Mesophyll conductance response to short‐term changes in p CO₂ is related to leaf anatomy and biochemistry in diverse C₄ grasses

Summary

• Mesophyll CO₂ conductance (g _m) in C₃ species responds to short‐term (minutes) changes in environment potentially due to changes in leaf anatomical and biochemical properties and measurement artefacts. Compared with C₃ species, there is less information on g _m responses to short‐term changes in environmental conditions such as partial pressure of CO₂ (pCO₂) across diverse C₄ species and the potential determinants of these responses.

• Using 16 C₄ grasses we investigated the response of g _m to short‐term changes in pCO₂ and its relationship with leaf anatomy and biochemistry.

• In general, g _m increased as pCO₂ decreased (statistically significant increase in 12 species), with percentage increases in g _m ranging from +13% to +250%. Greater increase in g _m at low pCO₂ was observed in species exhibiting relatively thinner mesophyll cell walls along with greater mesophyll surface area exposed to intercellular air spaces, leaf N, photosynthetic capacity and activities of phosphoenolpyruvate carboxylase and Rubisco. Species with greater CO₂ responses of g _m were also able to maintain their leaf water‐use efficiencies (TE_i) under low CO₂.

• Our study advances understanding of CO₂ response of g _m in diverse C₄ species, identifies the key leaf traits related to this response and has implications for improving C₄ photosynthetic models and TE_i through modification of g _m.

Introduction

Mesophyll CO₂ conductance (g _m) describes the movement of CO₂ from substomatal cavities across the intercellular air space, cell walls and membranes to the site of first carboxylation. This carboxylation occurs in the mesophyll chloroplast in species with the C₃ photosynthetic pathway and mesophyll cytosol in species with the C₄ photosynthetic pathway (Evans & von Caemmerer, 1996). g _m varies across plant groups due to variation in leaf anatomy and biochemistry, changes dynamically in response to environmental stimuli and has a significant impact on the plant and ecosystem‐level photosynthetic CO₂ uptake and water‐use efficiency (Evans et al., 2009; Flexas et al., 2014; Knauer et al., 2019b; Pathare et al., 2020a). Despite its importance for photosynthetic CO₂ uptake and water‐use at both plant and ecosystem levels and its variation across diverse plants groups, g _m is only beginning to be explicitly implemented into global models that upscale leaf‐scale photosynthetic processes to canopy and global scales (Rogers et al., 2017; Knauer et al., 2019a,b; von Caemmerer, 2021). The implementation of g _m is complicated because there is a lack of detailed information on responses of g _m to short and long‐term changes in environmental conditions (e.g. light, precipitation, temperature and CO₂ concentration) across diverse plant groups (Rogers et al., 2017; Knauer et al., 2019a,b). Most investigations on the responses of g _m to changes in environmental conditions have focussed on C₃ species (von Caemmerer & Evans, 2015; Xiong et al., 2015; Carriqui et al., 2018; Shrestha et al., 2018), but there is less information on the response of g _m to changing environmental conditions in diverse C₄ species (Ubierna et al., 2017, 2018; Kolbe & Cousins, 2018; Sonawane et al., 2021). A better understanding of how C₄–g _m responds to changing environmental conditions is essential for improving the models of C₄ photosynthesis at both the leaf and global scales (Rogers et al., 2017; Knauer et al., 2019a,b; von Caemmerer, 2021) as well as for potentially increasing water‐use efficiency of C₄ crops through manipulation of g _m (Cousins et al., 2020; Pathare et al., 2020a).

The influence of g _m on C₃ photosynthesis has been well studied for the past several years and there has been a significant understanding of C₃–g _m and its responses to changes in long‐term growth conditions and short‐term measurements conditions. In general, g _m varies greatly among diverse C₃ species and limits C₃ photosynthesis as much as stomatal conductance (g _sw) (Flexas et al., 2014; Muir et al., 2014; Barbour & Kaiser, 2016; Veromann‐Jürgenson et al., 2017). Variation in C₃–g _m is influenced by leaf ontogenic and anatomical traits such as leaf development and senescence (Grassi & Magnani, 2005; Barbour et al., 2016), surface area of chloroplasts appressed to intercellular air space (S _c) (Tosens et al., 2012; Peguero‐Pina et al., 2016), mesophyll cell wall thickness (T _CW) (Veromann‐Jürgenson et al., 2017; Ellsworth et al., 2018; Evans, 2021) and leaf thickness (Flexas et al., 2008; Muir et al., 2014). In terms of responses to long‐term growth conditions, C₃–g _m is influenced by water stress, elevated CO₂, salinity, nutrient supplement and growth latitude (Flexas et al., 2008; Momayyezi & Guy, 2017; Mizokami et al., 2018; Shrestha et al., 2018). Additionally, rapid responses of g _m (within minutes) have been observed in response to short‐term changes in leaf temperature, quantity and quality of light, relative humidity and CO₂ concentrations (Hassiotou et al., 2009; von Caemmerer & Evans, 2015; Xiong et al., 2015), although not always (Loreto et al., 1992; Tazoe et al., 2009). However, there is no consensus on the exact cause of rapid responses of g _m to changes in environmental conditions such as CO₂.

Some studies have suggested that rapid changes in g _m in C₃ plants can be explained by changes in chloroplast position and movement that could lead to short‐term change in S _c (Oguchi et al., 2005; Tholen et al., 2008; Terashima et al., 2011). However, mesophyll cell wall thickness (T _CW) and composition are considered invariable in the short term (minutes) and may not explain the rapid responses of g _m (Evans et al., 2009; Terashima et al., 2011; Carriqui et al., 2018). Alternatively, the rapid responses of C₃–g _m have also been attributed to changes in activities of key photosynthetic enzymes such as carbonic anhydrase (CA), which catalyses the conversion of CO₂ to HCO₃ ⁻ (Evans et al., 2009; Momayyezi & Guy, 2017) and the facilitation effect of CO₂‐permeable aquaporins (Uehlein et al., 2008; Flexas et al., 2012; Kaldenhoff, 2012; Groszmann et al., 2017; but please refer to Kromdijk et al., 2019; Huang et al., 2021). Still, other groups have suggested that the rapid responses of C₃–g _m could be the result of systematic methodological errors or the use of oversimplified models. These include mathematical dependency of g _m on other variables (such as A _net and C _i) used to calculate it in fluorescence and Δ¹³C methods, neglecting the contribution of respiratory and photorespiratory CO₂ release to the total CO₂ pool in the leaf and inaccurate measurement of day respiration or estimates of the Rubisco fractionation factor in the Δ¹³C method (Tholen & Zhu, 2011; Gu & Sun, 2014; Carriqui et al., 2018; Ubierna et al., 2019). The exact mechanism underlying the CO₂ responses of C₃–g _m therefore remains controversial. However, considerable evidence based on diverse species and methods of estimating g _m have suggested that the C₃–g _m values increase at low partial pressures of CO₂ (pCO₂).

