effect of light intensity on rate of photosynthesis experiment

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Biology Teaching Resources

Photosynthesis Virtual Lab

This lab was created to replace the popular waterweed simulator which no longer functions because it is flash-based. In this virtual photosynthesis lab , students can manipulate the light intensity, light color, and distance from the light source.

A plant is shown in a beaker and test tube which bubbles to indicate the rate of photosynthesis. Students can measure the rate over time. There is an included data table for students to type into the simulator, but I prefer to give them their own handout ,

The handout is a paper version for students to write on as the work with the simulator. The document is made with google docs so that it can be shared with remote students.

There are several experiments that can be done in the lab that would complement this virtual experiment. For example, students can use elodea and measure the number of bubbles released when the plant is under a bright light. Algae beads can also be used to measure changes in pH as the plants consume carbon dioxide.

In experiment 2, students specifically look at light color to determine which wavelength of light increases the rate of photosynthesis. Students should discover that green light has a very slow rate. Their collected data is then compared to a graph of the absorption spectrum of light.

Shannan Muskopf

Practical Biology

A collection of experiments that demonstrate biological concepts and processes.

Observing earthworm locomotion

Practical Work for Learning

Published experiments

Investigating factors affecting the rate of photosynthesis, class practical.

In this experiment the rate of photosynthesis is measured by counting the number of bubbles rising from the cut end of a piece of Elodea or Cabomba .

Lesson organisation

The work could be carried out individually or in groups of up to 3 students (counter, timekeeper and scribe).

Apparatus and Chemicals

Students may choose to use:.

Thermometer, –10 °C –110°C

Coloured filters or light bulbs

Push-button counter

Potassium hydrogencarbonate powder or solution (Hazcard 95C describes this as low hazard)

For each group of students:

Student sheets, 1 per student

Beaker, 600 cm 3 , 1

Metre ruler, 1

Elodea ( Note 1 ) or other oxygenating pond plant ( Note 2 )

Electric lamp

Clamp stand with boss and clamp

Health & Safety and Technical notes

Normal laboratory safety procedures should be followed. There is a slight risk of infection from pond water, so take sensible hygiene precautions, cover cuts and wash hands thoroughly after the work is complete.

Read our standard health & safety guidance

1 Elodea can be stored in a fish tank on a windowsill, in the laboratory or prep room. However it is probably a good idea to replace it every so often with a fresh supply from an aquarist centre or a pond. (It’s worth finding out if any colleague has a pond.) On the day of the experiment, cut 10 cm lengths of Elodea , put a paper-clip on one end to weigh them down and place in a boiling tube of water in a boiling tube rack, near a high intensity lamp, such as a halogen lamp or a fluorescent striplight. Check the Elodea to see if it is bubbling. Sometimes cutting 2–3 mm off the end of the Elodea will induce bubbling from the cut end or change the size of the bubbles being produced.

2 Cabomba (available from pet shops or suppliers of aquaria – used as an oxygenator in tropical fish tanks) can be used as an alternative to Elodea , and some people find it produces more bubbles. It does, though tend to break apart very easily, and fish may eat it very quickly.

3 If possible, provide cardboard to allow students to shield their experiment from other lights in the room.

Ethical issues

Look out for small aquatic invertebrates attached to the pond weed used, and remove them to a pond or aquarium.

• 1 Beijing Key Laboratory for Forest Resources and Ecosystem Processes, Beijing Forestry University, Beijing, China
• 2 Optoelectronic College, Beijing Institute of Technology, Beijing, China
• 3 School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia

Photoinhibition decreases photosynthetic capacity and can therefore affect the plant survival, growth, and distribution, but little is known about how it affects on kindred tree species. We conducted field experiments to measure the photosynthetic, growth and physiological performances of two maple species ( Acer mono and A. pseudosieboldianum ) seedlings at four light intensities (100%, 75%, 55%, and 20% of full light) and evaluated the adaptability of seedlings. We found that: (1) A. mono seedlings have larger light saturated photosynthetic rates ( A max ), the light saturation point (LSP), and lower light compensation point (LCP) than A. pseudosieboldianum seedlings, thus indicating that the former has a stronger light utilization ability. (2) A. mono seedlings under 75% light intensity and had higher seedling height (SH), basal stem diameter (BSD), leaf number (LN), leaf area per plant (LAPP) and total dry weight (TDW), while A. pseudosieboldianum seedling at 55% light intensity displayed greater growth advantages, which agreed with their response of light saturated photosynthetic rate. Morphological plasticity adjustments such as decreased root shoot ratio (RSR) and increased specific leaf area (SLA) showed how seedlings adapt to weak light environments. (3) 100% and 20% light intensities increased the malondialdehyde (MDA) content of two maple seedlings, indicating that very strong or very weak light could lead to the imbalance of reactive oxygen species (ROS) metabolism. The regulation of antioxidant enzyme activities such as superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), as well as the content of osmoregulation substances such as free proline and soluble protein, are the main mechanisms of plant adaptation to light stress. Although both A. mono and A. pseudosieboldianum are highly shade tolerant, subtle differences in the photosynthetic, morphological and physiological traits underpinning their shade tolerance suggest A. pseudosieboldianum has the advantage to deal with the light threat. Future studies should focus on the expression level of photosynthesis-related genes and cell, to better understand the adaptation mechanism of plants to light variation which facilitates forest development, either natural or via silvicultural practices. This information expands our understanding of the light-regulating mechanism of trees, which contributes to develop management practices to support natural forest regeneration.

Introduction

Photoinhibition often occurs when light energy is excessive, which reduces photochemical efficiency and even causes photooxidative system damage ( Ma et al., 2015 ; Dias et al., 2018 ). Furthermore, low light intensity influences photosynthesis, which is central to plant productivity, and can therefore severely restrict plant growth ( Zhu et al., 2014 ), and even death ( Wang et al., 2021 ). During the evolutionary process, plants had various adaptive strategies to decrease the potential damage caused by light stress ( Walters et al., 1993 ). Many studies have shown that plants can reduce the direct absorption of light energy by modifying morphological and photosynthetic plasticity, such as decreasing specific leaf weight (SLW), increasing specific leaf area (SLA) or enhancing light utilization capacity through the reduction in the light saturation point (LSP) and lower light compensation point (LCP) ( Kaelke et al., 2001 ; Zhu et al., 2014 ; Sugiura et al., 2016 ). Moreover, plant species can adjust their physiological characteristics in response to the variation in light intensity. For example, high levels of antioxidant enzyme activity which enable the rapid clearance of reactive oxygen species (ROS) ( Ma et al., 2015 ; Ozturk et al., 2021 ). Similarly, osmoregulation substances also play a key role in protecting plants from injury ( Kishor et al., 2005 ; Kučerová et al., 2019 ).

The early growth and survival of seedlings are very important for their successful supplement into the young tree stage, and light intensity plays a determinant role in this stage ( Loik and Holl, 1999 ; Razzak et al., 2017 ). However, in forest development and succession, the light environment varies greatly at both temporal and spatial scales ( Avalos and Mulkey, 2014 ). For example, the destruction and fragmentation of forests are bound to cause sharp changes in light intensity, which may not be beneficial for the regeneration of many trees ( Paquette et al., 2012 ; Yao et al., 2014 ). Even in the forest, the distribution of light is uneven due to the gap and stratification ( Popma and Bongers, 1988 ; Tripathi et al., 2020 ). The adaptability of seedlings to different light environments may determine the status of the tree species in the forest community ( Valladares et al., 2002 ; Rabara et al., 2017 ). In addition, previous studies on seedlings in canopy gaps or forest edges suggest that native tree seedlings may be inhibited by high light ( Yu and Hao, 1998 ; Wu et al., 2006 ).

Maple trees, Acer mono and Acer pseudosieboldianum , belong to the Aceraceae family, which are late succession and shade-tolerant species widely distributed in the natural mixed-broadleaved Korean pine forests in Changbai Mountains, Northeast, China ( Ye et al., 2014 ). These two maple trees are also widely used in landscape architecture construction due to their bright colors ( Xie et al., 2021 ). Previous field investigations found that numerous A. mono has developed into the dominant species in the main story, while A. pseudosieboldianum is the most important constructor in a forest sub-story ( Zhu et al., 2007 ; Ye et al., 2014 ; Zhang et al., 2015 ). Both maple trees are shade tolerant and kindred species, but they have different distribution patterns and abundances in the forest, which may be caused the differentiation in light requirements for the establishment and growth of seedlings ( Paquette et al., 2012 ). Hence, the identification of light requirements is necessary to understanding the regeneration of tree species and facilitating forest development, either natural or via silvicultural practices.

Here, we investigated the light acclimation capacity of A. mono and A. pseudosieboldianum seedlings in response to light conditions, and we hypothesized that: 1) A. mono seedlings may exhibit high photosynthetic efficiency under high light, while A. pseudosieboldianum seedlings may be limited. 2) The photosynthetic, morphological and physiological traits underpinning seedlings’ shade tolerance may give A. pseudosieboldianum an advantage in coping with light threats.

Materials and methods

Seed collection and seedling propagation.

We collected, A. mono and A. pseudosieboldianum seeds from mixed-broadleaved Korean pine forests (127°40’~128°16’ E, 41°35’~42°25’ N) in Changbai Mountains Northeast, China, from late September to early October 2020. Twenty independent individual maple trees were selected. The wings of the seeds were removed during seed collection, and the seeds were soaked in warm water at 45°C (initial temperature) in the laboratory to break the dormancy. The soaked time lasts for 7 days, and the water was renewed every 12 hours. The seeds were mixed with the appropriate amount of sand and put into a pot (30 cm inner diameter, 35 cm height, with good air permeability). Then, the pot with seeds was buried in the ground at 60 cm depth.

We dug out the pots with seeds on April 10, 2021, and then separated the seeds from the pots. The seeds were soaked in 0.5% KMnO 4 solution to disinfect for 3 h, and sterilized seeds were thoroughly rinsed with purified water. The seedbed was built at the Northeast Asia botanical garden in Changbai Mountains. For the seedbed soil disinfection, 1:1500 phoxim was used for insecticidal treatment, then 1:500 carbendazim was used for sterilization, and sowed seeds on 15 April.

