The strategies of water–carbon regulation of plants in a subtropical primary forest on karst soils in China

Coexisting plant species in a karst ecosystem may use diverse strategies of trade off between carbon gain and water loss to adopt to the low soil nutrient and low water availability conditions. An understanding of the impact of CO₂ diffusion and maximum carboxylase activity of Rubisco (V_cmax) on the light-saturated net photosynthesis (A) and intrinsic water use efficiency (iWUE) can provide insight into physiological strategies of the water–carbon regulation of coexisting plant species used in adaptation to karst environments at the leaf scale. We selected 63 dominant species (across 6 life forms) in a subtropical karst primary forest in southwestern China, measured their CO₂ response curves, and calculated the corresponding stomatal conductance to CO₂ (g_s), mesophyll conductance to CO₂ (g_m), and V_cmax. The results showed that g_s and g_m varied about 7.6- and 34.5-fold, respectively, and that g_s was positively related to g_m. The contribution of g_m to the leaf CO₂ gradient was similar to that of g_s. g_s ∕ A, g_m ∕ A and g_t ∕ A was negatively related to V_cmax ∕ A. The relative limitations of g_s (l_s), g_m (l_m), and V_cmax (l_b) to A for the whole group (combined six life forms) were significantly different from each other (P < 0.05). l_m was the largest (0.38 ± 0.12), followed by l_b (0.34 ± 0.14), and l_s (0.28 ± 0.07). No significant difference was found between l_s, l_m, and l_b for trees and tree/shrubs, while l_m was the largest, followed by l_b and l_s for shrubs, grasses, vines and ferns (P < 0.05). iWUE varied about 3-fold (from 29.52 to 88.92 µmol CO₂ mol⁻¹ H₂O) across all species, and was significantly correlated with g_s, V_cmax, g_m ∕ g_s, and V_cmax ∕ g_s. These results indicated that karst plants maintained relatively high A and low iWUE through the covariation of g_s, g_m, and V_cmax as an adaptation to a karst environment.

1 Introduction

Diverse strategies of trade off between carbon gain and water loss are critical for the survival of coexisting plant species. In order to adapt to harsh environments, coexisting plant species develop distinct patterns of strategies for carbon–water regulation (light-saturated net photosynthesis (A) and intrinsic water use efficiency; iWUE) (Sullivan et al., 2017). iWUE is the ratio of A to stomatal conductance to H₂O (g_sw) (Moreno-Gutierrez et al., 2012). Plants with high iWUE are better able to adapt to nutrient- and water-limited environments (Flexas et al., 2016). Due to the greater hydraulic erosion and complex underground drainage network (Nie et al., 2014; Chen et al., 2015), karst soils cannot retain enough nutrients and water for plant growth even though precipitation is high (1000–2000 mm) (Liu et al., 2011; Fu et al., 2012; Chen et al., 2015). An understanding of the impact of CO₂ diffusion and the maximum carboxylase activity of Rubisco (V_cmax) on A and iWUE in karst plants can provide insight into the physiological strategies of water–carbon regulation of plants used in adaptation to karst environments at the leaf scale. Until now, variability in A and iWUE has only been reported in 13 co-occurring trees and 12 vines (Chen et al., 2015), and 12 co-occurring tree species (Fu et al., 2012) in two tropical karst forests in southwestern China.

Based on Fick's first law, A has been shown to be only limited by leaf stomatal conductance to CO₂ (g_s= g_sw/1.6) and V_cmax (Flexas et al., 2012; Buckley and Warren, 2014); originally, mesophyll conductance to CO₂ (g_m) was proposed to be infinite, i.e., CO₂ concentration in chloroplast (C_c) was equal to the CO₂ concentration in intercellular air space (C_i). However, g_m varies greatly among species (Warren and Adams, 2006; Flexas et al., 2013). Recent studies have confirmed that A was jointly constrained by g_s, g_m, and V_cmax, and that their relative contribution to A was species-dependent and site-specific (Carriqui et al., 2015; Tosens et al., 2016; Galmes et al., 2017; Peguero-Pina et al., 2017a, b; Veromann-Jurgenson et al., 2017).

Variation in iWUE (=A ∕ g_sw) depends on the relative changes in A (g_s, g_m, V_cmax) and g_sw (g_sw= 1.6 g_s) (Flexas et al., 2013; Gago et al., 2014). Theoretical relationships between iWUE and g_s, g_m, and V_cmax have been deduced using two approaches. First, based on Fick's first law of CO₂ diffusion, Flexas et al. (2013) deduced that iWUE was a function of g_m ∕ g_s and CO₂ gradients (the ambient CO₂ concentration (C_a)–the CO₂ concentration at the sites of carboxylation (C_c)) within a leaf. And second, combining Fick's first law of CO₂ diffusion and the Farquhar biochemical model (Farquhar and Sharkey, 1982), Flexas et al. (2016) deduced that iWUE was a function of V_cmax ∕ g_s, C_c, CO₂ compensation point of photosynthesis (Γ^∗), and the effective Michaelis–Menten constant of Rubisco for CO₂ (K_m). Until now, most previous studies have focused on the role of CO₂ diffusion in limiting iWUE, and have suggested that iWUE was negatively related to g_s, and positively related to g_m ∕ g_s (Flexas et al., 2013). Gago et al. (2014) used a meta-analysis with 239 species, and were the first to confirm that iWUE was positively related to V_cmax ∕ g_s. Although both g_m ∕ g_s and V_cmax ∕ g_s were positively correlated with iWUE, there was only a weak correlation between g_m ∕ g_s and V_cmax ∕ g_s, which indicates that iWUE can be improved by increasing V_cmax or g_m (proportionally higher than g_s), not both (Gago et al., 2014).