There has been a recent increase in research on the short‐term and long‐term variability of g _m in diverse C₄ species and in the leaf traits that could explain this variability (Barbour et al., 2016; Cano et al., 2019; Pathare et al., 2020a,b). Our recent study demonstrated that, as for C₃ species, g _m varied significantly among diverse C₄ grasses and had significant effects on photosynthetic rates and leaf water‐use efficiencies (Pathare et al., 2020a). This variation in C₄–g _m was correlated with leaf‐level traits such as leaf thickness, stomatal ratio (SR), adaxial stomatal densities and S_mes. We also demonstrated that C₄ grasses adapted to low precipitation habitats exhibited traits related to greater g _m but lower leaf hydraulic conductance compared with grasses from habitats with relatively high precipitation (Pathare et al., 2020b). These studies have advanced our understanding of the variability of g _m in C₄ grasses, as well as how g _m is influenced by leaf‐level traits and is affected by long‐term growth conditions such as precipitation. However, there is still a limited understanding of how g _m in diverse C₄ grasses responds to short‐term changes in environmental conditions such as CO₂. The few previous studies have largely focussed on a few species such as sorghum, maize and Setaria (Osborn et al., 2017; Kolbe & Cousins, 2018; Ubierna et al., 2018; Sonawane & Cousins, 2020). To the best of our knowledge, no studies to date have explored if g _m responses to short‐term changes in pCO₂ vary among diverse C₄ grasses and what potential anatomical and biochemical traits could explain this variation in CO₂ response of C₄–g _m.

The overall objectives of the current study were (1) to investigate the response of g _m to short‐term changes in pCO₂ in diverse C₄ species, (2) identify the anatomical and biochemical traits that may explain the variable CO₂ response of C₄–g _m, and (3) evaluate the impact of varying CO₂ responses of g _m on changes in photosynthesis and leaf water‐use efficiency. To address the above objectives, we estimated g _m under changing pCO₂ in 16 diverse C₄ grasses (please refer to Pathare et al., 2020a for details), using the most recent method described by Ogee et al. (2018). We also investigated the relationship of leaf anatomical traits, previously known to influence C₄–g _m, with variable CO₂ responses of g _m in the 16 C₄ grasses. Furthermore, we investigated the impacts of photosynthetic capacity on the CO₂ response of g _m in these C₄ grasses through measurements of maximum photosynthetic rates (A _max), leaf nitrogen content (N_area), activities of key enzymes of C₄ photosynthetic pathway such as CA, phosphoenolpyruvate carboxylase (PEPC) and ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) and PEPC affinity for its substrate HCO₃ ⁻ (K _m). N_area is integral to the proteins of photosynthetic machinery (such as PEPC, Rubisco and CA) that, along with the leaf structure, are responsible for the drawdown of CO₂ inside the leaf (Parkhurst, 1994; Wright et al., 2004; Evans et al., 2009; DiMario et al., 2018).

Materials and Methods

Plant growth

Sixteen C₄ grasses (Table 1) were selected for this study. In the graphs, each species is identified by four letter word that combines the first letter of the genus and first three letters of the species name. The plants were raised from seeds and grown in 3‐l free drainage pots in a controlled environment growth chamber (model GC‐16; Enconair Ecological Chambers Inc., Winnipeg, MB, Canada). The photoperiod was 14 h including a 2 h ramp at the beginning and end of the light period. Light and dark temperatures were maintained at 26 and 22°C, respectively. Light was provided by 400‐W metal halide and high‐pressure sodium lamps with maximum photosynthetic photon flux density (PPFD) of c. 1000 μmol photons m⁻² s⁻¹ at plant height. One individual per species was grown per pot in a Sunshine mix LC‐1 soil (Sun Gro Horticulture, Agawam, MA, USA) with five or six replicate pots per species. The plants were irrigated daily to pot saturation and fertilised twice a week with Peters 20–20–20 (2.5 g l⁻¹). Pots were randomised daily within the growth chamber.

Table 1.

Mean ± SE (n = 3–6) values along with the corresponding letters of post‐hoc Tukey's test for important leaf‐level anatomical and biochemical traits measured for the 16 C₄ grasses.

Species	Species code	T CW (μm)	Smes (μm2 μm−2)	SR (unitless)	SDada (number mm−2)	Leaf thickness (μm)	PEPC activity (μmol m−2 s−1)	Rubisco activity (μmol m−2 s−1)	k CA (μmol m−2 s−1 Pa−1)	K m (μM HCO3 −1)	A max (μmol CO2 m−2 s−1)	Narea (gm−2)
Chloris gayana	cgay	0.09 ± 0.012 a	9.3 ± 0.5 bcf	0.62 ± 0.08 c	106.2 ± 6.74 fhi	153 ± 9.4 bc	112 ± 13 bf	32 ± 3 bh	19 ± 1 cdeg	29 ± 0.8 ad	35.6 ± 1 abc	1.81 ± 0.16 abcd
Danthoniopsis dinteri	ddin	0.11 ± 0.005 ab	18.6 ± 1.3 a	1.45 ± 0.08 ab	142.9 ± 4.5 abc	241 ± 1.7 a	156 ± 8 bc	81 ± 4 a	49 ± 4 ab	33 ± 1.4 abcd	42.4 ± 1.1 a	2.25 ± 0.07 a
Digitaria sanguinalis	dsan	0.11 ± 0.009 abcd	8.9 ± 0.6 bc	0.52 ± 0.02 c	40.4 ± 3.26 de	155 ± 8.3 bc	721 ± 33 a	30 ± 1 b	13 ± 2 cde	44 ± 1.6 ef	33.2 ± 2.8 bcde	1.5 ± 0.09 bcd
Eriachne aristidea	eari	0.08 ± 0.004 a	13.1 ± 0.2 de	2.33 ± 0.11 d	130 ± 5.53 abf	216 ± 3.6 ad	348 ± 46 de	79 ± 4 ac	74 ± 6 af	36 ± 1.1 abceg	37.7 ± 2.2 abc	2.22 ± 0.09 a
Echinochloa colona	ecol	0.11 ± 0.006 abc	10 ± 0.3 bcdef	0.82 ± 0.02 ce	68.7 ± 4.17 dg	168 ± 3.5 bef	408 ± 11 de	50 ± 2 de	72 ± 1 af	46 ± 2.1 f	42.5 ± 0.9 a	1.71 ± 0.03 abcd
Eragrostis curvula	ecur	0.17 ± 0.03 cdef	10.6 ± 0.6 bcdef	1.09 ± 0.07 ef	97.8 ± 7.39 hi	176 ± 17.9 bdef	97 ± 14 f	43 ± 0 dfgh	35 ± 2 bg	34 ± 0.7 abcdg	38 ± 0.7 abc	2.22 ± 0.22 a
Eleusine indica	eind	0.12 ± 0.006 abcde	10 ± 0.4 bcdef	1.41 ± 0.07 abf	155.8 ± 2.91 ac	186 ± 1.8 bdef	149 ± 15 bcf	41 ± 3 bdfgh	14 ± 2 cde	33 ± 1.3 abcd	41.1 ± 0.5 ab	2.05 ± 0.14 abc
Eriochloa sericea	eser	0.1 ± 0.001 abc	11.3 ± 0.4 bcdef	1.41 ± 0.11 af	89.8 ± 2.55 gh	184 ± 2.8 bdef	496 ± 16 ad	59 ± 2 ce	86 ± 2 f	41 ± 0.9 efg	35.5 ± 2.8 abc	2.08 ± 0.1 ac
Heteropogon contortus	hcon	0.18 ± 0.004 ef	9.9 ± 0.3 bcdf	0.56 ± 0.02 c	53.9 ± 2.98 d	119 ± 8.5 c	116 ± 21 bf	36 ± 2 bfgh	9 ± 1 c	35 ± 1.2 abcdg	25.6 ± 2.7 de	1.24 ± 0.07 d
Ischaemum afrum	iafr	0.1 ± 0.012 a	12.4 ± 0.3 def	0.81 ± 0.03 ce	157.3 ± 10.11 c	153 ± 5.3 bc	323 ± 28 de	48 ± 2 deg	25 ± 1 deg	39 ± 0.9 cefg	33.6 ± 1.6 bcd	1.43 ± 0.08 d
Paspalum dilatatum	pdil	0.21 ± 0.005 f	8.5 ± 0.3 b	0.68 ± 0.05 c	52 ± 3.62 d	150 ± 9.7 bc	185 ± 8 cg	38 ± 1 bdfgh	15 ± 3 cde	31 ± 0.9 abd	30.6 ± 0.4 cde	1.39 ± 0.1 d
Paspalum macrophyllum	pmac	0.18 ± 0.022 def	9.8 ± 0.4 bcdf	0.12 ± 0.01 g	12 ± 1.04 e	159 ± 6.2 bce	162 ± 8 bc	30 ± 2 b	12 ± 1 cd	37 ± 2.2 bceg	25.3 ± 1.1 e	1.47 ± 0.05 bd
Panicum maximum	pmax	0.12 ± 0.005 abcd	11.9 ± 0.2 cdef	1.76 ± 0.12 b	111.8 ± 5.14 fhi	161 ± 2.4 bce	155 ± 9 bcf	44 ± 3 dfg	69 ± 5 af	30 ± 2.7 abd	38.1 ± 0.9 abc	1.72 ± 0.01 abcd
Panicum miliaceum	pmil	0.11 ± 0.001 abc	11.8 ± 1.4 cdef	1.31 ± 0.04 af	88.6 ± 4.52 gh	212 ± 11.4 ad	199 ± 17 cg	31 ± 2 b	78 ± 3 f	30 ± 1.1 abd	38.6 ± 0.4 abc	1.77 ± 0.04 abcd
Panicum virgatum	pvir	0.17 ± 0.03 bcdef	13.6 ± 1.3 e	1.28 ± 0.04 af	117.3 ± 7.89 bfi	210 ± 12.9 adf	296 ± 38 eg	62 ± 5 ace	165 ± 22 h	29 ± 0.2 d	36.9 ± 1.5 abc	2.27 ± 0.22 a
Urochloa dictyoneura	udic	0.08 ± 0.003 a	9.9 ± 0.6 bcdf	1.11 ± 0.03 ef	129 ± 5.08 abf	201 ± 4.6 adef	154 ± 8 bcf	34 ± 2 bfh	29 ± 2 beg	46 ± 1.7 f	37.4 ± 0.6 abc	1.29 ± 0.05 d

