# Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths
(Amaranthus spp. L.)

Mildred Osei-Kwarteng, Emmanuel Ayipio, Dany Moualeu-Ngangue, Gerhard Buck-Sorlin, Hartmut Stützel

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► To cite this version:

Mildred Osei-Kwarteng, Emmanuel Ayipio, Dany Moualeu-Ngangue, Gerhard Buck-Sorlin, Hartmut Stützel. Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (Amaranthus spp. L.). PLoS ONE, 2022, 17 (6), pp.e0270674. 10.1371/journal.pone.0270674. hal-03713100

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RESEARCH ARTICLE

# Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (*Amaranthus* spp. L.)

Mildred Osei-Kwarteng<sup>1,2*</sup>, Emmanuel Ayipio<sup>3,4</sup>, Dany Moualeu-Ngangue<sup>1</sup>, Gerhard Buck-Sorlin<sup>5</sup>, Hartmut Stützel<sup>1</sup>

1 Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany,

2 Department of Horticulture, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Nyankpala, Tamale, Ghana, 3 CSIR-Savannah Agricultural Research Institute, Nyankpala, Ghana, 4 Auburn University, Department of Horticulture, Auburn, Alabama, United States of America, 5 IRHS, INRAE, Institut Agro, Université d'Angers, Beaucouzé, France

* osei-kwarteng@gem.uni-hannover.de, osei.kwarteng@uds.edu.gh

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## Abstract

Photosynthetic light response curve parameters help us understand the interspecific variation in photosynthetic traits, leaf acclimation status, carbon uptake, and plant productivity in specific environments. These parameters are also influenced by leaf traits which rely on species and growth environment. In accessions of four amaranth species (*Amaranthus hybridus*, *A. dubius*, *A. hypochondriacus*, and *A. cruentus*), we determined variations in the net photosynthetic light response curves and leaf traits, and analysed the relationships between maximum gross photosynthetic rate, leaf traits, and whole-plant productivity. Non-rectangular hyperbolae were used for the net photosynthesis light response curves. Maximum gross photosynthetic rate (*P*<sub>gmax</sub>) was the only variant parameter among the species, ranging from 22.29 to 34.21 µmol m<sup>-2</sup> s<sup>-1</sup>. Interspecific variation existed for all the leaf traits except leaf mass per area and leaf inclination angle. Stomatal conductance, nitrogen, chlorophyll, and carotenoid contents, as well as leaf area correlated with *P*<sub>gmax</sub>. Stomatal conductance and leaf nitrogen explained much of the variation in *P*<sub>gmax</sub> at the leaf level. At the plant level, the slope between absolute growth rate and leaf area showed a strong linear relationship with *P*<sub>gmax</sub>. Overall, *A. hybridus* and *A. cruentus* exhibited higher *P*<sub>gmax</sub> at the leaf level and light use efficiency at the whole-plant level than *A. dubius*, and *A. hypochondriacus*. Thus, *A. hybridus* and *A. cruentus* tended to be more efficient with respect to carbon assimilation. These findings highlight the correlation between leaf photosynthetic characteristics, other leaf traits, and whole plant productivity in amaranths. Future studies may explore more species and accessions of *Amaranthus* at different locations or light environments.

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### OPEN ACCESS

**Citation:** Osei-Kwarteng M, Ayipio E, Moualeu-Ngangue D, Buck-Sorlin G, Stützel H (2022) Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (*Amaranthus* spp. L.). PLoS ONE 17(6): e0270674. https://doi.org/10.1371/journal.pone.0270674

**Editor:** Umakanta Sarker, Bangabandhu Sheikh Mujibur Rahman Agricultural University, BANGLADESH

**Received:** December 15, 2021

**Accepted:** June 14, 2022

**Published:** June 30, 2022

**Copyright:** © 2022 Osei-Kwarteng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability Statement:** All relevant data are within the manuscript and its Supporting information files.

**Funding:** MOK received a scholarship for her PhD. studies from a collaboration between the Ministry of Education, Ghana, and the German Academic Exchange Service (DAAD), Germany. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.

**Competing interests:** The authors have declared that no competing interests exist.

**Introduction**

The response of photosynthesis to light is vital in predicting carbon fixation in the field because leaf photosynthesis rate is mainly influenced by the variations in light under field conditions [1, 2]. Photosynthetic light response curves describe the relationship between leaf net photosynthesis rate and the photosynthetic photon flux density (PPFD) incident on the leaf surface [1, 3, 4]. They reveal the current acclimation state of a leaf, which helps to understand carbon uptake and productivity of plants in specific environments [3, 5, 6]. Leaf photosynthesis response to light can be described by several models such as the rectangular and non-rectangular hyperbola or exponential functions and their modifications [1, 3–9]. The non-rectangular hyperbola model is among the most commonly used due to its broad applicability to C₃ and C₄ species [5, 7, 10–12]. The parameters of these models are often included in canopy photosynthesis and ecosystem models of plant productivity and gas exchange [1, 12, 13].

Some leaf traits are indicators of plant acclimation to the growth light environment [14, 15]. Interspecific and intraspecific photosynthetic variation among and across species can be explained by leaf traits such as nitrogen content, chlorophyll (Chl) content, leaf dry mass per unit area (LMA), leaf angle, and stomatal conductance (g_s) [1, 3, 12, 16–19].

Plants develop photosynthetic characteristics and other leaf traits depending on the local light environment [20–23]. Most of our knowledge on photosynthetic light response curves and leaf traits stems from plants grown under controlled conditions, which is insufficient to assess plant acclimation to natural growth light conditions [22, 24, 25]. Plants grown under natural light conditions experience rapid fluctuations in light due to solar movement, weather, and canopy characteristics [21, 25–28], and variations in light can occur at timescales ranging from seconds to weeks [24, 29, 30]. Consequently, acclimation to natural light conditions may result in different physiological, biochemical, and morphological properties of plants [22, 24]. Acclimation of plants to alterations in natural light environments depends on the plant species (phenotypic plasticity) and the environment to which it is adapted [26, 28, 30–35]. Acclimation may occur at the leaf and whole-plant levels [21, 22, 25, 30, 36]. Plants grown under natural light conditions may combine the characteristics of both low light and high light-grown plants, allowing them to use light efficiently [24, 37]. For instance, some classical responses of plants to high light conditions include: high chlorophyll (Chl) a to Chl b ratio (Chl a/b) [22–24, 28, 33, 38–43]; low total Chl content [38, 42–45]; high LMA [22, 24, 38]; low Chl b content; higher leaf photosynthesis rates [24, 33, 38, 43, 46, 47], erect leaf orientation [22], and the opposite is experienced under low light conditions [22, 46, 48]. Quantum yield of CO₂ uptake is at its upper limit under low light conditions and also often shows no significant difference between high and low light-grown plants [4, 23, 32]. The convexity factor tends to be higher in low light and vice versa, with intermediate values found under medium light conditions [11, 23].

Amaranthes (*Amaranthus* spp. L.) are NAD-dependent malic enzyme (NAD-ME) subpathway C₄ type, annuals, herbaceous, dicotyledonous, or rarely short-lived perennials with worldwide distribution. The genus consists of about 87 species originating from the tropics [49, 50]. Consequently, amaranths perform best in warm climates and thrive under high irradiance levels [50–54]. They grow well at day temperatures above 25°C and night temperatures not lower than 15°C. The genus includes vegetable (*A. tricolor* L., *A. blitum* L., *A. dubius* L., *A. cruentus* L., and *A. viridis* L.), grain (*A. hypochondriacus* L., *A. cruentus* L., and *A. caudatus* L.), weed (*A. palmeri*, *A. retroflexus*, and *A. hybridus*) and ornamental species (brightly coloured *A. tricolor*, *A. caudatus* and *A. hypochondriacus*) [53, 55–57]. The leaves of all the species can be consumed depending on regional preferences [53, 58]. Variations in photosynthetic capacity among 12 amaranth species were found to be positively correlated with stomatal conductance,
nitrogen and Chl contents, and LMA [16]. However, no comparative studies have been con-
ducted in amaranth species under natural growth light conditions on the photosynthesis light
response, leaf traits, and how interspecific variations in photosynthetic light response curve
parameters at the leaf level are related to other leaf traits and whole-plant productivity.

The objectives of this study were: 1) to determine the variations in the net photosynthetic light response (P<sub>N</sub>/I) curves and leaf traits, 2) to explain the variations in the variant parameter [i.e., maximum gross photosynthetic rate (P<sub>gmax</sub>)] by the leaf traits, and 3) to explore how the variation in P<sub>gmax</sub> correlates with growth rate and leaf area at the whole-plant level in four amaranth species. We hypothesised that there is variation in the leaf photosynthetic light response curves and leaf traits among the amaranth species. The differences in the leaf photo-synthetic light response curves correlate with leaf traits and whole plant productivity.