It is noteworthy that Flexas et al. (2016) and Gago et al. (2014) found that most of the previous work on constraints ofg_s, g_m, and V_cmax on A were conducted in crops or saplings, and only a few studies were in natural ecosystems. For example, g_m was the main factor limiting A in two Antarctic vascular grasses (Saez et al., 2017), and in 35 Australian sclerophylls (Niinemets et al., 2009b) in different habitats. The A of two closely related Mediterranean Abies species growing in two different habitats was mainly constrained by g_m in one, and by g_s in the other habitat (Peguero-Pina et al., 2012). Beyond that, it still remains unknown how g_s, g_m, and V_cmax regulate A and iWUE across species in natural ecosystems.

In this study, we selected 63 dominant plant species, including 6 life forms (tree (n= 29), tree/shrub (n= 11), shrub (n= 11), grass (n= 11), vine (n= 5), and fern (n= 3)), from a subtropical primary forest in the “karst critical zone (karst CZ)” of southwestern China, and measured their A and CO₂ response curves. g_m was calculated using the curve-fitting method (Ethier and Livingston, 2004). The obtained g_m was used to transform the A–C_i into A–C_c response curves, and then to calculate the A and V_cmax. Our objective was to determine and distinguish the limitations of CO₂ diffusion (g_s and g_m) and V_cmax on A and iWUE in different life forms in this karst primary forest, and to understand the patterns of strategies for carbon–water regulation in karst plants.

2 Materials and methods

2.1 Site information

This study was conducted in a subtropical primary forest (26^∘14^′48${}^{\prime \prime }$ N, 105^∘45^′51${}^{\prime \prime }$ E; elevation, 1460 m), located in the karst CZ of southwestern China. This region has a typical subtropical monsoon climate, with a mean annual precipitation of 1255 mm, and mean annual air temperature of 15.1 ^∘C (Zeng et al., 2016). The soils are characterized by a high ratio of exposed rock, shallow and nonhomogeneous soil cover, and complex underground drainage networks, e.g., grooves, channels, and depressions (Chen et al., 2010; Zhang et al., 2011; Wen et al., 2016). Soils and soil water are easily leached into underground drainage networks. Soil texture is silt clay loam, and soil PH is 6.80 ± 0.16 (Chang et al., 2018). The total nitrogen and phosphorus content in soil is 7.30 ± 0.66 and 1.18 ± 0.35 g Kg⁻¹, respectively, which was similar with that of non-karst CZs (Wang et al., 2018). However, the soil quantities (16.04–61.89 Kg m⁻²) and nitrogen and phosphorus storage (12.04 and 1.68 t hm⁻²) is much lower than that of non-karst CZs, due to the thin and heterogeneous soil layer (Jobbagy and Jackson, 2000; He et al., 2008; Li et al., 2007; Lu et al., 2010). The typical vegetation type is mixed evergreen and broadleaf deciduous primary forest, dominated by Itea yunnanensis Franch., Carpinus pubescens Burk., and Lithocarpus confinis Huang., etc. (Wang et al., 2018).

2.2 Leaf gas-exchange measurements

In July and August 2016, 63 species (Table S1 in the Supplement) were selected for measurements of the A and CO₂ response curves. The species sampled were selected according to their abundance in the study site. They are the main components of this forest, including 55 woody species (46 deciduous and 10 evergreen species) and 5 herb species. To distinguish the strategies of water–carbon regulation of plants among different life forms, the species were grouped into 6 life forms, including (1) tree (n= 29), (2) tree/shrub (n= 11), (3) shrub (n= 11), (4) grass (n= 11), (5) vine (n= 5), and (6) fern (n= 3). “Tree/shrub” is a kind of low wood plant between tree and shrub. Ferns grow in the understory; vines climb up to the shrub canopy to get light.

Details of leaf sampling and measurements of the CO₂ response curve are briefly described as follows. Branches exposed to the sun were excised from the upper part of the crown (trees, tree/shrubs, shrubs and vines) or aboveground portion (grasses, ferns), and immediately recut under water to maintain xylem water continuity. Back in the laboratory, branches and aboveground portions were kept at 25 ^∘C for 30 min. Fully expanded and mature leaves were induced for 30 min at a saturating light density (1500 µmol m⁻² s⁻¹). CO₂ response curve measurements were performed when A andg_s was stable. Three leaves per species were collected and measured. A total of 189 leaves were collected from adult individuals of 63 species.