Experimental design

To obtain light transmittance, photosynthetically active radiation (PAR) sensors (S-LIA-M003) with HOBO Micro Station Loggers (H21-002) (Onset Computer Corporation, USA) were installed in the forest gap, forest edge and understory of mixed-broadleaved Korean pine forests. The time step for data recording was set at 30 minutes. The light transmittance was calculated according to the following formula:

Four light intensity gradients were set up with different layers of black shade nets in the Northeast Asia botanical garden of Changbai Mountains, Northeast, China. The setup with 100% full light (L 100 ) served as a control, three weak light intensities were set up according to the light transmittance to simulate the forest gap, forest edge and understory. Three weak light intensity treatments were 75% (L 75 ), 55% (L 55 ), and 20% (L 20 ) of full light, which were set up with one layer, two layers, and three layers of nets, and each layer of shading net had three holes. In addition, branches from neighboring trees overtopping the experimental area were removed to secure homogeneous illumination.

On June 5, 2021, the healthy and homogenous seedlings (mean height of A. mono and A. pseudosieboldianum were 19.42 ± 5.32 cm and 21.32 ± 7.57 cm respectively, mean ± SD) were transferred to twenty plastic pots (20 cm inner diameter, 25 cm height, with holes in the bottom, six seedlings per pot) filled with a mixture of black soil, sand, branny, and pearlite (2:2:1:1, v/v/v, 40 kg m -3 ). During the first 15 days, all pots were placed in the built layers shading net for seedling retarding. On July 20, 2021, twenty plastic pots were randomly divided into four groups with five repetitions in each group and moved into the shade nets. In the early stages of the trial, the seedlings were watered every two days.

Photosynthetic measurements

Fully developed leaves (the second, third and the fourth from the top) of three robust seedlings of each tree species were randomly selected under each light environment from August 18 to 25, 2021. Photosynthesis ( P n ) was measured using a portable photosynthesis system (LI-6400, LiCor, Lincoln, NE, USA) at 10 levels of the photosynthetic photon flux density (PPFD), starting from 0, then 40, 60, 80, 100, 150, 200, 400, 600, 800, and 1200 μmol·m -2 ·s -1 . During the measurements, the ambient CO 2 concentrations, the temperature of the leaf chamber and air relative humidity were fixed for 380 μmol·mol –1 , 30°C and 50% respectively. The data were recorded between 8:30 and 11:30 a.m. To fit the photosynthetic light-response curve, we used the non-rectangular hyperbolic photosynthetic model proposed by Ye et al. (2013) . The light-saturated photosynthetic rates ( A max ), LCP, LSP and dark respiration rate ( R d ) were derived from the photosynthetic light-response curve.

Morphological measurements

We harvested seedlings on October 15, 2021. Each seedling, together with its taproots, was carefully removed from the soil, placed into sealed bags, and then transported to the laboratory. The seedlings were carefully washed with tap water and dried using filter papers. The number of leaves in the seedlings was counted, and the seedling height (SH) and basal stem diameter (BSD) were measured using a vernier caliper. The leaf area per plant (LAPP) was measured by a scanner (Canon scan lide120) and analysed using image analyzer (Image J). The seedlings were sorted into leaves, stems, and roots and subsequently dried in a dry oven at 85°C for 48h until constant mass, and then weighed with an electronic balance (EX224ZH 1/10000g; Ohaus Instruments, Changzhou, China). The total dry weight (TDW), root shoot ratio (RSR) and SLA were calculated based on Kelly et al. (2015) :

Physiological measurements

Leaves of two maple seedlings were randomly selected from one pot per treatment on September 1, 2021. The leaves were cut and mixed, they were randomly divided into three groups as three repetitions. The activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) were determined by the guaiacol method ( Beauchamp and Fridovich, 1971 ), UV absorption method ( Thomas et al., 1982 ), and azoblue tetrazole photoreduction method ( Díaz-Vivancos et al., 2008 ). The content of malondialdehyde (MDA), soluble protein and free proline were determined by the thiobarbituric acid technique ( Deng et al., 2012 ), Coomassie Brilliant Blue G-250 method and ninhydrin staining ( Bates et al., 1973 ; Kučerová et al., 2019 ).

Data analysis

We used the One-way ANOVA to analyze the differences in photosynthetic, morphological and physiological parameters of the two species under different light intensities and the differences between different species under the same light intensity, and Duncan’s multiple range test was used to detect differences between means. All analyses were performed within SPSS (Version 21.0) and Origin 2019.

Light response curves

The light response curves of maple seedlings varied with species. When PPDF< 200 μmol·m -2 ·s -1 , the light response curves of the two species under different light intensities were similar, and the P n increased sharply with the increase of PPDF ( Figures 1A, B ). When PPDF>400 μmol·m -2 ·s -1 , the P n of A. pseudosieboldianum seedlings tended to be stable and reached the LSP ( Figure 1A ). The P n of A. mono tended to be stable when the PPDF>600 μmol·m -2 ·s -1 ( Figure 1B ).

Figure 1 Light-photosynthetic response curves of two maple seedlings under different light intensities. (A) , A. pseudosieboldianum ; (B) , A. mono .

Photosynthetic parameters

The two maple seedlings exposed to 100% intensity showed the lowest A max ( Table 1 ). The A max of A. pseudosieboldianum seedlings was the highest under 55% intensity, while that of A. mono was the highest under 75% intensity. With the decrease in light intensity, the LSP of A. pseudosieboldianum seedlings decreased gradually, and the LSP under 100% intensity was significantly greater than 75%, 55% and 20% intensity ( P< 0.05); A similar response was observed for A. mono seedlings, but it was not significant under the different light intensities. Compared with the 100% light intensity, 75%, 55% and 20% intensity decreased LCP for two species, and the LCP of A. mono seedlings under 100% intensity was significantly greater than 75%, 55% and 20% intensity ( P< 0.05). The R d of two maple seedlings decreased gradually with the increase of light intensity, but a significant difference was not observed.

Table 1 Photosynthetic characteristics of two maple seedlings under different light intensity treatments.

Morphological characters

The shading was beneficial to the growth of two maple seedlings. For example, 55% light intensity resulted in the highest SH, BSD, LN, LAPP, and TDW of A. pseudosieboldianum seedlings, and the seedlings under 75%, 55%, and 20% light intensity were significantly higher than those under 100% light intensity ( P< 0.05) ( Table 2 ). The SH, BSD, LN, and TDW of A. mono seedlings under 75% and 55% light intensity were significantly higher than those under 100% and 55% light intensity ( P< 0.05), and the LAPP was significantly different under different light intensity ( P< 0.05). Two maple seedlings showed decreased RSR in response to dropped light intensity while the SLA increased ( Table 2 ).

Table 2 The growth parameters of two maple seedlings under different light intensity treatments (mean ± SD).

Antioxidant enzymes activity and MDA content

The SOD activity of A. mono seedling seedlings under 20% and 100% light intensity was higher than that under 55 and 20% light intensity(significance was not observed); and the SOD activity of A. pseudosieboldianum seedlings under 100% light intensity was significantly higher than 55% light intensity ( P< 0.05) ( Figure 2A ). Compared with the 100% light intensity, 75% light intensity decreased POD and CAT activity, while 55% and 20% light intensity increased POD and CAT activity of A. mono seedlings, especially 20% light was significantly higher than 75% light ( P< 0.05). Compared with 100% light intensity, 75%, 55% and 20% light intensity reduced the POD and CAT activity of A. pseudosieboldianum seedlings, and the CAT activity under 100% light was significantly higher than that of 55% and 20% light ( P< 0.05) ( Figures 2B, C ). The 20% light intensity resulted in the lowest MDA content of A. mono seedlings, while the MDA content of A. pseudosieboldianum seedlings was the lowest when the light intensity was 100% ( Figure 2D ).

Figure 2 Effect of light intensity on leaf antioxidant enzymes ( A , SOD; B , POD; C , CAT) and MDA (D) . Small letters indicate significant differences under different light intensities ( P < 0.05).

Content of soluble protein and free proline contents

The soluble protein content of two maple seedlings was significantly different under different light intensities ( Figure 3A , P< 0.05). Among them, 75% light intensity resulted in the lowest soluble protein content of A. mono seedlings, while the soluble protein content of A. pseudosieboldianum seedlings was the lowest when the light intensity was 55%. The free proline content of A. mono seedlings under 20% light intensity was significantly higher than 100%, 75% and 55%, but there was no significant difference among the latter three ( Figure 3B ).

Figure 3 Effect of light intensity on osmoregulation substance ( A , soluble protein; B , free proline contents). Small letters indicate significant differences under different light intensities ( P < 0.05).

Photosynthesis

The light-photosynthetic response curve is the key to understand the photochemical efficiency and photochemical processes of plants ( Loik and Holl, 1999 ; Razzak et al., 2017 ). We found that when PPDF > 400 μmol·m -2 ·s -1 , P n of A. pseudosieboldianum seedlings tended to be stable ( Figure 1A ) while P n of A. mono seedlings tended to be stable when the PPDF>600 μmol·m -2 ·s -1 ( Figure 1B ). This result is consistent with our assumption that as PPDF availability increased, the A. pseudosieboldianum seedlings were difficult to absorb electrons through photochemical processes and on the contrary, A. mono seedlings could deal effectively with the increase in light energy. This variation modes of photosynthetic characteristics may be related to the inherent genetic physiological, and it is also the result of the long-term adaptation of tree species to the environment ( Fariba et al., 2014 ). We also found that A. mono seedlings have higher A max , LSP and lower LCP than A. pseudosieboldianum seedlings in four light gradients ( Table 1 ). This result suggests that the photosynthetic potential for A. mono is high, which may also be the reason why this tree species occupies the forest’s main storey in the natural mixed-broadleaved Korean pine forests. Furthermore, we found that the A max of A. pseudosieboldianum seedlings was the largest at 55% light intensity, while A. mono seedlings exhibited the largest A max at 75% light intensity ( Table 1 ), reflecting that 55% and 75% of full light may be the optimum light levels for the two species respectively. In the field, the optimum light of A. pseudosieboldianum and A. mono is congruent with the habitat choice, which prefers forest gaps, forest edges, and the top of the canopy ( Wu et al., 2006 ; Ye et al., 2014 ).

