Contribution of PsbS Function and Stomatal Conductance to Foliar Temperature in Higher Plants.

Natural capacity has evolved in higher plants to absorb and harness excessive light energy. In basic models, the majority of absorbed photon energy is radiated back as fluorescence and heat. For years the proton sensor protein PsbS was considered to play a critical role in non-photochemical quenching (NPQ) of light absorbed by PSII antennae and in its dissipation as heat. However, the significance of PsbS in regulating heat emission from a whole leaf has never been verified before by direct measurement of foliar temperature under changing light intensity. To test its validity, we here investigated the foliar temperature changes on increasing and decreasing light intensity conditions (foliar temperature dynamics) using a high resolution thermal camera and a powerful adjustable light-emitting diode (LED) light source. First, we showed that light-dependent foliar temperature dynamics is correlated with Chl content in leaves of various plant species. Secondly, we compared the foliar temperature dynamics in Arabidopsis thaliana wild type, the PsbS null mutant npq4-1 and a PsbS-overexpressing transgenic line under different transpiration conditions with or without a photosynthesis inhibitor. We found no direct correlations between the NPQ level and the foliar temperature dynamics. Rather, differences in foliar temperature dynamics are primarily affected by stomatal aperture, and rapid foliar temperature increase during irradiation depends on the water status of the leaf. We conclude that PsbS is not directly involved in regulation of foliar temperature dynamics during excessive light energy episodes.

Introduction

Plants possess natural capacity to absorb light energy in excess of what is sufficient for photochemistry; thus, in full sunlight, only a portion of light energy absorbed by Chls is used for CO2 fixation (Asada 1999, Ruban et al. 2004). This requires a very efficient system that is able to optimize light absorption, quenching and photochemistry. The excess excitation energy (EEE) defines all the absorbed energy that exceeds the amount required for photochemistry, and as such must be dissipated through both fluorescence and heat. Failure to dissipate and quench EEE is highly damaging to plants, often leading to chlorosis, bleaching or bronzing of leaves due to imbalanced reactive oxygen species (ROS) metabolism and EEE dissipation as heat (Asada 1999, Karpinski et al. 1999, Niyogi 2000, Apel and Hirt 2004, Ruban et al. 2004, Laloi et al. 2007, Breusegem et al. 2008, Li et al. 2009). Later on, it was demonstrated that EEE leads to the induction of programmed cell death and hypersensitive response (Mühlenbock et al. 2008).

Non-photochemical quenching (NPQ) of Chl fluorescence is a phenomenon in which the intensity of Chl fluorescence is decreased (quenched) during actinic illumination for reasons other than photosynthetic electron transport in PSII, representing a decrease in energy conversion efficiency from light to photochemistry in the photosystem. NPQ consists of, starting from the fastest component, the ΔpH- and PsbS-dependent process (qE), zeaxanthin formation (qZ), state transition (qT) and photoinhibition (qI) (Baker 2008, Li et al. 2009, Bonente et al. 2011, Brooks et al. 2013). At the end of this queue there is a—recently described—slowly reversible, ΔpH- and PsbS-independent NPQ mechanism. Its formation is prevented by Suppressor of Quenching 1 (SOQ1) and thereby the efficiency of light harvesting is maintained (Brooks et al. 2013). The full operation of qE requires three components: xanthophylls with an epoxy group removed from the 3-hydroxo β-rings (Demmig et al. 1987, Niyogi et al. 1998), a transthylakoidal pH gradient and PsbS (Li et al. 2002b, Li et al. 2004, Niyogi et al. 2005). In high light it can dissipate up to 75% of the energy absorbed by plants (Demmig-Adams et al. 1996).

PsbS is a nuclear-encoded 22 kDa protein belonging to the Chl a/b/xanthophyll-binding proteins [light-harvesting complex (LHC)] superfamily which acts in the chloroplast as a lumen pH sensor (Kim et al. 1994, Dominici et al. 2002). This protein has a characteristic feature different from typical members of this superfamily: it has four transmembrane α-helices instead of three. Most probably it is situated between the core of PSII and intrinsic antenna proteins (Kim et al. 1994). PsbS has two glutamate residues (Glu122 and Glu226) responsible for pH sensing, and both of them are needed for regulation of NPQ (Li et al. 2004). An increase of the transthylakoidal pH gradient triggers the protonation of Glu122 and Glu226 in PsbS (Niyogi et al. 2005), but is not sufficient fully to activate PsbS and regulate the conformational and allosteric changes of the LHC core (Caffarri et al. 2009) and changes in Chl orientation (Ruban and Horton 1999, Bergantino et al. 2003, Ruban et al. 2007). The npq4-1 line has a single, semi-dominant nuclear mutation in PsbS (deletion of the entire PsbS-encoding gene) and displays deregulated qE capacity (Li et al. 2000).

Growth as well as seed production of npq4-1 compared with wild-type plants is unaffected in light-limiting conditions (Li et al. 2000) but impaired in the outdoor EEE-generating conditions (Külheim et al. 2002). Recently, it has been revealed that EEE-induced permanent photodamage was associated with induction of cell death, e.g. via NPQ- and plastoqinone pool redox status-dependent chloroplast retrograde signaling and induction of systemic acquired acclimation (SAA) (Idso et al. 1981, Karpinski et al. 1999, Mateo et al. 2004, Hashimoto et al. 2006, Merlot et al. 2007, Mühlenbock et al. 2008, Plessis et al. 2011, Hashimoto-Sugimoto et al. 2013, Janka et al. 2013, Sankaran et al. 2013). What is interesting is that it has been reported that the amount of PsbS protein affects herbivore behavior (feeding and oviposition), but no direct causal links had been found so far (Johansson Jänkänpää et al. 2013). As many recent studies (e.g. Lemoine et al. 2014, Lu et al. 2015) proved the influence of ambient temperature on insect herbivory, it is possible that leaf temperature also affects this kind of behavior. If so, identifying the main factors that contribute to regulation of leaf temperature during variable light condition can lead to creation of new plant varieties with modified susceptibility to insect pests.

In this study, we want to test the hypothesis that PsbS plays a key role in dissipation of absorbed EEE in the form of heat, as suggested in most articles (e.g. Ikeuchi et al. 2014). For this purpose, we used an advanced thermal imaging camera with high image and time resolution, lanolin to exclude evaporative cooling and a set of Arabidopsis lines containing different levels of PsbS, i.e. the PsbS-overproducing line oePsbS, the wild type and the PsbS-deficient mutant npq4-1 (Li et al. 2002a). We applied a novel approach based on sequential illumination of plants with increasing light intensity and simultaneous leaf temperature measurement. We compared functions fitted to the temperature increase plotted over light intensity, which we defined as foliar temperature dynamics. If the above hypothesis is true, then we should observe: (i) higher foliar temperature in leaves with an increased PsbS level and NPQ value, i.e. the highest temperature in the PsbS-overexpressing plant (oePsbS), moderate in the wild type and the lowest in the PsbS null mutant (npq4-1); and (ii) lower foliar temperature in DCMU-treated plants of all three genotypes—wild type, npq4-1 and oePsbS—as this herbicide completely disables NPQ. Moreover, we tested the contribution of stomatal conductance to Arabidopsis foliar temperature. We introduced the concept of foliar temperature dynamics, which is the slope of the linear function that describes the increase of surface leaf temperature upon short, transient and increasing sequential episodes of excess light.