The CO₂ response curves were measured with 11 CO₂ concentration gradients in the chamber following the procedural guidelines described by Long and Bernacchi (2003). The photosynthetic photon flux density was 1500 µmol m⁻² s⁻¹. The leaf temperature was 25 ^∘C, controlled by the block temperature. The humidity in the leaf chamber was maintained at ambient conditions. Leaf area, thickness (LT), and dry mass were measured after the CO₂ response measurements. Leaf mass per area (LMA) was calculated by dividing the corresponding dry mass by leaf area. Finally, leaf density (LD) was calculated by dividing the corresponding LMA by LT. More details are given in Wang et al. (2018).

2.3 Response curve analyses

A and the corresponding g_sw (g_s=g_sw/1.6), C_a, and C_i were extracted from the CO₂ response curve under saturating light (1500 µmol m⁻² s⁻¹) conditions, with the CO₂ concentration inside the cuvette set to 400 µmol mol⁻¹ (Domingues et al., 2010). V_cmax was estimated by fitting A–C_c curves (Ethier and Livingston, 2004). The obtained values of g_m were used to transform the A–C_i into A–C_c response curves as C_c=C_i–A ∕ g_m.

Three methods are most commonly used for g_m estimation. Those methods have been reviewed by Warren (2006) and Pons et al. (2009). Briefly, g_m can be calculated by the stable isotope method (Evans, 1983; Sharkey et al., 1991; Loreto et al., 1992), the J method (Bongi and Loreto, 1989; Dimarco et al., 1990; Harley et al., 1992; Epron et al., 1995; Laisk et al., 2005), and the “curve-fitting” method (Ethier and Livingston, 2004; Sharkey et al., 2007). All of these methods are based on gas-exchange measurements (Pons et al., 2009), and some common assumptions (Warren, 2006). Thus, the accuracy of each method is unknown to some extent (Warren, 2006).

g_m was estimated by the “curve-fitting” method in this study. Although the “curve-fitting” method is less precise than the stable isotope method, it is much more readily available and has been used for several decades (Warren, 2006; Sharkey, 2012). Accurate measurements of A and C_i are prerequisites for estimating g_m using the “curve-fitting” method (Pons et al., 2009). Warren (2006) pointed out that highly accurate measurements require small leaf areas and low flow rates. We confirmed that the calculated C_c and the initial slope of the A–C_c curves were positive, suggesting that the measured g_m was reliable (Warren, 2006).

2.4 Theory of trade-off between carbon and water at leaf scale

The exchange of H₂O and CO₂ between the leaf and the atmosphere is regulated by stomata (Gago et al., 2014). According to Fick's first law of diffusion, A and g_s are related as follows:

$$\begin{array}{}\text{(1)}& A=g_{\mathrm{s}}\left(C_{\mathrm{a}}-C_{\mathrm{i}}\right),\end{array}$$

where A is the photosynthetic rate (µmol CO₂ m⁻² s⁻¹), C_a is the ambient CO₂ concentration (µmol mol⁻¹), and C_i is the intercellular CO₂ concentration (µmol mol⁻¹).

Mesophyll is the barrier for CO₂ inside the leaf. A and mesophyll conductance to CO₂ (g_m) are related as follows:

$$\begin{array}{}\text{(2)}& A=g_{\mathrm{m}}(C_{\mathrm{i}}-C_{\mathrm{c}}),\end{array}$$

where C_c is the CO₂ concentration at the sites of carboxylation (µmol mol⁻¹). C_c not only depends on CO₂ supply by g_m, but also on CO₂ demand (the maximum carboxylase activity of Rubisco, V_cmax).

2.4.1 The relationship between iWUE and g_m ∕ g_s

iWUE is a function of CO₂ diffusion conductances (e.g., g_s and g_m) and leaf CO₂ concentration gradients. We can express A as the product of the total CO₂ diffusion conductance (g_t) from ambient air to chloroplasts, and the corresponding CO₂ concentration gradients by combining Eq. (1) and (2) (Flexas et al., 2013):

$$\begin{array}{}\text{(3)}& A=g_{\mathrm{t}}\left[(C_{\mathrm{a}}-C_{\mathrm{i}})+(C_{\mathrm{i}}-C_{\mathrm{c}})\right],\end{array}$$

where g_t= 1/(1/g_s+ 1/g_m). This equation demonstrates that CO₂ concentration gradients in leaves are constrained by stomatal and mesophyll resistance to CO₂. Therefore, iWUE can be expressed as

$$\begin{array}{}\text{(4)}& {\displaystyle \frac{A}{g_{\mathrm{sw}}}}={\displaystyle \frac{\mathrm{1}}{\mathrm{1.6}}}\left({\displaystyle \frac{g_{\mathrm{m}}/g_{\mathrm{s}}}{\mathrm{1}+g_{\mathrm{m}}/g_{\mathrm{s}}}}\right)\left[\left(C_{\mathrm{a}}-C_{\mathrm{i}}\right)+(C_{\mathrm{i}}-C_{\mathrm{c}})\right].\end{array}$$

Equation (4) means that iWUE is positively related to g_m ∕ g_s, but not to g_m itself (Warren and Adams, 2006; Flexas et al., 2013; Buckley and Warren, 2014; Cano et al., 2014).