In this study, the A max of two species under 100% light intensity was significantly lower than 75% and 55% treatments, indicating that the photosynthesis of maple seedlings was limited under strong light. This response to excess light energy is common in other shade tolerant species such as A. Saccharum ( Marilou and Christian, 1998 ), Pinus koraiensis ( Zhu et al., 2014 ), Fagus grandifoli ( Collin et al., 2017 ), and Quercus virginian ( Thyroff et al., 2019 ). Moreover, we found that the LSP and LCP of the two species dropped with the weakening of light intensity, which was consistent with the previous results showing the relatively low LCP and LSP of shade tolerant species were conducive to plants to utilize the light energy more efficiently under weak light environment, thereby increasing the accumulation of organic matter ( Ma et al., 2015 ). Lower R d is generally considered as the adaptive response of plants to cope with shaded conditions and obtain the maximum carbon benefit ( Dias et al., 2018 ). R d of two species under 75%, 55%, and 20% light intensity was lower than that of 100% treatment in our study, although not significant. This suggests that under shading conditions, seedlings reduce the loss of photosynthetic products and maintain the balance of carbon metabolism by decreasing R d , which was also confirmed by Yao et al. (2014) in the study of Abies holophylla .

Seedling growth

Light is a key factor affecting the early growth of tree seedlings in the forest ( Collin et al., 2017 ). Seedling regeneration may fail in shaded habitats with insufficient light ( Dias et al., 2018 ). As a result, seedlings must rely on forest gaps or forest edges to achieve individual regeneration. Previous studies have shown that the greater the light intensity, the better the seedling growth ( Gehring, 2003 ; Kelly et al., 2015 ), however, two maple seedlings exposed to 100% light intensity resulted in significantly lower SH and LAPP compared with the seedlings grown under the 75%, 55%, and 20% light treatments in this study, and BSD, LN and TDW also had a similar trend ( Table 2 ). These results showed that full light has little benefit to the maple seedling growth and is expected that maple trees are reputed to be a late succession and shade tolerant species. Moreover, under the canopy of closed adult plants in the natural mixed-broadleaved Korean pine forests in Northeast China, maples often form a dense seedling bank with a state of growth inhibition, and these seedlings can survive for many years ( Ye et al., 2014 ). Notably, SH, BSD, LN, LAPP, and TDW of A. pseudosieboldianum seedlings were the largest under 55% light intensity, while A. mono seedlings grew best under 75% light intensity ( Table 2 ). The different growth responses of two species to the different light levels may be explained by the photosynthetic variables previously observed in our study, and thus, the optimum light intensity required for seedlings determines their growth. A similar result was also reported in Camptotheca acuminata ( Ma et al., 2015 ) and Tetracentron sinense ( Lu et al., 2020 ).

The modifying of morphological plasticity is an adaptive response of plants to environmental stress (e.g., drought, high salinity and shade) and is also an important way for plants to improve population fitness and resource acquisition ability ( Kitajima, 1994 ; Tripathi et al., 2020 ). In the present study, we found that the SLA of A. pseudosieboldianum seedlings under 20% light intensity was significantly higher than that of 100% treatment, while the RSR under 20% light intensity was significantly lower than that of 100%, 75%, and 55% treatments ( Table 2 ). Similarly, in A. mono seedling, the light of decreased intensity resulted in the increase of SLA and the decrease of RSR ( Table 2 ). This morphological response to variation in light availability has been observed in many other studies ( Popma and Bongers, 1988 ; Avalos and Mulkey, 2014 ; Tang et al., 2015 ). This may be the result of the trade-off between plant biomass aboveground and underground and light stress ( Kitajima, 1994 ). Generally, soil moisture under strong light limits the upward extension of seedlings and eventually affects their growth and survival, thus seedlings allocate more photosynthetic products to the underground to form better developed roots, which is conducive to the absorption of water and nutrition; conversely, the biomass allocation of seedlings under weak light transferred to the aboveground, which can enhance the ability of plants to capture light ( Walters et al., 1993 ; Kaelke et al., 2001 ; Kelly et al., 2015 ; Tang et al., 2015 ). Moreover, we found that SLA and RSR in A. pseudosieboldianum seedlings were higher than that of A. mono seedlings across the light intensity ( Table 2 ). This result is consistent with Canham (1988) which found that shade tolerant species are generally more morphological plastic than less tolerant ones, which helps to improve the resistance and the ability to obtain resources of an individual tree seedling in the weak light environment, hence ensure the long-term reproduction of tree population ( Paquette et al., 2012 ).

Physiological characteristics

In stressful environmental conditions, the imbalance of ROS metabolism and the damage to the cell membrane system can lead to the increase of lipid peroxidation in biomembranes and permeability ( Yi et al., 2020 ), thus resulting in the accumulation of MDA in leaf cells, the product of membrane lipid peroxide, and then decreasing the photosynthetic capacity ( Ozturk et al., 2021 ). In this study, although a significant difference was not observed, the MDA content of two species under 100% and 20% light intensity was higher than that of 75% and 55% treatments ( Figure 2D ). This result agrees well with a recent study that shows full light and deep shade aggravate oxidative damage to lipid membranes ( Wang et al., 2021 ). However, plants have a complete antioxidant enzyme system including SOD, POD, and CAT, which can avoid the damage caused by ROS ( Tang et al., 2015 ). In this study, compared with 75% light intensity, 100% light intensity increased the activities of SOD, POD and CAT of two species ( Figures 2A–C ), indicating that the scavenging ability of ROS was enhanced in the full light environment. This result agrees with the report on olive trees by Sofo et al. (2004) . Similar results were also observed under 20% light intensity and the 20% light intensity enhances antioxidant enzyme activity of two species compared to 55% light intensity ( Figures 2A–C ), which could be due to the fact that the seedlings suffer from light threat under 20% light intensity more grievous than that under 55% light treatment. As a result, seedlings are bound to improve the activity of antioxidant enzymes to resist light stress and reduce light damage ( Ozturk et al., 2021 ).

Another immediate response of plants to cope with light stress is osmotic regulation ( Ozturk et al., 2021 ; Wang et al., 2021 ). For example, free proline can stabilize the construction of membranes and protein by eliminating ROS ( Bates et al., 1973 ; Kishor et al., 2005 ), and soluble protein protects cells against structural-metabolic disruptions and maintain osmolarity ( Ozturk et al., 2021 ). In the present study, an obvious rise in the content of soluble protein and free proline of two species was observed under 100% and 20% light intensities ( Figures 3A and B ), which was consistent with the lower photosynthetic capacity under these two light intensities, indicating that the seedlings increase osmotic regulators to adapt full light and deep shade. Similar results were reported that high levels of soluble protein and free proline maintain cell stability and reduce high/low photo damage ( Wang et al., 2021 ). It is worth noting that the proline and soluble protein content, as well as the above-mentioned three enzyme activities of A. mono seedlings, were the lowest at 75% light intensity, while these of A. pseudosieboldianum seedlings displayed a minimum at 55% light treatment ( Figures 2A–C and Figures 3A, B ), thus the subtle difference supporting their shade tolerance in the plasticity physiological shows that A. pseudosieboldianum more so than A. mono .

Our work demonstrates that full light and deep shade limited the growth of two maple seedlings, the optimum light intensity for the growth of the A. mono and A. pseudosieboldianum seedlings was 75% and 55% of full light, respectively, which can account for the niche of two maple trees in the natural mixed-broadleaved Korean pine forests in Changbai Mountains, Northeast, China. On the other hand, the differentiation in light requirements improves a theoretical basis that in artificial seedling raising and management, appropriate shading should be given to ensure that they are in an optimal light environment. Moreover, while marked differences in growth exist in two maple species, the response in shade conditions is similar, such as increasing antioxidant enzyme activity or osmoregulation substance content, or increasing SLA and reducing RSR, and these responses guarantee the establishment of two tree species in long-term shaded environments. Future studies need to focus on the expression level of photosynthesis-related genes and cell structure, to better understand the adaptation mechanism of higher plants to light variation. Such information expands our understanding of the light-regulating mechanism of endangered plant species and contributes to develop management practices to promote natural forest regeneration.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JL designed the research project and provided theoretical guidance. JZ collected and analyzed the data. JZ, JG, and BD wrote the manuscript. All authors contributed to the article and approved the submitted version.

This research was funded by the National Science and Technology Basic Resources Survey Project (SQ2019FY101602).

Acknowledgments

We would like to thank Shixiong Wu for his technical assistance.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: maple, light intensity, photosynthetic, morphological, physiological

Citation: Zhang J, Ge J, Dayananda B and Li J (2022) Effect of light intensities on the photosynthesis, growth and physiological performances of two maple species. Front. Plant Sci. 13:999026. doi: 10.3389/fpls.2022.999026

Received: 20 July 2022; Accepted: 06 September 2022; Published: 12 October 2022.

Reviewed by:

Copyright © 2022 Zhang, Ge, Dayananda and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Junqing Li, [email protected]

† These authors contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Science is LIT

Explore How Light Affects Photosynthesis

Algae are aquatic, plant-like organisms that can be found in oceans, lakes, ponds, rivers, and even in snow. But don’t worry, if you’re not near a waterway, it can easily be ordered from Amazon or Carolina Biological. Algae range from single-celled phytoplankton (microalgae) to large seaweeds (macroalgae). Phytoplanktons can be found drifting in water and are usually single-celled. They can also grow in colonies (group of single-cells) that are large enough to see with the naked eye. The specific types of algae that can be used in this experiment are  Scenedesmus, Chlamydomonas, or  Chlorella , all of which are phytoplanktons or microalgae. 

Experimental variables

• Color filter paper
• Table/desk lamp
• Light bulbs (varying intensities and colors)

Laboratory Supplies

• Transfer pipettes
• Vials with caps
• Freshwater Algae ( Scenedesmus , Chlorella , or Chlamydomonas )
• Small beakers or cups

Laboratory Solutions

• 2% Calcium Chloride
• 2% Sodium alginate
• Cresol red/thymol blue pH indicator solution

Solution Preparations

2% calcium chloride (cacl 2 ).

• 20 g of CaCl 2
• Fill to 1000 mL with water

2% CaCl 2 is stable at room temperature indefinitely.

2% Sodium alginate (prepared in advance)

• 2 g sodium alginate
• Fill to 100 mL with water

It takes a while for the alginate to go into solution. We recommend to dissolve by stirring using a magnetic stir bar overnight at room temperature. Store at 4 °C for up to 6 months or use immediately.

