhou et al., 2013; Drake et al., 2017). In addition, it remains to be tested how plasticity or endogenous regulation could impact ξ estimates within and across PFTs (Peguero-Pina et al., 2016; de Dios et al., 2017; Miner and Bauerle, 2017; Wolz et al., 2017).

Impact of water availability on optimal stomatal behaviour

We found that under low water availability, stomata behaved according to optimization theory in all our study species. The low-watering treatment had the expected physiological effects: it reduced A_net, g_sc, ${\widehat{V}}_{cmax}$, and Ψ_pd relative to well-watered plants (Fig. 1, Table 1), although Ψ_pd did not decrease significantly in P. pinaster (Table 1), which maintained Ψ_pd well above levels required to induce stem cavitation for this species (Bouche et al., 2016). Decreased A_net is directly affected by stomatal closure because of diminished substrate supply, but also by NSL (Farquhar and Sharkey, 1982). Most likely, decreased A_net under low water availability resulted from reductions in both types of NSL, that is, g_m (Théroux-Rancourt et al., 2014; Wang et al., 2018) and carboxylation capacity (Flexas et al., 2006; Gimeno et al., 2010). Here, we did not explicitly quantify the contributions of each NSL to reduced photosynthesis under water stress (Grassi and Magnani, 2005; Zhou et al., 2013; Drake et al., 2017) and we did not measure g_m. Yet, we found that ${\widehat{V}}_{cmax0}$ decreased at midday and the CAP formulation provided the best fit under both watering levels (Fig. 2). Thus, we suggest that reduced carboxylation capacity is likely to be a major driver underlying rapid (sub-daily) stomatal regulation, irrespective of water availability.

We observed some differences in stomatal behaviour among species from the six PFTs as a consequence of reduced water availability. The marginal water cost to C gain (ξ or g₁) was conserved under low water availability, except for the two deciduous angiosperm trees, for which ξ decreased under low water availability. Conserved ξ or g₁ under decreasing water availability has previously been reported for species with a large drought-tolerance range, while more drought-sensitive species were capable of modulating their marginal water cost under water stress (Héroult et al., 2013; Zhou et al., 2013; Gimeno et al., 2016; Miner and Bauerle, 2017). For the fern, we found a tight coupling of g_sc and A_net according to optimization theory only under low water availability, suggesting that the hydro-passive mechanism underlying the stomatal response to increasing D_w might be functional only below a certain water availability threshold (Martins et al., 2016). According to the CAP formulation, Dewar et al. (2018) predict a larger decrease in $\xi \sqrt{{\widehat{V}}_{cmax0}}$ with Ψ_leaf for species with a higher root-to-leaf conductivity (K_rl). Our results support this prediction (Fig. 4), as we found that for the two deciduous angiosperm trees, which were expected to exhibit high K_rl (Domec et al., 2017), our estimates of $\xi \sqrt{{\widehat{V}}_{cmax0}}$ fell close to the CAP model predictions for the higher range of K_rl. For the conifer (P. pinaster), the water-stress treatment did not reduce Ψ_pd and our estimates of $\xi \sqrt{{\widehat{V}}_{cmax0}}$ from low-watered and well-watered plants overlapped (Fig. 4), a response that typifies isohydric behaviour, consistent with previous observations for this species (Ripullone et al., 2007).