Species code was created using first letter of the genus and first three letters of the species. Results of one‐way ANOVA are given in Supporting Information Table S2. Information on species biochemical subtypes can be found in Table S3. Values for T _CW, S_mes, SR, SD_ada, Leaf thickness, k _CA and N_area have been published in Pathare et al. (2020a), whereas values for K _m have been published in DiMario et al. (2021). Mesophyll cell wall thickness (T _CW), total mesophyll cell surface area exposed to intercellular air space per unit of leaf surface area (S_mes), stomatal ratio (SR), adaxial stomatal density (SD_ada), phosphoenolpyruvate carboxylase activity (PEPC), carbonic anhydrase activity expressed as first‐order rate constant (k _CA), PEPC affinity for HCO⁻ ₃ (K _m), maximum photosynthetic capacity (A _max) and leaf N content (N_area).

Gas exchange measurements and estimation of g _m

The measurements of net photosynthetic rates (A _net, μmol CO₂ m⁻² s⁻¹), stomatal conductance to water vapour (g _sw, mol water m⁻² s⁻¹), intercellular CO₂ concentrations (C _i, Pa) and mesophyll conductance to CO₂ (g _m, μmol m⁻² s⁻¹ Pa⁻¹) were performed at four different CO₂ levels inside the chamber or C _a (34, 27, 20 and 14 Pa) using an LI‐6400XT infrared gas analyser (Li‐Cor, Lincoln, NE, USA). Intrinsic water‐use efficiency (TE_i, μmol CO₂ mol⁻¹ water) was calculated as A _net/g _sw for each CO₂ level. Maximum photosynthetic rates (A _max, μmol CO₂ m⁻² s⁻¹) were measured at saturating light of c. 1200 μmol photons m⁻² s⁻¹ and pCO₂ of c. 1500 μmol mol⁻¹.

Several methods have been used to estimate C₄–g _m. Pfeffer & Peisker (1998) calculated the g _m from the initial slope of a photosynthetic CO₂ response curve and assumed no CO₂ dependence of g _m. However, C₄–g _m is sensitive to pCO₂ (Kolbe & Cousins, 2018; Ubierna et al., 2018) and therefore the initial slope method may be problematic. Using anatomical traits such as S_mes for estimating C₄–g _m could also be subject to errors due to assumptions made for values of T _CW, porosity and membrane permeability (Pengelly et al., 2010). The ∆¹³C in vitro V _pmax method (Ubierna et al., 2017) estimates C₄–g _m by retrofitting models of C₄ photosynthesis model (von Caemmerer, 2000) and the ∆¹³C (Farquhar & Cernusak, 2012) with gas exchange, kinetic constants and in vitro PEPC activities. The ∆¹³C in vitro V _pmax method may also result in inaccurate estimates of C₄–g _m due to errors associated with the estimation of in vitro PEPC and CA activities and enzyme kinetic parameters. The ∆¹⁸O method (Gillon & Yakir, 2000; Barbour et al., 2016; Barbour & Kaiser, 2016) utilises simultaneous measurements of the oxygen isotope composition (δ¹⁸O) of transpired H₂O and oxygen isotope discrimination (Δ¹⁸O) in CO₂ to calculate the CO₂ concentration at the site of isotope exchange by CA. The Δ¹⁸O method assumes full isotopic equilibrium between CO₂ and H₂O at the site of CA (θ = 1), which may not always be true and therefore g _m could be underestimated. In the current study we used the method described by Ogee et al. (2018) that builds upon the Δ¹⁸O method (Barbour et al., 2016; Ogee et al., 2018). This method utilises a newly developed model of C₄ photosynthetic discrimination that provides an estimate of the isotopic equilibrium between CO₂ and H₂O inside the leaf and g _m and accounts for the physical separation between mesophyll and bundle sheath cells in C₄ leaves and the contribution of respiratory fluxes (Ogee et al., 2018). For estimating g _m, isotopologs of CO₂ and H₂O were measured using a LI‐6400XT infrared gas analyser (Li‐Cor) coupled to a tunable diode laser absorption spectroscope (TDLAS, model TGA 200A; Campbell Scientific, Logan, UT, USA) and a cavity‐ring down absorption spectroscope (Picarro, Sunnyvale, CA, USA), as described previously (Ubierna et al., 2017; Kolbe & Cousins, 2018; Pathare et al., 2020a). The entire LI6400XT, the 2 cm × 6 cm leaf chamber (6400‐11; Li‐Cor) and LI‐6400‐18‐RGB light source were placed in a growth cabinet (model EF7, Conviron; Controlled Environments Inc., North Branch, MN, USA) with fluorescence lamps (F48T12/CW/VHO; Sylvania, Wilmington, MA, USA) set at a PPFD of c. 250 μmol photons m⁻² s⁻¹ and air temperature was maintained at 25°C. For each species, four‐point CO₂ response curve of g _m, g _sw, A _net and TE_i was performed. During measurements CO₂ sample (CO₂S) was set to c. 34, 27, 20 and 14 Pa. At each CO₂ level, the leaves were allowed to adjust for at least 30 min or until stable values of A _net and g _sw were achieved. Data for isotopologs of CO₂ and H₂O and physiological parameters (A _net, g _sw, C _i, TE_i) were collected and averaged over the next 20–30 min for each CO₂ level (c. 10–15 cycles of TDLAS) with the Li6400XT set to log data only when the TDLAS analysed the sample line. At each CO₂ level, three biological replicates were measured for the species eari and five biological replicates were measured for species' pvir and eser. For the remaining 13 species, measurements were conducted on four biological replicates. After the completion of above measurements, lights were tuned off and the leaves were allowed to stabilise for 15 min before logging the rates of dark respiration (R _n, μmol CO₂ m⁻² s⁻¹). Mesophyll conductance was estimated for each species at each of the four pCO₂ levels using the Ogee et al. (2018) method. Key input parameters used in calculation of isotope parameters and estimation of g _m are given in Supporting Information Table S1. Further details of equations and calculations of fractionation factors can be found in Ogee et al. (2018); Ubierna et al. (2017). While estimating g _m, we assumed that ϕr or the fraction of respired CO₂ not produced in the bundle sheath cells of C₄ plants = 0.5 (von Caemmerer, 2000) and analysed the impacts of changing ϕr values (0–1) on the calculation of g _m (Fig. S1). We also performed a sensitivity analysis of g _m to changes in leaf temperature (ranging from 23 to 28°C) (Fig. S2). For C₄ plants, photorespiration was assumed to be negligible and therefore not accounted for while estimating g _m. While estimating g _m at different pCO₂ (34, 27, 20 and 14 Pa), it was assumed that the pH of the mesophyll cytosol was constant due to the small micromolar shifts in dissolved CO₂ at different pCO₂ (DiMario et al., 2018). We assumed that the day respiration (Vr, μmol CO₂ m⁻² s⁻¹) was equal to the dark respiration (R _n) for the C₄ species.