Cresol red/Thymol blue pH indicator solution (10x)

• 0.1 g cresol red
• 0.2 g thymol blue
• 0.85 g sodium bicarbonate (NaHCO 3 )
• 20 mL ethanol
• Fill to 1L with fresh boiled water

Measure indicators and mix with ethanol. Measure sodium bicarbonate and mix with warm/hot water. Mix the solutions together and fill with remaining freshly boiled water up to 1L final solution. The 10x stock solution is stable for at least a year.

In preparation for doing the experiment, prepare 1x indicator solution by diluting the 10x indicator solution with distilled water (e.g. 20 ml 10x into 200 mL final solution).

Experimental Bench Set-Up

• ~10 mL of 2% CaCl 2 in a cup or beaker
• ~3-5 mL of sodium alginate in cup or beaker
• Cup with ~10 mL of water
• Empty cup or beaker that holds a minimum of 30 mL

Preparing Algae for Experiment

• Prepare a concentrated suspension of algae. Without centrifuge : leave ~50 mL of algae suspension to settle (preferably overnight), then carefully pour off the supernatant to leave ~3-5 mL of concentrated algae. With centrifuge : Centrifuge ~50 mL of algae suspension at low speed for 10 minutes and then carefully pour off the supernatant, leaving behind ~3-5 mL of concentrated algae.
• In a small beaker, add equal volumes of sodium alginate and then add in the concentrated algae. Gently mix algae and sodium alginate together using a transfer pipette until its evenly distributed.
• Using the transfer pipette, carefully add single drops of the algae/sodium alginate mixture into the CaCl 2 to make little “algae balls”
• Once all of the “algae balls” are in the CaCl 2 solution, allow them to harden for 5 minutes
• Place the strainer over the empty cup or beaker, and pour over the entire solution of “algae balls” and CaCl 2 into the strainer allowing the CaCl 2 to pass through, leaving just the algae in the strainer
• Keeping the strainer over the container, pour the water over the “algae balls” to rinse the remain CaCl 2
• Transfer your newly made “algae balls” to a new cup or beaker

Setting up Photosynthesis Experiment

• Distance from light (using ruler) – group can set up vials different distances from one light source
• Different color lights (using color filter paper or different color light bulbs) – group can set up by covering the vials with different colored films and arrange them the same distance away from the light source or set up 1 vial in front of a different colored lamp same distance away.
• With or without light – group places 1 vial in front of an illuminated lamp and another has the vial or lamp covered with black paper the same distance away

• When starting your experiment, be sure to take note of the time that you placed your vial in front of the light source. Vials should be left for ~1-2 hours.

What would happen if the algae photosynthesizes (increase O2) in a solution that started at pH8.2?

Analyzing photosynthesis results

• After 1-2 hours, return to the experiment. Without disturbing the vials, analyze and take pictures of results. Have students write down the time that their experiment ended.
• Using the color chart above, determine which pH matches your sample the closest.
• Have students determine if they got what they expected and discuss amongst their group members.

Explain how the rate of photosynthesis is affected by their different variables.

What were your conclusions from this experiment? If you were to repeat the experiment, what would you change and why? What’s the relationship with O2 and CO2 during the process of photosynthesis? Is there a “best” source of light that allowed the algae to photosynthesize better?

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Photosynthetic Physiology of Blue, Green, and Red Light: Light Intensity Effects and Underlying Mechanisms

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Red and blue light are traditionally believed to have a higher quantum yield of CO 2 assimilation ( QY , moles of CO 2 assimilated per mole of photons) than green light, because green light is absorbed less efficiently. However, because of its lower absorptance, green light can penetrate deeper and excite chlorophyll deeper in leaves. We hypothesized that, at high photosynthetic photon flux density ( PPFD ), green light may achieve higher QY and net CO 2 assimilation rate ( A n ) than red or blue light, because of its more uniform absorption throughtout leaves. To test the interactive effects of PPFD and light spectrum on photosynthesis, we measured leaf A n of “Green Tower” lettuce ( Lactuca sativa ) under red, blue, and green light, and combinations of those at PPFD s from 30 to 1,300 μmol⋅m –2 ⋅s –1 . The electron transport rates ( J ) and the maximum Rubisco carboxylation rate ( V c,max ) at low (200 μmol⋅m –2 ⋅s –1 ) and high PPFD (1,000 μmol⋅m –2 ⋅s –1 ) were estimated from photosynthetic CO 2 response curves. Both QY m,inc (maximum QY on incident PPFD basis) and J at low PPFD were higher under red light than under blue and green light. Factoring in light absorption, QY m,abs (the maximum QY on absorbed PPFD basis) under green and red light were both higher than under blue light, indicating that the low QY m,inc under green light was due to lower absorptance, while absorbed blue photons were used inherently least efficiently. At high PPFD , the QY inc [gross CO 2 assimilation ( A g )/incident PPFD ] and J under red and green light were similar, and higher than under blue light, confirming our hypothesis. V c,max may not limit photosynthesis at a PPFD of 200 μmol m –2 s –1 and was largely unaffected by light spectrum at 1,000 μmol⋅m –2 ⋅s –1 . A g and J under different spectra were positively correlated, suggesting that the interactive effect between light spectrum and PPFD on photosynthesis was due to effects on J . No interaction between the three colors of light was detected. In summary, at low PPFD , green light had the lowest photosynthetic efficiency because of its low absorptance. Contrary, at high PPFD , QY inc under green light was among the highest, likely resulting from more uniform distribution of green light in leaves.

Introduction

The photosynthetic activity of light is wavelength dependent. Based on McCree’s work ( McCree, 1971 , 1972 ), photosynthetically active radiation is typically defined as light with a wavelength range from 400 to 700 nm. Light with a wavelength shorter than 400 nm or longer than 700 nm was considered as unimportant for photosynthesis, due to its low quantum yield of CO 2 assimilation, when applied as a single waveband ( Figure 1 ). Within the 400–700 nm range, McCree (1971) showed that light in the red region (600–700 nm) resulted in the highest quantum yield of CO 2 assimilation of plants. Light in the green region (500–600 nm) generally resulted in a slightly higher quantum yield than light in the blue region (400–500 nm) ( Figure 1 ; McCree, 1971 ). The low absorptance of green light is partly responsible for its low quantum yield of CO 2 assimilation. Within the visible spectrum, green leaves have the highest absorptance in the blue region, followed by red. Green light is least absorbed by green leaves, which gives leaves their green appearance ( McCree, 1971 ; Zhen et al., 2019 ).

The normalized action spectrum of the maximum quantum yield of CO 2 assimilation for narrow wavebands of light from ultra-violet to far-red wavelengths ( McCree, 1971 ). Redrawn using data from Sager et al. (1988).

Since red and blue light are absorbed more strongly by photosynthetic pigments than green light, they are predominantly absorbed by the top few cell layers, while green light can penetrate deeper into leaf tissues ( Nishio, 2000 ; Vogelmann and Evans, 2002 ; Terashima et al., 2009 ; Brodersen and Vogelmann, 2010 ), thus giving it the potential to excite photosystems in deeper cell layers. Leaf photosynthesis may benefit from the more uniform light distribution throughout a leaf under green light. Absorption of photons by chloroplasts near the adaxial surface may induce heat dissipation of excess excitation energy in those chloroplasts, while chloroplasts deeper into the leaf receive little excitation energy ( Sun et al., 1998 ; Nishio, 2000 ). Blue and red photons, therefore, may be used less efficiently and are more likely to be dissipated as heat than green photons.

The misconception that red and blue light are used more efficiently by plants than green light still occasionally appears ( Singh et al., 2015 ), often citing McCree’s action spectrum or the poor absorption of green light by chlorophyll extracts. The limitations of McCree’s action spectrum were explained in his original paper: the quantum yield was measured under low photosynthetic photon flux density ( PPFD ), using narrow waveband light, and expressed on an incident light basis ( McCree, 1971 ), but these limitations are sometimes ignored. The importance of green light for photosynthesis has been well established in more recent studies ( Sun et al., 1998 ; Nishio, 2000 ; Terashima et al., 2009 ; Hogewoning et al., 2012 ; Smith et al., 2017 ).

From those studies, one trend has emerged that has not received much attention: there is an interactive effect of light quality and intensity on photosynthesis ( Sun et al., 1998 ; Evans and Vogelmann, 2003 ; Terashima et al., 2009 ). At low PPFD , green light has the lowest QY inc (quantum yield of CO 2 assimilation on incident light basis) because of its low absorptance; at high PPFD , on the other hand, red and blue light have a lower QY inc than green light, because of their high absorptance by photosynthetic pigments, which shifts much of the light absorption closer to the upper leaf surface. This reduces both the quantum yield of CO 2 assimilation in cells in the upper part of a leaf and light availability in the bottom part of a leaf.

The interactive effect between light quality and intensity was illustrated in an elegant study that quantified the differential quantum yield, or the increase in leaf CO 2 assimilation per unit of additional light ( Terashima et al., 2009 ). The differential quantum yield was measured by adding red or green light to a background illumination of white light of different intensities. At low background white light levels, the differential quantum yield of red light was higher than that of green light, due to the low absorptance of green light. But as the background light level increased, the differential quantum yield of green light decreased more slowly than that of red light, and was eventually higher than that of red light ( Terashima et al., 2009 ). The red light was absorbed efficiently by the chloroplasts in the upper part of leaves. With a high background level of white light, those chloroplasts already received a large amount of excitation energy from white light and up-regulated non-photochemical quenching (NPQ) to dissipate excess excitation energy as heat, causing the additional red light to be used inefficiently. Green light, on the other hand, was able to reach the chloroplasts deeper in the mesophyll and excited those chloroplasts that received relatively little excitation energy from white light. Therefore, with high background white light intensity, additional green light increased leaf photosynthesis more efficiently than red light ( Terashima et al., 2009 ).

In this paper, we present a comprehensive study to explore potential interactive effect of light intensity and light quality on C 3 photosynthesis and underlying processes. We quantified the photosynthetic response of plants to blue, green, and red light over a wide PPFD range to better describe how light intensity and waveband interact. In addition, we examined potential interactions among blue, green, and red light, using light with different ratios and intensities of the three narrow waveband lights. To get a better understanding of the biochemical reasons for the effects of light spectrum and intensity on CO 2 assimilation, we constructed assimilation – internal leaf CO 2 ( C i ) response curves ( A/C i curves) under blue, green, and red light, as well as combinations of the three narrow waveband lights at both high and low PPFD . We hypothesized that effects of different light spectra would be reflected in the electron transport rate ( J ) required to regenerate consumed ribulose 1,5-bisphosphate (RuBP), rather than the maximum carboxylation rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) ( V c,max ).