Results

The concept of foliar temperature dynamics

We measured leaf surface temperature changes with a forward looking infrared (FLIR) camera and obtained valuable information about PsbS and NPQ functions in regulation of foliar temperature dynamics in response to the variable EEE. Foliar temperature dynamics are defined as the slope of the linear function T = a I + b {where T is foliar temperature (°C), I is light intensity (μmol m−2 s−1), a is the slope [because a = (T – b)/I, the unit of a is (°C m2 s μmol−1)] and b is the intercept (°C)} that describes the increase of surface leaf temperature upon short, transient and increasing sequential episodes of excess light. There are two reasons why we chose the linear function: (i) because the amount of absorbed photons by Chls during increasing light intensity increases linearly (Baker 2008, Lambers et al. 2008) and illumination with one particular light intensity causes constant adjustment of fast quenching of Chl fluorescence (such as NPQ); and (ii) R2 and adjusted R2 parameters are satisfyingly high (in most cases it is >0.9; the lowest R2 is 0.8, which is still high). Using polynomials of a higher degree could lead to overfitting—a mathematical phenomenon where a fitted function describes not only the correlation but also the noise. In the study of Kaňa and Vass (2008) this function is not linear, but they used light of much lower intensity, i.e. up to 1,200 mol m−2 s−1, so it contains only two measuring points which we used, i.e. 0 and 750 μmol m−2 s−1. It is possible that at lower light intensities this relationship is non-linear, and at higher intensities it starts to be linear. Slopes of linear functions fitted to temperature data plotted against light intensity provide information about the dynamism of leaf heating and the efficiency of cooling (in darkness, between excess light treatments). A larger slope value means that the foliar temperature rises more rapidly.

The effect of lower efficiency of photosynthesis on the temperature of various plant organs

Green parts of mosaic leaves of Ficus benjamina were characterized by 2-fold higher Chl fluorescence than cream-colored parts of the leaf (Fig. 1A, B). Maximal photosynthetic efficiency and NPQ were also higher in a green part than in a cream part of a leaf (Fig. 1F). The foliar temperature of such a leaf measured in darkness was similar in green and cream parts, but under conditions of increasing light the green part always displayed a significantly higher temperature than the cream part (Fig. 1C–E). At the highest light intensity (4,000 μmol of photons m−2 s−1), the difference between average foliar temperature values measured on these two parts was 8°C (Fig. 1D, E). The background temperature during this experiment increased by about 2°C. These results suggest that foliar temperature dynamics are positively correlated with Chl content. This suggests that leaf temperature is dependent on the amount of the absorbed energy quanta of light.

Fig. 1

The effect of lower efficiency of photosynthesis on the temperature of various plant organs. (A) Thermogram of the leaf in darkness. (B) Thermogram of the leaf under high light (4,000 μmol photons m−2 s−1). (C) Fm: the maximum fluorescence in the dark-adapted state. (D) Real leaf photograph. (E) Average leaf temperature under a sequence of increasing light illumination separated by periods of darkness.

In order to test whether other non-photosynthetic organs of a plant, e.g. roots, do not react like leaves with increased temperature in response to exposure to EEE conditions, additional experiments were performed. Two organs (root and leaf) of horseradish (Armoracia rusticana) with different functions in the plant were measured in 1 min. darkness/2 min light cycles. Dark-adapted plant organs showed differences in temperature. The leaf was approximately 4°C warmer than the root (). Upon exposure to light, the leaf increased its temperature significantly, but temperature changes in the root were similar to those of the background (). At high light intensity, the difference between the leaf and the root temperature was 10°C (). The other above-ground plant organs such as the inflorescence also showed a lower temperature rise than the leaf. The surface temperature in these organs was measured in silver birch (Betula pendula) in 1 min darkness/2 min light cycles. At a high light intensity (3,000 μmol photons m−2 s−1) a 15°C difference between the leaf and the inflorescence temperature was observed (). These results confirmed that foliar temperature dynamics were dependent on Chl content and photosynthesis efficiency.

Mathematical model

The energy balance of the leaf is described by Equation 1 (Kaňa and Vass 2008):

The complete set of factors that affect leaf temperature in our experiments is presented in Fig. 2. The net amount of energy entering or leaving the leaf (S, unit: W m−2) affecting the rate of increase of the heat content of leaf tissue over time depends on the net radiant flux density absorbed (Rn, unit: W m−2; see, for example, Jones 2004), the rate of heat produced by metabolism (M, unit: W m−2), the rate of heat loss through evaporation of water (–λE, transpiration, unit: W m−2) and the rate of heat loss by conduction or convection to the environment (–C, unit: W m−2). It is also directly related to the density of the leaf (ρleaf, unit: g m−3), the specific heat of the leaf (cp, unit: J g−1 °C−1), its thickness (lleaf, unit: m) and temperature (Tleaf, unit: °C). From the right side of Equation 1, we know that the net amount of energy entering or leaving the leaf (S) is directly related to the density of the leaf (ρleaf), the specific heat of the leaf (cp), its thickness (lleaf) and temperature changes in time (dT/dt). Because in our study ρleaf, cp and lleaf are the same in all genotypes and treatments (we used leaves of the same age and geometry), only dT/dt influences S; therefore, foliar temperature changes reflect changes in the net amount of energy entering or leaving the leaf.

Fig. 2

Design of the thermographic experiment and the theory behind it. (A) typical thermograms in darkness (top) and during excess light irradiation (below); arrows indicate leaves covered with lanolin (Lanolin) and a drop of 100 μl of distilled water under the leaf (shorter arrow) or by the plant, without being in contact with it (longer arrow). (B) Scheme of possible energy fluxes in the leaf during excess light irradiation, that takes into account all three types of leaves used in the experiment: the ‘A’ leaf without any cover, the ‘L’ leaf covered with lanolin and the ‘W’ leaf floating on the drop of water. (C) Principle of calculation of the ACWSI: for each experimental variant and each leaf type, we fitted straight lines T = aI + b (where T is the maximal leaf temperature during 3 min of irradiation or 1 min of darkness, I is light intensity, a is the calculated slope, and b is the calculated intercept); AL, AA and AW are areas below the fitted lines calculated for ‘L’, ‘A’ and ‘W’ leaves, respectively.

For their thermal research, Kaňa and Vass (2008) proposed an elegant mathematical model which links the efficiency of utilization of incident irradiation (I) in PSII (ΦII) and leaf temperature. It requires some assumptions, which we adjusted to our model and list below.

Rn can be replaced by incident irradiation (I), as: (i) energy losses due to infrared (IR) radiation emitted at room temperature are small and (ii) energy gains due to absorbance of IR radiation at room temperature are overwhelmingly small in relation to applied excess light irradiation (up to 6,000 μmol s−1 m−2; for details see the Materials and Methods).