Measurement of anatomical traits and habitat mean annual precipitation

Light and electron microscopy techniques were used to measure important structural and anatomical traits such as adaxial stomatal density (SD_ada, number mm⁻²), abaxial stomatal density (SD_aba, number mm⁻²), SR (unitless) expressed as ratio of SD_ada : SD_aba, leaf thickness (μm), mesophyll surface area exposed to intercellular air spaces (S_mes, μm² μm⁻²) and T _CW (μm). The details of sample preparation for microscopy and measurements are presented in Pathare et al. (2020a) and in the Methods S1. Values for mean annual precipitation (MAP) for habitats where the C₄ grasses commonly occur were obtained as indicated in Pathare et al. (2020b).

Enzyme assays and measurement of leaf nitrogen content

Immediately following gas exchange measurements, leaf samples were taken from the same leaf and frozen in liquid nitrogen for enzyme assays. Measurements of CA, phosphoenolpyruvate carboxylase (PEPC, μmol m⁻² s⁻¹) and ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco, μmol m⁻² s⁻¹) activities were performed at 25°C, as described previously (Sharwood et al., 2016; Sonawane & Cousins, 2019; Pathare et al., 2020a). Carbonic anhydrase activities were expressed as the first‐order rate constant (k _CA, μmol m⁻² s⁻¹ Pa⁻¹). In addition to enzyme activities, PEPC affinity for HCO⁻ ₃ (K _m, μM HCO₃ ⁻¹) values were derived using a membrane‐inlet mass spectrometer. K _m values have been published in DiMario et al. (2021). For measuring leaf nitrogen content, leaf samples were taken from the same leaf on which gas exchange measurements were performed. Samples were dried in a hot air oven at 60°C for 72 h. Leaf nitrogen content was measured using a Eurovector elemental analyser and expressed on a leaf area basis (N_area, g m⁻²).

Estimating CO₂ responses of physiological traits (g _m, g _sw, A _net and TE_i )

Changes in g _m in response to decrease in pCO₂ (from 34 to 14 Pa) were analysed using two different methods. First, the percentage change in g _m in response to a decrease in pCO₂ (from 34 to 14 Pa) was calculated for each biological replicate for the 16 species as follows:

$$\left(\text{trait value}\mathrm{at}14\mathrm{Pa}-\text{trait value}\mathrm{at}34\mathrm{Pa}\right)\times 100/\left(\text{trait value}\mathrm{at}34\mathrm{Pa}\right)$$

Similar to g _m, we also calculated percentage change in A _net, g _sw, C _i and TE_i in response to the decrease in pCO₂ (from 34 to 14 Pa). pCO₂ levels of 34 and 14 Pa were chosen, as these two pCO₂ levels were the highest and lowest levels, respectively, used in current study. Relationships of percentage change in physiological traits with key leaf anatomical and biochemical traits are given in the Figs S3–S8.

In a second method, we used an equation (y = a × (34/CO₂) ^b ) to model the changes in g _m in response to changes in CO₂ (here C _a). In the equation, y indicates the g _m, coefficient a (μmol m⁻² s⁻¹ Pa⁻¹) is the value of g _m at 34 Pa pCO₂, CO₂ indicates the pCO₂ inside the leaf chamber (C _a) and coefficient b (unitless) indicates the sensitivity of g _m to changes in C _a. For further analysis (Figs 2, 3, 4 please refer to later paragraphs), we considered coefficient b as a proxy for the CO₂ response of g _m, with a relatively greater value of b indicating a greater increase in g _m with a decrease in C _a. Mean ± SE values for model coefficients a and b for the 16 C₄ grasses are given in Table S3 (please refer to later paragraphs). Average values for three biochemical subtypes (NAD‐ME, NADP‐ME and PCK) are also included in Table S3. Fig. 1 shows the relationship of g _m and C _a for 16 C₄ grasses along with the model line, whereas Fig. S9 shows relationship of g _m and C _a along with the model line for the three C₄ subtypes.

Fig. 2.

Relationship of coefficient b (or sensitivity of g _m, unitless) with (a) mesophyll cell wall thickness (T _CW), (b) mesophyll surface area exposed to intercellular air spaces (S_mes), (c) ratio of T _CW : S_mes, (d) stomatal ratio (SR), (e) stomatal density adaxial (SD_ada) and (f) leaf thickness among the 16 C₄ grasses measured in current study. Linear models were used for deriving regression coefficients (R ²) in all panels, except panels (a) and (c) for which we used a polynomial model. Significance of R ²: *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. Each circle represents the mean ± SE value for each species (n = 3–6). Species names are indicated by the codes given in Table 1.

Fig. 3.

Relationship of coefficient b (or sensitivity of g _m, unitless) with (a) phosphoenolpyruvate carboxylase (PEPC) activity, (b) Rubisco activity, (c) carbonic anhydrase (CA) activity expressed as k _CA, (d) PEPC affinity for HCO₃ ⁻ (K _m), (e) maximum photosynthetic capacity (A _max) and (f) leaf N content (N_area) among the 16 C₄ grasses measured in current study. Linear models were used for deriving regression coefficients (R ²) in all panels. In panel (c) R ² = 0.22⁺ after removing influential species pvir. Significance of R ²: +, marginally significant; *, P ≤ 0.05. Each circle represents the mean ± SE value for each species (n = 3–6). Species names are indicated by codes given in Table 1.

Fig. 4.

Relationship of coefficient b (or sensitivity of g _m, unitless) with ratio of mesophyll cell wall thickness (T _CW) to (a) phosphoenolpyruvate carboxylase (PEPC) activity (b) Rubisco activity (c) PEPC affinity for HCO₃ ⁻ (K _m), (d) carbonic anhydrase (CA) activity expressed as k _CA, (e) maximum photosynthetic rates (A _max) and (f) leaf N content (N_area) among the 16 C₄ grasses measured in current study. Polynomial models were used to derive regression coefficients (R ²) in all panels. Significance of R ²: *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. Each circle represents the mean ± SE value for each species (n = 3–6). Species names are indicated by codes given in Table 1.

