Response of Leaf Photosynthesis–Transpiration Coupling to Biotic and Abiotic Factors in the Typical Desert Shrub Artemisia ordosica

Abstract

The environmental regulatory mechanism underlying the coupling of leaf photosynthesis and transpiration in Artemisia ordosica, a typical desert shrub in China, remains unclear. To understand this mechanism, we measured the net leaf photosynthetic rate (P_n), transpiration rate (E), and stomatal conductance (g_s) from May to October 2019 using a portable photosynthesis analyser. Photosynthetically active radiation, air temperature, relative humidity, and soil water content were simultaneously measured. Both E and P_n are positively correlated with g_s. P_n and E exhibited a nonlinear quadratic correlation from May to July and a linear correlation in August and September. The changes in the maximum photosynthetic (P_n−max) and carboxylation rates were mainly affected by air temperature and light. Seasonally, P_n−max initially exhibited an increasing trend, peaking in June and then decreasing. Under low temperature and light conditions, P_n−E was linearly correlated and the coupling relationship was stable. Under higher temperatures and radiation, P_n−E exhibited a nonlinear quadratic correlation, and decoupling occurred with increasing temperature and light intensity. The results of this study provide a better understanding of the responses of desert shrub ecosystems to climate change.

1. Introduction

Desert ecosystems are among the most widely distributed terrestrial ecosystems globally, accounting for more than 40% of the total global land area [1,2] and approximately 52% of the arid and semi-arid areas in China [3,4,5]. In recent years, owing to the influence of global climate change, extreme weather events have occurred frequently in arid and semi-arid areas of Northwest China, posing more serious challenges to the fragile desert ecosystem [6,7,8]. Therefore, exploring the photosynthetic characteristics of the typical desert vegetation in fluctuating environments is helpful to further understand the adaptability of desert vegetation to the changing environments and, thus, provide scientific support for dealing with future extreme climate change.

Photosynthesis is the primary physiological process of plant growth, directly providing a material basis for the growth and development of plants and directly or indirectly providing available energy sources for other organisms in the terrestrial ecosystem [9,10]. As an important function in maintaining the water resource balance in ecosystems, plant transpiration has ecological significance for social and economic development [11]. Leaves are the main organs for photosynthesis and transpiration in plants, and water dissipation is accompanied by plant leaf photosynthesis. In recent years, the coupling mechanism of carbon and water in leaves has remained unclear, and an increasing number of researchers are trying their best to explore this issue [12,13]. Therefore, exploring the coupling mechanisms of carbon and water at the leaf scale has become an important topic in plant eco-physiological research.

Exploring the photosynthetic and transpiration characteristics of plants provides not only a theoretical basis for understanding carbon assimilation behaviour in ecosystems but also a biochemical basis for further understanding the coupling mechanism of carbon and water [14,15]. The stomata are an important channel for controlling water and air exchange between plant leaves and the external environment, and their bidirectional regulation of photosynthetic and transpiration mechanisms is the basis for studying the coupling mechanism of carbon and water in plants. Exploring the carbon and water cycling mechanisms of plants at the leaf scale is helpful for a better understanding of photosynthesis and transpiration in relation to climate change.

The photosynthetic and transpiration processes of plants are influenced by both biotic and abiotic factors. Photosynthetic and transpiration-related stomatal behaviours change with interactions with the external environment. Some researchers have suggested that conductance (g_s) decreases exponentially with increasing vapour pressure deficit with insufficient soil moisture [16,17]. Others believe that increasing temperature leads to increased saturated water vapour pressure inside and outside the leaves, thus, increasing stomatal conductance and enhancing photosynthesis and transpiration. In contrast, stomatal conductance and the photosynthetic rate of plants decrease because of the effect of photosynthetic enzyme activity when the temperature decreases [18,19]. Current studies on the mechanisms of plant photosynthesis and transpiration have started with the exploration of the mechanism of the porosity theory proposed by Farquhar [20] for the realisation of carbon–water coupling at the ecosystem scale using flux data. In recent years, with the rapid development of remote sensing technology, large-scale carbon and water coupling models have been optimised [21,22,23]. However, there are few studies on the response of plant photosynthesis–transpiration coupling mechanisms at the leaf scale, especially in desert shrub species.