Figure 1 Relationships between (a) CO₂ gradient between ambient air and intercellular air space (C_a–C_i, µmol mol⁻¹) and stomatal conductance to CO₂ (g_s, mol CO₂ m⁻² s⁻¹); (b) CO₂ gradient between intercellular air space and chloroplasts (C_i–C_c, µmol mol⁻¹) and mesophyll conductance to CO₂ (g_m, mol CO₂ m⁻² s⁻¹); (c) CO₂ concentration gradient between ambient air and chloroplasts (C_a–C_c, µmol mol⁻¹) and total conductance to CO₂ (g_t, mol CO₂ m⁻² s⁻¹); and (d) C_a–C_c and the maximum carboxylase activity of Rubisco (V_cmax, µmol CO₂ m⁻² s⁻¹). Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

2.4.2 The relationship between iWUE and V_cmax ∕ g_s

When Fick's first law and the Farquhar biochemical model (Farquhar and Sharkey, 1982) are combined, iWUE is also a function of V_cmax. Based on the Farquhar biochemical model (Farquhar and Sharkey, 1982), when A is limited by Rubisco, it can be expressed by the following equation (Sharkey et al., 2007):

$$\begin{array}{}\text{(5)}& A={\displaystyle \frac{V_{\mathrm{cmax}}(C_{\mathrm{c}}-{\mathrm{\Gamma }}^{\ast })}{(C_{\mathrm{c}}+K_{\mathrm{m}})}}-R_{\mathrm{d}},\end{array}$$

where Γ^∗ is the CO₂ compensation point of photosynthesis in the absence of non-photorespiratory respiration in light (R_d), and K_m is the effective Michaelis–Menten constant of Rubisco for CO₂. Combining Eqs. (1) and (5) (Flexas et al., 2016), we obtain the following:

$$\begin{array}{}\text{(6)}& {\displaystyle \frac{V_{\mathrm{cmax}}}{g_{\mathrm{s}}}}={\displaystyle \frac{(C_{\mathrm{c}}+K_{\mathrm{m}})(C_{\mathrm{a}}-C_{\mathrm{i}})(A+R_{\mathrm{d}})}{(C_{\mathrm{c}}-{\mathrm{\Gamma }}^{\ast })A}}.\end{array}$$

Because R_d is much smaller than A in actively photosynthesizing leaves, V_cmax ∕ g_s can be approximated as

$$\begin{array}{}\text{(7)}& {\displaystyle \frac{V_{\mathrm{cmax}}}{g_{\mathrm{s}}}}\approx {\displaystyle \frac{(C_{\mathrm{c}}+K_{\mathrm{m}}\left)\right(C_{\mathrm{a}}-C_{\mathrm{i}})}{(C_{\mathrm{c}}-{\mathrm{\Gamma }}^{\ast })}}={\displaystyle \frac{(C_{\mathrm{c}}+K_{\mathrm{m}})}{(C_{\mathrm{c}}-{\mathrm{\Gamma }}^{\ast })}}{\displaystyle \frac{A}{g_{\mathrm{s}}}}.\end{array}$$

Consequently, iWUE can be expressed as

$$\begin{array}{}\text{(8)}& {\displaystyle \frac{A}{g_{\mathrm{sw}}}}={\displaystyle \frac{\mathrm{1}}{\mathrm{1.6}}}{\displaystyle \frac{V_{\mathrm{cmax}}}{g_{\mathrm{s}}}}{\displaystyle \frac{(C_{\mathrm{c}}-{\mathrm{\Gamma }}^{\ast })}{(C_{\mathrm{c}}+K_{\mathrm{m}})}}.\end{array}$$

2.5 Statistical analysis

2.5.1 Quantitative analysis of limitations on A

The relative contribution of g_s (l_s), g_m (l_m), and V_cmax (l_b) to A can be separated by a quantitative limitation model introduced by Jones (1985) and further developed by Grassi and Magnani (2005). The sum of l_s, l_m, and l_b is one. l_s, l_m and l_b can be calculated as follows:

$$\begin{array}{}\text{(9)}& {\displaystyle }& {\displaystyle }{l}_{\mathrm{s}}={\displaystyle \frac{g_{\mathrm{t}}/g_{\mathrm{s}}\cdot \partial A/\partial C_{\mathrm{c}}}{g_{\mathrm{t}}+\partial A/\partial C_{\mathrm{c}}}}\text{(10)}& {\displaystyle }& {\displaystyle }{l}_{\mathrm{m}}={\displaystyle \frac{g_{\mathrm{t}}/g_{\mathrm{m}}\cdot \partial A/\partial C_{\mathrm{c}}}{g_{\mathrm{t}}+\partial A/\partial C_{\mathrm{c}}}}\text{(11)}& {\displaystyle }& {\displaystyle }{l}_{\mathrm{b}}={\displaystyle \frac{g_{\mathrm{t}}}{g_{\mathrm{t}}+\partial A/\partial C_{\mathrm{c}}}},\end{array}$$

where ∂A ∕ ∂C_c was calculated as the slope of A–C_c response curves over a C_c range of 50–100 µmol mol⁻¹. l_s, l_m, and l_b have no units. A is co-limited by the three factors when l_s≈ 0.3, l_m≈ 0.3, and l_b≈ 0.4 (Galmes et al., 2017).