Fig. 1.

Response of mesophyll conductance (g _m) to changes in the partial pressure of CO₂ (pCO₂) inside the leaf chamber (C _a) in 16 diverse C₄ grasses. Data for each of the species are shown separately from panels (a) to (p) along with the CO₂ response of g _m (black solid line) modelled using the equation, g _m = a × (34/C _a) ^b , where coefficient ‘a’ is the value of g _m at 34 Pa pCO₂ and coefficient ‘b’ is the rate of change in g _m with change in C _a. Mean ± SE values for the model constants (a and b) for each species are shown in Supporting Information Table S3. Measurements were performed at constant light (photosynthetic photon flux density (PPFD) = 1200 μmol m⁻² s⁻¹) and leaf temperature (25°C). Values in each panel represent the mean ± SE (green colour) with n = 3–6. Grey points indicate the replicate values for each species and CO₂ level. Response of g _m to changes in pCO₂ for each species is plotted in separate panel of Fig. 1. Species code has been indicated as the first letter of the genus and first three letters of the species (please refer to Table 1 for full names of species). P‐values from one‐way ANOVA along with Tukey's letters are shown.

Statistical analyses

All statistical analyses were performed using R software (v.4.1.0, R Foundation for Statistical Computing, Vienna, Austria). Normality and equal variances were tested and, when necessary, square root or log transformations were used to improve the data homoscedasticity (Zar, 2007). One‐way ANOVA with post‐hoc Tukey's test was used to examine differences in leaf‐level anatomical and biochemical traits among the 16 diverse C₄ grasses. Results for post‐hoc Tukey's test are given in Table 1. Results of one‐way ANOVA for traits used in the current study are given in Table S2. For key physiological traits such as g _m, ∆¹⁸O, A _net, g _sw, C _i and TE_i, two‐way ANOVA was performed with species and pCO₂ as the main effects using the aov function in R (R Core Team, 2018). Results of two‐way ANOVA are given in Table 2 and mean values are presented in Figs S1–S6. For the ANOVAs, P‐values ≤ 0.05 were considered as statistically significant. We grouped the species into biochemical subtypes and analysed whether CO₂ responses of g _m varied among the subtypes (Table S3; Fig. S9).

Table 2.

Summary of two‐way ANOVA P‐values and F‐values along with numerator degree of freedom (df) for the physiological traits measured on 16 diverse C₄ grasses at four different pCO₂.

Traits	Species (df = 15)		CO2 (df = 3)		Species × CO2 (df = 45)
	P‐value	F‐value	P‐value	F‐value	P‐value	F‐value
g m	< 0.001	39.18	< 0.001	64.85	< 0.001	2.54
∆18O	< 0.001	5.34	< 0.001	74.92	0.04	1.46
g sw	< 0.001	37.64	< 0.001	228.39	0.32	1.10
A net	< 0.001	63.20	< 0.001	201.63	0.96	0.64
C i	< 0.001	12.44	< 0.001	370.49	0.12	1.29
TEi	< 0.001	22.29	< 0.001	2764.65	0.16	1.24

Mean values for these physiological traits list below are presented in Supporting Information Figs S1–S5. Statistically significant P‐values (≤ 0.05) are highlighted in bold. Mesophyll conductance to CO₂ (g _m; estimated using the Ogee et al., 2018 method), leaf net C¹⁸O¹⁶O discrimination (∆¹⁸O) to changes in CO₂ inside leaf, stomatal conductance to water (g _sw), net CO₂ assimilation rates (A _net), leaf intercellular [CO₂] (C _i) and leaf‐level water‐use efficiency (TE_i = A _net/g _sw).

Regression analyses were performed to examine the relationship of coefficient b with the key anatomical and biochemical traits such as T _CW, S_mes, ratio of T _CW : S_mes, SR, SD_ada, leaf thickness, PEPC activity, Rubisco activity, k _CA, K _m, A _max and N_area. To account for the combined effect of anatomy and biochemistry on the CO₂ responses of g _m, we derived ratios of T _CW to biochemical traits (activities of PEPC, Rubisco and CA, K _m, A _max and N_area). We examined the relationship between coefficient b and the ratio of T _CW to biochemical traits. Similarly, we also examined the relationships between percentage change in g _m and key anatomical and biochemical traits and the ratio of T _CW to the biochemical traits mentioned above. Both, coefficient b and percentage change in g _m showed similar relationships with anatomical and biochemical traits. In the main text we used coefficient b as a proxy for CO₂ response of g _m (Figs 2, 3, 4). The relationship of percentage change in physiological traits with key leaf anatomical and biochemical traits are given in Figs S3–S8. For the regression analysis, P‐values ≤ 0.05 were considered as statistically significant. The function outlierTest from the R package car (Fox & Weisberg, 2019) was used to identify any potential influential points in the relationships. Influential data points (with Bonferroni P‐value ≤ 0.05) were removed while deriving regression statistics if required (Figs 3c,f, S3, S5, S7). To complement the regression analysis, we also performed a principal component analysis (PCA; Methods S2; Fig. S8; Table S4) with leaf traits, percentage changes in g _m, A _net, g _sw and TE_i and habitat MAP (R package factominer; Le et al., 2008).

Results

CO₂ response of physiological traits

The 16 C₄ grasses (Table 1) with previously demonstrated variation in leaf‐level anatomical traits and g _m (Pathare et al., 2020a,b) were chosen to explore the potential variation in the CO₂ response of g _m and its relationship with leaf traits and TE_i. Responses of g _m to changes in C _a and C _i are given in Figs 1 and S10, respectively. Responses of other physiological traits (∆¹⁸O, g _sw, A _net, C _i and TE_i) to changes in C _a are given in Figs S11–S15. Species differed significantly in the CO₂ response of g _m (Figs 1, S10) and g _sw (Fig. S12). In general, across the 16 C₄ grasses, g _m increased as C _a and C _i decreased with percentage increase at lowest C _a, ranging from +13% to +250%. The increase in g _m was statistically significant in 12 out of 16 species. As with g _m, g _sw increased with decreasing C _a (Fig. S12). However, unlike g _m, the magnitude of increase in g _sw was lower. Specifically, the percentage increase in g _sw ranged from c. 40–80% when C _a decreased from 34 to 14 Pa. Alternatively, both A _net and TE_i decreased with decreases in C _a (Figs S13, S15). Particularly, A _net decreased by c. 23–40% and TE_i decreased by c. 53–64%.