The whole metabolic energy flux goes into photosynthesis (M = –P). P is energy flux into photosynthesis (unit: W m−2); we put the minus sign here because photosynthesis is a heat sink.

Heat content is unchanging (thermal equilibrium is reached).

Lateral heat conduction has been neglected, because of the thinness of 4-week-old Arabidopsis leaves which are about 170 μm thick (Wuyts et al. 2010, Wuyts et al. 2012).

Only vertical heat fluxes from the leaf surface to the boundary layer above and to the leaf tissue below are assumed.

We made two further assumptions: (i) that for leaves covered with lanolin or floating on a drop of water, λE = 0, and for the latter the cooling role is taken over by the physical unregulated process of evaporation (ΔEenv); and (ii) in view of high thermal capacity of water and Fourier’s law, a drop of water is a perfect sink of heat transferred vertically from its surface (Csink).

By incorporating the above assumptions and observations into Equation 1, we obtain the following Equations 2–4: where SA, SL and SW are the net amounts of energy entering or leaving the control leaf, a leaf covered with lanolin and a leaf floating on water, respectively. These equations perfectly apply to our study.

To compare energy fluxes between genotypes, we should consider Equation 2, as it reflects differences in natural conditions, where stomata can freely close and open. In all lines, irradiation (I) has the same value, because we exposed all the plants to the same light intensities. To assume that C has the same value in different conditions, we need a more complex argument. In light of Fourier’s law, it is directly proportional to the material’s conductivity and temperature gradient. We chose leaves of a similar age, so the quantity, quality and density of particular tissues is also similar; therefore, we can infer that the material (and the material’s conductivity) is the same. A temperature gradient establishes between the heat source (here the plant area with the highest temperature) and the heat sink (here the plant area with the lowest temperature). The gradient depends not only on the temperature difference between source and sink, but also on the distance between them. At this point, there are some differences between Kaňa and Vass’s study (2008) and ours. (i) Kaňa and Vass illuminated only a small leaf area (8 mm in diameter), and we illuminated the whole plant. Thus, the heat sink in Kaňa and Vass’s study was much colder and was placed on the same leaf as the heat source, so the temperature gradient was higher—and the contribution of conduction to leaf temperature was higher than in our study. (ii) Suppose that conduction really significantly affects foliar temperature. Then conduction cools down the hottest leaves the most, and the coolest leaves the least. This means that lines fitted to temperature data of the hottest leaves are the most flattened and those of the coolest leaves the least flattened; thus differences between treatments and genotypes would be smaller. So it is possible that we have made a type II error, but not a type I error, and possibly we do not spot differences where they really are. What is left in this equation is: energy loss by evaporation (–λE) and by photosynthetic activity (–P). We cannot assume that –λE values are the same, so before we draw any conclusions concerning the relationship between temperature and photosynthesis, we should first exclude differences in evaporation between genotypes.

Energy loss by evaporation (–λE) depends on two factors: the water status of the leaf and stomatal conductance. The water status of the leaf contributes to the leaf temperature, because water has high a thermal capacity, and thus withered leaves are hotter than well-hydrated leaves. This factor is mathematically expressed by the modified crop water stress index (CWSI), i.e. (ICWSI) (Idso 1982, Jones 1999) and is defined as: where Ts is the surface temperature of a leaf able to close and open stomata, Tbase is the temperature of a leaf with fully open stomata and Tmax is the temperature of a leaf with completely closed stomata. In our case, Ts refers to an untreated leaf (Tair), Tbase refers toa leaf floating on a 200 μl drop of water (Twater) and Tmax refers to a leaf covered with lanolin (Tlanolin). We rewrote this equation to produce Equation 6:

The ICWSI value rises when Tair approaches Tlanolin, which happens when stomata of an untreated leaves are closing, and the maximal value of 1 means that they are completely closed. The water status of the leaf (expressed in numbers by CWSI and ICWSI) is a factor that contributes to the leaf temperature. The above-described model was developed for crops undergoing water stress, where the differences in temperatures of stressed and non-stressed plants are quite large (see, for example, Jones et al. 2009); we need a more sophisticated mathematical tool to assess the weight of evaporative cooling in the temperature of a single leaf undergoing excess light stress. For this purpose, we determined the maximal leaf temperature and plotted it against light intensity. We fitted straight lines to those points and calculated the area between the fits and the x-axis. We calculated ACWSI from following the equation (Equation 7; see also Fig. 2C): and: where AA is the area under the line fitted for temperatures of an untreated leaf, AW is the area under the line fitted for temperatures of a leaf floating on a 20 μl drop of water, AL is the area under a line fitted for temperatures of a leaf covered with lanolin, T0 is the maximal foliar temperature in darkness (0 μmol m−2 s−1), and T6,000 is the maximal foliar temperature during irradiation of 6,000 μmol m−2 s−1; the superscripta ‘L’, ‘A’ and ‘W’, and bA, bL and bW are intercepts of linear functions fitted to temperature data plotted against light intensity obtained for untreated leaves, leaves covered with lanolin and leaves floating on a drop of water, respectively. The light intensity difference (ΔI) is the difference between the highest and lowest light intensity applied to a plant; in our case it is 6,000 μmol m−2 s−1 – 0 μmol m−2 s−1 = 6,000 μmol m−2 s−1.

The value of ACWSI rises when AA approaches AL, which happens when stomata are closing. The value of ACWSI reaches 1 when stomata are totally closed. The lower this value, the greater the participation of stomata in leaf temperature.

Because the slope is generally a value by which y (temperature) rises for every 1 unit of x (i.e. 1 μmol m−2 s−1), and temperature difference (T6000 – T0) is the value by which temperature rises from 0 to 6,000 μmole m−2 s−1: Incorporating Equation 11 into Equations 8–10, we get Equations 12–14: Incorporating Equations 12–14 into Equation 7, we get Equation 15 that describes the relationship between ACWSI and aL, aA and aW:

The effect of transpiration on leaf temperature under changing light conditions

The wild-type Arabidopsis leaves exposed to 300 μmol m−2 s−1 showed a rapid foliar temperature increase by 10°C in 1 min. In the following period during light exposure, we observed temperature fluctuations resulting presumably from fine-tuning of stomatal conductance and transpiration (). To assess the role of stomatal movements, ICWSI (Equation 6) was calculated for all genotypes under ambient conditions and after application of DCMU which blocks reduction of the secondary electron acceptor, QB, in PSII, and, as a consequence, slows down the oxidation of QA and reduction of the plastoquinone pool and inhibits NPQ (Szechyńska-Hebda et al. 2010) (). With increasing light intensity, the index increased linearly and reached a peak at 3,000 μmol m−2 s−1, and dropped at 6,000 μmol m−2 s−1. We observed no significant differences between genotypes and treatments (). These results strongly suggest that the observed huge foliar temperature differences at a given light intensity in Arabidopsis leaves of the tested genotypes and treatments did not result from differences in stomatal aperture and transpiration.

