Photosynthetic induction and its diffusional, carboxylation and electron transport processes as affected by CO₂ partial pressure, temperature, air humidity and blue irradiance

Abstract

Background and Aims Plants depend on photosynthesis for growth. In nature, factors such as temperature, humidity, CO₂ partial pressure, and spectrum and intensity of irradiance often fluctuate. Whereas irradiance intensity is most influential and has been studied in detail, understanding of interactions with other factors is lacking.

Methods We tested how photosynthetic induction after dark–light transitions was affected by CO₂ partial pressure (20, 40, 80 Pa), leaf temperatures (15·5, 22·8, 30·5 °C), leaf-to-air vapour pressure deficits (VPD_leaf-air; 0·5, 0·8, 1·6, 2·3 kPa) and blue irradiance (0–20 %) in tomato leaves (Solanum lycopersicum).

Key Results Rates of photosynthetic induction strongly increased with CO₂ partial pressure, due to increased apparent Rubisco activation rates and reduced diffusional limitations. High leaf temperature produced slightly higher induction rates, and increased intrinsic water use efficiency and diffusional limitation. High VPD_leaf-air slowed down induction rates and apparent Rubisco activation and (at 2·3 kPa) induced damped stomatal oscillations. Blue irradiance had no effect. Slower apparent Rubisco activation in elevated VPD_leaf-air may be explained by low leaf internal CO₂ partial pressure at the beginning of induction.

Conclusions The environmental factors CO₂ partial pressure, temperature and VPD_leaf-air had significant impacts on rates of photosynthetic induction, as well as on underlying diffusional, carboxylation and electron transport processes. Furthermore, maximizing Rubisco activation rates would increase photosynthesis by at most 6–8 % in ambient CO₂ partial pressure (across temperatures and humidities), while maximizing rates of stomatal opening would increase photosynthesis by at most 1–3 %.

INTRODUCTION

When a dark-adapted leaf is illuminated, photosynthesis (A) starts, and increases over a period of time to a stable steady-state rate. This process, photosynthetic induction, was discovered almost a century ago (Osterhout and Haas, 1918), and its underlying mechanisms have been studied extensively (Pearcy and Way, 2012; Kaiser et al., 2016). The main mechanisms that affect photosynthetic induction, and A in fluctuating irradiance, are activation of Calvin cycle enzymes and stomatal opening (Pearcy et al., 1996). Additionally, the history of irradiance intensity, plant functional type and environmental conditions modulate the amplitude and kinetics of photosynthetic induction. While previous studies (reviewed in Kaiser et al., 2015) have shown that environmental factors such as leaf external CO₂ partial pressure (C_a), leaf temperature (T_leaf), leaf-to-air vapour pressure deficit (VPD_leaf-air) and blue irradiance can modulate the responses of A to variable irradiance, no study has systematically compared the effects of all of these factors on the photosynthetic response to dark–light transitions.

Due to the wind-induced movement of leaves, canopies and clouds, irradiance incident on a leaf can fluctuate, often resulting in time-dependent changes in A and reductions in irradiance use efficiency compared to the theoretical situation of instantaneous changes in assimilation. Currently, there is renewed interest in the dynamic components of photosynthesis, as (1) faster activation of Rubisco could increase resource use efficiency and productivity (Carmo-Silva et al., 2015), (2) stomata that react faster to changes in irradiance could increase intrinsic water use efficiency (WUE_i; Lawson and Blatt, 2014), (3) faster relaxation of non-photochemical quenching (NPQ) could increase photosynthetic quantum yield in limiting irradiance (Murchie and Niyogi, 2011) and (4) predictions of assimilation that account for dynamics could lead to more accurate forecasts of plant productivity (Kaiser et al., 2015). To address these questions, the behaviour of dynamic photosynthesis in C₃ crops must be thoroughly understood. However, most effort has been directed towards understorey shrubs and trees, and only a few studies have investigated dynamic photosynthesis and its environmental modulation in C₃ species with high photosynthetic capacity (Yamori et al., 2012; Carmo-Silva and Salvucci, 2013; Soleh et al., 2016). Such experiments are necessary to quantify limitations to dynamic photosynthesis and to assess how each limiting factor is affected by environmental conditions.

The enzymes that regenerate ribulose-1,5-bisphosphate (RuBP) are activated rapidly during photosynthetic induction (Sassenrath-Cole and Pearcy, 1992). Consequently, RuBP supply to Rubisco is considered to be non-limiting after the first minute of induction (Woodrow and Mott, 1989; Pearcy et al., 1996). Rubisco itself typically takes 7–10 min to fully activate in vivo (Pearcy et al., 1996), and the extent of its limitation during photosynthetic induction and the apparent time constant of its activation (τ_R) can be calculated from gas exchange data (Woodrow and Mott 1989). A low stomatal conductance (g_s) can impose an additional diffusional limitation on induction. By estimating the assimilation rate that would occur if CO₂ partial pressure in the chloroplast (C_c) were identical to C_a (i.e. leaf conductance being infinite), the diffusional limitation acting on transient and steady-state A can be quantified. This diffusional limitation normally includes a component in the mesophyll, which is quantified as mesophyll conductance (g_m). Mesophyll conductance may vary with irradiance, C_a and temperature (Flexas et al., 2007, 2008; von Caemmerer and Evans, 2015). However, to our knowledge, no study has examined possible changes of g_m during induction and their implications on diffusional limitation.

During photosynthetic induction, electron and proton transport processes undergo rapid changes, affecting the efficiency of electron transport through photosystem II (Φ_PSII) and NPQ. As in the case of steady-state A, linear electron transport rate (ETR) correlates linearly with gross photosynthesis (A_gr) during induction (Košvancová-Zitova et al., 2009; Yamori et al., 2012), and changes in the slope of this relationship can be used to infer changes in photorespiration. NPQ often overshoots at the start of induction (e.g. Johnson et al., 1994), which is probably due to the decrease of lumen pH that develops when ETR is limited by low photosynthetic metabolic activity. Hence, measuring Φ_PSII and NPQ concurrent with gas exchange can provide detailed information on processes affecting photosynthetic induction.

Dynamic A and its modulation by environmental factors must be better understood in order to improve it. Tomato (Solanum lycopersicum), a C₃ model species with intermediate leaf photosynthetic capacity and an important crop in open field and protected cultivation, was used in this study. During photosynthetic induction after a dark–light transition, it was shown how transient diffusional and biochemical limitations, stomatal and mesophyll conductance, apparent Rubisco activation, WUE_i and electron transport processes are affected by C_a, T_leaf, VPD_leaf-air and blue irradiance. The benefits and costs of faster Rubisco activation or stomatal opening on dynamic photosynthesis are discussed.

MATERIALS AND METHODS

Plant material

Tomato seeds (Solanum lycopersicum ‘Cappricia’; Rijk Zwaan, De Lier, the Netherlands) were germinated in Rockwool plugs (Grodan, Roermond, the Netherlands), which after 1 week were transferred to Rockwool cubes (10 cm × 10 cm × 7 cm; Grodan). Plants were grown in a climate chamber with 16/8-h photoperiod, 22/20 °C (day/night) temperature, 70 % relative humidity and 320 µmol m⁻² s⁻¹ photosynthetically active radiation (PAR), measured at table height. Irradiance was provided by a mixture of white, red and far-red LEDs with emission peaks at 440, 550, 660 and 735 nm. Rockwool cubes were standing in a layer (height: 1–2 cm) of nutrient solution (Yara Benelux B.V., Vlaardingen, the Netherlands), which was replenished every 1–2 d and contained 12·4 mm ${\text{NO}}_3^{-}$, 7·2 mm K⁺, 4·1 mm Ca²⁺, 3·3 mm ${\text{SO}}_4^{2-}$, 1·8 mm Mg²⁺, 1·2 mm ${\text{NH}}_4^{+}$, 1·1 mm ${\text{PO}}_4^{3-}$, 30 μm ${\text{BO}}_3^{3-}$, 25 μm Fe³⁺, 10 μm Mn²⁺, 5 μm Zn²⁺, 0·75 μm Cu²⁺ and 0·5 μm ${\text{MoO}}_4^{2-}$ (EC 2·1 dS m⁻¹, pH 5·5). When plants were between 5 and 6 weeks old, leaves 4 and 5, counting from the bottom, were used for measurements. At this stage, growth of these leaves was almost complete (data not shown).