Parameters of the non-rectangular hyperbola such as the maximum gross photosynthetic rate (P<sub>gmax</sub>), apparent quantum yield at zero PPFD [α (I<sub>0</sub>)], convexity (θ) and dark respiration rate (R<sub>D</sub>) were estimated for each gas exchange measurement. Key leaf traits such as stomatal conductance (g<sub>s</sub>), nitrogen per unit leaf area (N<sub>a</sub>), leaf dry mass per unit area (LMA), Chlorophyll (Chl, Chl a, Chl b), carotenoid (Car) content, leaf area (LA) and leaf inclination angle were measured.

Under the natural growth light environments of this study, interspecific variation in P<sub>gmax</sub> and some key leaf traits were observed among the amaranth species. Interspecific variation in P<sub>gmax</sub> was mainly explained by g<sub>s</sub> and N<sub>a</sub>, while at the whole-plant level, P<sub>gmax</sub> was strongly influenced by the variations in light use efficiency (slope of the natural logarithm of absolute growth rate and leaf area per plant).

**Materials and methods**

**Plant materials**

Accessions of four cultivated Amaranthus species, namely, *A. hybridus* ('IP7'; weed), *A. dubius* ('Mombo 2'; vegetable), *A. hypochondriacus* ('TZ-SMN-102'; grain), and *A. cruentus* 'Ex-Zim/Madiira 1'; vegetable) were obtained from the Asian Vegetables Research and Development Centre (AVRDC), Arusha, Tanzania (S1 Fig). All the species were reported to have been collected (i.e., country of collection) from Africa, but the origin of *A. hybridus* is unknown; *A. dubius* and *A. hypochondriacus* are from Tanzania, and *A. cruentus* from Zimbabwe. The number of days to flowering (from sowing to 50% inflorescence when characterised in Tanzania) are 31, 25, 35, 73 for *A. hybridus*, *A. dubius*, *A. hypochondriacus*, and *A. cruentus*, respectively [59]. The four amaranth species were chosen because of their genetic diversity (variation), contrasting plant architecture (morphology), and since they are taxonomically well characterised and commercially important in East Africa [52, 53, 57, 60–63].

**Experimental site, cultivation, and experimental design**

The experiment was conducted at the Institute of Horticultural Production Systems, Leibniz
University of Hannover, Germany (52.2°N, 9.7°E). Seeds were sown on March 18, 2014, in
trays with Potgrond (peat) tray substrate (Klasmann-Deilmann, Geest, Germany) and raised
in a growth cabinet at 22°C/ 20°C, day and night temperature, respectively. The nutrient com-
position of the Potgrond substrate was: 210 mg L⁻¹ N, 240 mg L⁻¹ P₂O₅, 270 mg L⁻¹ K₂O 100
mg L⁻¹ Mg and 150 mg L⁻¹ S, with a pH of 6.0. Vigorous plants were transplanted into 10 litre
pots (diameter of ca. 26 cm (top) & 19 cm (bottom); height, 24 cm) of a 1:1 mixture of sand
and Potgrond peat-based substrate. Pots were arranged at a spacing of 60 cm and 40 cm,
between and within rows, respectively. Plants were grown under natural light conditions in
the glasshouse without supplementary light from lamps. However, the temperature was
regulated as 24/22°C, day/night air temperatures, respectively. Ventilators were opened when the air temperature was higher than 26°C. Plants were watered daily with 0.5–1% (50-100g/ 100L H₂O) Ferty® 2 MEGA [16+6+26 (+3.4)] to avoid nutrient and water stress. The experiment was conducted as a randomised complete block design and replicated four times. Photosynthesis measurements were conducted on two of these replications.

The weather data were recorded at 12-minutes intervals by the Institute's weather station, situated at 36 m from the glasshouse.

## Gas exchange and leaf trait measurements

Data on the leaf traits and photosynthesis gas exchange were collected on May 7, 12, and 20, 2014, corresponding to 50, 55, and 63 days after sowing (DAS), respectively (S1 Table). All gas exchange measurements were made on the uppermost youngest fully expanded leaves. These leaves were selected from different plants at each measurement date. A portable photosynthesis gas exchange system (LI-6400, LI-COR, Inc., Lincoln, NE, USA) equipped with a red/blue light-emitting diode (LED) light source was used for the simultaneous measurement of photosynthesis and stomatal conductance. Measurements were made at 400 µmol mol⁻¹ ambient atmospheric CO₂ concentration, a flow rate of 300 µmol s⁻¹, mean leaf temperature of 25° C ± 1.6°C, and a vapour pressure deficit (VPD) of 1.3 ± 0.3 kPa. Measurements were made from 09:00 h to 15:00 h, at photosynthetic photon flux density (PPFD) levels of 0, 50, 100, 150, 200, 250, 300, 400, 450, 500, 600, 800, 1,000, 1,200 and 1,500 µmol (photon) m⁻² s⁻¹. The light curves were started at the lowest PPFD. Leaves were adapted for at least 5–20 min per light level to ensure that photosynthesis and stomatal conductance were stable before data logging.

Following the gas exchange measurements, the same youngest fully expanded leaves were used for leaf trait measurements. Leaves were placed on ice in a cool box and taken to the laboratory to determine leaf area and leaf pigments. Leaf area was measured with a leaf area meter LI-3100 (LI-COR, Lincoln, NE, USA). Leaves were oven-dried at 70°C for at least 96 h and weighed to determine their dry mass.

Chl and Car contents were determined in a whole-pigment extract of leaf tissues by UV-VIS spectroscopy [40]. The absorbance of the extract was measured at 470.0 nm, 648.6 nm, and 664.2 nm for the calculation of the Chl a, Chl b and Car contents [40]. Nitrogen content was determined by the Nelson and Sommers [64] procedure.

Leaf inclination angle was obtained with a three-dimensional (3D) digitiser (Fastrak, Polhemus Inc., Colchester, VT, USA) [65, 66]. Leaf inclination angle is expressed in the range from zero to 180 degrees, where zero indicates an upward vertical leaf and 180 a downward dropping leaf [66].

## Estimation of growth rate and the relationship between growth rate and leaf area

The oven-dried weight of the above-ground plant parts (shoots) at five measurement dates (28, 42, 50, 55, and 63 DAS) (S1 Table) and the intervals (14, 8, 5, and 8 days) between the measurement dates were used to calculate the growth rate. Absolute growth rate (AGR; increment in dry weight per unit time) of the plants across the measurement intervals was calculated as

$$AGR = \frac{W_2 - W_1}{t_2 - t_1} \qquad (1)$$

where W₁ and W₂ are the dry weights at the beginning and the end of the interval at times t₁ and t₂, respectively [67]. Plant growth rate and total leaf area per plant were assessed to explore the variation in P_gmax at the whole-plant level. In crops, light interception is often
exponentially related to leaf area [68–70], and growth can be considered the product of light interception and light use efficiency [69, 70]. Thus, we can expect a linear relationship between the natural logarithm of absolute growth rate and leaf area per plant, where the slope of the relationship should indicate light use efficiency.

## Photosynthesis model

The four-parameter non-rectangular hyperbola leaf photosynthesis model [4] was employed in this study. We used a Microsoft Excel routine (Solver) to estimate the four key parameters of the $P_N/I$ curve. The routine uses the non-linear least square curve fitting procedure (generalised reduced gradient method) [4].

The model is of the form:

$$P_N = \frac{\alpha(I_0) \times I + P_{gmax} - \sqrt{\alpha(I_0) \times I + (P_{gmax})^2 - 4\theta \times \alpha(I_0) \times I \times P_{gmax}}}{2\theta} - R_D \quad (2)$$

Where $I$ [µmol photon) m⁻² s⁻¹] is the photosynthetic photon flux density (PPFD); $P_{gmax}$ [µmol (CO₂) m⁻² s⁻¹] is the asymptotic estimate of the maximum gross photosynthetic rate; $\alpha$ ($I_0$) [µmol (CO₂) µmol⁻¹ (photon)], is the apparent quantum yield at $I = 0$ (based on incident light) [4, 37, 71]; $\theta$ [dimensionless] is the convexity or rate of bending of the curve (the ratio of physical-to-total resistance (carboxylation resistance + physical resistance [8, 72]) and $R_D$ [µmol (CO₂) m⁻² s⁻¹] is dark respiration (measured at $I = 0$, intercept on the Y-axis). $P_N$ [µmol (CO₂) m⁻² s⁻¹] is the net photosynthetic rate. It is important to note that the definition of apparent quantum yield, $\alpha$ ($I_0$), does not correspond to the original concept of the maximum quantum yield (α) of photosynthesis light response. The maximum quantum yield, $\alpha$, is usually defined as the slope of the curve at the linear portion in the range of PPFD between 0 and 200 µmol (photon) m⁻² s⁻¹ [4, 73–76]. In contrast, $\alpha$ ($I_0$) is the derivative of the four-parameter non-rectangular hyperbola at $I = 0$ [4]. Thus $\alpha$ ($I_0$) is instead the maximum value of quantum yield higher than any point on the $P_N/I$ curve [4]. The parameters of the non-rectangular hyperbola were estimated separately for each leaf.