In this study, Artemisia ordosica, a typical desert shrub species in Mu Us Sandy Land, Ningxia, was selected to analyse variations in leaf stomatal conductance (g_s), the transpiration rate (E), and the net photosynthetic rate (P_n) in response to environmental factors. The goal was to understand the coupling between photosynthesis and transpiration and its regulatory mechanism in response to environmental factors. Specifically, this study addresses the following: (1) the environmental factors controlling leaf photosynthesis and transpiration; (2) the factors involved in the abiotic regulation of leaf photosynthesis; (3) the effect of environmental factors on the relationship between photosynthesis and transpiration (P_n−E); (4) the conditions that could strengthen the coupling effect between the P_n−E; and (5) the conditions that inhibit the coupling effect of P_n−E, and even the decoupling phenomenon.

2. Materials and Methods

2.1. Experimental Design

2.1.1. Study Site

The study site is located at the National Observation and Research Station for Desert Ecosystem in Mu Us Sandy Land, Yanchi, Ningxia (37°42′31″ N, 107°13′47″ E, with an altitude of 1560 m). It is in the ecological ecotone of transition from semi-arid to arid regions, arid grassland areas to desert grassland areas, and agricultural to pastoral areas. The area has a typical mid-temperate continental monsoon climate, with an annual average temperature of 8.1 °C, potential annual evaporation of 2024 mm, and a frost-free period of 165 days. The annual precipitation is 287 mm, mainly from July to September, accounting for 68% of the total annual precipitation. The soil type is mainly aeolian sandy soil, the surface soil organic carbon content is approximately 13.7g·kg⁻¹, and the soil pH range is 7.0–9.2. A. ordosica is the dominant species in the study area. Other species include Salix psammophila, Hedysarummongolicum, and Hedysarumscoparium.

A. ordosica is an iconic plant in desert steppes with drought and barrenness tolerance. It is widely distributed in the deserts and semi-desert steppes of Northwest China. The leaf expanding period is from April to early May, the expanded leaf period is from June to late August, and the deciduous period is from September to October.

2.1.2. Determination of Gas Exchange Parameters

From May to October 2019, a 10 m × 10 m plot was set near the flux tower at the site. A group of five A. ordosica plants was randomly selected from each plot. There were three healthy leaves from each A. ordosica plant selected to measure gas exchange parameters, including the net photosynthetic rate (P_n), transpiration rate (E), and stomatal conductance (g_s), using a portable photosynthetic analyser (LI-6400; LI-COR Inc., Lincoln, NE, USA).

2.1.3. Measurement of CO₂ Response Curve

Plant leaves were induced for more than 30 min under the following conditions: photosynthetically active radiation (PAR) of 1800 μmol·m⁻²·s⁻¹ (light source using LI-6400 LED red and blue light source), temperature of 25 °C, a flow rate of 400 μmol·mol⁻¹, and relative humidity of 55%. After the PAR and stomatal conductance were relatively stable, CO₂ concentrations of 400, 200, 100, 50, 0, 400, 700, 1000, 1200, 1400, 1800, and 2000 μmol·mol⁻¹ in the reference chamber were controlled using the Li-6400 injection system. The photosynthetic rate of the leaves was determined after 3 min acclimation to each CO₂ concentration.

2.1.4. Determination of Environmental Factors

Meteorological data were monitored using meteorological sensors on the flux tower (Figure 1). PAR, air temperature (T_a), and relative humidity (RH) were measured using a PAR sensor (PAR-LITE; Kipp and Zonen, Delft, The Netherlands) and a temperature and humidity sensor (HMP155A; Vaisala, Vantaa, Finland) mounted on the flux tower. The soil moisture content (SWC) at depths of 10 and 30 cm was determined using a soil moisture probe (ECH₂O-5TE; Decagon Devices, Pullman, WA, USA). Rainfall (P) was measured using a tip-bucket rain gauge (TE525WS; Campbell Scientific Inc., Logan, UT, USA) installed in an open space near the sampling site. All data were monitored for 30 min.