Optimal stomatal behaviour in developing leaves

We found that optimal stomatal behaviour in developing leaves mimicked that of their mature counterparts (Fig. 3). Contrary to our expectations, we did not find lower A_net in developing leaves. Similar A_net would imply that in developing leaves respiratory costs associated with construction would be compensated for in mature leaves with greater maintenance costs (Zhou et al., 2015). Alternatively, or in addition, developing leaves would compensate for extra respiratory costs with greater carboxylation capacity (Medlyn et al., 2002; Rajaona et al., 2013; Locke and Ort, 2014), but this latter explanation is not supported by our estimates of carboxylation capacity using the one-point method (Supplementary Fig. S1) (De Kauwe et al., 2016). More importantly, the CAP formulation predicted similar coupling between g_sc and A_net with no clear differences in the marginal water cost to C gain (ξ or g₁) between mature and developing leaves. The CAP formulation fitted best for both leaf ages, except for the developing leaves of pine and oak, although the CAP formulation also successfully predicted g_sc in the developing leaves of these species. This result supports our initial expectation that the photosynthetic apparatus of developing leaves would be sensitive to diurnal oscillations in Ψ_leaf and their stomata would operate to minimize the likelihood of dehydration damage (Chaves et al., 2002). Similar stomatal behaviour for both leaf ages suggests that developing leaves might not be less tolerant to dehydration, although the underlying protective mechanisms could vary. For example, it has been shown that developing leaves can tolerate similar water stress to mature leaves, albeit at a greater carbon cost as they rely on the accumulation of compatible solutes (Sperdouli and Moustakas, 2014). This strategy would compensate for a lack of morphological adaptation expressed later in ontogeny. Furthermore, our results could also suggest that g_m limits photosynthesis in mature and developing leaves to a similar extent and that g_m does not necessarily decrease during leaf development. Developmental changes that result in lower g_m, such as thicker cell walls, could be partially compensated, for example, with an increase in the chloroplast per surface area ratio (Evans et al., 2009). Overall, our results did not support that the type of formulation and the marginal water cost to C gain in the developing stage would differ from those applied to mature canopies. Instead, our results indicated that stomatal behaviour was conserved along leaf ontogeny and across PFTs, despite potentially large differences in construction costs (Wright et al., 2004; Onoda et al., 2017). So far, DGVM have ignored possible changes in stomatal behaviour with leaf ontogeny, even for deciduous forests in temperate regions (Keenan et al., 2013). Our study, together with other recent observations (Macinnis-Ng et al., 2017), suggest that the CAP formulation could serve as a homogeneous approach to predict g_sc under changing environmental conditions, irrespective of leaf ontogenetic stage.

Conclusions

We have shown that the optimal formulation assuming that water costs to carbon gain arise from reduced carboxylation capacity (CAP) provides a suitable fit for predicting stomatal behaviour irrespective of leaf ontogenetic stage, PFT, and water availability. Our results do not imply that NSL to photosynthesis are restricted to reduced carboxylation capacity. Instead, we argue that sub-daily marginal changes in g_m are unlikely to be influencing the rapid stomatal responses to atmospheric drought. Our results suggest that the CAP formulation successfully captures the co-variation of g_sc and A_net with increasing D_w, irrespective of PFT, water stress, or leaf ontogenetic stage, for the timescale at which stomata operate. Our data also support that the use of a single-parameter CAP formulation is valid for most PFTs, with the caveat that the CAP parameter (ξ or g₁) should not be assumed to be static in response to water availability for all PFTs, particularly for those most sensitive to drought.

Supplementary data

Supplementary data are available at JXB online.

Protocol S1. Estimated carboxylation capacity with the one-point method.

Fig. S1. Estimated carboxylation capacity.

Fig. S2. Internal to ambient CO₂ molar ratio against vapour pressure deficit.

Fig. S3. Stomatal conductance plotted against the indexes of the two formulations.

Fig. S4. Leaf mass per area and leaf thickness for all species and two leaf ages.

Fig. S5. Model slope parameters plotted against leaf mass per area and leaf thickness.

Glossary

Abbreviations:

A_net

net photosynthesis

C_a

ambient CO₂ concentration

C_i

leaf intercellular CO₂ concentration

DGVM

dynamic global vegetation models

D_w

vapour pressure deficit

E

transpiration

g_m

mesophyll conductance

g_s

stomatal conductance to water

g_sc

stomatal conductance to CO₂

K_rl

root-to-leaf conductivity

K_sl

soil-to-leaf conductivity

LMA

leaf mass per area

NSL

non-stomatal limitations

PFT

plant functional type

Γ*

CO₂ photorespiratory compensation point

Ψ_leaf

leaf water potential

Ψ_pd

predawn leaf water potential