Gas exchange and chlorophyll fluorescence measurements

All measurements were performed using the LI-6400 photosynthesis system (Li-Cor Biosciences, Lincoln, NB, USA) equipped with the leaf chamber fluorometer (Li-Cor Part No. 6400-40, area 2 cm²).

Photosynthetic induction.

To assess the response of gas exchange to a step increase in irradiance, leaves were first dark-adapted at the treatment levels described below until g_s was constant (60–120 min). Then, irradiance was increased to 1000 µmol m⁻² s⁻¹ in a stepwise change and gas exchange values were logged every second for 60 min. Although such a dark–light transition does not resemble a natural situation, we chose these extreme irradiance levels in an attempt to maximize the effect of the treatment levels (see below) on photosynthetic induction. An irradiance of 1000 µmol m⁻² s⁻¹ was ∼5 % below saturation, which was a compromise between using a fully saturating irradiance (determined in pilot experiments, see Supplementary Data File S1) and the desire to avoid photoinhibition of photosynthesis. The flow rate of air was 500 µmol s⁻¹. Other than when adjusted as part of a treatment, the standard conditions in the cuvette were: 39·7–40·3 Pa C_a (range of lowest to highest value), 0·7–1·0 kPa VPD_leaf-air, 22·3–23·3 °C T_leaf and 90:10 % red/blue irradiance mixture provided by LEDs. The values of all cuvette conditions reported here are averages over whole induction curves. Peak intensities of red and blue LEDs were at wavelengths of 635 and 465 nm, respectively. Treatments were applied individually and included: 20, 40 and 80 Pa C_a, 15·5, 22·8 and 30·5 °C T_leaf, 0·5, 0·8, 1·6 and 2·3 kPa VPD_leaf-air (0·4, 0·9, 1·7 and 2·5 VPD_air) and 0, 1, 5, 10 and 20 % blue irradiance in a red irradiance background. For each treatment, five biological replicates were used (n = 5). All measurements were performed in a lab except the 15·5 and 30·5 °C T_leaf treatments, which were performed in climate chambers. Despite efforts to keep VPD_leaf-air similar between T_leaf treatments, it was, on average, 0·97 kPa at 15·5 °C, 0·80 kPa at 22·8 °C and 0·84 at 30·5 °C (Supplementary Data File S2). Transient A_n, g_s and C_i were averaged over five data points using a moving average filter to reduce measurement noise. Assimilation was corrected for CO₂ leaks using dried leaves (Long and Bernacchi, 2003).

To analyse the effect of C_a and T_leaf on photosynthetic electron transport processes, another set of induction curves was performed on different leaves, with the same cuvette conditions as described above. ETR was estimated from measurements of Φ_PSII, which was calculated from measurements of F_s (fluorescence yield under continuous actinic irradiance) and F_m′ (maximum fluorescence yield during a saturating irradiance pulse). The measurements of F_m′ were also used to calculate NPQ according to the Stern–Volmer quenching model (i.e. as 1 − F_m/F_m′) and using F_m from dark-adapted leaves. Measurements of F_m′ were made once a minute during the first 10 min of induction, and once every 2 min thereafter. To ensure the accurate measurement of F_m′, the multi-phase flash (MPF) protocol of the Li-Cor fluorometer was used (Loriaux et al., 2013). Using MPFs instead of single saturating pulses prevents underestimation of maximum chlorophyll fluorescence yield in light-adapted leaves of high photosynthetic capacity. F_m′ estimated by the MPF was ∼4 % larger than measured F_m′ (Supplementary Data File S3). Settings of the MPF were determined in preliminary measurements. These were 8500 and 1–2 µmol m⁻² s⁻¹ flash and measuring beam intensity, respectively; 60 % decrease of flash intensity during the 2^nd phase of the MPF; and 0·3, 0·7 and 0·4 s duration of the three flash phases. These settings yielded high correlations (R² ≈ 0·99) between flash intensity and F_m′ during flash phase 2 after the first or second minute of induction (data not shown). Preliminary data indicated limited effects of VPD_leaf-air on Φ_PSII or NPQ (data not shown); therefore, those measurements were not repeated here.

A/C_i curves.

To estimate the parameters V_Cmax, ETR_max, TPU and Γ*, A/C_i curves were first performed in photorespiratory and then in non-photorespiratory conditions (21 and 2 kPa oxygen, respectively; Supplementary Data File S4). Leaves were first adapted to 50 Pa CO₂ and 21 kPa O₂ for ∼30 min, then CO₂ partial pressure was reduced in a stepwise manner until 5 Pa, each step taking ∼4 min. Then, CO₂ was again raised to 50 Pa for ∼15 min, after which it was increased to 150 Pa in several steps, each step taking ∼5 min. Then, O₂ partial pressure was reduced to 2 kPa, and the procedure was repeated. Altogether, A was logged at 11 CO₂ partial pressures per O₂ partial pressure, and each complete A/C_i curve took ∼2·5 h. Data were logged every 5 s, and averages of 10 values at each C_a step, after steady-state A had visibly been reached, were used. Other cuvette conditions were: 1000 µmol m⁻² s⁻¹ PAR, 0·8 kPa VPD_leaf-air and 23 °C T_leaf.

A/PAR curves.

To estimate parameters R_d and s (lumped parameter used to scale the product of irradiance and Φ_PSII onto ETR), irradiance-limited curves were performed in 2 % oxygen [File S4]. The intercept of the linear A/(PAR × Φ_PSII × 0·25) relationship was R_d, while the slope was s (Yin et al., 2009). Leaves were adapted to 200 µmol m⁻² s⁻¹, until A and g_s were stable. Then, leaves were exposed to a range of PAR values between 0 and 200 µmol m⁻² s⁻¹. Assimilation was determined as described for the A/C_i curves. Φ_PSII was determined as described above. Other cuvette conditions were: 40 Pa C_a, 0·8 kPa VPD_leaf-air and 22 °C T_leaf.