2.2. Statistical Analyses

A CO₂ response model was used to fit the P_n−C_i curve [24], and photosynthetic parameters, including the initial carboxylation rate, maximum net photosynthetic rate, saturated intercellular CO₂ concentration, CO₂ compensation point, and photorespiration rate, were obtained. P_n was determined as follows:

$$P\mathrm{n}=\alpha \frac{1-\beta C}{1+\gamma C}C-R\mathrm{p}$$

(1)

Saturated CO₂ concentration was determined as follows:

$$Csat=\frac{\sqrt{\left(\beta +\gamma \right)/\beta }-1}{\gamma }$$

(2)

When C = C_sat, the maximum net photosynthetic rate (P_n−max) was expressed as follows:

$$P_{\mathrm{n}-\max }=\alpha {\left(\frac{\sqrt{\beta +\gamma }-\sqrt{\beta }}{\gamma }\right)}^2-R\mathrm{p}$$

(3)

where P_n is the net photosynthetic rate (μmol·m⁻²·s⁻¹), α is the initial slope of the photosynthetic CO₂ response curve, which is also called the initial carboxylation efficiency, β and γ are constants independent of CO₂ concentration, C is the intercellular CO₂ concentration or atmospheric CO₂ concentration (μmol·mol⁻¹), R_p is the photorespiration rate, and C_sat is the CO₂ saturation point.

Regression analysis was used to analyse the relationships between photosynthetic parameters and environmental factors. R software (R Foundation for Statistical Computing, Vienna, Austria) was used to establish a linear mixed-effect model. All statistical analyses were performed using R software.

3. Results

3.1. Seasonal Dynamics of Environmental Factors

The seasonal dynamics of the environmental factors are shown in Figure 2. During the observation period, environmental factors exhibited clear seasonal patterns and variations (Figure 2). The maximum value of PAR (PAR_max) was 1902 μmol·m⁻²·s⁻¹ (day 192), and the minimum value was 288.4 μmol·m⁻²·s⁻¹ (day 177, Figure 2a). The minimum and maximum of the mean daily temperatures (T_a) were 9.1 °C (day 139) and 29.2 °C (day 207), respectively (Figure 2b). The maximum RH was 94.5% (day 177), and the minimum value was 14.2% (day 142, Figure 2c). The total rainfall during the observation period was 260.4 mm (Figure 2d), mostly in June and July. There were three larger rainfall events with single rainfalls of 20 mm (day 176), 23.1 mm (day 196), and 45 mm (day 215). The moisture content of the 10 cm soil was greatly affected by rainfall. The soil moisture content at 30 cm was relatively stable and changed significantly after the three large rainfall events.

3.2. Seasonal Dynamics of Gas Exchange Parameters

The seasonal dynamics of the gas exchange parameters are presented in

Table 1. Gas exchange parameters of Artemisia ordosica at the study site from May to September 2019.

	Pn	E	gs
Month	(μmol·mol−1)	(mmol·m−2·s−1)	(mol·m2·s−1)
May	24.04 (2.01)	12.35 (0.07)	0.51 (0.14)
June	23.04 (3.97)	13.62 (2.27)	0.45 (0.12)
July	28.09 (1.13)	13.58 (2.30)	0.57 (0.15)
August	20.25 (3.23)	8.98 (2.43)	0.44 (0.11)
September	23.54 (4.23)	9.03 (2.37)	0.51 (0.19)

The data in the table represent the average values of P_n, E, and g_s for each month between May and September, and the standard errors are in parentheses.

and Figure 3. It can be seen from Table 1 that leaf P_n, E, and g_s are all at a minimum in August, and the mean values were 20.25 ± 3.23 μmol·mol⁻¹, 8.98 ± 2.43 μmol·mol⁻¹, and 0.44 ± 0.11 mol·m²·s⁻¹, respectively. In July, P_n, E, and g_s were at a maximum with mean values of 28.09 ± 3.23 μmol·mol⁻¹, 13.58 ± 2.30 μmol·mol⁻¹, and 0.57 ± 0.15 mol·m²·s⁻¹, respectively. (Table 1). Further analysis of the relationship between P_n, E, and g_s showed that P_n−g_s presented a nonlinear quadratic correlation (p< 0.05, Figure 3a). The R² values during May, June, July, August, and September were 0.87, 0.89, 0.86, 0.91, and 0.9, respectively. When g_s was low, P_n changed faster, and when stomatal conductance reached a certain level, P_n slowed with a change in g_s.