Materials and Methods

Plant material.

Lettuce “Green Towers” plants were grown from seed in 1.7 L round pots filled with soilless substrate (Fafard 4P Mix, Sun Gro Horticulture, Agawam, MA, United States). The plants were grown in a growth chamber (E15, Conviron, Winnipeg, Manitoba, Canada) at 23.2 ± 0.8°C (mean ± SD), under white fluorescent light with a 14-hr photoperiod, vapor pressure deficit (VPD) of 1.20 ± 0.43 kPa and a PPFD of 200–230 μmol⋅m –2 ⋅s –1 at the floor level, and ambient CO 2 concentration. Plants were sub-irrigated when necessary with a nutrient solution containing 100 mg⋅L –1 N, made with a complete, water-soluble fertilizer (Peter’s Excel 15-5-15 Cal-Mag fertilizer, Everris, Marysville, OH, United States).

Leaf Absorptance, Transmittance, and Reflectance

Leaf absorptance was determined using a method similar to that of Zhen et al. (2019) . Three plants were randomly selected. A newly expanded leaf from each plant was illuminated with a broad-spectrum halogen bulb (70W; Sylvania, Wilmington, MA, United States) for leaf transmittance measurement. Transmittance was measured with a spectroradiometer (SS-110, Apogee, Logan, UT, United States). The halogen light spectrum was taken as reference measurement with the spectroradiometer placed directly under the halogen bulb in a dark room. Then, a lettuce leaf was placed between the halogen bulb and spectroradiometer, with its adaxial side facing the halogen bulb and transmitted light was measured. Leaf transmittance was then calculated on 1 nm resolution. Light reflectance of the leaves was measured using a spectrometer with a leaf clip (UniSpec, PP systems, Amesbury, MA, United States). Light absorptance was calculated as 1− r e f l e c t a n c e − t r a n s m i t t a n c e . We verified that this method results in similar absorptance spectra as the use of an integrating sphere. Absorptance of each of the nine light spectra used in this study were calculated from the overall leaf absorptance spectrum and the spectra of the red, green, and blue LEDs.

Leaf Photosynthesis Measurements

All gas exchange measurements were made with a leaf gas exchange system (CIRAS-3, PP Systems). Light was provided by the LEDs built into the chlorophyll fluorescence module (CFM-3, PP Systems). This module has dimmable LED arrays of different colors, with peaks at 653 nm [red, full width at half maximum (FWHM) of 17 nm], 523 nm (green, FWHM of 36 nm), and 446 nm (blue, FWHM of 16 nm). Nine different combinations of red, green, and blue light were used in this study ( Table 1 ). Throughout the measurements, the environmental conditions inside the cuvette were controlled by the leaf gas exchange system. Leaf temperature was 23.0 ± 0.1°C, CO 2 concentration was 400.5 ± 4.1 μmol⋅mol –1 , and the VPD of air in the leaf cuvette was 1.8 ± 0.3 kPa (mean ± SD).

List of light spectrum abbreviations and their spectral composition.

Photosynthesis – Light Response Curves

To explore photosynthetic efficiency of light with different spectra, we constructed light response curves for lettuce plants using each light spectrum. Lettuce plants were exposed to 10 PPFD levels ranging from 30 to 1,300 μmol⋅m –2 ⋅s –1 (30, 60, 90, 120, 200, 350, 500, 700, 1,000, and 1,300 μmol⋅m –2 ⋅s –1 ) in ascending orders for light response curves. Photosynthetic measurements were taken on 40–66 days old lettuce plants. Lettuce plants were taken out of the growth chamber and dark-adapted for 30 min. Starting from the lowest PPFD , one newly expanded leaf was exposed to all nine spectra. Net CO 2 assimilation rate ( A n ) of the leaf was measured using the leaf gas exchange system. Under each light spectrum, three A n readings were recorded at 10 s intervals after readings were stable (about 4–20 min depending on PPFD after changing PPFD and spectrum). The three A n readings were averaged for analysis. After A n measurements under all nine light spectra were taken, the leaf was exposed to the next PPFD level and A n measurements were taken with the light spectra in the same order, until measurements were completed at all PPFD levels. Throughout the light response curves, C i decreased with increasing PPFD , from 396 ± 10 μmol⋅mol –1 at a PPFD of 30 μmol⋅m –2 ⋅s –1 to 242 ± 44 μmol⋅mol –1 at a PPFD of 1,300 μmol⋅m –2 ⋅s –1 . To account for the potential effect of plants and the order of the spectra on assimilation rates, the order of the different spectra was re-randomized for each light response curve, using a Latin square design with plant and spectrum as the blocking factors. Data were collected on nine different plants.

Regression curves (exponential rise to maximum) were fitted to the data for each light spectrum and replication (plant):

where R d is the dark respiration rate, QY m,inc is the maximum quantum yield of CO 2 assimilation (initial slope of light response curve, mol of CO 2 fixed per mol of incident photons) and A g,max is the light-saturated gross assimilation rate. The A n,max is the light-saturated net assimilation rate and was calculated as A n , m a x = A g , m a x - R d . The maximum quantum yield of CO 2 assimilation was also calculated on absorbed light basis as Q ⁢ Y m , a ⁢ b ⁢ s = Q ⁢ Y m , i ⁢ n ⁢ c l ⁢ i ⁢ g ⁢ h ⁢ t ⁢ a ⁢ b ⁢ s ⁢ o ⁢ r ⁢ p ⁢ t ⁢ a ⁢ n ⁢ c ⁢ e .

The instantaneous quantum yield of CO 2 assimilation based on incident PPFD ( QY inc ) was calculated as A g P ⁢ P ⁢ F ⁢ D for each PPFD at which A n was measured, where the gross CO 2 assimilation rate ( A g ) was calculated as A g = A n + R d . To account for differences in absorptance among the different light spectra, the quantum yield of CO 2 assimilation was also calculated based on absorbed light base, as Q ⁢ Y a ⁢ b ⁢ s = A g P ⁢ P ⁢ F ⁢ D × l ⁢ i ⁢ g ⁢ h ⁢ t ⁢ a ⁢ b ⁢ s ⁢ o ⁢ r ⁢ p ⁢ t ⁢ a ⁢ n ⁢ c ⁢ e , where light absorptance is the absorptance of lettuce leaves for each specific light spectrum. The differential QY , the increase in assimilation rate per unit of additional incident PPFD , was calculated as the derivative of Eq. 1:

Photosynthesis – Internal CO 2 Response ( A/C i ) Curves

To explore the underlying physiological mechanisms of assimilation responses to different light spectra, we constructed A/C i curves. Typically, A/C i curves are collected under saturating PPFD . We collected A/C i curves at two PPFD s (200 and 1,000 μmol⋅m –2 ⋅s –1 ) to explore interactive effects of light spectrum and PPFD on the assimilation rate. At a PPFD of 200 μmol⋅m –2 ⋅s –1 , red light has the highest A n and green light the lowest A n , while at PPFD of 1,000 μmol⋅m –2 ⋅s –1 , red and green light resulted in the highest A n and blue light in the lowest A n .

We used the rapid A/C i response (RACiR) technique that greatly accelerates the process of constructing A/C i curves ( Stinziano et al., 2017 ). We used a Latin square design, similar to the light response curves. A/C i curves were measured under the same nine spectra used for the light response curves. Nine lettuce plants were used as replicates. For each A/C i curve, CO 2 concentration in the leaf cuvette started from 0 μmol⋅mol –1 , steadily ramping to 1,200 μmol⋅mol –1 over 6 min. A reference measurement was also taken at the beginning of each replication with an empty cuvette to correct for the reaction time of the leaf gas exchange system. Post-ramp data processing was used to calculate the real A and C i with the spreadsheet provided by PP systems, which yielded the actual A/C i curves with C i range of about 100–950 μmol mol –1 . Throughout the data collection, leaf temperature was 24.4 ± 1.3°C and VPD in the cuvette was 1.4 ± 0.2 kPa.

Curve fitting for A/C i curves was done by minimizing the residual sum of squares, following the protocol developed by Sharkey et al. (2007) . Among our nine replicates, four plants did not show clear Rubisco limitations at low PPFD and for those plants Rubisco limitation ( V c,max ) was not included in the model ( Sharkey et al., 2007 ). We therefore report V c,max values for high PPFD only. The J was determined for all light spectra at both PPFD s. We therefore report V c,max was determined for all light spectra only at high PPFD . The quantum yield of electron transport [ QY(J) ] was calculated on both incident and absorbed PPFD basis as Q ⁢ Y ⁢ ( J ) i ⁢ n ⁢ c = J P ⁢ P ⁢ F ⁢ D and Q ⁢ Y ⁢ ( J ) a ⁢ b ⁢ s = Q ⁢ Y ⁢ ( J ) i ⁢ n ⁢ c l ⁢ i ⁢ g ⁢ h ⁢ t ⁢ a ⁢ b ⁢ s ⁢ o ⁢ r ⁢ p ⁢ t ⁢ a ⁢ n ⁢ c ⁢ e , respectively. We did not estimate triose phosphate utilization, because the A/C i curves often did not show a clear plateau.

Data Analysis

The QY m,inc , QY m,abs , and A g,max were analyzed with ANOVA to determine the effects of light spectrum using SAS (SAS University Edition; SAS Institute, Cary, NC, United States). A n , QY inc , and QY abs at each PPFD level and V c,max and J estimated from A/C i curves were similarly analyzed with ANOVA using SAS. A n at different PPFD levels were analyzed with regression analysis to detect interactive effect of blue, green, and red light on leaf assimilation rates using the fractions of red, blue, and green light as explanatory variables (JMP Pro 15, SAS Institute).

Leaf Absorptance

A representative spectrum of light absorptance, reflectance and transmittance of a newly fully expanded lettuce leaf is shown in Figure 2 . In the blue region, 400–500 nm, the absorptance by “Green Towers” lettuce leaves was high and fairly constant, averaging 91.6%. The leaf absorptance decreased as the wavelength increased from 500 to 551 nm where the absorptance minimum was 69.8%. Absorptance increased again at longer wavelengths, with a second peak at 666 nm (92.6%). Above 675 nm, the absorptance decreased steadily to <5% at 747 nm ( Figure 2 ). The absorptance spectrum of our lettuce leaves is similar to what McCree (1971) obtained for growth chamber-grown lettuce, with the exception of slightly higher absorptance in the green part of the spectrum in our lettuce plants. Using this spectrum, the absorptance of the blue, green, and red LED lights were calculated to be 93.2 ± 1.0%, 81.1 ± 1.9% and 91.6 ± 1.1%, respectively. Absorptance of all nine spectra was calculated based on their ratios of red, green, and blue light ( Table 2 ).