## Statistical analysis

A two-way ANOVA was conducted on the parameters of the $P_N/I$ curve and the leaf traits to test the effects of the species and measurement dates. Significant differences between means were determined using the Tukey honest significant difference (THSD) test at a 5% probability level. It is also important to report some effect size measures that indicate whether the observed statistical differences among groups are of practical significance. For a two-way ANOVA and small sample size, the effect size measure omega squared ($\omega^2$) is recommended [77–82]. $\omega^2$ also determines the percentage of the variation in the dependent variable attributable to the individual independent factors (i.e., species and measurement dates) [78]. ANOVA and $\omega^2$ were both computed using JMP Pro software version 13 (SAS Institute Inc., 2016). Correlation (Pearson's) analysis was used to establish the association between leaf traits and maximum gross photosynthetic rate ($P_{gmax}$). Linear regression analysis was also used to establish the relationship between the leaf traits and $P_{gmax}$.
<figure><img src="image_5.png" /><figcaption>**Fig 1.** Weather variables during the growing period. (A) Mean air temperature and relative humidity. (B) Daily mean photosynthetically active radiation (PAR) and daily light (PAR) integral (DLI). The red spots in A correspond to the five days (28, 42, 50, 55, and 63 DAS) for the growth rate measurements and the red spots in B correspond to the three days (50, 55, and 63 DAS) for the photosynthetic light response and leaf trait measurements. DAS denotes days after sowing.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g001

# Results

## Environmental variables

The mean air temperature, mean relative humidity, mean daily photosynthetically active radia- tion (PAR) and mean daily light integral (DLI) during the growing period are shown in Fig 1 below.

The range of the daily mean PAR and DLI in the glasshouse during the growth period were 35.2 to 257 µmol m⁻² s⁻¹ and 3.1 to 22.2 mol m⁻² d⁻¹, respectively. The mean temperature and relative humidity during the whole growth period were 22.7°C and 58.4%, respectively. Light intensities at and in the five- or ten-day intervals before measurements were highest for the third measurement date (May 20, 2014; 63 DAS), followed by the first (May 7, 2014; 50 DAS) and the second (May 12, 2014; 55 DAS) (Table 1).

## Net photosynthetic-light response ($P_N/I$) curves

Non-rectangular hyperbolae described the net photosynthesis light response ($P_N/I$) of the four amaranth species well at each measurement date (S2 Fig). The two-way ANOVA test showed

<table><caption>Table 1. Daily light integral (DLI) at three measurement dates, means across five (5 d) and ten days (10 d) prior, and the average from transplanting to the three measurement dates (TM).</caption><thead><tr><th rowspan="2">Measurement</th><th rowspan="2">Date (2014)</th><th rowspan="2">Days after sowing (DAS)</th><th colspan="4">Daily Light Integral (DLI) (mol PAR m<sup>-2</sup> d<sup>-1</sup>)</th></tr><tr><th>Daily</th><th>Mean (5 d)</th><th>Mean (10 d)</th><th>Mean (TM)</th></tr></thead><tbody><tr><td>-</td><td>-</td><td>-</td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>07 May</td><td>50</td><td>10.61</td><td>11.61</td><td>11.48</td><td>11.37</td></tr><tr><td>2</td><td>12 May</td><td>55</td><td>9.23</td><td>9.25</td><td>10.42</td><td>11.11</td></tr><tr><td>3</td><td>20 May</td><td>63</td><td>22.20</td><td>16.50</td><td>14.05</td><td>11.80</td></tr></tbody></table>

https://doi.org/10.1371/journal.pone.0270674.t001
<figure><img src="image_6.png" /><figcaption>**Fig 2.** The response of net photosynthetic rate, *P*<sub>N</sub>, to photosynthetic photon flux density (PPFD) on new fully expanded leaves of four amaranth species. The symbols for each species represent the mean pooled data of six light response curves (n = 6, two from each of the three measurement dates) since there was no measurement date effect on the light response curves. Lines joining the points were omitted for clarity. Bars represent ± SD. Measurement conditions were leaf temperature of 25°C, CO<sub>2</sub> concentration of 400 µmol mol<sup>-1</sup>, average relative humidity of 60–70%, and a vapour pressure deficit of 1.3 ± 0.3 kPa.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g002

no main effect of measurement date and an interactive effect of measurement dates and species on all the model parameters (S2 Table). The maximum gross photosynthesis rate (*P*<sub>gmax</sub>) was the only parameter that differed (p<0.001) among the species. Due to the lack of measurement date effects, the data on the *P*<sub>N</sub>/I curves were pooled for the species (Fig 2).

The mean separation test on *P*<sub>gmax</sub> categorised the species into two; high (*A. cruentus* and *A. hybridus*) and low *P*<sub>gmax</sub> (*A. dubius* and *A. hypochondriacus*). Apparent quantum yield at PPFD of zero, dark respiration, and convexity were not significantly different among the species (Table 2).

## Interspecific variations in leaf traits

The two-way ANOVA showed a significant species effect on most of the leaf traits except for LMA and leaf inclination angle (S3 Table). There was a measurement date effect on both total Chl and Chl *b* (S4 Table). Significant interactions between measurement date and species were also found for Chl *a* and Chl *b* ratio (Chl *a/b*) (S5 Table). *A. hybridus* had the highest values for all pigments, while *A. cruentus* had the highest *N*<sub>a</sub> and *g*<sub>s</sub> values (Table 3).
<table><caption>Table 2. Means and confidence intervals (95%) for apparent quantum yield at zero PPFD (α(I₀)), convexity (θ), dark respiration rate (R_D), and the mean comparison (Tukey honest significant test) of species effect on maximum gross photosynthesis (P_gmax).</caption><thead><tr><th>Species</th><th>P<sub>gmax</sub> (µmol (CO<sub>2</sub>) m<sup>-2</sup> s<sup>-1</sup>)</th><th>α(I<sub>0</sub>) (µmol (CO<sub>2</sub>) µmol (photon) -1)</th><th>R<sub>D</sub> (µmol (CO<sub>2</sub>) m<sup>-2</sup> s<sup>-1</sup>)</th><th>θ</th></tr></thead><tbody><tr><td>A.hybridus</td><td>32.57<sup>A</sup> (± 4.16)</td><td>0.07 (± 0.02)</td><td>2.05 (± 1.00)</td><td>0.66 (± 0.36)</td></tr><tr><td>A.dubius</td><td>22.29<sup>B</sup> (± 4.16)</td><td>0.08 (± 0.03)</td><td>1.09 (± 1.00)</td><td>0.41 (± 0.36)</td></tr><tr><td>A.hypochondriacus</td><td>21.86<sup>B</sup> (± 4.16)</td><td>0.08 (± 0.02)</td><td>1.02 (± 1.00)</td><td>0.49 (± 0.36)</td></tr><tr><td>A.cruentus</td><td>34.21<sup>A</sup> (± 4.16)</td><td>0.07 (± 0.02)</td><td>1.12 (± 1.00)</td><td>0.34 (± 0.36)</td></tr></tbody></table>

Mean values of P_gmax with different superscripts are significantly different at the 5% level. n = 24

https://doi.org/10.1371/journal.pone.0270674.t002

## Relation between maximum gross photosynthesis rate (P_gmax) and leaf traits

Positive correlations were found between P_gmax and leaf pigments except for Chl b. Correlations were highly significant for P_gmax and stomatal conductance and nitrogen per unit area (S6 Table).

Accordingly, P_gmax showed a strong positive linear relationship with gs measured at 1500 µmol (photons) m⁻² s⁻¹. A positive linear relationship was also found between N_a, leaf pigments, and P_gmax (Fig 3).

Leaf inclination angle showed a positive linear relationship with P_gmax only at the first measurement date (50 DAS). LMA exhibited a weak linear relationship with P_gmax only at the third measurement date (63 DAS; Fig 4).

## Relationship between whole-plant variables and maximum gross photosynthesis rate (P_gmax)

A strong linear relationship was found between the natural logarithm of absolute growth rate and leaf area per plant for all the species (Fig 5).

The slopes of the linear relationship varied among the species (Table 4). The differences in slopes corroborate the pattern of the variation in the P_gmax. Accordingly, A. cruentus and A. hybridus had high and similar slopes while A. dubius and A. hypochondriacus exhibited low slopes (Table 4).

## Discussion

In the present study, we investigated the net photosynthetic light response (P_N/I) curves, leaf traits, and productivity at the whole-plant level in four amaranth species. We observed variation in maximum gross photosynthetic rate, P_gmax, and some key leaf traits among the species.