E-g_s presented a positive linear correlation in different months (p < 0.05, Figure 3b), and the coupling relationship was stable; the R² values during May, June, July, August, and September were 0.93, 0.93, 0.92, 0.96, and 0.91, respectively. Figure 3c shows that P_n−E had a nonlinear quadratic relationship in the early growing season and a linear relationship in the late growing season. With the increased stomatal opening, the P_n−E fitting slopes decreased gradually, and the coupling relationship weakened gradually; the R² values during May, June, July, August, and September were 0.91, 0.92, 0.89, 0.95, and 0.89, respectively.

3.3. Seasonal Dynamics of Photosynthetic Parameters

Seasonal dynamics of the photosynthetic parameters are shown in Figure 4 and

Table 2. Photosynthetic parameters of the CO₂ response curve in different months.

Month	α	Pn−max	Csat	C0	Rp	R2
	(mol·mol−1)	(μmol·m−2·s−1)	(μmol·m−2·s−1)	(μmol·m−2·s−1)	(μmol·m−2·s−1)
May	0.027 (0.009)	12.076 (2.132)	1877.883 (78.321)	67.658 (12.212)	1.749 (0.811)	0.991
June	0.080 (0.032)	18.723 (4.321)	1536.522 (65.322)	43.334 (9.443)	3.133 (0.951)	0.997
July	0.044 (0.021)	12.388 (3.572)	2758.173 (100.275)	45.567 (5.251)	1.816 (0.342)	0.992
August	0.035 (0.045)	9.521 (1.946)	2031.811 (89.312)	33.051 (8.347)	1.081 (0.310)	0.998
September	0.020 (0.010)	6.214 (1.523)	1568.160 (72.143)	50.921 (7.215)	0.955 (0.241)	0.987

The data in the table represent the average values of α, P_n−max, C_sat, C₀, R_p, and R² for each month between May and September, and standard errors are in parentheses. α is initial carboxylation efficiency, P_n−max is maximum net photosynthetic rate, C_sat is saturated CO₂ concentration, C₀ is CO₂ compensation point, and R_p is light respiration rate.

. P_n rapidly increased with increasing CO₂, saturated, and then remained stable (Figure 4). Table 2 shows the parameters of the photosynthesis–CO₂ curves; the R² values during May, June, July, August, and September were 0.991, 0.997, 0.992, 0.998, and 0.987, respectively. The initial α and P_n−max in June reached a maximum with mean values of 0.080 ± 0.032 (mol·mol⁻¹) and 18.723 ± 4.321(μmol·m⁻²·s⁻¹), respectively; α was double that for July (0.044 ± 0.021 mol mol⁻¹) and approximately four-fold that for September (0.020 ± 0.010 mol·mol⁻¹). P_n−max was approximately twice that for August (9.521 ± 1.946 μmol·m⁻²·s⁻¹) and approximately thrice that for September (6.214 ± 1.523 μmol·m⁻²·s⁻¹). P_n−max showed an increasing trend from May to June and a decreasing trend from June to September. C_sat varied withthe months, being the highest in May (2758.173 ± 100.275 μmol·mol⁻¹) and approximately double that for June (1536.522 ± 65.322 μmol·mol⁻¹) and September (1568.160 ± 72.143 μmol·mol⁻¹). The CO₂ compensation point (C₀) was lowest in August (33.051 ± 8.347 μmol·mol⁻¹).

3.4. Response of Photosynthetic Parameters to Environmental Factors

The response of the photosynthetic parameters of A. ordosica to environmental factors is shown in Figure 5 and Figure 6. The results indicated that in the growing season, P_n−max was positively correlated with PAR, T_a, and SWC₁₀ and negatively correlated with RH, P, and SWC₃₀ at a 30cm depth (Figure 5). α was positively correlated with PAR, T_a, and SWC₃₀ and negatively correlated with RH, P, and SWC₁₀ (Figure 5).

A linear mixed-effects model was used to analyse the contribution of various environmental factors to the P_n−max and α of A. ordosica. The results suggested that P_n−max was negatively correlated with PAR and SWC₃₀ and positively correlated with T_a, RH, and SWC₁₀ (Figure 6). α was negatively correlated with RH and P but positively correlated with PAR, T_a, SWC₁₀, and SWC₃₀. The results also showed that both P_n−max and α were positively correlated with T_a and SWC₁₀ (Figure 5). The above analysis showed that A. ordosica photosynthesis was mainly affected by light and temperature during the growing season.