Light absorptance, reflectance, and transmittance spectrum of a newly fully expanded “Green Towers” lettuce leaf.

Light absorptance and transmittance of new fully expanded “Green towers” lettuce leaves under nine light spectra.

Light Quality and Intensity Effects on Photosynthetic Parameters

Light response curves of lettuce under all nine spectra are shown in Figure 3 , with regression coefficients in Supplementary Table 1 . It is worth noting that a few plants showed photoinhibition under 100B (decrease in A n with PPFD > 1,000 μmol⋅m –2 ⋅s –1 ). Those data were excluded in curve fitting for light response curves to better estimate asymptotes. Photoinhibition was not observed under other spectra.

Net assimilation ( A n ) – light response curves of “Green Towers” lettuce under nine light spectra. Error bars represent the standard deviation ( n = 9). Inserts show A n against PPFD of 30-90 μmol⋅m –2 ⋅s –1 s to better show the initial slopes of curves. The composition of the nine light spectra is shown in Table 1 . The light spectra in the graphs are (A) 100B, 100G, and 100R; (B) 100B, 80B20G, 20B80G, and 100G; (C) 100G, 80G20R, 20G80R, and 100R; and (D) 20B80R, 16B20G64R, and 100G.

The QY m,inc of lettuce plants was 22 and 27% higher under red light (74.3 mmol⋅mol –1 ) than under either 100G (60.8 mmol⋅mol –1 ) or 100B (58.4 mmol⋅mol –1 ), respectively ( Figure 4A and Supplementary Table 1 ). Spectra with a high fraction of red light (64% or more) resulted in a high QY m,inc ( Figure 4A ), while 80G20R resulted in an intermediate QY m,inc ( Figure 4A ). To determine whether differences in QY m,inc were due to differences in absorptance or in the ability of plants to use the absorbed photons for CO 2 assimilation, we also calculated QY m,abs . On an absorbed light basis, 100B light still resulted in the lowest QY m,abs (62.7 mmol⋅mol –1 ) and red light resulted in the highest QY m,abs (81.1 mmol⋅mol –1 ) among narrow waveband lights ( Figure 4B ). Green light resulted in a QY m,abs (74.9 mmol⋅mol –1 ) similar to that under red light, but significantly higher than that of blue light ( Figure 4B ). We did not find any interactions (synergism or antagonism) between lights of different colors, with all physiological responses under mixed spectra being similar to the weighted average of responses under single colors. Thus, for the rest of the results we focus on the three narrow waveband spectra.

Maximum quantum yield of CO 2 assimilation of “Green Towers” lettuce based on incident ( QY m,inc ) (A) and absorbed light ( QY m,abs ) (B) under nine different light spectra. Values are calculated as the initial slope of the light response curves of corresponding light spectra (see Figure 3 ). Bars with the same letter are not statistically different ( p ≤ 0.05). Error bars represent the standard deviation ( n = 9). The composition of the nine light spectra is shown in Table 1 .

Among the three narrow waveband lights, 100G resulted in the highest A g,max (20.0 μmol⋅m –2 ⋅s –1 ), followed by red (18.9 μmol⋅m –2 ⋅s –1 ), and blue light (17.0 μmol⋅m –2 ⋅s –1 ) ( Figure 5 and Supplementary Table 1 ). As with QY m,inc and QY m,abs , combining two or three colors of light resulted in an A g,max similar to the weighted averages of individual light colors.

Maximum gross assimilation rate ( A g,max ) of “Green Towers” lettuce under different light spectra, calculated from the light response curves. Bars with the same letter are not statistically different ( p ≤ 0.05). Error bars represent standard deviation ( n = 9). The composition of the nine light spectra is shown in Table 1 .

QY inc initially increased with increasing PPFD and peaked at 90–200 μmol⋅m –2 ⋅s –1 , then decreased at higher PPFDs ( Figure 6A ). The QY inc under 100R was higher than under either green or blue light at low PPFD (≤300 μmol⋅m –2 ⋅s –1 ). Although 100G resulted in lower QY inc than 100B at low PPFD (≤300 μmol⋅m –2 ⋅s –1 ), the decrease in QY inc under 100G with increasing PPFD was slower than that with 100B or 100R. Above 500 μmol m –2 s –1 , the QY inc with 100G was comparable to the QY inc with 100R, and higher than with 100B ( Figure 6A ). The QY abs with 100R was higher than that with either 100G or 100B at PPFDs from 60 to 120 μmol⋅m –2 ⋅s –1 ( p < 0.05). The QY abs with 100G was similar to 100B at low PPFD , but decreased slower than that with either 100R or 100B as PPFD increased. At PPFD ≥ 500 μmol⋅m –2 ⋅s –1 , QY abs was lowest under 100B among the three monochromatic lights ( p < 0.05) ( Figure 6B ).

The quantum yield of CO 2 assimilation of “Green Towers” lettuce as a function of incident ( QY inc ) (A) and absorbed PPFD ( QY abs ) (B) under blue, green, and red LED light. Error bars represent the standard deviation ( n = 9).

The differential QY , which quantifies the increase in CO 2 assimilation per unit of additional PPFD , decreased with increasing PPFD . The differential QY with 100R was higher than those with 100B and 100G at low PPFD . At a PPFD of 30 μmol⋅m –2 ⋅s –1 , the differential QY was 70.5 mmol⋅mol –1 for 100R, 59.4 mmol⋅mol –1 for 100G, and 55.8 mmol⋅mol –1 for 100B ( Figure 7 ). However, the differential QY with 100R decreased rapidly with increasing PPFD and was lower than the differential QY with 100G at high PPFD ( Figure 7 ). At high PPFD , the differential QY with 100G was highest among three monochromatic light ( Figure 7 ). For instance, at a PPFD of 1,300 μmol⋅m –2 ⋅s –1 , the differential QY with 100G was 1.09 mmol⋅mol –1 , while those with 100B and 100R were 0.64 mmol⋅mol –1 and 0.46 mmol⋅mol –1 , respectively ( Figure 7 ).

The differential quantum yield of CO 2 assimilation ( differential QY ) of “Green Towers” lettuce under blue, green, and red LED light as a function of the PPFD . The differential QY is the increase in net assimilation per unit additional PPFD and was calculated as the first derivate of the light response curves ( Figure 3 ). The insert shows the differential quantum yield plotted at PPFDs of 1,000–1,300 μmol m –2 s –1 s to better show differences at high PPFD (note the different y -axis scale).

Effect of Light Spectrum and Intensity on J and V c,max

J of lettuce leaves at low PPFD was lowest under 100G (47.4 μmol⋅m –2 ⋅s –1 ), followed by 100B (56.1 μmol⋅m –2 ⋅s –1 ), and highest under 100R (64.1 μmol⋅m –2 ⋅s –1 ) ( Figure 8A ). At high PPFD , on the other hand, J of leaves exposed to 100G (115.3 μmol⋅m –2 ⋅s –1 ) and 100R (112.1 μmol⋅m –2 ⋅s –1 ) were among the highest, while J of leaves under 100B was the lowest (97.0 μmol⋅m –2 ⋅s –1 ) ( Figure 8A ). At high PPFD , V c,max of leaves under blue light (59.3 μmol⋅m –2 ⋅s –1 ) was lower than V c,max of leaves under 16B20G64R light (72.1 μmol⋅m –2 ⋅s –1 ), but none of the other treatments differed significantly ( Figure 8 ). When PPFD increased from 200 to 1,000 μmol⋅m –2 ⋅s –1 , J under green light increased by 143%, while J under blue and red light increased by 73% and 75%, respectively ( Figure 8A ). J and V c,max at high PPFD were strongly correlated ( R 2 = 0.82) ( Supplementary Figure 3 ).

Electron transport rate ( J ) at PPFD s of 200 (left bars) and 1,000 μmol m –2 s –1 (right bars) (A) and maximum Rubisco carboxylation rate ( V c,max ) at a PPFD of 1,000 μmol m –2 s –1 (B) of “Green Towers” lettuce, as estimated from A/C i curves under different light spectra. Bars with the same letter are not statistically different ( p ≤ 0.05). Error bars represent the standard deviation ( n = 9). The light composition of the nine light spectra is shown in Table 1 .

Interactive Effect of Light Spectrum and PPFD on Photosynthesis

There was an interactive effect of light spectrum and PPFD on photosynthetic properties of lettuce. Under low light conditions (≤200 μmol⋅m –2 ⋅s –1 ), the QY inc of lettuce leaves under green light was lowest among blue, green, and red light ( Figure 6A ), due to its lower absorptance by lettuce leaves. After accounting for absorptance, green photons were used at similar efficiency as blue photons, while red photons were used most efficiently ( Figure 6B ). The QY m,abs under green and red light were higher than under blue light ( Figure 4B ). At high PPFD , green and red light had similar quantum yield, higher than that of blue light, both on an absorbed and incident light basis ( Figure 6A ). Multiple factors contributed to the interactive effect of light spectrum and PPFD on quantum yield and photosynthesis.

Light Absorptance and Non-Photosynthetic Pigments Determine Assimilation at Low PPFD

QY m,inc with blue and green light was lower than with red light ( Figure 4A ), consistent with McCree’s action spectrum ( McCree, 1971 ). But when taking leaf absorptance into account, QY m,abs was similar under green and red light and lower under blue light ( Figure 4B ). Similarly, at low PPFD (≤200 μmol⋅m –2 ⋅s –1 ), QY inc of lettuce leaves was highest under red, intermediate under blue, and lowest under green light. When accounting for leaf absorptance, QY abs under red light remained highest and QY abs under both green and blue light were similar at low PPFD ( Figure 6A ). Consistent with our data, previous studies also documented that, once absorbed, green light can drive photosynthesis efficiently at low PPFD ( Balegh and Biddulph, 1970 ; McCree, 1971 ; Evans, 1987 ; Sun et al., 1998 ; Nishio, 2000 ; Terashima et al., 2009 ; Hogewoning et al., 2012 ; Vogelmann and Gorton, 2014 ). For example, the QY m,abs of spinach ( Spinacia oleracea ) and cabbage ( Brassica oleracea L. ) was highest under red light, followed by that under green light and lowest with blue light. But on incident light basis, QY m,inc of under green light was lower than under red or blue light ( Sun et al., 1998 ).