2.5.2 Data analysis

Data were analyzed either as a whole group (six life forms combined) or by individual life forms. The bivariate linear regressions of leaf gas exchange parameters were performed using the standardized major axis (SMA) regression fits, and all of the data were made on log_e-transformed data (Table S2).

To test for differences among life forms, SMA regression fits were used to compare the slope of regression lines where significant relationships had already been obtained. Note that grass, vine, and fern were not considered due to the small sample size. A similar trend was obtained, and no significant difference was found between life forms although significant relationships were not obtained for some bivariate linear regressions. Accordingly, six life forms were grouped together to analyze the strategy of water–carbon regulation of plants.

The difference of relative limitation of g_s, g_m, and V_cmax to A for life forms or as a whole group were performed using one-way ANOVA and Duncan's multiple comparison. The probability of significance was defined at p < 0.05.

Figure 2 Relationships between (a) V and G_s; (b) V and G_m; and (c) V and G_t. V is the ratio of photosynthetic capacity (V_cmax) to light-saturated net photosynthesis (A, µmol CO₂ m⁻² s⁻¹); G_s is the ratio of stomatal conductance to CO₂ (g_s, mol CO₂ m⁻² s⁻¹) to A; G_m is the ratio of mesophyll conductance to CO₂ (g_m, mol CO₂ m⁻² s⁻¹) to A; and G_t is the ratio of total conductance to CO₂ (g_t, mol CO₂ m⁻² s⁻¹) to A. Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

3 Results

3.1 Interrelation among g_s, g_m, g_t, and V_cmax

Figure 3 Relationships between light-saturated net photosynthesis (A, µmol CO₂ m⁻² s⁻¹) and (a) stomatal conductance to CO₂ (g_s, mol CO₂ m⁻² s⁻¹); (b) mesophyll conductance to CO₂ (g_m, mol CO₂ m⁻² s⁻¹); and (c) the maximum carboxylase activity of Rubisco (V_cmax, µmol CO₂ m⁻² s⁻¹). Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

CO₂ concentration gradients in leaves were controlled by CO₂ diffusion conductance and V_cmax. Figure 1 shows the relationship between CO₂ gradients (C_a–C_i, C_i–C_c, and C_a–C_c) in leaves and the corresponding CO₂ diffusion conductance (g_s, g_m, and g_t) (Fig. 1a–c), and between C_a–C_c and V_cmax (Fig. 1d). CO₂ concentration gradients (C_a–C_i, C_i–C_c, and C_a–C_c) were significantly negatively associated with the corresponding CO₂ diffusion conductance (g_s, g_m, and g_t) (P < 0.001). V_cmax was positively associated with C_a–C_c (P < 0.001).

g_s, g_m, and g_t were significantly positively related to each other (P < 0.001) (Fig. S1 in the Supplement). The contribution of g_m to the leaf CO₂ gradient was similar to that of g_s. The contribution of g_s (57.51–155.13 µmol mol⁻¹) to C_a–C_c (98.50–282.94 µmol mol⁻¹) varied from 28 to 86 %, and the contribution of g_m (18.15–179.36 µmol mol⁻¹) to C_a–C_c varied from 14 to 72 %. But the variation range of g_m (0.02–0.69 mol CO₂ m⁻² s⁻¹) was 4.5 times that of g_s (0.05–0.38 mol CO₂ m⁻² s⁻¹).

Figure 4 Relationships between (a) stomatal conductance to CO₂ (g_s, mol CO₂ m⁻² s⁻¹) and l_s (g_s limitation on light-saturated net photosynthesis; A); (b) mesophyll conductance to CO₂ (g_m, mol CO₂ m⁻² s⁻¹) and l_m (g_m limitation on A); and (c) the maximum carboxylase activity of Rubisco (V_cmax, µmol CO₂ m⁻² s⁻¹) and l_b (V_cmax limitation on A). Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

No relationship was found between the CO₂ diffusion conductance (g_s, g_m, and g_t) and V_cmax (Fig. S2). However, after normalization of g_s, g_m, g_t, and V_cmax for A (normalized parameters are hereafter called G_S=g_S ∕ A, G_m=g_m ∕ $A,G_{\mathrm{t}}=g_{\mathrm{t}}$ ∕ A, and V=V_cmax ∕ A), V was significantly positively correlated with G_m and G_t (P < 0.001) (Fig. 2b, c), and was slightly positively correlated with G_s (P < 0.05) (Fig. 2a), which represented the trade-off between CO₂ supply and demand.

3.2 Contribution of g_s, g_m, and V_cmax to A

The variation in A was attributed to variation in g_s, g_m, g_t, and V_cmax. A was positively correlated with g_s (Fig. 3a), g_m (Fig. 3b), and V_cmax (Fig. 3c). We used the quantitative limitation model (Eqs. 9, 10 and 11) to separate g_s (l_s), g_m (l_m), and V_cmax (l_b) limitations to A. l_s, l_m, and l_b were negatively associated with g_s, g_m, and V_cmax, respectively (Fig. 4). The contributions by g_s, g_m, and V_cmax to limiting A were different for each species (Fig. S3). l_s varied 2.6-fold (from 0.17 to 0.45), l_m varied 10.5-fold (from 0.05 to 0.55), and l_b varied 6.2-fold ( from 0.11 to 0.68) across species. Overall, l_m (0.38 ± 0.12) was significantly larger than l_b (0.34 ± 0.14) and l_s (0.28 ± 0.07) (P < 0.05).