Variation in leaf anatomical and biochemical traits

The results of one‐way ANOVA suggested that the 16 C₄ grasses varied significantly (P < 0.001; Table 1) in all the leaf‐level anatomical and biochemical traits. T _CW showed a significant 2.6‐fold variation with values ranging from c. 0.08 to 0.21 μm. S_mes showed a 2.4‐fold variation with values ranging from c. 8.5 to 19 μm² μm⁻². The C₄ grasses measured here also showed a highly significant variation in stomatal traits such as SR and SD_ada (P < 0.001; Tables 1, S2). SR varied by 4.7‐fold, with values ranging from 0.5 to 2.4, whereas SD_ada showed a 13‐fold variation with values ranging from 12 to 160 number mm⁻². Leaf thickness showed a significant two‐fold variation with values ranging from 120 to 240 μm (P < 0.001; Tables 1, S2). Similar to the variation in anatomical traits mentioned above, the biochemical traits (that is leaf‐level activities of PEPC, Rubisco and k _CA) also varied significantly among the 16 C₄ grasses included in the current study (P < 0.001; Tables 1, S2). PEPC activities showed a 7.5‐fold variation with values from 96 to 721 μmol m⁻² s⁻¹. Rubisco activities showed a 2.6‐fold variation with values from 30 to 80 μmol m⁻² s⁻¹. k _CA showed an 18‐fold variation with values from 9 to 165 μmol m⁻² s⁻¹ Pa⁻¹. PEPC affinity for HCO₃ ⁻ (K _m) showed a 1.6‐fold variation across the 16 C₄ grasses with values ranging from c. 29 to 46 μM HCO₃ ⁻ (P < 0.001; Tables 1, S2). There was a 1.7‐fold variation in maximum photosynthetic rates (A _max) with values ranging from c. 25 to 42.5 μmol CO₂ m⁻² s⁻¹ (P < 0.001; Table 1). N_area also varied significantly among the grasses (P < 0.001; Table 1) with values ranging from 1.24 to 2.26 g m⁻².

Relationships of CO₂ response of g _m with anatomical and biochemical traits

We used two proxies to account for the changes in g _m in response to change in C _a. First, percentage change in g _m in response to decrease in C _a (from 34 to 14 Pa) (Figs S3–S8) and second, the coefficient b derived from an equation used to model the changes in g _m in response to changes in C _a. In the main text, we used coefficient b as a proxy for CO₂ response of g _m, (Figs 2, 3, 4). Coefficient b showed a strong negative relationship with T _CW (R ² = −0.74, P < 0.001; Fig. 2a) and ratio of T _CW : S_mes (R ² = −0.64, P < 0.001; Fig. 2c) and a nonsignificant positive relationship with S_mes (R ² = 0.14, P = 0.11; Fig. 2b). In addition, coefficient b showed a significant positive relationship with SR (R ² = 0.34, P < 0.05; Fig. 2d), SD_ada (R ² = 0.32, P < 0.01; Fig. 2e) and leaf thickness (R ² = 0.38, P < 0.01; Fig. 2f). Coefficient b also showed a nonsignificant positive relationship with biochemical traits such as PEPC activity (R ² = 0.19, P = 0.12; Fig. 3a) and A _max (R ² = 0.20, P = 0.08; Fig. 3e) and a significant positive relationship with Rubisco activity (R ² = 0.24, P < 0.05; Fig. 3b). However, coefficient b did not show a statistically significant relationship with K _m (Fig. 3c,d). Coefficient b showed significant positive relationships with k _CA (R ² = 0.26, P < 0.05; Fig. 3c) and N_area (R ² = 0.31, P < 0.05; Fig. 3f) after removing the influential species (pvir and udic, respectively).

To account for the combined effects of leaf anatomy (specifically T _CW that acts as a resistance in series to CO₂ diffusion) and biochemistry (that can have a facilitating effect) on CO₂ response of C₄–g _m, we derived the ratio of T _CW with PEPC activity, Rubisco activity, k _CA, K _m, A _max and N_area and investigated the relationship between coefficient b and these ratios (Fig. 4). Coefficient b showed a significant negative relationship with T _CW/PEPC (R ² = −0.44, P < 0.01; Fig. 4a), T _CW/Rubisco (R ² = −0.57, P < 0.01; Fig. 4b), T _CW/K _m (R ² = −0.47, P < 0.01; Fig. 4c), T _CW/k _CA (R ² = −0.47, P < 0.05; Fig. 4d), T _CW/A _max (R ² = −0.62, P < 0.001; Fig. 4e) and T _CW/N_area (R ² = −0.75, P < 0.001; Fig. 4f). In summary, C₄ grasses with thinner cell walls combined with greater S_mes, N_area, A _max and activities of key enzymes were able to achieve greater increases in g _m at lower C _a. Similar results were observed when we plotted percentage change in g _m against key anatomical and biochemical traits mentioned above (Figs S3–S8).

Relationship of CO₂ response of g _m with CO₂ response of TE_i , g _sw and A _net

We investigated the impacts of changes in g _m with C _a on corresponding changes in TE_i, g _sw and A _net (Fig. S7). For this we used percentage change in the trait's value when C _a decreased from 34 to 14 Pa. As g _m and g _sw increase with decreases in C _a, percentage change for both conductances is reported in the manuscript as percentage increase. Alternatively, A _net and TE_i decrease with decreases in CO₂S, therefore the percentage change for these two traits have been reported in the manuscript as percentage decrease. Percentage change in TE_i showed a strong negative correlation with percentage change in g _m (R ² = −0.54, P < 0.001; Fig. S7a). Particularly, C₄ grasses that were able to achieve a greater increase in g _m at lower C _a showed a lesser decrease in TE_i (indicated by a less negative value for TE_i in Fig. S11a). In addition, percentage change in g _sw showed a negative relationship with percentage change in g _m (R ² = −0.28, P < 0.05; Fig. S7b), that is species that showed a greater increase in g _m at lower C _a also showed a lesser increase in g _sw. A significant relationship was not observed between percentage change in g _m and A _net (Fig. S7c).

CO₂ response of g _m in the C₄ biochemical subtypes

We also analysed the CO₂ responses of g _m among the subtypes (Table S3; Fig. S9). NADP‐ME (coefficient b = 0.618) and PCK (coefficient b = 0.0.73) subtypes showed 20% and 43% greater sensitivity of g _m to C _a respectively compared with NAD‐ME (coefficient b = 0.512). However, there were no statistically significant differences in coefficient b among the subtypes (Fig. S9e), which could be due to the low replication. With only seven NADP‐ME, five PCK and four NAD‐ME replicate species our study does not provide the statistical power to discuss subtype effects. Therefore, here we focus on species‐level differences.

Discussion

In general C₃–g _m increases under short‐term decreases in pCO₂ (Flexas et al., 2007; Bunce, 2010; Douthe et al., 2011), although not always (von Caemmerer & Evans, 1991; Loreto et al., 1992; Tazoe et al., 2009). The main candidates suggested to affect the CO₂ response of C₃–g _m include chloroplast movement and therefore changes in S _c, changes in activities of CA (Evans et al., 2009; Momayyezi & Guy, 2017) and the facilitation effect of CO₂‐permeable aquaporins (Uehlein et al., 2008; Flexas et al., 2012; Kaldenhoff, 2012). However, the C₃–g _m response to pCO₂ may also result from systematic errors associated with the use of methods such as gas exchange, chlorophyll fluorescence and discrimination against ¹³C or the use of oversimplified models (Pons et al., 2009; Yin & Struik, 2009; Tholen & Zhu, 2011; Gu & Sun, 2014). For instance, the apparent response of C₃–g _m to short‐term changes in pCO₂ has partly been attributed to changes in photorespiratory and nonphotorespiratory release of CO₂ (or day respiration) to the total CO₂ pool in the leaf, particularly under low pCO₂. Ignoring these CO₂ pools while estimating C₃–g _m also overlooks the effects of spatial distribution of mitochondria and chloroplast on pathlength of CO₂ movement. Inaccurate measurements of day respiration or estimates of the Rubisco fractionation factor in the ∆¹³C method and measurements conducted at low [O₂] have also been suggested as potential sources of artefacts in determining the C₃–g _m response to pCO₂ (Pons et al., 2009; Gu & Sun, 2014).