3.5. Effects of Environmental Factors on Photosynthesis and Transpiration Coupling

The effects of environmental factors on the coupling between photosynthesis and transpiration in A. ordosica are shown in Figure 7a,b. Figure 7 shows that different light and temperature levels have different effects on the coupling of photosynthesis and transpiration. There was a linear correlation between P_n and E at low PAR (R² = 0.98, p < 0.05); the relationship expression is y = 1.54x + 1.44, and the state values of P_n and E were 14.14 (μmol·m⁻²·s⁻¹) and 23.10 (mmol·m⁻²·s⁻¹), respectively. At high levels of PAR, however, P_n and E showed a nonlinear quadratic correlation (R² = 0.97, p < 0.05; Figure 7a); the relationship expression is y = −0.06x² + 2.56x − 3.22 and the state values of P_n and E were 13.93 (μmol·m⁻²·s⁻¹) and 24.95 (mmol·m⁻²·s⁻¹), respectively. This indicates that photosynthesis–transpiration coupling was stronger at low PAR levels and decoupled gradually as light intensity increased.

The effect of T_a on the coupling of photosynthesis and transpiration was similar to that of PAR. At low T_a levels, P_n and E showed a positive linear correlation (R² = 0.98, p < 0.05), and the relationship expression is y = 1.54x + 5.22, and the state values of P_n and E were 14.15 (μmol·m⁻²·s⁻¹) and 23.27 (mmol·m⁻²·s⁻¹), respectively. At high levels of T_a, however, P_n and E showed a nonlinear quadratic correlation, and the coupling relationship broke down with temperature increasing (R² = 0.96, p < 0.05; Figure 7b); the relationship expression is y = −0.04x² + 2.56x − 3.30, and the state values of P_n and E were 13.92 (μmol·m⁻²·s⁻¹) and 25.06 (mmol·m⁻²·s⁻¹), respectively.

4. Discussion

4.1. Correlation between P_n, g_s, and E

Plants themselves control photosynthesis and transpiration by adjusting the stomatal opening but this process is regulated by both the external environment and their own physiological state [25,26,27]. In the present study, we found that g_s-E showed a linear correlation, indicating that stomatal restriction of plant transpiration is relatively stable during the growing season. The nonlinear quadratic correlation of g_s-P_n indicated that the limiting effect of stomata on plant photosynthesis changes from strong to weak with environmental changes. When g_s was low, P_n increased nearly linearly with the gradual opening of the stomata, and the limiting effect of stomata was relatively stable. When stomata opened to a certain extent, photosynthesis decreased, indicating that stomatal restriction of photosynthesis changed when the external environment changed.

P_n−E showed a nonlinear quadratic correlation, indicating that stomata regulate photosynthesis and transpiration in the same direction. The P_n−E regression relationship could better reflect the coupling mechanism of carbon and water in plants [28]. The closer the linear relationship between them, the stronger the degree of plant carbon and water coupling [29]. The larger the fitting slope, the stronger the sensitivity of plants to water change [29]. In general, the relationship between photosynthetic rate, transpiration, and stomatal conductance is stable when the plant is in a stable environment. However, the coupling characteristics change when the environment changes. Studies have shown that, for most vegetation types, a quadratic curve can better fit the relationship between P_n and g_s [5,17]. In the present study, the coupling relationship between P_n and E in A. ordosica differed monthly. Compared with the stable linear correlation, the nonlinear quadratic correlation between P_n and E was unstable at both the leaf expansion (from May to June) and leaf senescence stages (in October), indicating that carbon–water coupling in A. ordosica was more susceptible to the external environment at leaf expansion and senescence stages. During theleaf senescence stages, stomata restrict photosynthesis and transpiration synchronously because of the decrease in the environmental temperature and plant physiological state, and the coupling relationship between photosynthesis and transpiration shows a linear correlation.