<table><caption>Table 3. Mean comparison (Tukey Honest Significant Difference) and 95% confidence intervals of amaranth species for stomatal conductance (g_s), nitrogen content per unit area (N_a), chlorophyll (Chl), and carotenoids (Car) contents. n = 24 (g_s), n = 48 for the rest of the traits.</caption><thead><tr><th>Species</th><th>g<sub>s</sub> (mol m<sup>-2</sup> s<sup>-1</sup>)</th><th>N<sub>a</sub> (g m<sup>-2</sup>)</th><th>Chl a (mmol m<sup>-2</sup>)</th><th>Chl b (mmol m<sup>-2</sup>)</th><th>Total Chl (mmol m<sup>-2</sup>)</th><th>Car (mmol m<sup>-2</sup>)</th></tr></thead><tbody><tr><td>A. hybridus</td><td>0.20<sup>A</sup> (± 0.07)</td><td>1.95<sup>AB</sup> (± 0.34)</td><td>0.55<sup>A</sup> (± 0.11)</td><td>0.15<sup>A</sup> (± 0.03)</td><td>0.71<sup>A</sup> (± 0.13)</td><td>0.23<sup>A</sup> (± 0.04)</td></tr><tr><td>A. dubius</td><td>0.14<sup>AB</sup> (± 0.07)</td><td>1.85<sup>AB</sup> (± 0.34)</td><td>0.38<sup>B</sup> (± 0.11)</td><td>0.09<sup>C</sup> (± 0.03)</td><td>0.46<sup>B</sup> (± 0.13)</td><td>0.16<sup>B</sup> (± 0.04)</td></tr><tr><td>A. hypo-chondriacus</td><td>0.10<sup>B</sup> (± 0.07)</td><td>1.66<sup>B</sup> (± 0.34)</td><td>0.43<sup>B</sup> (± 0.11)</td><td>0.10<sup>BC</sup> (± 0.03)</td><td>0.53<sup>B</sup> (± 0.13)</td><td>0.18<sup>B</sup> (± 0.04)</td></tr><tr><td>A. cruentus</td><td>0.19<sup>A</sup> (± 0.07)</td><td>2.03<sup>A</sup> (± 0.34)</td><td>0.46<sup>AB</sup> (± 0.11)</td><td>0.11<sup>B</sup> (± 0.03)</td><td>0.58<sup>AB</sup> (± 0.13)</td><td>0.19<sup>B</sup> (± 0.04)</td></tr></tbody></table>

The stomatal conductance (g_s) presented here was measured at the maximum light level (1500 µmol m⁻² s⁻¹).

https://doi.org/10.1371/journal.pone.0270674.t003
<figure><img src="image_7.png" /><figcaption>**Fig 3.** Relationship between leaf traits and maximum gross photosynthetic rate (*P*<sub>gmax</sub>) for four amaranth species (*A. hybridus*, *A. dubius*, *A. hypochondriacus*, and *A. cruentus*). *A–F* (stomatal conductance (g<sub>a</sub>) measured at the maximum PPFD of 1500 µmol (photons) m<sup>-2</sup> s<sup>-1</sup>, nitrogen per unit area (N<sub>a</sub>), carotenoids (Car), Chlorophyll (Chl *a*, total Chl, Chl *b*). The three data points for each species represent the means of the variables at the three measurement dates (50, 55, and 63 days after sowing). Measurement conditions for the gas exchange measurements were leaf temperature of 25°C, CO<sub>2</sub> concentration of 400 µmol mol<sup>-1</sup>, average relative humidity of 60–70%, and a vapour pressure deficit of 1.3 ± 0.3 kPa.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g003
<figure><img src="image_8.png" /><figcaption>**Fig 4.** Relation between maximum gross photosynthesis rate ($P_{gmax}$) and leaf inclination angle (left) and leaf dry mass per unit area (LMA) (right) at measurement date 1 (50 DAS) and 3 (63 DAS), respectively. The four data points represent the means of each species. Bars represent the ± SD of the means. DAS denotes days after sowing.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g004

At the leaf level, stomatal conductance predominantly explained this variation. At the whole-plant level, the slope of the linear relationship between the natural logarithm of absolute growth rate and leaf area per plant varied among the species and was strongly correlated with $P_{gmax}$.

$P_{gmax}$ is widely used for the ecophysiological characterisation of plant species and comparative analysis of growth conditions [83]. Many plant anatomical or morphological, chemical (e.g. Chl content per leaf area), physiological (e.g. photosynthesis rate per leaf area) and growth traits (e.g. growth rate) are better related to the daily light integral (DLI; i.e., the PAR integrated over the day) than to instantaneous or peak values of PAR at any specific moment in time [35, 84]. Hence the average DLI during an experimental treatment can be used to quantify the light intensity experienced by plants [35]. The average DLIs calculated from the onset of the experiment in the glasshouse to each measurement date were similar for the three measurement dates (Table 1). The similarity in the average DLI presumably is why measurement dates had no significant effect on the parameters of the $P_{N/I}$ curves [35, 84]. Hence, the differences in $P_{gmax}$ observed among the species represented the species' innate acclimated photosynthetic performance under the conditions of growth [75, 85, 86]. The observed $P_{gmax}$ are in the range reported in previous studies for amaranths [45, 75, 87]. Also, the trend of the variations in $P_{gmax}$ among the species agrees with the findings of [16]. These researchers found that weedy amaranths such as *A. hybridus* and fast-growing amaranths such as *A. cruentus* exhibit a higher photosynthesis rate than grain (*A. hypochondriacus*) and vegetable amaranths (*A. dubius*).

Quantum yields of normal healthy leaves do not differ among species under non-stressed growth conditions [36, 44, 88, 89]. Ehleringer et al. [90] also found that the quantum yield of both $C_4$ and $C_3$ species is not dependent on the growth light and temperature conditions. Our values are similar to the theoretical quantum yield for $C_4$ plants (i.e., 0.07, when there is no
<figure><img src="image_9.png" /><figcaption>Fig 5. The relationship between the natural logarithm of absolute growth rate (AGR) and leaf area per plant of four amaranth species (*A. hybridus*, *A. dubius*, *A. hypochondriacus*, and *A. cruentus*). The four data points for each species represent the means of the variables at the four measurement date intervals. Bars represent the ± SD of the means.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g005

CO₂ leakage from the bundle sheath to the mesophyll and 0.063 µmol (CO₂) µmol (photon)⁻¹ when there is leakage) [90–92]. Our values are also consistent with those of NAD-ME enzyme type C₄ grass species, *Sporobolus cyrptandrus*, *Panicum virgatum*, and maize (Zea mays) [0.06 µmol (CO₂) µmol (photon)⁻¹] [7, 90]. Harley and Ehleringer [75] determined the quantum yields of four amaranth species, including three species used in this study. They also found no significant difference among the species.

The coefficient θ represents the photosynthetic efficiency in the intermediate light range above the linear section determined by the maximum quantum yield. Photosynthesis in the intermediate light range is most efficient when θ is high [11]. Commonly observed leaf θ values range from 0.5 to 0.99 [11, 13, 71, 93], and two of our values were in this range. Nevertheless, all our values (Table 2) were in the range observed in C₄ plants [7, 93].
<table><caption>Table 4. The slope of the linear relationship between the natural logarithm of absolute growth rate (AGR) and leaf area per plant.</caption><thead><tr><td>Species</td><td>Slope (g d<sup>-1</sup>m<sup>-2</sup>)</td></tr></thead><tbody><tr><td>A.hybridus</td><td>11.32<sup>A</sup></td></tr><tr><td>A.dubius</td><td>2.26<sup>B</sup></td></tr><tr><td>A.hypochondriacus</td><td>0.84<sup>B</sup></td></tr><tr><td>A.cruentus</td><td>12.48<sup>A</sup></td></tr></tbody></table>

There was a strong linear relationship between the slope and $P_{gmax}$ (Fig 6).

https://doi.org/10.1371/journal.pone.0270674.t004

$R_D$ is known to vary depending on the acclimation state or ambient light environment [94, 95]. The observed $R_D$ was reasonably proportional to the observed $P_{gmax}$, indicating the coupling relationship between photosynthesis rate and respiration rate [96].

## Interspecific variation in leaf traits

The species showed interspecific variation in leaf traits except for LMA and leaf inclination angle. The variation in $g_s$, $N_a$, and Chl content agrees with the findings of [16], who found interspecific variation in 12 amaranth species. LMA indicates the position of species along a gradient of resource-rich to resource-poor environments [97]. Average DLI during plant growth determines the LMA of plants [84, 98]. In our study, the average DLI received by the plants at the measurement dates were similar (Table 1), which is consistent with the similar LMA among the species and measurement dates. At the species level, *A. hybridus* and *A. cruentus* had higher total Chl content, Chl *a*, $N_a$, and $g_s$ than *A. dubius* and *A. hypochondriacus*, which corroborated their higher $P_{gmax}$ (Tables 2 and 3) [2, 16, 99, 100].