Both our data ( Figure 4B ) and those of Sun et al. (1998) show that QY m,abs with blue light is lower than that with red and green light, indicating that blue light is used intrinsically less efficiently by lettuce. Blue light, and, to a lesser extent, green light is absorbed not just by chlorophyll, but also by flavonoids and carotenoids ( Sun et al., 1998 ). Those pigments can divert energy away from photochemistry and thus reduce the QY abs under blue light. Flavonoids (e.g., anthocyanins) are primarily located in the vacuole and cannot transfer absorbed light energy to photosynthetic pigments ( Sun et al., 1998 ). Likewise, free carotenoids do not contribute to photochemistry ( Hogewoning et al., 2012 ). Carotenoids in light-harvesting antennae and reaction centers channel light energy to photochemistry, but with lower transfer efficiency than chlorophylls ( Croce et al., 2001 ; de Weerd et al., 2003a , b ; Wientjes et al., 2011 ; Hogewoning et al., 2012 ). Therefore, absorption of blue light by flavonoids and carotenoids reduces the quantum yield of CO 2 assimilation. Thus, even with the high absorptance of blue light by green leaves, QY m,abs of leaves under blue light was the lowest among the three monochromatic lights ( Figure 4B ). It is likely that the lower QY abs under green light than that under red light was also due to absorption of green light by carotenoids and flavonoids ( Hogewoning et al., 2012 ). At high PPFD , absorption of blue light by flavonoids and carotenoids still occurs, but this is less of a limiting factor for photosynthesis, since light availability is not limiting under high PPFD .

Light Dependence of Respiration and Rubisco Activity May Reduce the Quantum Yield at Low PPFD

At PPFD s below 200 μmol⋅m –2 ⋅s –1 , the QY inc and QY abs of lettuce showed an unexpected pattern in response to PPFD ( Figure 6 ). Unlike the quantum yield of PSII, which decreases exponentially with increasing PPFD ( Weaver and van Iersel, 2019 ), QY inc and QY abs increased initially with increasing PPFD ( Figure 6 ). A similar pattern was previously observed by Craver et al. (2020) in petunia ( Petunia × hybrida ) seedlings. This pattern could result from light-dependent regulation of respiration ( Croce et al., 2001 ), alternative electron sinks such as nitrate reduction ( Skillman, 2008 ; Nunes-Nesi et al., 2010 ), or Rubisco activity ( Campbell and Ogren, 1992 ; Zhang and Portis, 1999 ). In our calculations, we assumed that the leaf respiration in the light was the same as R d . However, leaf respiration in the light is lower than in the dark, in a PPFD -dependent manner ( Brooks and Farquhar, 1985 ; Atkin et al., 1997 ), which can lead to overestimation of A g with increasing PPFD . When we accounted for this down-regulation of respiration, using the model by Müller et al. (2005) to correct A g , QY inc , and QY abs , we found that depression of respiration by light did not explain the initial increase in QY inc and QY abs we observed ( Supplementary Figure 4 ). Alternative electron sinks in the chloroplasts that are upregulated in response to light can explain the low QY inc , and QY abs at low PPFD , because they compete with the Calvin cycle for reducing power (ferredoxin/NADPH). Such processes include photorespiration ( Krall and Edwards, 1992 ), nitrate assimilation ( Nunes-Nesi et al., 2010 ), sulfate assimilation ( Takahashi et al., 2011 ) and the Mehler reaction ( Badger et al., 2000 ) and their effect on QY inc , and QY abs would be especially notable under low PPFD ( Supplementary Figure 5 ).

Upregulation of Rubisco activity by Rubisco activase in the light may also have contributed to the increase in QY inc and QY abs at low PPFD ( Campbell and Ogren, 1992 ; Zhang and Portis, 1999 ). In the dark, 2-carboxy-D-arabinitol-1-phosphate (CA1P) or RuBP binds strongly to the active sites of Rubisco, preventing carboxylation activity. In the light, Rubisco activase releases the inhibitory CA1P or RuBP from the catalytic site of Rubisco, in a light-dependent manner ( Campbell and Ogren, 1992 ; Zhang and Portis, 1999 ; Parry et al., 2008 ). At PPFD < 120 μmol⋅m –2 ⋅s –1 , low Rubisco activity may have limited photosynthesis.

Light Distribution Within Leaves Affects QY at High PPFD

Except for the initial increase at low PPFD , both QY inc and QY abs decreased with increasing PPFD . QY inc decreased slower under green than under red or blue light ( Figure 6A ). At a PPFD ≥ 500 μmol⋅m –2 ⋅s –1 , QY inc under green light was higher than that under blue light ( Figure 6A ). Accordingly, A n under blue light was lower than under green and red light at PPFD s above 500 μmol⋅m –2 ⋅s –1 ( Figure 3A ). The lower QY inc under blue light than under green and red light at high PPFD can be explained by disparities in the light distribution within leaves.

Blue and red light were strongly absorbed by lettuce leaves (93.2 and 91.6%, respectively), while green light was absorbed less (81.1%) ( Table 2 ). Similar low green absorptance was found in sunflower ( Helianthus annuus L.), snapdragon ( Antirrhínum majus L.) ( Brodersen and Vogelmann, 2010 ), and spinach ( Vogelmann and Han, 2000 ). In leaves of those species, absorption of red and blue light peaked in the upper 20% of leaves, and declined sharply further into the leaf. Absorption of red light decreased slower with increasing depth than that of blue light ( Vogelmann and Han, 2000 ; Brodersen and Vogelmann, 2010 ). Green light absorption peaked deeper into leaves, and was more evenly distributed throughout leaves, because of low absorption of green light by chlorophyll ( Vogelmann and Han, 2000 ; Brodersen and Vogelmann, 2010 ). The more even distribution of green light within leaves, as compared to red and blue light, can explain the interactive effects between PPFD and light spectrum on leaf photosynthesis. It was estimated that less than 10% of blue light traveled through the palisade mesophyll and reached the spongy mesophyll in spinach, while about 35% of green light and 25% of red light did so ( Vogelmann and Evans, 2002 ). It was also estimated that chlorophyll in the lowermost chloroplasts of spinach leaves absorbed about 10% of green and <2% of blue light, compared to chlorophyll in the uppermost chloroplasts ( Vogelmann and Evans, 2002 ; Terashima et al., 2009 ).

The more uniform green light distribution within leaves may be a key contributor to higher leaf level QY inc under high PPFD because less heat dissipation of excess light energy is needed ( Nishio, 2000 ; Terashima et al., 2009 ). Reaction centers near the adaxial leaf surface receive more excitation energy under blue, and to a lesser extent under red light, than under green light, because of the differences in absorptance. Consequently, under high intensity blue light, NPQ is up-regulated in the chloroplasts near the adaxial leaf surface to dissipate some of the excitation energy ( Sun et al., 1998 ; Nishio, 2000 ), lowering the QY inc under blue light. Since less green light is absorbed near the adaxial surface, less heat dissipation is required. When incident light increased from 150 to 600 μmol⋅m –2 ⋅s –1 , the fraction of whole leaf CO 2 assimilation that occurred in the top half of spinach leaves remained the same under green light (58%), but decreased from 87 to 73% under blue light. This indicates more upregulation of heat dissipation in the top of the leaves under blue, than under green light ( Evans and Vogelmann, 2003 ). On the other hand, the bottom half of the leaves can still utilize the available light with relatively high QY inc , since the amount of light reaching the bottom half is relatively low, even under high PPFD ( Nishio, 2000 ). By channeling more light to the under-utilized bottom part of leaves, leaves could achieve higher QY inc even under high intensity green light. In our study, high QY inc under green light and low QY inc under blue light at high PPFD ( Figure 6 ) can be thus explained by the large disparities in the light environment in chloroplasts from the adaxial to the abaxial side of leaves due to differences in leaf absorptance. Similarly, differential QY of lettuce leaves was highest under green light and lower under blue and red light at high PPFD (>300 μmol⋅m –2 ⋅s –1 ) ( Figure 7 ), also potentially because of the more uniform distribution of green light and the uneven distribution of blue and red light in leaves.

Along the same line, A n of lettuce leaves was the lowest under blue light at PPFD > 500 μmol⋅m –2 ⋅s –1 ( Figure 3 ). Also, A n of lettuce leaves approached light saturation at lower PPFD s under blue and red light, than under green light ( Figure 3A ). Under blue, green, and red light, lettuce leaves reached 95% of A n,max at PPFD s of 954, 1,110 and 856 μmol⋅m –2 ⋅s –1 , respectively. This can be seen more clearly in the differential QY at high PPFD ( Figure 7 ). At a PPFD of 1,300 μmol⋅m –2 ⋅s –1 , green light had a differential QY of 1.09 mmol⋅mol –1 , while that of red and blue light was only 0.46 and 0.69 mmol⋅mol –1 , respectively ( Figure 7 ). Green light also resulted in a higher A g,max (22.9 μmol⋅m –2 ⋅s –1 ) than red and blue light (21.8 and 19.3 μmol⋅m –2 ⋅s –1 , respectively) ( Figure 5 ). As discussed before, the high A g,max under green light resulted from the more uniform light distribution under green light, allowing deeper cell layers to photosynthesize more. Previous research similarly found that at high PPFD (>500 μmol⋅m –2 ⋅s –1 ), A n of both spinach and cabbage were lower under blue light than under white, red and green light ( Sun et al., 1998 ). Overall, under high PPFD , the differences in light distribution throughout a leaf are important to quantum yield and assimilation rate, since it affects NPQ up-regulation ( Sun et al., 1998 ; Nishio, 2000 ). However, light distribution within a leaf is less important at low than at high PPFD , because upregulation of NPQ increases with increasing PPFD ( Zhen and van Iersel, 2017 ).

Light Spectrum Affects J , but Not V c,max

We examined the effect of light quality and intensity on J and V c,max ( Figure 8 ). For the light-dependent reactions, the interactive effect between light spectra and PPFD found for CO 2 assimilation and quantum yield was also observed for J ( Figure 8A ). At low PPFD (200 μmol⋅m –2 ⋅s –1 ), green light resulted in the lowest J and red light in the highest J among single waveband spectra. But at a PPFD of 1,000 μmol⋅m –2 ⋅s –1 , red and green light resulted in the highest J and blue light in the lowest J ( Figure 8A ), similar to the differences in A g .