Calculations

All calculations described here were performed on single replicates, and then used for further (statistical) analysis. Photosynthetic induction was calculated after Chazdon and Pearcy (1986): transient A (µmol m⁻² s⁻¹) was expressed as a percentage of the final rate (A_f), corrected for the initial, dark-adapted rate (A_i)

$$Photosynthetic\mathrm{ }induction=\mathrm{ }\frac{A-A_i}{A_f-A_i}·100$$ (1)

The relative rate of increase of g_s (mol m⁻² s⁻¹) during induction was calculated similarly. For the calculation of several parameters, gas exchange data were corrected for transient changes in C_i or C_c (using g_m as in Table 1 in Supplementary Data File S5) during induction. For diffusional limitation (LD; %), A was multiplied by the percentage by which A would increase if CO₂ partial pressure in the chloroplast (C_c, Pa) during induction was equal to leaf external partial pressure, C_a ($A_{C_a}^{*}$). For biochemical limitation (LB; %) and the apparent rate constant of Rubisco activation (τ_R; min), A was multiplied by the percentage by which A would increase if transient C_i was similar to final, steady-state C_i ${(A}_{C_i}^{*}$), following Woodrow and Mott (1989). However, unlike Woodrow and Mott (1989), for calculations of $A_{C_a}^{*}$ and $A_{C_i}^{*}$ no linear relationship between C_i and the CO₂ compensation point ${(\mathrm{\Gamma }}^{*}$, Pa) was assumed. Instead, information from complete A/C_i curves was used to correct A using the steady-state, curvilinear response of A to C_i. In the case of $A_{C_a}^{*}$, A was corrected for the minimum of either Rubisco activity-limited A (A_c), RuBP-limited A (A_j) or triose phosphate utilization-limited A (A_t) at C_a (in the numerator) and at C_c (in the denominator):

$$A_{C_{\mathrm{a}}}^{\mathrm{*}}=A·\frac{{min\left\{A_c\right(C}_a),\mathrm{ }{\mathrm{ }A}_j\left(C_a\right),{\mathrm{ }A}_t(C_a\left)\right\}}{min\left\{A_c\right(C_c),{\mathrm{ }A}_j\left(C_c\right),{\mathrm{ }A}_t(C_c\left)\right\}}$$ (2)

A_c, A_j and A_t were calculated after the FvCB model (Farquhar et al., 1980) modified to account for TPU limitation (Sharkey 1985). In eqns (3)–(5), the calculations for A at C_a are shown. For calculating A at C_c, C_i or C_if, C_a was replaced by any of these variables (not shown here):

$$A_c\left(C_a\right)=V_{\mathrm{C}\mathrm{m}\mathrm{a}\mathrm{x}}\left(\frac{C_a-{\mathrm{\Gamma }}^{\mathrm{*}}}{C_a+\mathrm{ }{K}_c\mathrm{ }·\mathrm{ }\left(1+\frac{O}{K_o}\right)}\right)-R_{\mathrm{d}}$$ (3)

$$A_j\left(C_a\right)={\mathrm{E}\mathrm{T}\mathrm{R}}_{\mathrm{m}\mathrm{a}\mathrm{x}}\left(\frac{C_a-{\mathrm{\Gamma }}^{\mathrm{*}}}{4\mathrm{ }·\mathrm{ }{C}_a+\mathrm{ }8\mathrm{ }·\mathrm{ }{\mathrm{\Gamma }}^{\mathrm{*}}}\right)-R_{\mathrm{d}}$$ (4)

$$A_t\left(C_a\right)=3·\mathrm{T}\mathrm{P}\mathrm{U}-R_{\mathrm{d}}$$ (5)

where V_Cmax (µmol m⁻² s⁻¹) is maximum velocity of Rubisco for carboxylation, R_d is day respiration (µmol m⁻² s⁻¹), O (kPa) is the chloroplast O₂ partial pressure, K_c (Pa) and K_o (kPa) are the Michaelis-Menten constants of Rubisco for CO₂ and for O₂, respectively, ETR_max (µmol m⁻² s⁻¹) is the maximum rate of electron transport in the absence of regulation and TPU (µmol m⁻² s⁻¹) is the triose phosphate utilization rate. Parameters V_Cmax, ETR_max and TPU were estimated using the Excel routine of Sharkey et al. (2007). The first five points of A/C_i curves at 21 kPa O₂ partial pressure were used to estimate V_Cmax (initial slope), the next four points to estimate ETR_max and the uppermost two points to estimate TPU (n=3). R_d and Γ* were determined after Yin et al. (2009). Additionally, R_d was corrected for respiration under the gasket of the gas exchange cuvette (Pons and Welschen, 2002). Parameters K_c and K_o were taken from Sharkey et al. (2007). All parameters were temperature-adjusted (Bernacchi et al., 2001); their values are given in Table 1. We acknowledge that the use of a steady-state model to correct A during transients may be inaccurate (e.g. V_Cmax and J_max change during induction; Soleh et al., 2016), and that further work should be dedicated to refining this method. LD was determined by analogy to stomatal limitation as in Urban et al. (2007):

$$\mathrm{L}\mathrm{D}=\mathrm{ }\frac{A_{C_{\mathrm{a}}}^{\mathrm{*}}-A}{A_{\mathrm{f}}-A_{\mathrm{i}}}·100$$ (6)

Table 1.

Parameters used in the calculations of diffusional limitation, biochemical limitation and the apparent time constant of Rubisco (eqns 3–5)

Parameter	Unit	Temperature (°C)
		15·5	22·8	30·5
ETRmax	µmol m−2 s−1	94·33	148·16	232·97
Kc	Pa	9·29	21·36	49·25
Ko	kPa	12·04	15·37	19·63
Rd	µmol m−2 s−1	0·77	1·23	2·00
TPU	µmol m−2 s−1	5·98	10·32	17·84
VCmax	µmol m−2 s−1	43·35	84·86	166·44
Γ*	Pa	3·62	5·34	7·88

Parameters ETR_max, TPU and V_Cmax were determined from A/C_i curves after Sharkey et al. (2007), K_c and K_o were taken from Sharkey et al. (2007), R_d and Γ* were determined from A/PAR and A/C_i curves after Yin et al. (2009). All parameters were temperature-adjusted after Bernacchi et al. (2001).

LB was calculated by using $A_{C_i}^{*}$, i.e. final steady-state C_i (C_if) in the numerator and C_i in the denominator of eqn (2) instead of C_a and C_c, respectively. LB was calculated after Urban et al. (2007):

$$\mathrm{L}\mathrm{B}=\mathrm{ }\frac{A_{\mathrm{f}}-A_{C_{\mathrm{i}}}^{\mathrm{*}}}{A_{\mathrm{f}}-A_{\mathrm{i}}}·100$$ (7)

${\tau }_R$ was calculated after Woodrow and Mott (1989):

$${\tau }_R=\frac{\mathrm{\Delta }time}{\mathrm{\Delta }\mathrm{ln }·\mathrm{ }(A_{\mathrm{i}}\mathrm{ }-{\mathrm{ }A}_{C_{\mathrm{i}}}^{\mathrm{*}})}$$ (8)

For the C_a and VPD_leaf-air treatments, data from minutes 2–5 during induction were used for Δtime [it has been determined by Woodrow and Mott (1989) that during this phase Rubisco activation is the main limiting factor], while in the case of varying T_leaf, data were taken from minutes 5–8 during induction, to account for a possible slower activation of RuBP regeneration in the beginning of induction due to low T_leaf. WUE_i (μmol mmol⁻¹) was calculated as:

$${\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}}=\frac{A}{g_{\mathrm{s}}}$$ (9)

Φ_PSII and NPQ were calculated after Genty et al. (1989) and Bilger and Björkman (1991), respectively. The coefficient of photochemical quenching (q_P) and PSII maximum efficiency (F_v′/F_m′) was calculated after Oxborough and Baker (1997). ETR was calculated after Yin et al. (2009):

$$\mathrm{E}\mathrm{T}\mathrm{R}={\mathrm{\Phi }}_{\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}·\mathrm{P}\mathrm{A}\mathrm{R}·s$$ (10)

where s is a unitless lumped calibration factor used to scale Φ_PSII to ETR (Yin et al., 2009). The maximum change in A (in percent) that would occur if either Rubisco instantly became fully activated or g_s immediately reached its final steady-state level (g_sf, Table 2) directly after the onset of illumination was calculated as the average of LB and LD between minutes 2 and 60 during induction, respectively. LB and LD data from the first minute after the onset of illumination were left out, as the activation of RuBP regeneration is known to be the main limiting factor of photosynthetic induction during that phase (Pearcy et al., 1996). The changes in WUE_i (in percent) were calculated as:

$${{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}}}_{\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{R}\mathrm{u}\mathrm{b}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{o}}=\left(\frac{\frac{A_f}{g_s}-{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}}}{{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}\mathrm{f}}}\right)*100$$ (11)

and

$${{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}}}_{\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{g}\mathrm{s}}=\left(\frac{\frac{A}{g_{sf}}-{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}}}{{\mathrm{W}\mathrm{U}\mathrm{E}}_{\mathrm{i}\mathrm{f}}}\right)*100$$ (12)

after which their averages during minutes 2–60 were determined. WUE_{i_instantRubisco} and WUE_{i_instantgs} are the changes in WUE_i that would occur if Rubisco became immediately fully activated, or g_s increased immediately to its final value. WUE_if is final, steady-state WUE_i.