As we mentioned in the previous section, our model requires exclusion of the uneven contribution of stomatal conductance to foliar temperature in various genotypes before testing the role of PsbS in regulation of foliar temperature dynamics. We set up the following experimental system. We compared slopes of the curves fitted to a leaf temperature plotted against light intensity for lanolin-treated leaves (‘L’, no transpiration), untreated (‘A’, ambient transpiration) and water-treated leaves on the adaxial side (‘W’, maximal transpiration) (Figs. 2C, 3, 4; ; ). Variants with higher values of the slope produced more heat or had a higher ratio of closed stomata when comparing leaves without any treatment (lanolin nor water). The difference resulting from stomatal movement is neglected when comparing only lanolin-covered leaves or only leaves floating on water, as ‘L’ leaves mimic leaves with closed stomata and ‘W’ leaves mimic leaves with open stomata, irrespective of the genotypes and treatments.

There were differences in the slopes of lines fitted to leaf temperatures between ‘L’, ‘A’ and ‘W’ leaves, measured after 1 min of darkness, when leaves cooled down between excess light treatments (Fig. 3, left). For the wild-type leaves we obtained the steepest slope for ‘W’ leaves (1.666×10−3), shallower for ‘L’ (1.146×10−3) and the shallowest for ‘A’ leaves (0.882×10−3). In npq4-1, we observed that the slope of ‘W’ leaves (1.552×10−3) was steeper than the slopes of ‘L’ and ‘A’ leaves (0.965×10−3 and 0.923×10−3, respectively). For the oePsbS leaves, we calculated the steepest slope for ‘W’ leaves (1.61×10−3), shallower for ‘L’ leaves (1.042×10−3) and the shallowest for ‘A’ leaves (0.79×10−3). These results indicate that cooling down of the leaf after intense irradiation (which is here described as the slopes of lines fitted to leaf temperature plotted against irradiation intensity for data recorded in darkness) depends on stomatal movements (Fig. 3, left; ).

Fig. 3

The influence of stomatal conductance on maximal leaf temperature recorded during increasing EEE stress (Excess light) or during 1 min of darkness (Darkness). Asterisks in the insets indicate the level of significance of the differences in slopes (*P < 0.05, **P < 0.01, ***P < 0.005).

We also compared slopes of lines fitted to maximal temperatures recorded for each EEE dose during irradiation (Fig. 3, right; ). For the wild type, we calculated the steepest slope for ‘L’ leaves (3.796×10−3), shallower for ‘A’ leaves (3.344×10−3) and the shallowest for ‘W’ leaves (2.518×10−3). The slope calculated for npq4-1 ‘W’ leaves (2.613×10−3) was shallower than slopes determined for ‘L’ and ‘A’ leaves (3.587×10−3 and 3.459×10−3, respectively). For oePsbS, we calculated the steepest slope for ‘L’ leaves (3.641×10−3), shallower for ‘A’ leaves (3.378×10−3) and the shallowest for ‘W’ leaves (2.528×10−3). Interestingly, these results indicate that the rapid foliar temperature increase observed during intense irradiation is not dependent on the PsbS content and NPQ value (genotypic differences) but depends rather on leaf water status.

To assess the weight of stomatal function in leaf temperature and compare it between genotypes, we modified CWSI (ACWSI). The areas we used for calculating this index (AL, AA and AW) are limited from below by the T = 0 (°C) function (the graphical representation of this function overlaps the x-axis): due to this approach, the areas depend not only on temperature dynamics (slope), but also on the initial temperature (intercept) (see Fig. 2C; Equation 7). If we took any other T = d cutting line, where d is lower than or equal to the lowest temperature value recorded in this experiment, ACWSI would be equal to that presented below. In the formula (Equation 7), in both the numerator and the denominator, there are differences in two areas, which means that during calculation the common part of two fields is always cut away, including the area under the T = d line. The ACWSI is very small (from −0.52 in the wild type to 0.25 in DCMU-treated oePsbS) in leaves during 1 min of cooling in darkness, after excess light treatment. In npq4-1, this value is −0.05, and DCMU treatment causes it to decrease to −0.21. In oePsbS, it is −0.31 and DCMU treatment causes it to increase to 0.25 (). ACWSI calculated for irradiated leaves is much higher. In the wild type, it is 0.68 and DCMU treatment causes it to rise to 0.90. In npq4-1, it is 0.79 and DCMU treatment increases it to 0.84. In oePsbS, it is 0.75 and DCMU increases this index to 0.93 (). These results confirm the stomatal closing effect of DCMU (Sharkey and Raschke 1981, Schwartz and Zeiger 1984, Tominaga et al. 2001, Olsen et al. 2002, Messinger et al. 2006).

The effect of DCMU treatment on leaf temperature under changing light conditions

We observed some significant differences between control and DCMU-treated leaves in the slopes of lines fitted to leaf temperatures measured after 1 min of darkness, when leaves cooled down between 3 min excess light treatments (, left; ). In the wild-type ‘A’ leaves, DCMU treatment increased the steepness of the slope (from 0.882×10−3 to 1.079×10−3). In the case of ‘L’ and ‘W’ leaves of the wild type, the differences were insignificant. We also did not observe significant differences in the slopes of lines fitted for npq4-1 ‘L’, ‘A’ and ‘W’ leaves. In oePsbS ‘L’ leaves, DCMU treatment increased the steepness of the slope (from 1.042×10−3 to 1.153×10−3), and the shift was even larger in oePsbS ‘A’ leaves (from 0.79×10−3 to 1.156×10−3), while changes in its ‘W’ leaves were insignificant (, left; ).

We also compared slopes of lines fitted to maximal temperatures recorded for each light intensity during irradiation (, right; ). We did not observe significant changes either in the wild type or in npq4-1 ‘L’, ‘A’ and ‘W’ leaves. In oePsbS ‘L’, ‘A’ and ‘W’ leaves, DCMU treatment caused an increase in the steepness of the slope (from 3.641×10−3, 3.378×10−3 and 2.528×10−3 to 3.848×10−3, 3.993×10−3 and 2.688×10−3, respectively) (, right; ). The above results show that the effect of DCMU on leaf temperature dynamics during intense irradiation is dependent on PsbS levels and NPQ capacity.

The effect of PsbS on leaf temperature under changing light conditions

We observed some differences between genotypes in the slopes of the lines fitted to leaf temperatures measured after 1 min of darkness, when leaves cooled down between 3 min excess light treatments (Fig. 4, left; ). In the wild-type leaves covered with lanolin (‘L’ leaves), the calculated slope (1.146×10−3) was steeper than for oePsbS ‘L’ leaves (1.042×10−3). We observed no differences between slopes calculated for wild-type, npq4-1 and oePsbS ‘A’ and ‘W’ leaves. In DCMU-treated npq4-1 ‘L’ leaves, the slope was shallower than in DCMU-treated wild-type ‘L’ leaves (0.927×10−3 and 1.063×10−3, respectively). We observed no differences between slopes calculated for wild-type, npq4-1 and oePsbS ‘A’ or ‘W’ leaves (Fig. 4, left; ).