There was no clear evidence of Rubisco limitations to photosynthesis at a PPFD of 200 μmol⋅m –2 ⋅s –1 , so the rate of the light-dependent reactions likely limited photosynthesis. This is corroborated by the strong correlation between A g and J at a PPFD of 200 μmol⋅m –2 ⋅s –1 . Although Rubisco limitations to photosynthesis were observed at a PPFD of 1,000 μmol⋅m –2 ⋅s –1 , there were no meaningful differences in V c,max in response to light spectrum, in contrast to J ( Figure 8 ).

When PPFD increased 5×, from 200 to 1,000 μmol⋅m –2 ⋅s –1 , there was only a 1.7 to 2.4× increase in J , indicating a lower QY(J) inc at higher PPFD . This matches the lower QY inc and the asymptotic increase in A n in response to increasing PPFD ( Figure 3 ). The relative increase of J under green light (143%) was greater than that under both blue and red light (73 and 75%, respectively) as PPFD increased. This similarly can be attributed to a more uniform energy distribution of green light among reaction centers throughout a leaf and weaker upregulation of non-photochemical quenching with increasing green light intensity ( Sun et al., 1998 ; Nishio, 2000 ; Evans and Vogelmann, 2003 ), as discussed before.

There was a strong correlation between J and A g under the nine light spectra at both PPFD levels ( Figure 9A ). QY abs and QY(J) abs are similarly strongly correlated ( Figure 9B ). Unlike J , V c,max was largely unaffected by light spectra ( Figure 8B ) and was not correlated with A g (data not shown). There was, however, a strong correlation between J and V c,max at a PPFD of 1,000 μmol⋅m –2 ⋅s –1 ( R 2 = 0.82, Supplementary Figure 3 ), suggesting that J and V c,max are co-regulated. Similarly, Wullschleger (1993) noted a strong linear relationship between J and V c,max across 109 C 3 species. The ratio between J and V c,max in our study (1.5–2.0) similar to the ratio found by Wullschleger (1993) . These results suggest that the interactive effect of light spectra and PPFD resulted from effects on J , which is associated with light energy harvesting by reaction centers, rather than from V c,max .

The correlation between gross CO 2 assimilation rate ( A g ) estimated from light response curves and electron transport rate ( J ) estimated from A/C i curves (A) , and between the quantum yield of CO 2 assimilation ( QY abs ) and the quantum yield of electron transport on an absorbed light basis [ QY(J) abs ] (B) , under low PPFD (200 μmol m –2 s –1 ) and high PPFD (1,000 μmol m –2 s –1 ) under nine light spectra averaged over nine “Green Towers” lettuce plants. The color scheme representing the nine spectra is the same as Figure 8 .

No Interactive Effects Among Blue, Green, and Red Light

The Emerson enhancement effect describes a synergistic effect between lights of different wavebands (red and far-red) on photosynthesis ( Emerson, 1957 ). McCree (1971) attempted to account for interactions between light with different spectra when developing photosynthetic action spectra and applied low intensity monochromatic lights from 350 to 725 nm with white background light to plants. His results showed no interactive effect between those monochromatic lights and white light ( McCree, 1971 ). We tested different ratios of blue, green, and red light and different PPFD s, and similarly did not find any synergistic or antagonistic effect of different wavebands on any physiological parameters measured or calculated.

Importance of Interactions Between PPFD and Light Quality and Its Applications

The interactive effect between PPFD and light quality demonstrates a remarkable adaptation of plants to different light intensities. By not absorbing green light strongly, plants open up a “green window,” as Terashima et al. (2009) called it, to excite chloroplasts deeper into leaves, and thus facilitating CO 2 assimilation throughout the leaf. While red light resulted in relatively high QY inc , QY abs and A n at both high and low PPFD ( Figures 3 , ​ ,6), 6 ), it is still mainly absorbed in the upper part of leaves ( Sun et al., 1998 ; Brodersen and Vogelmann, 2010 ). Green light can penetrate deeper into leaves ( Brodersen and Vogelmann, 2010 ) and help plants drive efficient CO 2 assimilation at high PPFD ( Figures 3 , ​ , 5 5 ).

Many early photosynthesis studies investigated the absorptance and action spectrum of photosynthesis of green algae, e.g., Haxo and Blinks (1950) or chlorophyll or chloroplasts extracts, e.g., Chen (1952) . Extrapolating light absorptance of green algae and suspension of chlorophyll or chloroplast to whole leaves from can lead to an underestimation of absorptance of green light by whole leaves and the belief that green light has little photosynthetic activity ( Moss and Loomis, 1952 ; Smith et al., 2017 ). Photosynthetic action spectra developed on whole leaves of higher plants, however, have long shown that green light effectively contributes to CO 2 assimilation, although with lower QY inc than red light ( Hoover, 1937 ; McCree, 1971 ; Inada, 1976 ; Evans, 1987 ). The importance of green light for photosynthesis was clearly established in more recent studies, emphasizing its role in more uniformly exciting all chloroplasts, which especially important under high PPFD ( Sun et al., 1998 ; Nishio, 2000 ; Terashima et al., 2009 ; Hogewoning et al., 2012 ; Smith et al., 2017 ). The idea that red and blue light are more efficient at driving photosynthesis, unfortunately, still lingers, e.g., Singh et al. (2015) .

Light-emitting diodes (LEDs) have received wide attention in recent years for use in controlled environment agriculture, as they now have superior efficacy over traditional lighting technologies ( Pattison et al., 2018 ). LEDs can have a narrow spectrum and great controllability. This provides unprecedented opportunities to fine tune light spectra and PPFD to manipulate crop growth and development. Blue and red LEDs have higher efficacy than white and green LEDs ( Kusuma et al., 2020 ). By coincidence, McCree’s action spectrum ( Figure 1 ; McCree, 1971 ) also has peaks in the red and blue region, although the peak in the blue region is substantially lower than the one in the red region. Therefore, red and blue LEDs are sometimes considered optimal for driving photosynthesis. This claim holds true only under low PPFD . Green light plays an important role in photosynthesis, as it helps plants to adapt to different light intensities. The wavelength-dependent absorptance of chlorophylls channels green light deeper into leaves, resulting in more uniform light absorption throughout leaves and providing excitation energy to cells further from the adaxial surface. Under high PPFD , this can increase leaf photosynthesis. Plant evolved under sunlight for hundreds of millions of years, and it seems likely that the relatively low absorptance of green light contributes to the overall photosynthetic efficiency of plants ( Nishio, 2000 ).

There was an interactive effect of light spectrum and PPFD on leaf photosynthesis. Under low PPFD , QY inc was lowest under green and highest under red light. The low QY inc under green light at low PPFD was due to low absorptance. In contrast, at high PPFD , green and red light achieved similar QY inc , higher than that of blue light. The strong absorption of blue light by chlorophyll creates a large light gradient from the top to the bottom of leaves. The large amount of excitation energy near the adaxial side of a leaf results in upregulation of nonphotochemical quenching, while chloroplasts near the bottom of a leaf receive little excitation energy under blue light. The more uniform distribution of green light absorption within leaves reduces the need for nonphotochemical quenching near the top of the leaf, while providing more excitation energy to cells near the bottom of the leaf. We also found that the interactive effect of light spectrum and PPFD on photosynthesis was a result of the light-dependent reactions; gross assimilation and J were strongly correlated. We detected no synergistic or antagonistic interactions between blue, green, and red light.

Data Availability Statement

Author contributions.

JL and MI designed the experiment, discussed the data, and revised the manuscript. JL performed the experiment, analyzed data, and prepared the first draft. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

Funding. This study was funded by the USDA-NIFA-SCRI award number 2018-51181-28365, project Lighting Approaches to Maximize Profits.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.619987/full#supplementary-material

Supplementary Figure 1

(Related to Figure 6 ) Quantum yield of CO 2 assimilation of “Green Towers” lettuce as a function of incident ( QY inc ) (A,C,E,G) and absorbed PPFD ( QY abs ) (B,D,F,H) under nine light spectra (see Table 1 ). Error bars represent standard deviation ( n = 9).

Supplementary Figure 2

(Related to Figure 7 ) Differential quantum yield of CO 2 assimilation ( differential QY ) of “Green Towers” lettuce under nine light spectra as a function of the PPFD . Inserts show differential QY at PPFD s of 1,000–1,300 μmol⋅m –2 s –1 s to better show differences at high PPFD (note the different y -axis scale). The composition of the nine light spectra is shown in Table 1 . The light spectra in the graphs are (A) 100B, 100G and 100R; (B) 100B, 80B20G, 20B80G and 100G; (C) 100G, 80G20R, 20G80R and 100R; and (D) 20B80R, 16B20G64R and 100G.

Supplementary Figure 3

(Related to Figure 6 ) The correlation between electron transport ( J ) and maximum Rubisco carboxylation rate ( V c,max ) of “Green Towers” lettuce estimated from A/C i curves under PPFD (1000 μmol m –2 s –1 ) under nine light spectra ( p < 0.001).

Supplementary Figure 4

(Related to Figure 6 ) The comparison between QY inc before (A) and after (B) correcting for light-suppression of respiration under blue, green, and red LED light. Note that the initial increase in QY inc became more pronounced after correction of light suppressed respiration.

Supplementary Figure 5

The comparison between QY abs before (A) and after (B) correcting for alternative electron sinks under blue, green, and red LED light. Assuming a simplified electron sink that diverts energy of 15 μmol m –2 s –1 of absorbed photons (an arbitrary value used for illustrative purposes only) away from the Calvin cycle under all PPFD s, the corrected QY abs was calculated based on remaining photons available to support Calvin cycle processes (B) . Note that the pattern of QY inc after correcting of alternative electron sink (B) is similar to quantum yield of PSII measured by chlorophyll fluorescence by Weaver and van Iersel (2019) .

Supplementary Table 1

Dark respiration rate (R d ), maximum quantum yield of CO 2 assimilation (QY m,inc ) and maximum gross assimilation rate (A g,max ) of “Green towers” lettuce derived from the light response curves for nine different spectra using Eq. 1. The light response curves are shown in Figure 3 . *See light composition of nine lights presented here in Table 1 .

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