Table 2.

Dynamic and steady-state parameters of photosynthetic induction in tomato leaves, as affected by C_a, T_leaf, VPD_leaf-air and blue light

Treatment	Dynamic parameters										Mean at start and end of induction
	IS60		tA50		tA90		tgs50		tgs90		Ai		Af		gsi		gsf
20 Pa	25·7 ± 3·0		3·2 ± 0·6b		18·5 ± 4·0b*		19·8 ± 1·2		46·7 ± 1·4b		−1·1 ± 0·6		11·7 ± 1·3a		0·22 ± 0·04		0·65 ± 0·05c
40 Pa	21·6 ± 2·7		2·6 ± 0·2a		10·8 ± 1·4ab*		18·7 ± 3·1		38·2 ± 5·6a		−1·6 ± 0·3		22·2 ± 1·4b		0·27 ± 0·06		0·56 ± 0·07b
80 Pa	21·9 ± 4·4		2·2 ± 0·3a		6·2 ± 0·3a*		18·2 ± 2·2		39·9 ± 4·7a		−1·3 ± 0·6		27·1 ± 2·3c		0·25 ± 0·06		0·46 ± 0·07a
15·5 °C	15·8 ± 4·5a*		2·7 ± 0·3b		12·6 ± 1·4		24·4 ± 4·8		42·5 ± 1·4		−1·1 ± 0·3b		15·6 ± 2·2a	†	0·17 ± 0·16a		0·34 ± 0·14a
22·8 °C	21·6 ± 2·7b*		2·6 ± 0·2b		10·8 ± 1·4		18·7 ± 3·1		38·2 ± 5·6		−1·6 ± 0·3ab		22·2 ± 1·4b	†	0·27 ± 0·06b		0·56 ± 0·07b
30·5 °C	37·8 ± 7·8c*		1·6 ± 0·4a		13·4 ± 1·6		17·2 ± 1·9		34·5 ± 2·2		−2·3 ± 0·5a		21·3 ± 3·8b	†	0·21 ± 0·03ab		0·36 ± 0·10a
0·5 kPa	22·3 ± 1·1		2·4 ± 0·8		10·7 ± 1·9A		20·7 ± 0·8b		45·3 ± 15·6c		−1·3 ± 0·1		21·5 ± 0·9		0·30 ± 0·01b		0·57 ± 0·02b
0·8 kPa	21·6 ± 3·9		2·6 ± 0·3		10·8 ± 2·6A		18·7 ± 5·3b		38·2 ± 13·8bc		−1·6 ± 0·8		22·2 ± 1·8		0·27 ± 0·04b		0·56 ± 0·14b
1·6 kPa	24·3 ± 2·7		2·8 ± 0·2		13·5 ± 1·4B		11·7 ± 3·1a		20·2 ± 5·6a		−1·5 ± 0·3		20·0 ± 1·4		0·11 ± 0·06a		0·34 ± 0·07a
2·3 kPa	25·5 ± 1·8		3·1 ± 0·1		11·5 ± 5·2Ab		8·7 ± 4·5a		31·2 ± 7·2ab		−1·7 ± 0·5		19·4 ± 0·7		0·09 ± 0·05a		0·26 ± 0·05a
0 % blue irradiance	24·6 ± 4·4		2·5 ± 0·4		13·8 ± 2·1		17·5 ± 3·1		33·2 ± 6·7		−1·7 ± 0·4		20·5 ± 1·3		0·19 ± 0·07		0·42 ± 0·07
1 % blue irradiance	23·0 ± 4·3		2·7 ± 0·3		13·0 ± 1·4		15·3 ± 3·8		30·8 ± 9·2		−1·9 ± 0·5		20·9 ± 2·1		0·16 ± 0·04		0·46 ± 0·08
5 % blue irradiance	21·5 ± 6·4		2·7 ± 0·3		14·7 ± 3·0		16·8 ± 1·8		35·2 ± 5·5		−2·2 ± 0·4		20·9 ± 1·7		0·17 ± 0·08		0·45 ± 0·09
10 % blue irradiance	21·6 ± 2·7		2·6 ± 0·2		10·8 ± 1·4		18·7 ± 3·1		38·2 ± 5·6		−1·6 ± 0·3		22·2 ± 1·4		0·27 ± 0·06		0·56 ± 0·07
20 % blue irradiance	18·6 ± 5·3		2·7 ± 0·4		12·4 ± 1·2		18·2 ± 1·3		37·6 ± 2·8		−1·4 ± 0·6		22·0 ± 2·5		0·22 ± 0·07		0·51 ± 0·09

Dynamic parameters include IS₆₀ (induction 60 s after illumination, %), t_A50, t_A90, t_gs50 and t_gs90 [time (min) to reach 50 and 90 % of photosynthetic induction and time to reach 50 and 90 % of full stomatal opening]. Steady-state parameters were calculated by averaging single values over 2 min (either in dark-adapted leaves or at the end of induction) and include A_i, A_f, g_si and g_sf (A and g_s in darkness and in 1000 µmol m⁻² s⁻¹, respectively; units: A expressed in µmol m⁻² s⁻¹ and g_s in mol m⁻² s⁻¹). Means followed by different letters differ significantly, according to a LSD test conducted at the P = 0·05 level (n = 5); absence of letters denotes absence of significant effects.

^*One-way ANOVA performed on log-transformed data.

^†Data compared using non-parametric Kruskal–Wallis test.

Statistical analysis

Most data are expressed as mean ± standard error (SE). Parameters shown in Table 2 and in Fig. 4 were tested for normality (Shapiro-Wilk test; Genstat 16^th edn, VSN International, Hempstead, UK) and homogeneity of variances (Fligner–Killeen test; R, R Core Team). On datasets where those requirements were fulfilled, one-way analysis of variance (ANOVA; Genstat) was performed, followed by Fisher’s protected LSD (Genstat) to determine significant differences between treatments. When datasets did not meet the requirement of normality or homogeneity of variances, they were log-transformed. On datasets where homogeneity of variances could be assumed, but the requirement of normality was not fulfilled, a non-parametric Kruskal–Wallis (Genstat) test was conducted.

Fig. 4.

Apparent time constants of Rubisco activation (τ_R) during photosynthetic induction, as affected by C_a (A), VPD_leaf-air (B) and leaf temperature (C). Small letters denote significant differences between treatments, error bars denote ± SE (n = 5).