Alternatively, in C₄ species, the photorespiratory release of CO₂ is relatively low and may not contribute significantly to estimates of g _m, even at low pCO₂ (Cousins et al., 2020). Also, in current study we estimated g _m in diverse C₄ grasses by using a method based on modelling of ∆¹⁸O that considers the contribution of respiratory fluxes (Ogee et al., 2018). Despite some uncertainties associated with the day respiration or water isotope gradient between mesophyll and bundle sheath cells, the Ogee et al. (2018) method provides robust estimates of C₄–g _m. Also, comparing many diverse C₄ species, as done in the current study, using a method that is not subject to the same limitations as those previously used for C₃ species is a reasonable approach to identify the CO₂ responses of C₄–g _m. Here, we discuss the CO₂ response of g _m for 16 diverse C₄ grasses and its relationships with leaf anatomical and biochemical traits.

CO₂ response of g _m varied among diverse C₄ grasses

Very few studies investigating the CO₂ response of C₄–g _m, primarily maize and Setaria viridis (Osborn et al., 2017; Kolbe & Cousins, 2018; Ubierna et al., 2018), have reported an increase in C₄–g _m with a decrease in pCO₂. Similarly, we demonstrated that g _m increases with a decrease in pCO₂ across the 16 diverse C₄ grasses. However, the magnitude of the increase in g _m varied greatly across the 16 C₄ grasses (+13% to +250%; Fig. 1). In the following sections we discuss the potential factors that could explain this variability in the CO₂ response of C₄–g _m.

CO₂ response of C₄ –g _m is related with T _CW, S_mes and photosynthetic capacity

g _m in C₃ and C₄ species is constrained by several anatomical and biochemical parameters such as T _CW, S_mes, S_c, CA activities and aquaporins (Cousins et al., 2020; Evans, 2021). However, the potential implication of leaf anatomy and biochemistry for the CO₂ response of g _m has not been studied for C₄ species. Most of the anatomical parameters remain unchanged under short‐term changes in environmental conditions such as pCO₂ (Evans et al., 2009; Terashima et al., 2011). Therefore, the only anatomical trait suggested to influence the CO₂ response of g _m is chloroplast movement that can affect g _m by changing S_c (Terashima et al., 2006; Tholen et al., 2008; Carriqui et al., 2018). However, chloroplast movement is unlikely to explain the variability of the CO₂ response of C₄–g _m observed in the current study, because S_mes and not S_c is a more accurate determinant of C₄–g _m, in which a greater S_mes results in greater g _m in C₄ species (Barbour et al., 2016; Pathare et al., 2020a). In contrast with S_c, S_mes should remain unchanged under short‐term changes in pCO₂. Therefore, although we observed a nonsignificant positive relationship between the CO₂ response of C₄–g _m and S_mes (Fig. 2b), S_mes may provide only a partial explanation for this variable response.

T _CW could account for > 50% of the total resistance to CO₂ diffusion inside leaves (Evans et al., 2009). Therefore, we further investigated the influence of T _CW on the variability of the CO₂ response of C₄–g _m. In general, C₃ species with relatively lower T _CW showed greater g _m (Onoda et al., 2017; Veromann‐Jürgenson et al., 2017; Evans, 2021). We did not observe a strong relationship between g _m and T _CW for the C₄ grasses under ambient CO₂ levels (34 Pa) (Pathare et al., 2020a). However, in the current study, the CO₂ response of C₄–g _m was related to T _CW (Fig. 2a). The CO₂ response of C₄–g _m also showed a strong relationship with T _CW after accounting for S_mes (as T _CW/S_mes; Fig. 2c). Particularly, C₄ grasses with relatively lower T _CW and greater S_mes, showed a greater increase in g _m at low pCO₂. It is unclear why the g _m of C₄ grasses with lower T _CW is more responsive to changes in pCO₂. As for S_mes, T _CW is unlikely to change under short‐term changes in pCO₂ and may not be the sole reason for the observed variable CO₂ response of C₄–g _m. However, T _CW may still provide a partial explanation for variability in the CO₂ response of C₄–g _m. Resistances to CO₂ diffusion through the liquid phase (comprised of apoplastic and cellular components from mesophyll cell wall to site of carboxylation) are greater compared with the gaseous phase (Evans et al., 2009; Flexas et al., 2021). CO₂ must dissolve in the water‐filled pores of the mesophyll cell walls and then diffuse to the plasma membrane and eventually to the site of CO₂ fixation. Because cell walls represent a significant proportion of liquid phase resistance, C₄ species with a lower T _CW may have the potential to achieve a greater change in g _m in response to changing pCO₂. However, the influence of lower T _CW on the CO₂ response of C₄–g _m may have also been augmented by greater S_mes, the facilitation effect of CO₂ transporting aquaporins and leaf N content and proteins of photosynthetic machinery that determine drawdown and fixation of CO₂ and therefore photosynthetic capacity (Parkhurst, 1994; Wright et al., 2004; Evans et al., 2009; Xiong & Flexas, 2021).

The role of CO₂‐permeable aquaporins in enhancing g _m has been well characterised in C₃ species (Uehlein et al., 2008). Only recently, it has been demonstrated that overexpressing a CO₂‐permeable aquaporin in plasma membranes of Setaria viridis (C₄ grass) can enhance C₄–g _m (Ermakova et al., 2021). We did not investigate the role of aquaporins in affecting the CO₂ response of C₄–g _m. However, we observed a greater CO₂ response of g _m in C₄ grasses with relatively lower T _CW. This indicates that g _m in plants with lower T _CW may be more influenced by aquaporins (Evans, 2021). There is still a need to further investigate the role of aquaporins in the variable CO₂ response of C₄–g _m.

Here, we investigated the relationship between the CO₂ response of C₄–g _m and photosynthetic capacity as indicated by A _max, N_area and activities of key C₄ photosynthetic enzymes (PEPC, CA and Rubisco). C₄ species with a greater CO₂ response of C₄–g _m tended to show greater activities of PEPC and Rubisco and greater A _max and N_area. Also, the CO₂ response of C₄–g _m was strongly related to the ratio of T _CW/photosynthetic capacity traits (Fig. 4), in which a greater CO₂ response of C₄–g _m was evident in species with lower T _CW and greater photosynthetic capacity. This suggests that lower T _CW may have decreased the resistance to the movement of CO₂ into the mesophyll cells (Evans, 2021; Flexas et al., 2021), whereas the greater photosynthetic capacity may have increased the demand for CO₂, resulting in greater drawdown of CO₂ and the necessity of maintaining a greater CO₂ supply through an increase in g _m at low pCO₂ (Wright et al., 2004; Evans et al., 2009). Furthermore, the enzyme CA catalyses the conversion of CO₂/HCO₃ ⁻ in the cytosol and ensures sufficient HCO₃ ⁻ substrate supply to PEPC (Studer et al., 2014; DiMario et al., 2018). Because CO₂ diffuses much faster in liquid phase in HCO₃ ⁻ form, greater CA activities in combination with lower T _CW could have maintained a rapid supply of HCO₃ ⁻ substrate to PEPC and further enhanced g _m at low pCO₂ in some C₄ grasses (Fig. 4d).