Figure 5 Limitation to light-saturated net photosynthesis (A) in six life forms by stomatal conductance to CO₂ (l_s), mesophyll conductance to CO₂ (l_m), and the maximum carboxylase activity of Rubisco (l_b). Error bars denominate standard deviation (1σ).

To further understand how A was limited by g_s, g_m, and V_cmax among life forms, we grouped the 63 species into 6 life forms: tree, tree/shrub, shrub, grass, vine, and fern. The results showed that there was no significant difference between l_s, l_m, and l_b for trees and tree/shrubs. l_m of shrubs and grasses was significantly higher than that of l_s and l_b (P < 0.05). l_m of vines and ferns was significantly higher than that of l_s (P < 0.05) (Fig. 5).

Figure 6 Relationships between the observed intrinsic water use efficiency (iWUE, µmol CO₂ mol⁻¹ H₂O) and (a) light-saturated net photosynthesis (A, µmol CO₂ m⁻² s⁻¹); (b) stomatal conductance to CO₂ (g_s, mol CO₂ m⁻² s⁻¹); (c) mesophyll conductance to CO₂ (g_m, mol CO₂ m⁻² s⁻¹); and (d) the maximum carboxylase activity of Rubisco (V_cmax, µmol CO₂ m⁻² s⁻¹). Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

3.3 Effect of g_s, g_m, and V_cmax on iWUE

iWUE varied from 29.52 to 88.92 µmol CO₂ mol⁻¹ H₂O. In theory, iWUE is regulated by g_s (g_sw= 1.6 g_s), g_m, and V_cmax. However, a simple correlation analysis showed that iWUE was negatively related to g_s (Fig. 6b), and not related to A (Fig. 6a), g_m (Fig. 6c), or V_cmax (Fig. 6d).

A correlation analysis was used to test how g_m ∕ g_s and V_cmax ∕ g_s affected iWUE. The results showed that iWUE was positively correlated with g_m ∕ g_s (Fig. 7a) and V_cmax ∕ g_s (Fig. 7b). However, there was no significant relationship between g_m ∕ g_s and V_cmax ∕ g_s. iWUE was regulated by covariation between g_s, g_m, and V_cmax.

Figure 7 The relationships of the intrinsic water use efficiency (iWUE, µmol CO₂ mol⁻¹ H₂O) and (a) the ratio of mesophyll conductance to CO₂ (g_m) to (g_s) (g_m ∕ g_s), and (b) the ratio of the maximum carboxylase activity of Rubisco (V_cmax) to g_s (V_cmax ∕ g_s). Lines refer to regression line for 63 species. T, TS, S, G, V, and F represent tree, tree/shrub, shrub, grass, vine, and fern, respectively.

4 Discussion

4.1 Covariation in g_s, g_m, and V_cmax in regulating A

A was constrained by g_s, g_m, and V_cmax acting together; however, variability in the relative contribution of these three factors depended on species and habitats (Tosens et al., 2016; Galmes et al., 2017; Peguero-Pina et al., 2017a; Veromann-Jurgenson et al., 2017). A was significantly correlated with g_s, g_m, and V_cmax (Fig.3a–c). g_s was positively related to g_m (Fig. S1c), while no relationship was found between the CO₂ diffusion conductance (g_s and g_m) and V_cmax (Fig. S2). The relative limitations of g_s, g_m, and V_cmax were separated by a quantitative limitation model (Jones, 1985; Grassi and Magnani, 2005). The results showed that l_s, l_m, and l_b of the 63 species varied over a large range (Fig. S3), indicating plants have diverse strategies to coordinate CO₂ diffusion (g_s and g_m) and V_cmax to maintain relative high A. The order of factor limitations to A was l_m > l_b >l_s (P < 0.05) (Fig. S3). Furthermore, we tested the relationship between the relative limitations and the corresponding limitation factors. The results showed that l_s, l_m, and l_b were negatively associated with g_s, g_m, and V_cmax, respectively (Fig. 4). The relationship was also stronger for g_m–l_m (r²= 0.65) than V_cmax–l_b (r²= 0.27) and g_s–l_s (r²= 0.19).

g_s was more highly correlated with A, while the results showed that A was more limited by g_m. This could be due to two possible reasons. Firstly, compared to the linear relationship between A and g_s, a nonlinear trend was found between A and g_m when g_m > 0.4 (Fig. 3a, b). Secondly, leaf structure plays an important role in regulating g_m and V_cmax and, consequently, in determining A (Veromann-Jurgenson et al., 2017). Negative relationships between A ∕ LMA and LT (r²= 0.16, p= 0.002), and A ∕ LMA and LT (r²= 0.3, p < 0.001)were also observed (Fig. S4c, d), while A was not correlated with LT and LD (Fig. S4a, b).