Plants grown under natural light conditions possess high acclimation capacity to alterations in light, which is measurable in the pigment composition of thylakoids [24, 41]. Thus under natural growth light conditions, plants combine the characteristics of low and high light-grown plants for an efficient utilisation of light [24]. The observed decrease in total Chl and Chl *b*; and increase in Chl *a/b* ratio at the last measurement date corroborated the known properties of plants grown in natural fluctuating light conditions [24, 41]. Our data shows that the PAR and DLI prior to, or on, the first and second measurement dates (7 and 12 May 2014; 50 and 55 DAS) were similar and lower than at the last measurement date (20 May 2014; 63 DAS) (Table 1 and Fig 1B). Both Chl *b* and total Chl are known to decrease in high growth light environments due to the reduced proportion in light-harvesting complex proteins in favour of electron transport, photophosphorylation, and carbon fixation components [33, 38, 41, 43–45, 48]. Chl *a/b* ratio is a primary index of the acclimation to light, which measures the proportion of light-harvesting complex to other Chl components [101]. A higher ratio occurs in high growth light environments where Chl *a* content or the photosystem I chlorophyll increases and the proportion of light-harvesting chlorophyll *a/b*-protein complex decreases [41, 42, 48]. The increase in the Chl *a/b* ratio was species-specific, as noted by [28, 42]. *A. dubius* and *A. cruentus* showed a significant increase in Chl *a/b* ratio at the last measurement date. In contrast, *A. hybridus* and *A. hypochondriacus* maintained a similar Chl *a/b* ratio across the measurement dates (S5 Table). This suggests that *A. dubius* and *A. cruentus* could reduce their light-harvesting complex proteins when the growth light environment improved [29, 41–43]. The observed range of values was similar to reported values for $C_4$ plants, including amaranths [41, 42]. The central role of Chl *b* and Car is to broaden the absorption spectrum of plants for maximal light capture [33, 35, 48]. Among the species, *A. hybridus* differed in Chl *b* and Car content suggesting a broader spectrum for maximal light capture (Table 3).
<figure><img src="image_10.png" /><figcaption>Fig 6. The relationship between maximum gross photosynthetic rate ($P_{gmax}$) and the slope between the natural logarithm of absolute growth rate and leaf area per plant.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0270674.g006

The interspecific variation observed in $g_s$, in the present study, ranging from 0.14 to 0.20 mol m⁻² s⁻¹, is similar to values in [34] found in 12 amaranth species (0.17 to 0.26 mol m⁻² s⁻¹). Their values were slightly higher than ours, probably due to the differences in the leaf temperatures (30°C and 25°C) used for the gas exchange measurements. Liu and Stützel [51] reported variation in $g_s$ between 0.35 mol m⁻² s⁻¹ and ca. 0.60 mol m⁻² s⁻¹ among four genotypes of amaranth. Our values are comparatively low, apparently due to the low temperatures (24/22°C, day/night) during our study compared to the high temperatures (30/20°C, day/night) in their research. Urban et al. [102] showed that $g_s$ increased by about 40% when the temperature was increased by 10°C at a constant VPD of 1 kPa in both broadleaf and coniferous species. Low light environments can also contribute to stomatal closure [103, 104].
Exploring the relationship between leaf traits and the maximum gross photosynthesis rate (*P*<sub>gmax</sub>)

Interspecific variation in *P*<sub>gmax</sub> was directly related to biochemical and physiological leaf traits such as stomatal conductance, nitrogen, and Chl content. In contrast, structural leaf traits such as leaf thickness were not directly involved [16]. Our observation confirms these findings. The interspecific variation in *P*<sub>gmax</sub> was also mainly explained by stomatal conductance and nitrogen content, although leaf pigments were also associated (Fig 3). Many C<sub>3</sub> and C<sub>4</sub> plant species showed similar positive linear relationships [16, 50, 99, 105–107]. According to von Caemmerer et al. [103], the striking correlation between photosynthetic capacity and *g*<sub>s</sub> maintains the Ci (intercellular CO<sub>2</sub> concentration) /Ca (ambient CO<sub>2</sub> concentration) ratio constant when photosynthetic capacity is modulated in the long-term by growth conditions. The fairly strong positive relationship between *P*<sub>gmax</sub> and leaf inclination angle at the first measurement date (Fig 4) suggests that as leaf inclination angle increased (i.e., became more horizontal from 50° to 100°), *P*<sub>gmax</sub> also increased. Also, leaf angles tend to be more horizontal under low light environments to increase the efficiency of direct light absorption [22, 108]. Plants maximise their total net photosynthetic gain by maximizing whole plant PPFD absorption and photosynthetic light use efficiency via simultaneous adjustments in leaf angle and leaf photosynthetic capacity [109].

Plant leaf area, growth rate, light use efficiency, and P<sub>gmax</sub>

Plant productivity, especially in low light environments, depends on the net photosynthetic rate of individual leaves but is also strongly dependent on the total leaf area displayed for light interception [36]. Our findings demonstrate that the slope between the natural logarithm (ln) of absolute growth rate and leaf area per plant, representing light use efficiency (LUE), was strongly associated with the variation in *P*<sub>gmax</sub>. The two species (*A. cruentus* and *A. hybridus*) with a higher *P*<sub>gmax</sub> showed higher slopes (Fig 5). Thus, *A. cruentus* and *A. hybridus* were more efficient in converting light energy into photosynthates [110].

**Conclusion**

Our data revealed interspecific variation in the maximum gross photosynthetic rate (*P*<sub>gmax</sub>), stomatal conductance, nitrogen content, and leaf pigments per unit area among four amaranth species. The variation in *P*<sub>gmax</sub> was mainly explained by stomatal conductance and nitrogen content at the leaf level. At the whole-plant level, light use efficiency showed a strong positive linear relationship with *P*<sub>gmax</sub>. Notable was the variation in total Chl, Chl *b*, and Chl *a/b* ratio at the measurement dates, which tended to combine the characteristics of both high and low light-grown plants. Overall, *A. cruentus* and *A. hybridus* were superior to *A. dubius* and *A. hypochondriacus* with respect to the *P*<sub>gmax</sub>, leaf traits, and light use efficiency. Thus, *A. hybridus* and *A. cruentus* tend to be more efficient in carbon acquisition. These findings highlight the correlation between leaf photosynthetic characteristics, other leaf traits, and whole plant productivity in amaranths. Future studies may explore more species and accessions of *Amaranthus* spp. at different locations or light environments.

**Supporting information**

S1 Fig. Images of the *Amaranthus* species (*A. hybridus*, *A. dubius*, *A. hypochondriacus* and *A. cruentus*) studied.
(DOCX)
**S2 Fig.** Net Photosynthetic light response curves (A-D) and the corresponding stomatal conductance response ($g_s$; E, F) at each light (Photosynthetic Photon Flux Density; PPFD) level in youngest fully expanded leaves of *A. hybridus*, *A. dubius*, *A. hypochondriacus*, and *A. cruentus* at three measurement dates (M). Measurement dates: M1 = May 7, 2014 (50 DAS); M2 = May 12, 2014 (55 DAS); and M3 = May 20, 2014 (63 DAS). DAS denotes days after sowing. Measurements were taken with the Licor-6400. Each curve for the measurement dates is an average of two biological replications (n = 2). Bars represent ± SD.
(TIF)

**S1 Table.** Data collection dates and the corresponding days after sowing.
(DOCX)

**S2 Table.** Analysis of variance table and the effect size measure, omega squared ($\omega^2$) for the parameters of the net photosynthetic light response curves. $P_{gmax}$- maximum gross photosynthetic rate (µmol (CO$_2$) m$^{-2}$ s$^{-1}$); apparent quantum yield at zero PPFD ($\alpha(I_0)$), µmol (CO$_2$) µmol (photon)$^{-1}$); dark respiration rate ($R_D$, µmol (CO$_2$) m$^{-2}$ s$^{-1}$); convexity ($\theta$); Df: degrees of freedom; SS: sum of squares; MS = Mean squares.
(DOCX)

**S3 Table.** Analysis of variance table and the effect size measure, omega squared ($\omega^2$) for leaf traits. Stomatal conductance ($g_s$); total chlorophyll (total Chl); chlorophyll a (Chl a); chlorophyll b (Chl b); chlorophyll a to chlorophyll b ratio (Chl a/b); carotenoids (Car); nitrogen content per unit area (N$_a$); leaf dry mass (LDM); leaf area (LA).
(DOCX)

**S4 Table.** Mean comparison test (Tukey Honest Significant Difference) of measurement dates effect on chlorophyll b and total chlorophyll content. n = 48.
(DOCX)

**S5 Table.** Mean comparison test (Tukey Honest Significant Difference) of the interaction effect of species and measurement dates on chlorophyll a and chlorophyll b ratio. n = 48.
(DOCX)

**S6 Table.** Pearson's correlation coefficients (r) and statistical significance for maximum gross photosynthetic rate and leaf traits in four amaranth species (*A. hybridus*, *A. dubius*, *A. hypochondriacus* and *A. cruentus*). $P_{gmax}$, maximum gross photosynthetic rate (µmol (CO$_2$) m$^{-2}$ s$^{-1}$); $g_s$, Stomatal conductance (mmol m$^{-2}$s$^{-1}$); N$_a$, nitrogen content per unit area (g m$^{-2}$); Car-carotenoids (mmol m$^{-2}$s$^{-1}$); total Chl, Total chlorophyll (mmol m$^{-2}$s$^{-1}$); Chl a, chlorophyll a (mmol m$^{-2}$s$^{-1}$); Chl b, Chlorophyll b (mmol m$^{-2}$s$^{-1}$). Values represent Pearson's correlation coefficient (r). Significance at P: <0.001***; <0.01**; <0.05*; NS—not significant.
(DOCX)

**Acknowledgments**

Our appreciation goes to the technical staff of the Institute of Horticultural Production Systems, Leibniz University Hannover, especially Mss Ilona Napp and Marie-Luise Lehmann. The publication of this article was funded by the Open Access Publishing Fund of Leibniz Universität Hannover.