RESULTS

Induction of photosynthetic CO₂ fixation

Rates of photosynthetic induction increased with C_a (Fig. 1A), affecting the time to reach 50 and 90 % of full induction (t_A50 and t_A90, respectively), but not induction 60 s after illumination (IS₆₀; Table 2). High T_leaf (30·5 °C) increased induction slightly in the first 5 min (Fig. 1C), affecting IS₆₀ and t_A50 but not t_A90 (Table 2). Elevated VPD_leaf-air slowed down induction after ∼5 min (Fig. 1E), increasing t_A90 in 1·6 kPa (Table 2). High VPD_leaf-air (2·3 kPa) induced oscillations of induction rates (Fig. 1E), without affecting the various induction parameters. However, it is difficult to determine those parameters in an oscillating time series. Varying blue irradiance (0–20 %) did not affect any parameter in Table 2, nor did it have noticeable effects on other parameters discussed here (data not shown).

Fig. 1.

Photosynthetic induction (A, C, E) and stomatal conductance (B, D, F) in dark-adapted tomato leaves, as affected by C_a (A, B), T_leaf (C, D) and VPD_leaf-air (E, F). Irradiance was raised from 0 to 1000 µmol m⁻² s⁻¹ at time = 0 and kept steady for 60 min. In A, C and E, the first 30 min of induction are shown. Mean ± SE (n = 5).

Stomatal conductance

Stomata opened faster in low C_a (Fig. 1B) and reached higher final conductance (g_sf, Table 2). However, because g_s levelled off earlier in intermediate and high C_a, the time to reach 90 % of full stomatal conductance (t_gs90) was significantly longer in low C_a (Table 2). Low (15·5 °C) and high T_leaf decreased g_s in darkness (g_si, Table 2) and decreased the extent of stomatal opening during induction (Fig. 1D), leading to lower steady-state g_sf compared to intermediate T_leaf (22·8 °C). Elevated VPD_leaf-air affected stomata by (1) decreasing g_si and g_sf, (2) increasing relative opening rates in the first 15 min of induction, (3) inducing damped stomatal oscillations at the highest VPD_leaf-air (2·3 kPa) and (4) causing stomata to reach steady-state g_s more quickly (or quasi steady-state in the case of an oscillating g_s; Fig. 1F, Table 2). Despite decreasing g_si by 40–55 % compared to low VPD_leaf-air, high VPD_leaf-air did not affect final A (A_f; Table 2), suggesting that in the steady state, diffusional limitation of A was no longer sensitive to VPD_leaf-air. Time courses of C_c during photosynthetic induction are shown in Supplementary Data File S6.

Intrinsic water use efficiency (WUE_i)

WUE_i, a result of dynamic changes in A and g_s, was strongly affected by C_a: both its steady-state level and its rate of change in the first 30 min of induction were increased in high compared to low C_a (Fig. 2A). At low and high T_leaf, g_s increased more slowly, with similar increases in A, in the beginning of induction, so both resulted in a higher WUE_i than for an intermediate T_leaf (Fig. 2B). A similar reasoning applies to VPD_leaf-air: because elevated VPD_leaf-air reduced g_s more strongly than A during and after induction, WUE_i was highest in 2·3 kPa, followed by 1·6 kPa (Fig 2C). The 0·5- and 0·8-kPa treatments showed lowest WUE_i and were no different from each other (Fig. 2C).

Fig. 2.

Intrinsic water use efficiency (WUE_i) during photosynthetic induction, as affected by C_a (A), T_leaf (B) and VPD_leaf-air (C). Mean ± SE (n = 5).

Diffusional and biochemical limitations during photosynthetic induction

Diffusional limitation quantifies the reduction in A due to C_c being lower than C_a. This is a complex parameter that depends on the combined effects of C_a, A and total leaf diffusive conductance on C_c, as well as the extent to which C_c imposes a limitation on A. Biochemical limitation quantifies the extent to which biochemical processes that activate during induction limit A during induction, but not in the steady state. Note that the sum of these limitations is not 100 %, as they are calculated not with respect to the total limitation for A, but to reference gaseous diffusion and biochemical states. In all treatments except at high VPD (2·3 kPa), transient diffusional limitation increased to its maximum within the first 15 min due to the activation of Rubisco, and then slowly relaxed to its steady-state level as stomata opened. Biochemical limitation was at its maximum in the very beginning of induction, and relaxed rapidly within the first 10–15 min. The extent, as well as the rates, of buildup and relaxation of diffusional and biochemical limitation scaled negatively with C_a (Fig. 3A, B). Diffusional limitation was higher in low compared to intermediate C_a, while there was no difference in biochemical limitation between these treatments. High C_a decreased the diffusional limitation and produced a faster relaxation of biochemical limitation than both low and intermediate C_a (Fig. 3A, B). When biochemical limitation had relaxed entirely at high C_a (∼10 min), ∼10 % of biochemical limitation remained at intermediate and low C_a, taking another 10 min to relax (Fig. 3B). High T_leaf induced strong diffusional limitation (Fig. 3C), while maintaining slightly positive effects on the rates of relaxation of biochemical limitation (Fig. 3D). The effects of high VPD_leaf-air (1·6 and 2·3 kPa) on g_s translated into very different kinetics of diffusional limitations during induction than moderate VPD_leaf-air. The 1·6-kPa treatment led to a faster decrease in diffusional limitation than 0·5 or 0·8 kPa, while 2·3 kPa produced an oscillating diffusional limitation (Fig. 3E). Biochemical limitation was affected less strongly, although it tended to relax more slowly in elevated VPD_leaf-air (Fig. 3F).

Fig. 3.

Diffusional limitation (A, C, E) and biochemical limitation (B, D, F) during photosynthetic induction, as affected by C_a (A, B), T_leaf (C, D) and VPD_leaf-air (E, F). In B, D and F, the first 30 min of induction are shown. Mean ± SE (n = 5).

Apparent time constants of Rubisco activation

The apparent time constant for Rubisco activation (τ_R), defined as the time to reach 63 % of final Rubisco activation, decreased with increasing C_a (Fig. 4A), reflecting faster activation of Rubisco with larger abundance of CO₂. Compared to τ_R in low C_a, τ_R at intermediate and high C_a was 20 and 56 % lower, respectively. Leaf temperature did not have a statistically significant effect on τ_R, although there was a trend towards higher τ_R in low T_leaf (Fig. 4B). Elevated VPD_leaf-air significantly increased τ_R, by 45 and 48 % in the 1·6- and 2·3-kPa treatments (compared with 0·5 kPa; Fig. 4C).

Slower apparent Rubisco activation in elevated VPD_leaf-air (compared to low VPD_leaf-air) was probably related to lower values of C_i, due to the lower g_s at high VPD_leaf-air. The decrease in C_i at the start of induction was stronger in elevated compared to low VPD_leaf-air. τ_R tended to increase with the relative rates of decrease in C_i, and data from the C_a treatments showed a similar trend (Fig. 5A), indicating that if C_i depleted too rapidly, apparent Rubisco activation was slowed down. Also, in an attempt to estimate the lowest CO₂ partial pressure reached in the chloroplast, C_c was calculated at the time of induction when C_i reached its lowest point. Plotting τ_R against this C_c, a tendency towards lower τ_R at higher C_c emerged (Fig. 5B), indicating that a very low C_c during induction slows down the activation of Rubisco. Different leaf temperatures could affect the rate of Rubisco activation in addition to their effect on C_i, so they were not taken into account in Fig. 5, which shows only the effect of C_i and C_c on τ_R.

Fig. 5.

Relationships between τ_R in the VPD_leaf-air and C_a treatments and (A) the rate of C_i depletion ($\frac{\mathrm{\Delta }{C}_i/\mathrm{\Delta }t}{initial C_i}*\left(-100\right)$), normalized by C_i in darkness (initial C_i) during the first 5 min of induction and (B) the lowest value of C_c during induction, using the lowest value of C_i during induction and corresponding values of A_n and g_m, then calculating $C_c=C_i-\frac{A_n}{g_m}$. Mean ± SE (n = 5).