Fig. 4

The contribution of PsbS-dependent NPQ to the increase in leaf temperature during increasing EEE stress (Excess light) or during 1 min of cooling in darkness (Darkness). Asterisks in the insets indicate the level of significance of the differences in slopes (*P < 0.05, **P < 0.01, ***P < 0.005).

We also compared the slopes of lines fitted to maximal temperatures recorded for each light intensity during irradiation. Unexpectedly, we observed only insignificant differences between slopes calculated for the wild type, npq4-1 and oePsbS lines in lanolin-covered (‘L’) leaves, in leaves in air (‘A’) and in leaves floating on water (‘W’). DCMU-treated oePsbS ‘L’ leaves displayed a steeper slope (3.848×10−3) compared with npq4-1 (3.408×10−3). We also observed a steeper slope in DCMU-treated oePsbS ‘A’ leaves (3.993×10−3) than in the wild type and npq4-1 (3.597×10−3 and 3.489×10−3, respectively). For DCMU-treated oePsbS ‘W’ leaves, we calculated a steeper slope (2.688×10−3) compared with the wild type (2.514×10−3) (Fig. 4, right; ). These results indicate that the PsbS content does not appear to affect the foliar temperature dynamics in the presence of DCMU. If it had, the slopes would be greater for lines fitted to data regarding leaves with a higher NPQ.

Correlation between foliar temperature rise during intense irradiation and NPQ

To find a correlation between the foliar temperature rise during intense irradiation and NPQ, we plotted the temperature difference (difference between the initial temperature and the maximal temperature reached during illumination with increasing light intensity) against NPQ of each data point (temperature and NPQ determined for the same leaf area) (Fig. 5). The points clustered depending only on NPQ, not temperature difference. These results suggest that the increase of the foliar temperature during intense illumination is independent of NPQ.

Fig. 5

Non-photochemical quenching plotted against temperature difference (the difference between the initial temperature and the maximal temperature determined during excess light irradiation).

The effect of transpiration on the photosynthetic parameters under changing light conditions

We applied lanolin on both sides of a leaf (the ‘L’ leaf) to inhibit transpiration, and applied a drop of 200 μl of distilled water under a leaf (the ‘W’ leaf) to maximize the transpiration cooling process. We compared the slopes of lines fitted to maximal temperatures recorded for each light intensity during irradiation of lanolin-treated leaves (the ‘L’ leaf) with those of control leaves (the ‘A’ leaf) (; ). Interestingly, we noticed some differences already before excess light treatment. In the DCMU-treated wild type, we observed a reduced Fv/Fm in the ‘A’ leaf in comparison with the ‘W’ leaf. We observed a similar reduction in other genotypes treated with DCMU, but the shift was not significant. Additionally, in wild-type plants, NPQ decreased after covering their leaves with lanolin. Other differences in the initial values of photosynthetic parameters between ‘L’, ‘A’ and ‘W’ were insignificant (; ).

After excess light treatment in plants of all genotypes, we observed significant differences in Fv/Fm between ‘L’, ‘A’ and ‘W’ leaves. Covering leaves with lanolin resulted in a significant Fv/Fm decrease, while leaves floating on the drop of water had an unchanged Fv/Fm value. In all DCMU-treated plants, this parameter was significantly higher in ‘W’ leaves as compared with ‘L’ and ‘A’ leaves. In the wild type, the qP value decreased and in the DCMU-treated wild type qP increased in ‘W’ leaves as compared with ‘A’ leaves. The wild type, npq4-1 and oePsbS displayed a significant reduction of ΦPSII in ‘L’ leaves compared with ‘A’ and ‘W’ leaves. Additionally, we observed significantly higher ΦPSII in the ‘W’ leaf compared with the ‘A’ leaf. In both DCMU-treated wild type and oePsbS, we observed a higher ΦPSII in ‘W’ compared with ‘A’ leaves. NPQ in wild-type ‘L’ leaves was significantly lower than in ‘A’ and ‘W’ leaves. We observed the same differences for npq4-1 leaves. NPQ measured in oePsbS ‘L’ and ‘W’ leaves was lower as compared with ‘A’ leaves. In DCMU-treated wild type, ‘L’ leaves displayed a slight increase in the NPQ value in comparison with ‘A’ leaves. In DCMU-treated npq4-1, the NPQ value was the highest in ‘L’ leaves, lower in ‘A’ leaves and the lowest in ‘W’ leaves. In DCMU-treated oePsbS, NPQ was significantly higher in ‘L’ leaves compared with ‘A’ and ‘W’ leaves (; ). These results indicate that inhibition of transpiration by covering leaves with lanolin caused photooxidative (photorespiratory) stress.

The effect of DCMU treatment on photosynthetic parameters under changing light conditions

As we expected, after DCMU treatment we observed decreases in photosynthetic parameters such as Fv/Fm (the herbicide blocks photochemical and non-photochemical quenching, which results in higher maximal fluorescence Fm), the coefficient of photochemical quenching (qP), the quantum yield of PSII (ΦPSII) and NPQ in all analyzed Arabidopsis lines. After treatment with increasing excess light, the reduction of values of these parameters was maintained. There was only one exception: NPQ measured in the lanolin-covered leaf of npq4-1 after excess light treatment was the same in non-treated and DCMU-treated plants. A possible explanation for this phenomenon might be that in this case all possible stresses occurred simultaneously (overwhelming amount of light energy, enormous heat stress due to the impossibility of transpiration and lack of PsbS protein), so the plant defense system against stress was least efficient and further constraints cannot worsen the already adverse situation (; ). These results confirm that DCMU treatment was correctly applied and interpreted.

The effect of PsbS protein on photosynthetic parameters under changing light conditions

We observed several differences between genotypes before EEE stress in terms of photosynthetic parameters. Fv/Fm was significantly lower in ‘L’ and ‘W’ leaves of the wild type compared with npq4-1. We observed no significant differences in this parameter between any other variants, including DCMU-treated plants of any genotype. The qP value in ‘L’ leaves was significantly higher in oePsbS than in npq4-1. We observed no significant differences in this parameter between any other variants, including DCMU-treated plants of any genotype. The npq1-4 ‘A’ leaves displayed increased ΦPSII compared with the wild type. We observed no significant differences in this parameter between any other variants, including DCMU-treated plants of any genotype. The NPQ value in all ‘L’, ‘A’ and ‘W’ leaves was the highest in oePsbS, lower in the wild type and the lowest in npq4-1. In DCMU-treated npq4-1 ‘L’, ‘A’ and ‘W’ leaves, this parameter was lower than in the DCMU-treated wild type. NPQ was also higher in DCMU-treated ‘L’ and ‘W’ oePsbS leaves compared with DCMU-treated npq4-1 ‘L’ and ‘W’ leaves (; ).