**Author Contributions**

**Conceptualization:** Mildred Osei-Kwarteng, Hartmut Stützel.
**Data curation:** Mildred Osei-Kwarteng, Emmanuel Ayipio.

**Formal analysis:** Mildred Osei-Kwarteng.

**Funding acquisition:** Mildred Osei-Kwarteng, Hartmut Stützel.

**Investigation:** Mildred Osei-Kwarteng.

**Methodology:** Mildred Osei-Kwarteng.

**Resources:** Mildred Osei-Kwarteng.

**Supervision:** Hartmut Stützel.

**Validation:** Dany Moualeu-Ngangue, Hartmut Stützel.

**Visualization:** Mildred Osei-Kwarteng, Dany Moualeu-Ngangue, Hartmut Stützel.

**Writing – original draft:** Mildred Osei-Kwarteng.

**Writing – review & editing:** Mildred Osei-Kwarteng, Dany Moualeu-Ngangue, Gerhard Buck-Sorlin, Hartmut Stützel.

## References

1. Lachapelle PP, Shipley B. Interspecific prediction of photosynthetic light response curves using specific leaf mass and leaf nitrogen content: effects of differences in soil fertility and growth irradiance. Ann Bot. 2012; 109:1149–57. https://doi.org/10.1093/aob/mcs032 PMID: 22442344

2. Pettigrew WT. Cotton genotypic variation in the photosynthetic response to irradiance. Photosynt. 2004; 42:567–71. https://doi.org/10.1007/S11099-005-0014-1

3. Xu JZ, Yu YM, Peng SZ, Yang SH, Liao LX. A modified nonrectangular hyperbola equation for photosynthetic light-response curves of leaves with different nitrogen status. Photosynt. 2014; 52:117–23. https://doi.org/10.1007/s11099-014-0011-3

4. de Lobo FA, de Barros MP, Dalmagro HJ, Dalmolin ÁC, Pereira WE, de Souza ÉC, et al. Fitting net photosynthetic light-response curves with Microsoft Excel—a critical look at the models. Photosynt. 2013; 51:445–56. https://doi.org/10.1007/s11099-013-0045-y

5. Poirier-Pocovi M, Lothier J, Buck-Sorlin G. Modelling temporal variation of parameters used in two photosynthesis models: influence of fruit load and girdling on leaf photosynthesis in fruit-bearing branches of apple. Ann Bot. 2018; 121:821–32. https://doi.org/10.1093/aob/mcx139 PMID: 29309513.

6. Fang L, Zhang S, Zhang G, Liu X, Xia X, Zhang S, et al. Application of five light-response models in the photosynthesis of *Populus* × *Euramericana* cv. 'Zhonglin46' leaves. Appl Biochem Biotechnol. 2015; 176:86–100. Epub 2015/04/02. https://doi.org/10.1007/s12010-015-1543-0 PMID: 25832179.

7. Moreno-Sotomayor A, Weiss A, Paparozzi ET, Arkebauer TJ. Stability of leaf anatomy and light response curves of field grown maize as a function of age and nitrogen status. J Plant Physiol. 2002; 159:819–26. https://doi.org/10.1078/0176-1617-00809

8. Marshall B, Biscoe PV. A model for C₃ leaves describing the dependence of net photosynthesis on irradiance. J Exp Bot. 1980; 31:29–39. https://doi.org/10.1093/jxb/31.1.29

9. Ye ZP. A new model for relationship between irradiance and the rate of photosynthesis in Oryza sativa. Photosynt. 2007; 45:637–40. https://doi.org/10.1007/s11099-007-0110-5

10. Akhkha A, Reid I, Clarke DD, Dominy P. Photosynthetic light response curves determined with the leaf oxygen electrode: minimisation of errors and significance of the convexity term. Planta. 2001; 214:135–41. https://doi.org/10.1007/s004250100599 PMID: 11762163.

11. Ogren E. Convexity of the photosynthetic light-response curve in relation to intensity and direction of light during growth. Plant Physiol. 1993; 101:1013–9. https://doi.org/10.1104/pp.101.3.1013 PMID: 12231754.

12. Marino G, Aqil M, Shipley B. The leaf economics spectrum and the prediction of photosynthetic light-response curves. Funct Ecol. 2010; 24:263–72. https://doi.org/10.1111/j.1365-2435.2009.01630.x

13. Thornley JHM. Instantaneous canopy photosynthesis: analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Ann Bot. 2002; 89:451–8. https://doi.org/10.1093/aob/mcf071 PMID: 12096806.
14. Poorter L. Leaf traits show different relationships with shade tolerance in moist versus dry tropical forests. New Phytol. 2009; 181:890-900. https://doi.org/10.1111/j.1469-8137.2008.02715.x PMID: 19140935.

15. Bündchen M, Boeger MRT, Reissmann CB, Geronazzo KM. Interspecific variation in leaf pigments and nutrients of five tree species from a subtropical forest in southern Brazil. An Acad Bras Cienc. 2016; 88 Suppl 1:467-77. https://doi.org/10.1590/0001-3765201620140605 PMID: 26959320.

16. Tsutsumi N, Tohya M, Nakashima T, Ueno O. Variations in structural, biochemical, and physiological traits of photosynthesis and resource use efficiency in *Amaranthus* species (NAD-ME-type C₄). Plant Prod Sci. 2017; 20:300-12. https://doi.org/10.1080/1343943X.2017.1320948

17. Evans JR. Photosynthesis and nitrogen relationships in leaves of C₃ plants. Oecologia. 1989; 78:9-19. https://doi.org/10.1007/BF00377192 PMID: 28311896.

18. Daymond AJ, Tricker PJ, Hadley P. Genotypic variation in photosynthesis in cacao is correlated with stomatal conductance and leaf nitrogen. Biol Plant. 2011; 55:99-104. https://doi.org/10.1007/s10535-011-0013-y

19. King DA. The functional significance of leaf angle in Eucalyptus. Aust J Bot. 1997; 45:619. https://doi.org/10.1071/BT96063

20. Deguchi R, Koyama K. Photosynthetic and morphological acclimation to high and low light environments in *Petasites japonicus* subsp. *giganteus*. Forests. 2020; 11:1365. https://doi.org/10.3390/f11121365

21. Morales A, Kaiser E. Photosynthetic acclimation to fluctuating Irradiance in plants. Front Plant Sci. 2020; 11:268. Epub 2020/03/24. https://doi.org/10.3389/fpls.2020.00268 PMID: 32265952.

22. Givnish TJ. Adaptation to sun and shade: a whole-plant perspective. Funct Plant Biol. 1988; 15:63. https://doi.org/10.1071/PP9880063

23. Lambers H, Chapin FS, Pons TL. Plant Physiological Ecology. New York, NY: Springer New York; 2008.

24. Schumann T, Paul S, Melzer M, Dörmann P, Jahns P. Plant growth under natural light conditions provides highly flexible short-term acclimation properties toward high light stress. Front Plant Sci. 2017; 8:681. Epub 2017/05/03. https://doi.org/10.3389/fpls.2017.00681 PMID: 28515734.

25. Athanasiou K, Dyson BC, Webster RE, Johnson GN. Dynamic acclimation of photosynthesis increases plant fitness in changing environments. Plant Physiol. 2010; 152:366-73. Epub 2009/11/25. https://doi.org/10.1104/pp.109.149351 PMID: 19939944.

26. Retkute R, Smith-Unna SE, Smith RW, Burgess AJ, Jensen OE, Johnson GN, et al. Exploiting heterogeneous environments: does photosynthetic acclimation optimize carbon gain in fluctuating light. J Exp Bot. 2015; 66:2437-47. Epub 2015/03/18. https://doi.org/10.1093/jxb/erv055 PMID: 25788730.

27. Lawson T, Kramer DM, Raines CA. Improving yield by exploiting mechanisms underlying natural variation of photosynthesis. Curr Opin Biotechnol. 2012; 23:215-20. Epub 2012/01/30. https://doi.org/10.1016/j.copbio.2011.12.012 PMID: 22296828.

28. Bailey S, Walters RG, Jansson S, Horton P. Acclimation of Arabidopsis thaliana to the light environment: the existence of separate low light and high light responses. Planta. 2001; 213:794-801. https://doi.org/10.1007/s004250100556 PMID: 11678285

29. Bailey S, Horton P, Walters RG. Acclimation of Arabidopsis thaliana to the light environment: the relationship between photosynthetic function and chloroplast composition. Planta. 2004; 218:793-802. Epub 2003/11/27. https://doi.org/10.1007/s00425-003-1158-5 PMID: 14648116.

30. Walters RG. Towards an understanding of photosynthetic acclimation. J Exp Bot. 2005; 56:435-47. Epub 2005/01/10. https://doi.org/10.1093/jxb/eri060 PMID: 15642715.