4.2. Effects of Environmental Factors on Plant Photosynthesis

Photosynthesis is one of the most important physiological characteristics of plants and reflects their production potential and survival strategies in response to environmental changes [30,31]. P_n−max represents the maximum photosynthetic potential of plants [32,33] and α represents the photosynthetic capacity of plants at low CO₂ concentrations. The CO₂ response curve reflects the feedback mechanism of plant photosynthesis with respect to CO₂ concentration [34,35]. The correlation between P_n−max, α, and environmental factors showed that light and temperature were positively correlated with P_n and α in the growing season, indicating that light and temperature were the main environmental factors affecting photosynthetic capacity in A. ordosica. Studies have shown that A. ordosica has different adaptive abilities to different light intensities, and its light energy utilisation rate under weak light is generally better than that under high light intensities [36]. Temperature is a sensitive factor affecting photosynthesis. High- and low-temperature stress can accelerate the degradation of photosynthetic pigment molecules, thereby affecting plant photosynthesis [37,38]. In addition, an increase in PAR and T_a leads to an increase in the transpiration rate, increasing the negative pressure of water transport and reducing cell turgor pressure [39], thus, leading to a decrease in the stomatal opening [40]. This, in turn, affects carbohydrate formation in plants [41].

4.3. Effects of Environmental Factors on Pn₋E Coupling

Photosynthesis and transpiration in plants are disproportionately driven by environmental factors, and the stomatal opening directly determines the magnitude of P_n and E. Some studies have suggested that light is the driving source of photosynthesis and water transpiration in leaves [42] and that there is a relatively stable proportional relationship between the light used to drive photosynthesis and the light used to drive transpiration [43]. Other studies have found that temperature directly affects the saturated water vapour pressure deficit inside and outside the leaves [44], thus, affecting stomatal openness and the transpiration rate and ultimately affecting plant photosynthesis. In the current study, it was found that leaf P_n and E showed diurnal and seasonal changes with changes in light and temperature during the growing season. Furthermore, we showed that the P_n and E of A. ordosica in different months were positively correlated with PAR and T_a.

The coupling relationship of P_n−E differed at different PAR and T_a levels. At low levels of PAR and T_a, P_n−E had a high-fit slope, indicating tight coupling between photosynthesis and transpiration. With an increase in temperature and light, photosynthesis was continuously enhanced, and P_n and E showed a linear increase, which led to an increase in CO₂ consumption and, thus, caused a decrease in intercellular CO₂ concentration. To absorb more CO₂, stomatal conductanceand transpiration increased. These results indicate that the coupling between photosynthesis and transpiration in A. ordosica was closer under low temperature and light conditions and was relatively stable due to the stomatal restriction. However, at high PAR and T_a levels, P_n−E showed a quadratic regression relationship, and high temperatures and strong light weakened the coupling between photosynthesis and transpiration. When PAR and T_a increased to a certain level, the stomata began to close due to protection mechanisms, and transpiration decreased but the degree of photosynthetic decline was greater than that of transpiration; therefore, the fitting slope of P_n−E decreased, consistent with the findings of Xu et al. [45]. High temperatures and strong light limit transpiration more than photosynthesis, resulting in photosynthetic transpiration decoupling. The overall temperature in the growing season showed a trend of first increasing and then decreasing; therefore, the P_n−E coupling relationship showed a trend of gradual strengthening and then weakening.

5. Conclusions

To determine the coupling relationship between photosynthesis and transpiration in A. ordosica and its regulation mechanism in response to environmental factors, an in situ monitoring experiment was conducted to analyse the responses of leaf stomatal conductance, the transpiration rate, and the net photosynthetic rate in regard to environmental factors. As a result, the following important conclusions were obtained. First, temperature and light are the main factors affecting photosynthesis and transpiration in the growing season through the regulation of stomatal changes. Second, under low temperatures and weak light conditions, the influence of stomata on the coupling between photosynthesis and transpiration was relatively stable. Third, under high temperatures and strong light, the restriction of stomata on photosynthesis is greater than that on transpiration, resulting in decoupling.

According to the analysis results of this study, as the climate becomes warmer and drier, shrubs will respond positively to temperature and light. Under the two climatic conditions of low temperature and weak light as well as high temperature and strong light, shrubs will decouple water and carbon, resulting in growth stagnation. Danger signals such as growth stagnation will occur in a short period of time, and then degradation or even death is highly likely. The results of this study provide a better understanding of the responses of desert shrub ecosystems to climate change.