After excess light treatment, Fv/Fm was significantly higher in ‘L’ leaves of oePsbS than in wild-type and npq4-1 ‘L’ leaves. In ‘A’ leaves of npq4-1 this parameter was lower than that of both wild-type and oePsbS ‘A’ leaves. Fv/Fm of ‘W’ leaves was the highest in oePsbS, lower in the wild type and the lowest in npq4-1. The qP value in wild-type ‘L’ leaves was higher than in npq4-1 ‘L’ leaves and lower than in oePsbS ‘L’ leaves. In ‘A’ npq4-1 leaves, this parameter was reduced compared with oePsbS. In ‘W’ wild-type leaves, this parameter was lower than in oePsbS. The npq1-4 ‘L’ leaves displayed increased ΦPSII compared with the wild type. In wild-type ‘W’ leaves, we observed lower ΦPSII compared with both npq4-1 and oePsbS. The NPQ value in all ‘L’, ‘A’ and ‘W’ leaves was the highest in oePsbS, lower in the wild type and the lowest in npq4-1. For DCMU-treated plants, this parameter was lowest in npq4-1 ‘L’ and ‘A’ leaves compared with both wild-type and oePsbS ‘L’ and ‘A’ leaves. In DCMU-treated ‘W’ leaves, we observed no significant differences in this parameter (; ). These results confirm the already described phenotype of npq4-1 and oePsbS Arabidopsis lines.

Discussion

Plants need to regulate the fate of absorbed photons in order to optimize photosynthesis and EEE dissipation in highly variable light conditions. In a basic picture, absorbed light energy distribution between photochemistry, Chl fluorescence and heat are in direct competition with each other. It has been suggested that increasing the rate of one of the processes should result in rate reduction of the others, and vice versa (Baker 2008, Li et al. 2009). The exact mechanism that enables plants to regulate and balance the rates of these processes under EEE light conditions has not yet been clarified.

Possible heat sources and heat sinks

In some conditions, external objects could be a source of ‘pseudo-heat’. Thermal radiation sourced from hot objects in the close environment could be reflected in the object of interest, changing its apparent temperature. We have made every effort to eliminate any object in the surroundings that could distort the thermal image of the plant.

There are several known reasons for a temperature rise in plants. The most spectacular one is thermogenesis during anthesis, described, for example, for the neotropical arum lily Philodendron selloum (Seymour et al. 1983, Seymour 1991, Seymour 1999). The plant increases the flower temperature up to 38–42°C through respiration to enhance odor production, which attracts entomophilous beetles (Meeuse and Raskin 1988). However, for anemophilous plants, this phenomenon does not apply and the heat provided from respiration is produced on a much smaller scale (Breidenbach et al. 1997), or by the need to adapt to cold conditions (Ordentlich et al. 1991, Nevo et al. 1992, Moynihan et al. 1995), which does not apply to our experiments. The only process that could explain a 10°C or higher temperature rise in a relatively short time during increasing EEE conditions of Ficus, horseradish, silver birch and Arabidopsis leaves (see Figs. 1, 2; ) is qE.

To prove this hypothesis, we designed a set of experiments. We compared the surface temperatures of plant organs that differ in the capacity for conducting photosynthesis and NPQ, i.e. a green leaf vs. a bleached leaf (Fig. 1), or a leaf vs. a root () or inflorescence (). Our results speak for themselves—under identical environmental and experimental conditions, plant organs with a higher Chl content which absorbed more energy quanta of light (Fig. 1), photosynthetic plant organs () and leaves in better physiological shape (Zobiole et al. 2012) () produce much more heat during EEE. In contrast, background temperature during EEE conditions increased only up to 2°C at the highest light intensities. This means that the light-emitting diode (LED) panels are not the direct source of heat observed in leaves exposed to EEE, but rather processes accompanying light absorption, photosynthesis, NPQ and transpiration.

Heat can be transferred between objects by means of lateral or vertical conductance, radiation or evaporation (transpiration). The lateral flux and radiation can be neglected, because of the thickness of Arabidopsis leaves (4-week-old leaves are about 170 μm thick; Wuyts et al. 2010, Wuyts et al. 2012), and the insignificance of the emittance at room temperature (Kaňa and Vass 2008), respectively. Vertical conductance is similar in each genotype, as we chose leaves of a similar size and age in each genotype. In leaves floating on water, the contact area and heat capacity of water are favorable to heat conductance.

The contribution of stomata in leaf temperature

One of the important roles of stomata is controlling leaf temperature by enabling transpiration.

We have reproduced the experiment of Kaňa and Vass (2008) with the following modifications. We used whole (not detached) wild-type and npq4-1 Arabidopsis plants treated or not treated with DCMU, which blocks photosynthetic electron transport between quinone B and the plastoquinone pool, therefore causing over-reduction of quinones A and B, oxidation of plastoquinone and reduction and suppression of NPQ (Wessels and van der Veen 1956, Astier et al. 1984, Haworth and Steinback 1987, Karpinski et al. 1999). We used very strong light (up to 6,000 μmol m−2 s−1) that closes all reaction centers (RCs). By applying light with increasing intensity (starting from 0 μmol m−2 s−1, where all RCs are open and ending with 6,000 μmol m−2 s−1 where all RCs are closed, we could measure the temperature of leaves during gradual closure of RCs. In the study of Kaňa and Vass (2008), a light of 6,000 μmol m−2 s−1 intensity was also used (only for 800 ms, as a saturating pulse, but the reason was the same).

We noticed some differences in slopes of linear functions fitted to leaf temperature data plotted against light intensity between leaves having near zero (‘L’), normal (‘A’) and maximal (‘W’) transpiration, which we interpret as a representation of stomatal aperture during the experiment (Fig. 3; ). To compare the significance of stomata in leaf temperature, we have calculated the ACWSI (Equation 6; Fig. 2C; ). Water has a high heat capacity, which means in practice that it heats up and cools down very slowly. When the light is off and leaves are allowed to cool down between 3 min periods of irradiation, the leaf that floats on the drop of water (‘W’) accumulates temperature and, despite the fact that it is initially cooler and heats up more slowly during light irradiation, AW calculated for this light condition is similar to or even greater than AA. This results in low or even negative ACWSI.

One of the best indicators of stomatal movement is ACWSI calculated for irradiated leaves, e.g. when the light was on. It is worth noting that in all cases DCMU caused an increase in ACWSI (). This observation is consistent with a previous study—stomatal opening caused by excess red light through the decrease of intracellular CO2 content (Roelfsema et al. 2002) is abolished by DCMU (Sharkey and Raschke 1981, Schwartz and Zeiger 1984, Tominaga et al. 2001, Olsen et al. 2002, Messinger et al. 2006). We observed the highest significance of stomatal opening in the wild type, lower in oePsbS and slightly lower in npq4-1 (). It should be noted that we observed no significant differences between slopes of npq4-1 ‘A’ and ‘L’ leaves (Fig. 3; ), which suggests that in this genotype stomata are closed during EEE. This could additionally explain the impaired growth of npq4-1 in the field (Külheim et al. 2002, Külheim and Jansson 2005, Krah and Logan 2010), as closed stomata cut off the access to CO2 and induce photoxidative stress, photorespiration and cell death (Mateo et al. 2004, Mühlenbock et al. 2008). DCMU causes stomatal closure (or inhibits their opening) to the greatest extent in the wild type (the difference between –DCMU and +DCMU ACWSI is 0.22), smaller in oePsbS (the difference between –DCMU and +DCMU ACWSI is 0.18) and the smallest in npq4-1 (the difference between –DCMU and +DCMU ACWSI is 0.05).