31. Murchie EH, Horton P. Acclimation of photosynthesis to irradiance and spectral quality in British plant species: chlorophyll content, photosynthetic capacity and habitat preference. Plant Cell Environ. 1997; 20:438-48. https://doi.org/10.1046/j.1365-3040.1997.d01-95.x

32. Boardman NK. Comparative photosynthesis of sun and shade plants. Annu Rev Plant Physiol. 1977; 28:355-77. https://doi.org/10.1146/annurev.pp.28.060177.002035

33. Catoni R, Granata MU, Sartori F, Varone L, Gratani L. *Corylus avellana* responsiveness to light variations: morphological, anatomical, and physiological leaf trait plasticity. Photosynt. 2015; 53:35-46. https://doi.org/10.1007/s11099-015-0078-5

34. Gratani L, Covone F, Larcher W. Leaf plasticity in response to light of three evergreen species of the Mediterranean maquis. Trees. 2006; 20:549-58. https://doi.org/10.1007/s00468-006-0070-6

35. Poorter H, Niinemets Ü, Ntagkas N, Siebenkäs A, Mäenpää M, Matsubara S, et al. A meta-analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance. New Phytol. 2019; 223:1073-105. Epub 2019/04/08. https://doi.org/10.1111/nph.15754 PMID: 30802971.
36. Björkman O. Responses to different quantum flux densities. In: Lange OL, Nobel PS, Osmond CB, Ziegler H, editors. Physiological Plant Ecology I. Berlin, Heidelberg: Springer Berlin Heidelberg; 1981. pp. 57–107.

37. Herrmann HA, Schwartz J-M, Johnson GN. From empirical to theoretical models of light response curves—linking photosynthetic and metabolic acclimation. Photosynth Res. 2020; 145:5–14. Epub 2019/10/25. https://doi.org/10.1007/s11120-019-00681-2 PMID: 31654195.

38. Han S, Chen SM, Song AP, Liu RX, Li HY, Jiang JF, et al. Photosynthetic responses of *Chrysanthemum morifolium* to growth irradiance: morphology, anatomy and chloroplast ultrastructure. Photosynt. 2017; 55:184–92. https://doi.org/10.1007/s11099-016-0219-5

39. Schiefthaler U, Russell AW, Bolhár-Nordenkampf HR, Critchley C. Photoregulation and photodamage in *Schefflera arboricola* leaves adapted to different light environments. Funct Plant Biol. 1999; 26:485. https://doi.org/10.1071/PP98102

40. Lichtenthaler HK. [34] Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Plant Cell Membranes. In Methods in enzymology. Academic Press; 1987. pp. 350–382.

41. Lichtenthaler HK, Babani F. Contents of photosynthetic pigments and ratios of chlorophyll a/b and chlorophylls to carotenoids (a+b)/(x+c) in C₄ plants as compared to C₃ plants. Photosynt. 2022; 60: 3–9. https://doi.org/10.32615/ps.2021.041

42. Tazoe Y, Noguchi K, Terashima I. Effects of growth light and nitrogen nutrition on the organization of the photosynthetic apparatus in leaves of a C4 plant, Amaranthus cruentus. Plant Cell Environ. 2006; 29:691–700. https://doi.org/10.1111/j.1365-3040.2005.01453.x PMID: 17080618.

43. Pearcy RW, Sims DA. Photosynthetic acclimation to changing light environments: Scaling from the leaf to the whole plant. In: Caldwell Martyn M., Pearcy Robert W., editors. Physiological Ecology, exploitation of environmental heterogeneity by plants. Academic Press. pp. 145–74.

44. Dias-Filho MB. Photosynthetic light response of the C₄ grasses *Brachiaria brizantha* and *B. humidicola* under shade. Sci Agric. 2002; 59:65–8.

45. Jha P, Norsworthy JK, Riley MB, Bielenberg DG, Bridges W. Acclimation of Palmer Amaranth (*Amaranthus palmeri*) to shading. Weed Sci. 2008; 56:729–34. https://doi.org/10.1614/WS-07-203.1

46. Grassi G, Bagnaresi U. Foliar morphological and physiological plasticity in *Picea abies* and *Abies alba* saplings along a natural light gradient. Tree Physiol. 2001; 21:959–67. https://doi.org/10.1093/treephys/21.12-13.959 PMID: 11498343.

47. Vialet-Chabrand S, Matthews JSA, Simkin AJ, Raines CA, Lawson T. Importance of fluctuations in light on plant photosynthetic acclimation. Plant Physiol. 2017; 173:2163–79. Epub 2017/02/09. https://doi.org/10.1104/pp.16.01767 PMID: 28184008.

48. Anderson JM, Chow WS, Park YI. The grand design of photosynthesis: Acclimation of the photosynthetic apparatus to environmental cues. Photosynth Res. 1995; 46:129–39. https://doi.org/10.1007/BF00020423 PMID: 24301575

49. Assad R, Reshi ZA, Jan S, Rashid I. Biology of amaranths. Bot Rev. 2017; 83:382–436. https://doi.org/10.1007/s12229-017-9194-1

50. Paredes-López O. Amaranth biology, chemistry, and technology. Boca Raton: CRC Press; 1994.

51. Liu F, Stützel H. Leaf expansion, stomatal conductance, and transpiration of vegetable amaranth (*Amaranthus* sp.) in response to soil drying. J Amer Soc Hort Sci. 2002; 127:878–83. https://doi.org/10.21273/JASHS.127.5.878

52. Dinssa FF, Hanson P, Ledesma DR, Minja R, Mbwambo O, Tilya MS, et al. Yield of vegetable amaranth in diverse Tanzanian production environments. Hortte. 2019; 29:516–27. https://doi.org/10.21273/HORTTECH04374-19

53. Das S. Amaranthus: A promising crop of future. 1st ed. Singapore: Springer Singapore; Imprint: Springer; 2016.

54. Escobedo-López D, Núñez-Colín CA, Espitia-Rangel E. Adaptation of cultivated amaranth (*Amaranthus* spp.) and their wild relatives in Mexico. J Crop Improv. 2014; 28:203–13. https://doi.org/10.1080/15427528.2013.869518

55. Brenner DM, Baltensperger DD, Kulakow PA, Lehmann JW, Myers RL, Slabbert MM, et al. Genetic resources and breeding of *Amaranthus*. Plant Breeding Reviews. 2000; 19:227–85.

56. Thapa R, Blair M. Morphological assessment of cultivated and wild amaranth species diversity. Agronomy. 2018; 8:272. https://doi.org/10.3390/agronomy8110272

57. Achigan-Dako EG, Sogbohossou OED, Maundu P. Current knowledge on *Amaranthus* spp.: research avenues for improved nutritional value and yield in leafy amaranths in sub-Saharan Africa. Euphytica. 2014; 197:303–17. https://doi.org/10.1007/s10681-014-1081-9
58. Andini R, Yoshida S, Yoshida Y, Ohsawa R. Amaranthus genetic resources in Indonesia: morphological and protein content assessment in comparison with worldwide amaranths. *Genet Resour Crop Evol.* 2013; 60:2115-28. https://doi.org/10.1007/s10722-013-9979-y

59. AVRDC Vegetable Genetic Resources Information System (AVGRIS). Amaranthus species. 2015 [cited 15 August 2021]. http://seed.worldveg.org/search/characterization/amaranthus.

60. Zehring J, Reim V, Schröter D, Neugart S, Schreiner M, Rohn S, et al. Identification of novel saponins in vegetable amaranth and characterization of their hemolytic activity. Food Res Int. 2015; 78:361-8. Epub 2015/09/13. https://doi.org/10.1016/j.foodres.2015.09.010 PMID: 28433304.

61. Schröter D, Baldermann S, Schreiner M, Witzel K, Maul R, Rohn S, et al. Natural diversity of hydroxy-cinnamic acid derivatives, flavonoid glycosides, carotenoids and chlorophylls in leaves of six different amaranth species. Food Chem. 2018; 267:376-86. Epub 2017/11/11. https://doi.org/10.1016/j.foodchem.2017.11.043 PMID: 29934181.

62. Stoilova T, Dinssa FF, Ebert AW, Tenkouano A. The diversity of African leafy vegetables: agromorphological characterization of subsets of AVRDC's germplasm collection. Acta Hortic. 2015; 1102:67-74. https://doi.org/10.17660/ActaHortic.2015.1102.7

63. Dinssa FF, Hanson P, Dubois T, Tenkouano A, Stoilova T, Hughes J, et al. AVRDC—The World Vegetable Center's women-oriented improvement and development strategy for traditional African vegetables in sub-Saharan Africa. Europ.J.Hortic.Sci. 2016; 81:91-105. https://doi.org/10.17660/eJHS.2016/81.2.3

64. Nelson DW, Sommers LE. Total nitrogen analysis of soil and plant tissues. J AOAC Int. 1980; 63:770-8. https://doi.org/10.1093/jaoac/63.4.770

65. Rakocevic M. Assessing the geometric structure of a white clover (Trifolium repens L.) Canopy using3-D Digitising. Ann Bot. 2000; 86:519-26. https://doi.org/10.1006/anbo.2000.1209

66. Kahlen K. 3D architectural modelling of greenhouse cucumber (Cucumis sativus L.) Using L-systems. Acta Hortic. 2006:51-8. https://doi.org/10.17660/ActaHortic.2006.718.4

67. Hunt R. Absolute growth rates. In: Hunt R, editor. Basic growth analysis. Dordrecht: Springer Netherlands; 1990. pp. 17-24.