Mesophyll conductance

Mesophyll conductance increased markedly during induction in all treatments, and the fastest changes were observed in the first 10 min of induction. Rates of increase and steady-state levels of g_m were higher at low than at high C_a. At different leaf temperatures, g_m increased with T_leaf. Details of dynamic g_m changes and their determination can be found in File S5.

Φ_PSII and NPQ during photosynthetic induction

The maximum, dark-adapted quantum efficiency of electron transport through photosystem II (F_v/F_m) ranged between 0·79 and 0·82 across C_a and T_leaf treatments. During induction, Φ_PSII increased to its steady-state level within 20 min. Between minutes 2 and 14, the relative rates of increase of Φ_PSII were significantly higher in high compared to low C_a. Furthermore, steady-state levels of Φ_PSII were highest in intermediate C_a (0·35), followed by the high (0·33) and low C_a treatments (0·28; Fig. 6A). During induction, NPQ initially increased towards a peak of ∼2 after 5 min. This peak was followed by a decline, which was most pronounced at intermediate C_a (Fig. 6C). The lowest value of NPQ (1·5) was found at intermediate C_a and occurred after ∼15 min in all C_a treatments, after which NPQ increased slowly. This last phase was similar at all CO₂ partial pressures, but values of NPQ were highest in low C_a (NPQ of 2), followed by high C_a (1·8) and the lowest value of NPQ (1·7) was found at intermediate C_a (Fig. 6C). Between minutes 2 and 5, high leaf temperature increased the relative rate of change of Φ_PSII compared to low T_leaf. Furthermore, steady-state Φ_PSII values scaled positively with T_leaf, reaching 0·42 at high, 0·35 at intermediate and 0·22 at low T_leaf (Fig. 6B). At intermediate and high T_leaf and varying C_a, the time courses of NPQ during induction were similar, rising rapidly to a maximum within 1–4 min, after which there was a decline to a minimum at ∼20 min (Fig 6C, D), followed by a rise to the steady-state value, except for the 30·5 °C treatment in which there was a continuous decline (Fig. 6D). At low T_leaf the response was different: an initial rapid increase in NPQ was less pronounced and was followed by a slow increase that did not reach a stable value during the experiment. Final NPQ values were therefore highest at low T_leaf (∼2), followed by intermediate (NPQ of 1·7) and high T_leaf (1·3). While changes in q_P paralleled Φ_PSII and were of the same magnitude, changes in F_v′/F_m′ were rather small (Supplementary Data File S7). As a result, Φ_PSII correlated linearly and positively with q_P, while F_v′/F_m′ correlated strongly and negatively with NPQ (data not shown).

Fig. 6.

Changes in Φ_PSII (A, B) and NPQ (C, D) during photosynthetic induction, as affected by C_a (A, C) and T_leaf (B, D). Mean ± SE (n = 5).

Electron transport and gross photosynthesis rates

Regressions of gross photosynthesis (A_gr = A_n+R_d) vs. ETR were predominantly linear (Fig. 7), but the slopes of this relationship increased with C_a and decreased slightly with T_leaf. Additionally, at low C_a and at high T_leaf, increases in A_gr became progressively independent of increases in ETR at high values of ETR and A_gr.

Fig. 7.

Relationship between electron transport rate and gross photosynthesis rate (A_n + R_d) during photosynthetic induction, as affected by C_a (A) and T_leaf (B). Arrows indicate the direction of change over time. Mean ± SE (n = 5).

DISCUSSION

The environmental factors CO₂ partial pressure, temperature and VPD_leaf-air had significant impacts on rates of photosynthetic induction, and on underlying diffusional, carboxylation and electron transport processes. For the first time, their effects have been compared using the same experimental set-up, and explored in a highly detailed manner. The results indicate the maximum gains that improvements in dynamic photosynthesis would have in various environments and atmospheres.

CO₂ partial pressure: effects via diffusional and biochemical limitations

By lowering diffusional and biochemical limitations, increased C_a sped up photosynthetic induction considerably. This was reflected in gas exchange (Fig. 1A) and chlorophyll fluorescence data (Fig. 6A, C; discussed below). Despite decreasing g_s and g_m, increased C_a actually lowered diffusional limitation. There are two reasons for this: firstly, due to the curvilinearity of the A/C_c response, a difference between A at C_a and A at C_c (which is the basis of the calculation of diffusional limitation) is larger at low C_a (e.g. 20 Pa) than at high C_a (e.g. 80 Pa). Secondly, the gradient for diffusion between C_a and C_c was steeper (File S6) with increases in C_a, thus decreasing diffusional limitation. A decrease in biochemical limitation was achieved by faster activation of Rubisco (Fig. 4A), but not by faster activation of RuBP regeneration, as visible from the similarity of the initial slopes (Fig. 1A) and the parameter IS₆₀ (Table 2). The positive effect of increased C_a on apparent Rubisco activation has been noted before (Mott and Woodrow, 1993; Woodrow et al., 1996), and is hypothesized to be due to faster carbamylation of Rubisco.

Because A increased faster and reached a higher value, and g_s increased to a smaller extent, WUE_i was strongly enhanced during and after photosynthetic induction (Fig. 2A) in high C_a. In absolute terms, elevated C_a is positive for WUE_i in fluctuating irradiance. After sudden drops in irradiance, WUE_i decreases quickly as A decreases more quickly than g_s (Lawson and Blatt, 2014). Since g_s is depressed in elevated C_a, the drops in WUE_i after decreases in irradiance are likely to be smaller compared to current atmospheric C_a. Stomatal opening, and the concomitant increase in C_i, decreased the rate of photorespiration in low C_a, as seen from the change in the slope of A_gr/ETR (Fig. 7A): when reaching higher values of A_gr, this was achieved almost without increases in ETR (i.e. there was a deviation from the previous linear relationship of A_gr/ETR), meaning that the rate of oxygenation decreased relative to the rate of carboxylation.

Effects of C_a on the rate of photosynthetic induction have been explored experimentally before (Chazdon and Pearcy, 1986; Naumburg and Ellsworth, 2000; Naumburg et al., 2001; Leakey et al., 2002; Tomimatsu and Tang, 2012; Tomimatsu et al., 2014; Soleh et al., 2016), and have been reviewed twice recently (Kaiser et al., 2015; Tomimatsu and Tang, 2016). Kaiser et al. (2015) found that across studies, t_A90 decreased near-linearly with increases in C_a, while t_A50 was unaffected. In the current study, t_A50 was significantly increased in low C_a, while t_A90 was three times lower in high (6·2 min) compared to low C_a (18·5 min; Table 2). Altogether, the stronger response to C_a observed in the current study (compared to the general response summarized by Kaiser et al., 2015) may be due to the use of C₃ plants with high photosynthetic rate compared to most species summarized by Kaiser et al. (2015).