We also investigated the relationship between the CO₂ response of C₄–g _m and K _m, a kinetic constant indicating PEPC affinity for HCO₃ ⁻ (DiMario et al., 2021). In general, lower K _m values (or high affinity of PEPC for HCO₃ ⁻) are expected to provide a selective advantage by maintaining high rates of C₄ photosynthesis, particularly under conditions such as drought when CO₂ availability is low due to restricted stomatal conductance. Here, we observed that K _m alone did not show a strong relationship with CO₂ response of C₄–g _m (Fig. 3d). However, after accounting for T _CW, we observed that T _CW/K _m showed a significant negative relationship with the CO₂ response of C₄–g _m (Fig. 4c), in which species with relatively lower T _CW and higher K _m (or lower affinity of PEPC for HCO₃ ⁻) exhibited a greater CO₂ response of g _m. This contrasts with the general expectation and could be explained by the lower T _CW leading to higher g _m and with the corresponding greater CA activities leading to higher HCO₃ ⁻ in mesophyll cells under low C _i conditions. The higher HCO₃ ⁻ concentration in the mesophyll cells of species with lower T _CW and greater CA can reduce the selective pressure on PEPC for lower K _m values.

Relationship of CO₂ response of g _m with CO₂ response of g _sw and TE_i

Previously, we demonstrated that C₄–g _m is positively related to TE_i under ambient pCO₂ (Pathare et al., 2020a). Our current study further suggests that, for C₄ grasses, g _m may also influence TE_i under short‐term changes in pCO₂. In general, both g _m and g_sw increased (Figs 1, S12) and TE_i decreased at low pCO₂ (Fig. S15). However, the magnitude of increase in g _m at low pCO₂ was greater (values from 13% to 250%) compared with the increase in g _sw (values from 40% to 80%). Also, C₄ grasses showing greatest increase in g _m at low pCO₂ also showed the lowest increase in g _sw (Fig. S7b). Consequently, although TE_i decreased at low pCO₂, the decrease was less in the species showing the greater CO₂ response of g _m (Fig. S7a). These types of species with greater CO₂ response of g _m may benefit in terms of maintaining TE_i under low CO₂ conditions, such as drought, compared with species whose g _m was less responsive to changes in pCO₂. Also, the PCA (Fig. S8) suggests that a greater CO₂ response of g _m is generally observed in C₄ grasses adapted to habitats with relatively low MAP.

Conclusions

We demonstrated that C₄–g _m increases with decreases in pCO₂ and the magnitude of this increase in g _m varies greatly among the 16 diverse C₄ grasses. Also, CO₂ responses of C₄–g _m is a composite trait that seems to be influenced by many leaf anatomical and biochemical parameters. The greatest increase in g _m at low pCO₂ was observed in C₄ grasses with lower T _CW along with greater S_mes and photosynthetic capacities. These C₄ grasses with a greater CO₂ response of g _m were also able to maintain their TE_i under low pCO₂, which may be advantageous under low CO₂ conditions such as drought. Our study advances the understanding of the CO₂ response of g _m in diverse C₄ species and identifies the key leaf anatomical and biochemical traits related to this response. This understanding is essential for improving C₄ photosynthetic models (von Caemmerer, 2021) that ultimately feed the larger Earth system models (Rogers et al., 2017; Knauer et al., 2019a,b) and in attempts to improve water‐use efficiency of C₄ crops through modification of g _m (von Caemmerer & Furbank, 2016).

Competing interests

None declared.

Author contributions

VSP and ABC designed the experiment. VSP, RJD and NK performed the measurements and analysed the data. VSP interpreted the data and led the writing with constructive inputs from RJD, NK and ABC.

Supporting information

Fig. S1 Sensitivity of mesophyll conductance (g _m; estimated by Ogee et al., 2018 method) to changes in fraction of CO₂ not produced in bundle sheath cells (ϕr).

Fig. S2 Sensitivity of mesophyll conductance (g _m; estimated by Ogee et al., 2018 method) to changes in leaf temperature (T _leaf).

Fig. S3 Relationship of model coefficient a (indicating value of g _m at 34 Pa pCO₂) with coefficient b (sensitivity of g _m to C _a; relatively lower b values indicate lower rate of change in g _m with C _a) and relationship between coefficient b and percentage change in g _m.

Fig. S4 Relationship of percent increase in g _m with mesophyll cell wall thickness (T _CW) mesophyll surface area exposed to intercellular air spaces (S_mes) ratio of T _CW : S_mes, stomatal ratio (SR), stomatal density adaxial (SD_ada) and leaf thickness among the 16 C₄ grasses measured in current study.

Fig. S5 Relationship of percent increase in g _m with PEPC activity Rubisco activity CA activity expressed as k _CA, PEPC affinity for HCO₃ ⁻ (K _m), maximum photosynthetic capacity (A _max) and leaf N content (N_area) among the 16 C₄ grasses measured in current study.

Fig. S6 Relationship of percent increase in g _m with ratio of mesophyll cell wall thickness (T _CW) to PEPC activity Rubisco activity PEPC affinity for HCO₃ ⁻ (K _m), CA activity expressed as k _CA, maximum photosynthetic rates (A _max) and leaf N content (N_area) among the 16 C₄ grasses measured in current study.

Fig. S7 Relationship of percent increase in g _m with percent decrease in leaf‐level water‐use efficiency (TE_i) expressed as A _net/g _sw (higher negative value indicates greater decrease in TE_i), percent increase in stomatal conductance to water (g _sw) and percent decrease in net photosynthetic rates (A _net) (higher negative value indicates greater decrease in A _net) for the 16 C₄ grasses measured in current study.

Fig. S8 PCA biplot showing major axes of variation in important leaf‐level anatomical and biochemical traits and percent change (increase or decrease) in response to CO₂ in physiological traits such as g _m, A _net, g _sw and TE_i for the 16 diverse C₄ grasses measured in current study.

Fig. S9 Response of mesophyll conductance (g _m) to changes in pCO₂ inside leaf chamber (C _a) in three C₄ biochemical subtypes (NAD‐ME, NADP‐ME, PCK).

Fig. S10 Response of mesophyll conductance to CO₂ (g _m) to changes in intercellular CO₂ (C _i) in 16 diverse C₄ grasses measured in current study.

Fig. S11 Response of leaf net C¹⁸O¹⁶O discrimination (∆¹⁸O) to changes in pCO₂ inside leaf chamber (C _a) in 16 diverse C₄ grasses measured in current study.

Fig. S12 Response of stomatal conductance to water (g _sw) to changes in pCO₂ inside leaf chamber (C _a) 16 diverse C₄ grasses measured in current study.

Fig. S13 Response of net CO₂ assimilation rates (A _net) to changes in pCO₂ inside leaf chamber (C _a) in 16 diverse C₄ grasses measured in current study.

Fig. S14 Response of leaf intercellular CO₂ concentration (C _i) to changes in pCO₂ inside leaf chamber (C _a) in 16 diverse C₄ grasses measured in current study.

Fig. S15 Response of leaf‐level water‐use efficiency (TE_i = A _net/g _sw) to changes in pCO₂ inside leaf chamber (C _a) in 16 diverse C₄ grasses measured in current study.

Methods S1 Measurement of anatomical traits.

Methods S2 Principal component analysis.

Table S1 Key input parameters used in calculation of isotope parameters and estimation of mesophyll conductance (g _m) for the 16 C₄ grasses at four pCO₂ levels (34, 27, 20 and 14 Pa) and a temperature of 25°C.

Table S2 Results of one‐way ANOVA with species as main effects for all the leaf‐level anatomical and biochemical traits measured for 16 C₄ grasses in current study.

Table S3 C₄ grasses used in current study along with their biochemical subtype and mean ± SE values for the equation (g _m = a × (34/C _a) ^b ) constants derived for the CO₂ response of g _m in the 16 C₄ grasses.

Table S4 Component loadings for important leaf‐level traits determined on 16 C₄ grasses.

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Data availability

Data available on request from the authors.