The importance of g_m in constraining A was variable, and depended on leaf structural traits; only LMA, LT, and LD were analyzed in this study. Large variability in g_m has been shown both between and within species with different life forms and habits (Gago et al., 2014; Flexas et al., 2016). Variability in g_m in this study is similar to that in global datasets (Gago et al., 2014; Flexas et al., 2016). There was no significant difference among life forms (P > 0.05). Previous studies have confirmed that LMA (Tomas et al., 2013), thickness of leaf cell wall (Peguero-Pina et al., 2017b), liquid phase of mesophyll (Veromann-Jurgenson et al., 2017), cell wall thickness of mesophyll (Tosens et al., 2016; Terashima et al., 2011), and surface area of mesophyll and chloroplast exposed to intercellular space (Veromann-Jurgenson et al., 2017) were the main limitations for g_m. The wide variability of g_m between different species and life forms in the same ecosystem seems to be related to the diversity of leaf anatomical traits.

No significant difference of LMA, LT, and LD was found among life forms (P < 0.05). Negative correlations of g_m (Terashima et al., 2005) or g_m ∕ LMA (Niinemets et al., 2009a; Veromann-Jurgenson et al., 2017) with LMA have been reported. In this study, there was a significant relationship between g_m ∕ LMA with LMA (P < 0.01); however, no relationship was found between g_m and LMA. g_m ∕ LMA was significantly negatively related to LD (p < 0.01) (Fig. S5c), and there was also a weak negative relationship with LT (p= 0.06) (Fig. S5d), demonstrating the negative effect of cell wall thickness on g_m (Terashima et al., 2006; Niinemets et al., 2009a). The strong investment in supportive structures was the main reason for the limitation of g_m on A (Veromann-Jurgenson et al., 2017). However, it is still unknown how leaf anatomical traits affect g_m and A, and this should be further explored.

g_s is responsible for the CO₂ exchange between atmosphere and leaves, and regulates the CO₂ fixation (A) and water loss (Lawson and Blatt, 2014). The variability of g_s was controlled by stomatal anatomy, i.e., stomata density and size, and mesophyll demands for CO₂ (Lawson and Blatt, 2014). However, the stomatal anatomy was not analyzed in this study. We only focused on how the relationship between g_s and g_m regulates A. A positive relationship has been observed between g_s and g_m (Flexas et al., 2013). For example, the restricted CO₂ diffusion from the ambient air to chloroplast is the main reason for a decreased A under water stress conditions, due to both the stomatal and mesophyll limitations (Olsovska et al., 2016). g_s was significantly positively related to g_m for 63 species (P < 0.001, Fig. S1) in this study, and no difference in the slopes of the regression lines between g_s and g_m was found among life forms, demonstrating that A was regulated by the covariation of g_s and g_m. However, the variability of g_m and l_m was larger than g_s and l_s, respectively (Figs. 1 and S3).

The wide variation in the range of l_b (0.11–0.68) highlighted the important role of V_cmax in regulating A. V_cmax was used to represent the CO₂ demand in the photosynthetic process in this study. The relative contribution of V_cmax to A not only depends on C_a–C_c, but also on leaf nutrient levels. A positive relationship was found between C_a–C_c and V_cmax (Fig. 1d). Furthermore, V_cmax ∕ LMA was also co-regulated by leaf nitrogen, phosphorus, and magnesium content (Jing et al. 2018). In addition, V_cmax ∕ LMA was negatively related to LT (p < 0.05) (Fig. S6c) and LD (p < 0.05) (Fig. S6d), while V_cmax was not correlated with LT and LD (Fig. S6a,b); this demonstrated that leaf structure plays an important role in regulating V_cmax.

The trade-off between CO₂ supply (g_s and g_m) and demand (carboxylation capacity of Rubisco) can help maintain a relatively high A (Galmes et al., 2017; Saez et al., 2017). In this study, we used V_cmax as a proxy for the carboxylation capacity of Rubisco, and the normalized V_cmax by A (V=V_cmax ∕ A) was significantly negatively correlated with the normalized g_t by A (G_t=g_t ∕ A) (P < 0.001) (Fig. 2c). This indicated that the trade-off between CO₂ supply and demand also existed among different species in the same ecosystems. For the Limonium genus(flowering plants) (Galmes et al., 2017), g_t was significantly positively related to Rubisco carboxylase specific activity, and significantly negatively related to Rubisco specificity factor to CO₂. In the case of Antarctic vascular (Saez et al., 2017) and Mediterranean plants (Flexas et al., 2014), A was mainly limited by low g_m, but it could be partially counterbalanced by a highly efficient Rubisco through high specificity for CO₂. This highlights the importance of the trade-off between CO₂ supply and demand in plant adaptation to karst environment. However, it is still unknown how leaf anatomical traits affect g_m, V_cmax, and A, and this should be further explored.