Leaf temperature: effects on the rate of RuBP regeneration and on stomatal opening

Effects of different leaf temperatures on the rate of photosynthetic induction were small compared to those of C_a and VPD_leaf-air (Fig. 1C), but they strongly affected the levels and kinetics of Φ_PSII and NPQ (Fig. 6B, D; discussed below). While apparent Rubisco activation rates were not significantly increased by elevated T_leaf (Fig. 4B), IS₆₀ was significantly larger and t_A50 significantly smaller (Table 2), suggesting a faster activation of the enzymes controlling the rate of RuBP regeneration (Sassenrath-Cole and Pearcy, 1992). This had slight effects on the initial relaxation of biochemical limitation (Fig. 3D). Stomatal opening was depressed at both low and high T_leaf (by 41–44 % compared to intermediate T_leaf): the difference between initial and final g_s was only 0·17 (low T_leaf) and 0·16 (high T_leaf), compared to 0·29 mol m⁻² s⁻¹ at intermediate T_leaf (Table 2). At the same time, the difference between initial and final A was virtually the same at intermediate and high T_leaf, while it was 30 % lower at low T_leaf (Table 2). Thus, while at low T_leaf (weak g_s and A increase) and intermediate T_leaf (strong g_s and A increase) diffusional limitation was low and comparable, at high T_leaf (combination of weak g_s increase and strong A increase) there was large diffusional limitation (Fig. 3C). The value of VPD_leaf-air was only 0·04 kPa larger at high compared to intermediate T_leaf (File S2), and was therefore not responsible for the increase in diffusional limitation at high T_leaf.

The effect of T_leaf on the rate of photosynthetic induction has been explored several times in a spectrum of species and growth conditions (Küppers and Schneider, 1993; Pepin and Livingston, 1997; Leakey et al., 2003; Yamori et al., 2012; Carmo-Silva and Salvucci, 2013). Across these studies, increasing T_leaf decreased t_A50 and t_A90 up to an optimum of ∼30 °C (i.e. smallest t_A50 and t_A90, meaning highest rate of induction), above which these indices increased again (Kaiser et al., 2015). Further, it was noted that effects of T_leaf on induction rates were not uniform between studies (Kaiser et al., 2015). The data in the current study add to the scatter: t_A50 was lower at high T_leaf, but t_A90 was unaffected by treatment levels (Table 2). Apparently, there is large interspecific variation in the temperature response of photosynthetic induction.

VPD_leaf-air: lower g_s affects apparent Rubisco activation kinetics, diffusional limitation and WUE_i

Increases in VPD_leaf-air (i.e. dryer air) strongly decreased g_s before, during and after photosynthetic induction (Fig. 1F). Very high VPD_leaf-air even induced stomatal oscillations (feeding back on A), a phenomenon whose mechanisms are still under debate (Buckley, 2005; Kaiser, 2009; Kaiser and Paoletti, 2014). By decreasing C_c (File S6), elevated VPD_leaf-air slowed down the rate of photosynthetic induction (Fig. 1E). This had strong effects on diffusional and, surprisingly, biochemical limitations (Fig. 3E, F), by decreasing the rate of apparent Rubisco activation (Fig 4B). A VPD_leaf-air effect on apparent Rubisco activation rates has, to our knowledge, not been found before. Slower apparent Rubisco activation is probably caused by lower C_i or C_c during induction, as indicated by the relationships of τ_R with the relative rate of C_i decrease and the lowest partial pressure of C_c reached during induction (Fig. 5). Further support for this hypothesis comes from a study on water stress: short-term leaf desiccation, which led to stomatal closure, decreased both C_c and initial (i.e. extracted) Rubisco activity (Flexas et al., 2006). While the rate of Rubisco activation after a dark–light transition and initial Rubisco activity are not the same, they are both likely to be affected by the rate or the total extent of carbamylation, respectively. Furthermore, apparent Rubisco activation rates after increases in irradiance correlated positively with C_i (see above).

While higher VPD_leaf-air undoubtedly had a negative impact on A after illumination was raised, it had positive effects on WUE_i (Fig. 2C). The global climate is predicted to be dryer (at least in mid-latitude and subtropical regions), warmer and enriched in CO₂ (IPCC, 2013). It can thus be hypothesized that WUE_i in such a climate will increase in fluctuating irradiance, as increases in all of these factors improved WUE_i (Fig. 2).

In contrast to C_a and T_leaf, published data describing the effects of VPD_leaf-air on rates of photosynthetic induction are scarce. Nevertheless, Tinoco-Ojanguren and Pearcy (1993a, b) reported that high VPD decreased steady-state g_s, slowed down photosynthetic induction and increased stomatal limitations in a pioneer rainforest tree (Piper auritum) and a shade-tolerant shrub (Piper aequale), similar to the present findings on tomato. Thus, stomatal dynamics of widely varying species seem to be similarly affected by elevated VPD_leaf-air.

Lack of effects of blue irradiance: possible reasons

Surprisingly, varying blue irradiance (0–20 %) had no effects on stomatal opening or photosynthetic induction (Table 2). Blue irradiance generally promotes rapid stomatal opening when combined with red irradiance, and could be a cue for overall radiation load (Shimazaki et al., 2007). In the current experiment, 1000 µmol m⁻² s⁻¹ may have provided such a strong stimulus for stomatal opening that the rate of opening could not have been accelerated by increasing the percentage of blue irradiance. Assmann and Grantz (1990a, b), however, superimposed blue irradiance on 900 µmol m⁻² s⁻¹ red irradiance in sugarcane and soybean and found an additional opening response (data on photosynthesis were not shown in these studies). The reported effects of blue irradiance on photosynthetic induction are ambiguous: Košvancová-Zitová et al. (2009) reported faster induction in beech (Fagus sylvatica L.) with increasing blue irradiance (25–75 % blue irradiance in 800 µmol m⁻² s⁻¹), while data reported in Zhang et al. (2011) for the orchid Cypripedium flavum showed the opposite (0–100 % blue irradiance in 250 µmol m⁻² s⁻¹). The effects of blue irradiance on induction are therefore variable with no clear correlations between the effects of blue irradiance and other environmental responses or preferences.

Changes in chlorophyll fluorescence parameters during photosynthetic induction

Changes in Φ_PSII during induction were primarily explained by changes in photochemical quenching (q_P) rather than F_v′/F_m′. Overall, this suggests that changes in NPQ, acting via decreases in F_v′/F_m′, did not contribute substantially to the changes in Φ_PSII (Baker et al., 2007); the total span of changes of F_v′/F_m′ was 0·55–0·65, while that for q_P was 0·05–0·7 (File S7). Changes in q_P occurred in the first 12 min of induction, making its time course similar to that of Φ_PSII, but distinct from that of NPQ (Fig. 6). Steady-state Φ_PSII was slightly higher in ambient compared to high C_a (Fig. 6A), while NPQ was slightly higher in high compared to ambient C_a (Fig. 6C). This may be explained by triose phosphate utilization limitation slowing down ETR in high C_a.

All C_a and T_leaf treatments (except low T_leaf) produced initial overshoots in NPQ (Fig. 6). It is hypothesized that the overshoot was caused by low metabolic activity that resulted in a low rate of electron transport, which caused a decrease in lumen pH, thereby activating NPQ. Upon the subsequent activation of Calvin cycle enzymes and increase in linear electron transport, the lumen pH increased and energy-dependent quenching (q_E) decreased, lowering NPQ. The slow build-up of zeaxanthin during induction would then have produced a slower increase in energy-dependent quenching (q_E) by enhancing the effect of pH on NPQ. This was visible between minutes 20 and 60 in all treatments except high T_leaf (Bilger and Björkman, 1991). Leaves that contained fully activated Rubisco in low irradiance did not exhibit an NPQ overshoot when transferred to high irradiance (Carmo-Silva and Salvucci, 2013). Also, in leaves containing less Rubisco activase, NPQ kept increasing throughout induction, indicating that Rubisco activation, and by implication photochemical quenching, increased more slowly (Yamori et al., 2012). Both examples demonstrate how the rate of change of metabolism sets the demand for the products of electron transport during photosynthetic induction, thereby affecting the transient excess irradiance condition and the parallel induction of NPQ.