Because of the above evidence for differences in stomatal conductance between genotypes, we further compared only leaves covered with lanolin. This treatment mimics closed stomata and eliminates the contribution of evaporative cooling to leaf temperature.

Is PsbS a simple regulator of EEE dissipation as heat?

The fact that the npq4-1 line that lacks PsbS displayed a temperature increase during excess light treatments (Fig. 4) puzzled us most: to date this protein was described as a positive regulator of heat dissipation (Li et al. 2000, Niyogi et al. 2005). During EEE, we observed no differences in slopes calculated for wild-type, npq4-1 or oePsbS plants, and the wild type cooled down more slowly during the 1 min breaks in irradiation. DCMU-treated oePsbS heated up more rapidly than the other DCMU-treated plants and it cooled down less quickly than DCMU-treated npq4-1 (Fig. 4; ). If PsbS was a regulator of EEE dissipation, the pattern of differences would be different. DCMU at 40 μM completely inhibits NPQ and linear electron transport (and then also the cyclic electron transport around PSI; Johnson 2010), so we expected to observe no differences in DCMU-treated plants and significant differences between DCMU-untreated lines. On the other hand, we cannot exclude a contribution of PSI in heat generation. DCMU blocks electron flow between PSII and Cyt b6f, but does not completely inhibit cyclic electron transfer (Hosler and Yocum 1987); therefore, it is possible that excited P700+ dissipates part of the trapped energy as heat and affects foliar temperature dynamics in DCMU-treated plants. However, PSI lacks PsbS, so if this protein regulated heat dissipation, all DCMU-treated genotypes would generate the same amount of heat. However, this is not true in the case of oePsbS in ‘L’ and ‘A’ leaves (Fig. 4, right, +DCMU; the statistical test found a difference between the WT and oePsbS in ‘W’ leaves, but this difference seems to have been found by chance). This leads us to the conclusion that PsbS is not a direct regulator of thermal dissipation. Moreover, our recent study (Ciszak et al. 2014) provided new evidence that PsbS is not a simple activator of NPQ, but its role is much more complex; it plays a role in overall regulation of the fate of the absorbed photon energy. In fact, the findings of other studies (Külheim et al. 2002, Külheim and Jansson 2005, Krah and Logan 2010) and our study (Ciszak et al. 2014) show that PsbS is a positive regulator of photosynthesis and plant productivity in artificial or natural EEE conditions, and is a negative regulator of NPQ.

Conclusion

Our results clearly indicate that there is no direct correlation between changes in NPQ value and foliar temperature difference (difference between foliar temperature during maximal light irradiation of 6,000 μmol m−2 s−1 and foliar temperature measured in darkness, before irradiation) during intense irradiation (see Fig. 5); therefore, we should rethink the relationship between PsbS, NPQ and dissipation of EEE as heat. There are other possible physical transformations of absorbed energy, not only into charge separation (photochemical quenching), electromagnetic waves (fluorescence) or the vibration of molecules (heat). The absorbed energy can be utilized in electron transfer that results in carotenoid radical cation formation (qZ) or can be used in spin reversal [i.e. singlet oxygen (1O2) formation]. Triplet Chl molecules are a source of 1O2, when no efficient quenchers other than triplet oxygen are near. Singlet oxygen not only causes photoinhibition by damaging D2 protein (Trebst et al 2002), but also takes part in molecular signaling (Leisinger et al 2001, Op den Camp et al 2003) or it may be quenched by carotenoids (Hirayama et al. 1994, Telfer 2002) or tocopherol (Trebst et al 2002). Our results have important implications for proper interpretation of Chl fluorescence parameters such as NPQ vs. foliar temperature dynamics during intense irradiation, e.g. in a large-scale and precise plant phenomics.

Future perspectives

Our results suggest that PsbS is not directly involved in regulation of EEE dissipation as heat. This raises the question of which factor plays a role in this process. One of several candidates is singlet oxygen. On the other hand, thermal imaging with a much greater time and picture resolution would allow more detailed examination of the heat produced in the photoprotective processes on subcellular levels. Application of infrared thermography at the microscopic scale is not of use because of the major limitation of this technique, i.e. it measures only the surface temperature. Nevertheless, recent advances in novel techniques of temperature measurement inside living cells, suh as, for example, intracellular nanodiamond-based nanothermometers (Kucsko et al. 2013), fluorescent polymers (Okabe et al. 2012) and green fluorescent protein (GFP) (Donner et al. 2012), open up new possibilities in this field of science.

Materials and Methods

Plant material and growth conditions

Various plant species (Ficus benjamina, Armoracia rusticana and Betula pendula) and various plant organs such as root, leaf, inflorescence and mosaic leaves were used to answer the question of whether processes involved in photosynthesis can generate a significant amount of heat.

Ficus benjamina was purchased in a commercial flower shop, and then it was grown in standard laboratory conditions as described before (Wituszynska et al. 2013). Armoracia rusticana and B. pendula were collected as wild plants in Wolica field. The localization of Wolica field was described before (Wituszynska et al. 2013).

For all the other experiments, we used A. thaliana (L.) Heynh. plants of the ecotype Columbia (Col-0). The npq4-1 mutant containing a deletion of the entire PsbS gene (AT1G44575) was derived from mutagenesis with fast-neutron bombardment (Li et al. 2000) and was kindly provided by Professor K. Niyogi. The PsbS-overexpressing line oePsbS was kindly provided by Professor K. Niyogi and Professor Jansson (Li et al. 2002a, Johansson Jänkänpää et al. 2013). Arabidopsis thaliana plants were grown in standard laboratory conditions as described before (Wituszynska et al. 2013).

Preparation procedures

DCMU pre-treatment of A. thaliana rosettes was used to block photosynthetic electron transport on the secondary electron acceptor QB (Wessels and van der Veen 1956, Astier et al. 1984, Haworth and Steinback 1987). Plants were shaded for 1 h and sprayed with DCMU (40 μM DCMU in 0.3% ethanol) or control solution (0.3% ethanol). After 2 h in total darkness, plants were carefully dried with paper tissue and used in further experiments.

Determination of temperature in different organs of various plant species in increasing light intensities

Different plant organs were dark adapted (30 min) and tested using an FLIR T650sc IR camera (FLIR Systems with software). Depending on the plant species and its parts compared, we applied different light experimental conditions. We used various dark–light cycles: 30 s of darkness and 30 s of light, 1 min of darkness and 1 min of light, and 1 min of darkness and 2 min of light. Light treatment was performed using a red LED panel with 627 nm wavelength emission, 25 nm wave width (Photon System Instrument). Plants were irradiated with four red light intensities (375, 750, 1,500 and 3,000 μmol m−2 s−1; or 450, 1,000, 2,000 and 4,00 μmol m−2 s−1). We used n = 5 biological replicates.