4.2 Covariation of g_s, g_m, and V_cmax in regulating iWUE

Compared with the global dataset under well-watered conditions (19.27–171.88 µmol CO₂ mol⁻¹ H₂O) (Flexas et al., 2016), iWUE (52.85 ± 13.08 µmol CO₂ mol⁻¹ H₂O) was somewhat lower in this study. iWUE varied from 29.53 to 88.91 µmol CO₂ mol⁻¹ H₂O, and the variability of iWUE was larger than in the karst tropical primary forest (Fu et al., 2012; Chen et al., 2015). The average iWUE of 12 vines and 13 trees in the karst tropical primary forest was 41.23 ± 13.21 µmol CO₂ mol⁻¹ H₂O (Chen et al., 2015), while that of six evergreen and six deciduous trees was 66.7 ± 4.9 and 49.7 ± 2.0 µmol CO₂ mol⁻¹ H₂O, respectively (Fu et al., 2012). The results demonstrated that karst plants use a diverse strategies of carbon–water regulation to adopt to the harsh karst environment.

Coexisting species have a diverse strategies of carbon–water regulation, ranging from “profligate/opportunistic” to “conservative”, which means that their ecophysiological niches are separate (Moreno-Gutierrez et al., 2012; Nie et al., 2014; Prentice et al., 2014). Species with high g_s, and low iWUE were defined to have a “profligate/opportunistic” water use strategy, and species with low g_s and high iWUE were defined to exhibit a “conservative” water use strategy (Moreno-Gutierrez et al., 2012). Consistent with previous research (Moreno-Gutierrez et al., 2012), coexisting plant species growing in the karst ecosystem had diverse water use strategies. However, karst plants tended to lose more water to gain more carbon, i.e., karst plants used a “profligate/opportunistic” water use strategy to adapt to the low nutrient availability and water stress conditions.

Prentice et al. (2014) studied the trade-off between carbon gain and water loss of woody species in contrasting climates, and found that species in hot and wet regions tend to lose more water in order to fix more carbon (high g_s ∕ A, low V_{cmax_Ci} ∕ A), and vice versa. Although karst soils cannot contain enough water for plant growth, the trade-off between carbon gain and water loss (high g_s ∕ A and low V_{cmax_Ci} ∕ A) was similar to that shown for plants growing in hot and wet regions (Prentice et al., 2014).

iWUE is regulated by the covariation of g_s, g_m, and V_cmax. In theory, water loss is regulated by g_s only, while carbon gain (A) is regulated by g_s, g_m, and V_cmax (Fig. 3) (Lawson and Blatt, 2014). However, in this study, iWUE was negatively related to g_s (R²= 0.30) and V_cmax (R²= 0.09), and not related to A, g_m (Fig. 6).

CO₂ diffusion and the Farquhar biochemical model indicated that iWUE is affected by g_m ∕ g_s and V_cmax ∕ g_s (Gago et al., 2014; Flexas et al., 2016). There was a hyperbolic dependency of iWUE on g_m ∕ g_s due to the roles of g_s and g_m in C_i and C_c, and of C_c in A (Flexas et al., 2016). In meta-analyses, both Gago et al. (2014) and Flexas et al. (2016) found that iWUE was significantly positively related to g_m ∕ g_s and V_cmax ∕ g_s. The results of this study are consistent with the meta-analyses (Fig. 7), demonstrating that plant species with relatively high g_m ∕ g_s or V_cmax ∕ g_s had relatively high iWUE. The relationship between iWUE and V_cmax ∕ g_s (R²= 0.50) was stronger than the relationship between iWUE and g_m ∕ g_s (R²= 0.20), demonstrating that iWUE was mainly regulated by V_cmax ∕ g_s. The reason for this may be that iWUE was correlated to g_s and V_cmax, and g_s was positively related to g_m.

However, plants cannot simultaneously have high g_m ∕ g_s and high V_cmax ∕ g_s. Similarly to the study by Gago et al. (2014), we found no relationship between g_m ∕ g_s and V_cmax ∕ g_s. Gago et al. (2014) thought that the poor relationship between g_m ∕ g_s and V_cmax ∕ g_s indicated that the iWUE may be improved by g_m ∕ g_s or V_cmax ∕ g_s separately; if both of them were simultaneously improved, an enhanced effect on iWUE could be anticipated. In addition, Flexas et al. (2016) showed in a simulation that the increase in iWUE caused by overinvestment in photosynthetic capacity would progressively lead to inefficiency in the trade-off between carbon gain and water use, causing an imbalance between CO₂ supply and demand.

5 Conclusions

This study provides information regarding the limitations of A and iWUE by g_s, g_m, and V_cmax in 63 species across 6 life forms in the field. The results showed that plants growing in karst CZs used diverse strategies of carbon–water regulation, but no difference was found among life forms. The covariation of CO₂ supply (g_s and g_m) and demand (V_cmax) regulated A, indicating that species maintained a relatively high A through covaring their leaf anatomical structure and V_cmax. iWUE was relatively low, but ranged widely, indicating that plants used the “profligate/opportunistic” water use strategy to maintain the survival, growth, and structure of the community. iWUE was regulated by g_s, V_cmax, g_m ∕ g_s, and V_cmax ∕ g_s, indicating that species with high g_m ∕ g_s or V_cmax ∕ g_s will have to be much more competitive to respond to the ongoing rapid warming and drought in the karst CZs.