Mesophyll conductance

The change in g_m during photosynthetic induction has, to our knowledge, never been assessed. This has been attempted here using the often-used variable J method (Harley et al., 1992) (File S5). However, because possible changes in alternative electron transport, stoichiometry of ATP and NADPH production, leaf absorbance (due to chloroplast movement), R_d, and the overall validity of g_m especially in the early phases of induction cannot be accounted for, we refrain from speculations on the correctness of g_m during photosynthetic induction, but note this as a topic that deserves more dedicated experimentation. Two more things are noteworthy: firstly, the steady-state values of g_m (Table in File S5) compare very well to published data (Bernacchi et al., 2002; Flexas et al., 2008; von Caemmerer and Evans, 2015). Secondly, the fact that at the beginning of photosynthetic induction none of the slopes of A_gr/ETR (Fig. 7) deviated strongly from linearity implies that neither changes in g_s nor changes in g_m limited induction, as in such a case C_c would have dropped momentarily (oxygenation would have increased relative to carboxylation). This suggests that potentially low g_m was not a (strongly) limiting factor during photosynthetic induction.

Methodological considerations

Diffusional and biochemical limitation were calculated for the first time assuming a curvilinear A/C_i relationship instead of the linear relationship previously used in such analyses (e.g. Woodrow and Mott, 1989; Jackson et al., 1991; Allen and Pearcy, 2000). This strongly affected the estimation of diffusional limitation at 40 and 80 Pa (Supplementary Data File S8). Most studies using this correction were performed with atmospheric or below-atmospheric C_a, where assuming a linear A/C_i relationship may be reasonable. However, some authors used a linear relationship at C_a of ≥ 70 Pa (Košvancová-Zitová et al., 2009; Tomimatsu and Tang, 2012). Their measures of stomatal limitation in high C_a are probably substantial overestimations.

In light-adapted leaves, the conventionally measured F_m′ (obtained using single saturating pulses) underestimated ‘true’ F_m′ (obtained using multiple saturating pulses), by approx. 4 %. It is shown here for the first time that this underestimation develops within 10 min during induction (File S3). Steady-state measurements on tobacco, pea and maize leaves (grown at 300 µmol m⁻² s⁻¹) showed comparably large underestimations of F_m′, translating into underestimations of Φ_PSII (Loriaux et al., 2013). Here, steady-state Φ_PSII would have been underestimated by 8–15 % if single rather than multi-phase pulses were used.

Improving crop photosynthesis in fluctuating irradiance: why and how?

Improving crop productivity via photosynthetic efficiency is considered a crucial pathway for future global food security (Zhu et al., 2010). Faster regulation of Rubisco activity may increase A in naturally fluctuating irradiance (Carmo-Silva et al., 2015). Also, a more dynamically regulated g_s, which can, for example, be reached by smaller stomata, could help save water by increasing dynamic WUE_i (Drake et al., 2013; Lawson and Blatt, 2014). Two scenarios were therefore explored using the present data on induction rates and stomatal opening in various atmospheres: changes in average A and WUE_i during photosynthetic induction in the case of (1) instantaneous Rubisco activation and (2) instantaneous g_s increase.

The analysis (Table 3) revealed that average A could increase by 6–8 % in ambient C_a (across VPD_leaf-air and T_leaf treatments), if Rubisco activated instantaneously. In elevated C_a, a form of Rubisco that activates instantaneously would be less advantageous (2·5 instead of 5·6 % increase in A), because Rubisco already activates faster in high C_a. The faster increase in A due to faster Rubisco activation would also positively impact WUE_i, by up to 12–19 % in ambient C_a. Rubisco activation can be sped up by manipulating the isoform composition of Rubisco activase (Zhang et al., 2002), although always-active Rubisco activase reduced growth in the Arabidopsis thaliana mutant rwt43 compared to its wild type (Carmo-Silva and Salvucci, 2013). The elucidation of how the activation state of Rubisco affects the balance of intermediates in the Calvin cycle should therefore be central to future research on improving dynamic photosynthesis.

Table 3.

Change ( %) in net photosynthesis rate or intrinsic water use efficiency (WUE_i) if either Rubisco activated directly after illumination or stomatal conductance directly increased to its final, steady-state value

Treatment	Net photosynthesis rate		WUEi
	Rubisco kinetics	Stomatal opening	Rubisco kinetics	Stomatal opening
20 Pa	7·5±1·5	3·6±0·8	24·7±1·7	−32·1±2·0
40 Pa	5·6±0·4	1·1±0·1	12·2±0·9	−20·6±1·4
80 Pa	2·5±0·6	0·6±0·1	5·8±0·8	−19·8±2·2
15·5 °C	5·2±0·7	1·4±0·3	11·7±1·8	−24·7±6·3
22·8 °C	5·6±0·4	1·1±0·1	12·2±0·9	−20·6±1·4
30·5 °C	4·1±0·9	3·3±1·2	11·5±1·3	−13·9±2·6
0·5 kPa	4·9±0·4	1·4±0·4	11·5±1·3	−23·3±3·6
0·8 kPa	5·6±0·4	1·1±0·1	12·2±0·9	−20·6±1·4
1·6 kPa	7·6±0·6	0·6±0·2	18·5±2·2	−15·6±2·5
2·3 kPa	8·0±0·8	0·7±0·1	17·3±2·1	−13·7±2·1

Values are means over minutes 2–60 during photosynthetic induction ± SE (n = 5).

Instantaneous stomatal opening would improve average photosynthesis rates by up to 1–3 % in ambient C_a and across air humidities and leaf temperatures. Thus, increasing the kinetics of Rubisco activation seems to be a more useful strategy than increasing g_s, especially as higher g_s would strongly decrease WUE_i (by 21–25 % in ambient C_a). Stomata that react faster to decreases in irradiance, on the other hand, would be very beneficial for dynamic WUE_i (Lawson and Blatt 2014); whether or not quickly reacting stomata enhance WUE_i is therefore dependent on the situation.

A transition from completely inactivated photosynthesis in darkness to near-saturating irradiance does not represent natural conditions; the modulation of dynamic photosynthesis by environmental factors and the benefits of faster Rubisco activation or stomatal opening may be smaller when photosynthesis is somewhat induced. Therefore, these numbers can only be used to provide a first guess for the benefits of ‘immediate’ Rubisco activation or stomatal opening.

CONCLUSIONS

Increased CO₂ partial pressure led to faster photosynthetic induction, by decreasing diffusional limitation and by speeding up the relaxation of biochemical limitation. Increased leaf temperature led to slightly faster induction rates, due to faster relaxation of biochemical limitation. Elevated leaf-to-air vapour pressure deficit mainly lowered the relaxation rates of biochemical limitation, by slowing down apparent Rubisco activation via decreased availability of CO₂. Increasing the rates of Rubisco activation would be more beneficial for dynamic photosynthesis than increasing initial stomatal conductance or the rate of stomatal opening.

SUPPLEMENTARY DATA

Supplementary data are available online at www.aob.oxfordjournals.org and consist of the following. File S1: preliminary irradiance response curves. File S2: traces of VPD_leaf-air during photosynthetic induction as affected by T_leaf. File S3: measured F_m′ underestimates true F_m′ in light-adapted but not in dark-adapted leaves. File S4: A/C_i and A/PAR curves. File S5: changes in g_m during photosynthetic induction. File S6: changes of chloroplast CO₂ partial pressure (C_c) during photosynthetic induction. File S7: q_P and F_v′/F_m′ during photosynthetic induction. File S8: implications of using curvilinear instead of linear A/C_c relationships when determining diffusional limitation.