Determination of leaf temperature in various genotypes of Arabidopsis thaliana in increasing light intensities

We used a red (with 627 nm wavelength emission, 25 nm wave width) LED panel (Photon System Instrument). Plants were irradiated with five light intensities of red light (375, 750, 1500, 3,000 and 6,000 μmol m−2 s−1), for 1–3 min for each intensity, separated by 1 min of very low light (2 μmol m−2 s−1 of ambient light) or darkness.

Leaf temperature measurements were performed using an FLIR T650sc IR camera with appropriate software (FLIR Systems). We placed a thin black polyethylene sheet (emissivity = 0.95) between the rosette and the plastic lid to enable the measurement of the background temperature. Three special leaves of similar size, age and shape were chosen before all measurements: a control leaf (not covered with any substance), a lanolin-treated leaf and a leaf floating on 200 μl of water, and close to the rosette we placed 200 μl of water (Fig. 2A). A thin layer of lanolin excludes the effect of transpiration and eliminates evaporative cooling of the leaf (Kaňa and Vass 2008). A drop of water under the third leaf eliminates transpiration but enhances cooling by abiotic evaporation and conductance (Fig. 2B). The three leaves were always in the same area of the measuring chamber to avoid errors resulting from the localization, e.g. angular variation, uneven excess light treatments or uneven focus of the camera. Thermal images were taken with a frequency of 7.5 frames s−1.

We circled two areas (technical replicates) within each of the previously chosen three leaves and noted their maximal values for each step of the experiment. We fitted lines to the data thus obtained in order to describe the relationship between temperature and light intensity; in each case a straight line was sufficient (R2 > 0.8). Using the F-test, we compared the fitted models. We used n = 6–7 biological replicates. Quantitative analysis of thermograms was performed in ResearchIR 3.4. All statistical analyses were performed in R.

Determination of photosynthetic parameters

To complement thermographic information, we measured pulse amplitude-modulated (PAM) fluorometry and calculated standard photosynthetic parameters, including: Fm, qP, ΦII, Fv/Fm and NPQ. A mosaic leaf of F. benjamina with visible differences in the intensity of the green color (Fig. 1D) was tested using FluorCam FC 800-C and associated software (Photon Systems Instruments).

In other experiments involving different Arabidopsis genotypes, we used a FluorCam 800MF (Photon System Instrument). We measured the parameters before and after completing the sequence of irradiation with increasing light intensities. Analysis of the data was performed using the associated software FluorCam7 (Photon Systems Instruments). All statistical analyses were performed in R.

Supplementary data

Supplementary data are available at PCP online.

Funding

This work was supported by the the Polish National Science Centre [Opus 4 (2012/07/B/NZ3/00228) and Maestro 6 (2014/14/A/NZ1/00218) projects]; the National Research and Development Center [PBS 3 (244622) project].

Disclosures

The authors have no conflict of interest to declare.

Table 1

Description of the linear model of the relationship between temperature and light intensity

Genotype	WT
Pre-treatment	control solution						DCMU
Light (on/off)	On			Off			On			Off
Leaf	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water
Intercepta	23.93	22.93	20.42	23.38	22.1	20.46	23.46	22.60	20.21	23.01	22.04	20.28
Slopeb	3.796×10−3	3.344×10−3	2.518×10−3	1.146×10−3	0.882×10−3	1.666×10−3	3.510×10−3	3.597×10−3	2.514×10−3	1.063×10−3	1.079×10−3	1.645×10−3
R2c	0.9743	0.9462	0.9794	0.9094	0.8289	0.949	0.9429	0.9558	0.9810	0.7783	0.8221	0.9559
R2 (adj)d	0.974	0.9455	0.9791	0.9083	0.8268	0.9484	0.9419	0.9550	0.9807	0.7745	0.8191	0.9551
A	211,908	197,772	167,844	160,908	148,476	152,748	203,940	200,346	166,512	157,194	151,662	151,290
A (% of L)	100.00	93.33	79.21	100.00	92.27	94.93	100.00	98.24	81.65	100.00	96.48	96.24
ACWSI	0.68			−0.52			0.90			0.06
Genotype	npq4-1
Pre-treatment	Control solution						DCMU
Light (on/off)	On			Off			On			Off
L eaf	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water
Intercepta	23.86	22.85	20.1	23.24	22.07	20.24	24.32	23.11	20.64	23.63	22.4	20.75
Slopeb	3.587 × 10−3	3.459 × 10−3	2.613 × 10−3	0.965×10−3	0.923×10−3	1.552×10−3	3.408×10−3	3.489×10−3	2.577×10−3	0.927×10−3	0.975×10−3	1.588×10−3
R2c	0.9579	0.954	0.9815	0.8392	0.8839	0.9482	0.9547	0.9399	0.9743	0.8438	0.8462	0.958
R2 (adj)d	0.9573	0.9534	0.9813	0.8369	0.8823	0.9474	0.9542	0.9391	0.974	0.8419	0.8443	0.9575
A	207,726	199,362	167,634	156,810	149,034	149,376	207,264	201,462	170,226	158,466	151,950	153,084
A (% of L)	100.00	95.97	80.70	100.00	95.04	95.26	100.00	97.20	82.13	100.00	95.89	96.60
ACWSI	0.79			−0.05			0.84			−0.21
Genotype	oePsbS
Pre-treatment	Control solution						DCMU
Light (on/off)	On			Off			On			Off
Leaf	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water	Lanolin	Air	Water
Intercepta	23.78	22.83	20.1	23.34	22.18	20.17	23.48	22.50	20.46	22.98	21.80	20.38
Slopeb	3.641×10−3	3.378×10−3	2.528×10−3	1.042×10−3	0.79×10−3	1.61×10−3	3.848×10−3	3.993×10−3	2.688×10−3	1.153×10−3	1.156×10−3	1.498×10−3
R2c	0.9788	0.9533	0.978	0.9376	0.8159	0.9528	0.9792	0.9657	0.9654	0.9206	0.9060	0.9211
R2 (adj)d	0.9785	0.9526	0.9777	0.9367	0.8133	0.9521	0.9788	0.9649	0.9647	0.9189	0.9039	0.9194
A	208,218	197,784	166,104	158,796	147,300	150,000	210,144	207,294	171,144	158,634	151,608	149,244
A (% of L)	100.00	94.99	79.77	100.00	92.76	94.46	100.00	98.64	81.44	100.00	95.57	94.08
ACWSI	0.75			−0.31			0.93			0.25

These data are illustrated in Figs. 3 and 4, and .

aUnit (°C).

bUnit (°C m2 s μmol−1).

cR2 is the coefficient of determination—a number that indicates how well data fit a statistical model; it quantifies the linear relationship in the analyzed sample of data.

dR2 (adj) (adjusted R2) is a modified version of R2 that has been adjusted for the number of predictors in the model; it is an estimate of the degree of relationship in the underlying population.