68. Portes TA, de Melo HC. Light interception, leaf area and biomass production as a function of the density of maize plants analyzed using mathematical models. Acta Sci Agron. 2014; 36:457. https://doi.org/10.4025/actasciagron.v36i4.17892

69. Saito T, Mochizuki Y, Kawasaki Y, Ohyama A, Higashide T. Estimation of leaf area and light-use efficiency by non-destructive measurements for growth modeling and recommended leaf area index in greenhouse tomatoes. Hort J. 2020; 89:445-53. https://doi.org/10.2503/hortj.UTD-171

70. Wilson JW. Analysis of growth, photosynthesis and light interception for single plants and stands. Ann Bot. 1981; 48:507-12. https://doi.org/10.1093/oxfordjournals.aob.a086154

71. Leverenz JW, Falk S, Pilström CM, Samuelsson G. The effects of photoinhibition on the photosynthetic light-response curve of green plant cells (Chlamydomonas reinhardtii). Planta. 1990; 182:161-8. https://doi.org/10.1007/BF00197105 PMID: 24197090

72. Akhkha A. Modelling photosynthetic light-response curve in Calotropis procera under salinity or water deficit stress using non-linear models. J Taibah Univ Sci. 2010; 3:49-57. https://doi.org/10.1016/S1658-3655(12)60020-X

73. Ye Z-P, Ling Y, Yu Q, Duan H-L, Kang H-J, Huang G-M, et al. Quantifying light response of leaf-scale water-use efficiency and its interrelationships with photosynthesis and stomatal conductance in C₃ and C₄ Species. Front Plant Sci. 2020; 11:374. Epub 2020/04/24. https://doi.org/10.3389/fpls.2020.00374 PMID: 32411151.

74. Ogren E, Evans JR. Photosynthetic light-response curves. Planta. 1993; 189. https://doi.org/10.1007/BF00195075

75. Harley PC, Ehleringer J. Gas exchange characteristics of leaves of four species of grain amaranth. Field Crops Res. 1987; 17:141-53. https://doi.org/10.1016/0378-4290(87)90088-8

76. Feng YL, Cao KF, Zhang JL. Photosynthetic characteristics, dark respiration, and leaf mass per unit area in seedlings of four tropical tree species grown under three irradiances. Photosynt. 2004; 42:431-7. https://doi.org/10.1023/B:PHOT.0000046163.83729.e5

77. Ferguson CJ. An effect size primer: A guide for clinicians and researchers. In: Kazdin AE, editor. Methodological Issues and Strategies in Clinical Research. Washington: American Psychological Association; 2016. pp. 301-10.

78. Yigit S, Mendes M. Which effect size measure is appropriate for one-way and two-way ANOVA models? A Monte Carlo simulation study. REVSTAT—Statistical Journal. 2018; 16:295-313.
79. Nakagawa S, Cuthill IC. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc. 2007; 82:591-605. https://doi.org/10.1111/j.1469-185X.2007.00027.x PMID: 17944619.

80. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013; 4:863. Epub 2013/11/26. https://doi.org/10.3389/fpsyg.2013.00863 PMID: 24324449.

81. Olejnik S, Algina J. Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol Methods. 2003; 8:434-47. https://doi.org/10.1037/1082-989X.8.4.434 PMID: 14664681.

82. Fey CF, Hu T, Delios A. The Measurement and communication of effect sizes in management research. Manag Organ Rev. 2022:1-22. https://doi.org/10.1017/mor.2022.2

83. Kaipiainen EL. Parameters of photosynthesis light curve in *Salix dasyclados* and their changes during the growth season. Russ J Plant Physiol. 2009; 56:445-53. https://doi.org/10.1134/S1021443709040025

84. Chabot BF, Jurik TW, Chabot JF. Influence of instantaneous and integrated light-flux density on leaf anatomy and photosynthesis. Am J Bot. 1979; 66:940. https://doi.org/10.2307/2442235

85. Pearcy RW, Tumosa N, Williams K. Relationships between growth, photosynthesis and competitive interactions for a C3 and C4 plant. Oecologia. 1981; 48:371-6. https://doi.org/10.1007/BF00346497 PMID: 28309755

86. El-Sharkawy MA. Prospects of photosynthetic research for increasing agricultural productivity, with emphasis on the tropical C₄ *Amaranthus* and the cassava C₃-C₄ crops. Photosynt. 2016; 54:161-84. https://doi.org/10.1007/s11099-016-0204-z

87. Lin ZE, Ehleringer J. Photosynthetic characteristics of *Amaranthus tricolor*, a C₄ tropical leafy vegetable. Photosynth Res. 1983; 4:171-8. https://doi.org/10.1007/BF00041812 PMID: 24458396

88. Murchie EH, Pinto M, Horton P. Agriculture and the new challenges for photosynthesis research. New Phytol. 2009; 181:532-52. Epub 2008/12/18. https://doi.org/10.1111/j.1469-8137.2008.02705.x PMID: 19140947

89. Singsaas EL, Ort DR, DeLucia EH. Variation in measured values of photosynthetic quantum yield in ecophysiological studies. Oecologia. 2001; 128:15-23. Epub 2001/06/01. https://doi.org/10.1007/s004420000624 PMID: 28547085.

90. Ehleringer J, Pearcy RW. Variation in quantum yield for CO₂ uptake among C₃ and C₄ Plants. Plant Physiol. 1983; 73:555-9. https://doi.org/10.1104/pp.73.3.555 PMID: 16663257.

91. Pearcy RW, Ehleringer J. Comparative ecophysiology of C₃ and C₄ plants. Plant Cell Environ. 1984; 7:1-13. https://doi.org/10.1111/j.1365-3040.1984.tb01194.x

92. Long S. Environmental responses. The biology of C4 photosynthesis. San Diego, California: Academic Press; 1999.

93. Prioul JL, Chartier P. Partitioning of transfer and carboxylation components of intracellular resistance to photosynthetic CO₂ fixation: a critical analysis of the methods used. Ann Bot. 1977; 41:789-800.

94. El-Sharkawy MA, Loomis RS, Williams WA. Photosynthetic and respiratory exchanges of carbon dioxide by leaves of the grain amaranth. J Appl Ecol. 1968; 5:243. https://doi.org/10.2307/2401288

95. Griffin KL, Tissue DT, Turnbull MH, Schuster W, Whitehead D. Leaf dark respiration as a function of canopy position in Nothofagus fusca trees grown at ambient and elevated CO₂ partial pressures for 5 years. Funct Ecol. 2001; 15:497-505. https://doi.org/10.1046/j.0269-8463.2001.00539.x

96. Bunce JA. Growth rate, photosynthesis and respiration in relation to leaf area index. Ann Bot. 1989; 63:459-63. https://doi.org/10.1093/oxfordjournals.aob.a087766

97. Puglielli G, Crescente MF, Frattaroli AR, Gratani L. Leaf mass per area (LMA) as a possible predictor of adaptive strategies in two species of *Sesleria* (Poaceae): Analysis of morphological, anatomical and physiological leaf traits. Ann Botan Fenn. 2015; 52:135-43. https://doi.org/10.5735/085.052.0201

98. Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol. 2009; 182:565-88. https://doi.org/10.1111/j.1469-8137.2009.02830.x PMID: 19434804.

99. Hikosaka K. Mechanisms underlying interspecific variation in photosynthetic capacity across wild plant species. Plant Biotechnology. 2010; 27:223-9. https://doi.org/10.5511/plantbiotechnology.27.223

100. Kiran TV, Rao YV, Subrahmanyam D, Rani NS, Bhadana VP, Rao PR, et al. Variation in leaf photosynthetic characteristics in wild rice species. Photosynt. 2013; 51:350-8. https://doi.org/10.1007/s11099-013-0032-3

101. Reed S, Schnell R, Moore JM, Dunn C. Chlorophyll a + b content and chlorophyll fluorescence in avocado. J Agric Sci. 2012; 4. https://doi.org/10.5539/jas.v4n4p29
102. Urban J, Ingwers M, McGuire MA, Teskey RO. Stomatal conductance increases with rising temperature. Plant Signaling Behav. 2017; 12:e1356534. Epub 2017/08/08. https://doi.org/10.1080/15592324.2017.1356534 PMID: 28786730.

103. von Caemmerer S, Lawson T, Oxborough K, Baker NR, Andrews TJ, Raines CA. Stomatal conductance does not correlate with photosynthetic capacity in transgenic tobacco with reduced amounts of Rubisco. J Exp Bot. 2004; 55:1157-66. Epub 2004/04/23. https://doi.org/10.1093/jxb/erh128 PMID: 15107451.

104. Vongcharoen K, Santanoo S, Banterng P, Jogloy S, Vorasoot N, Theerakulpisut P. Seasonal variation in photosynthesis performance of cassava at two different growth stages under irrigated and rain-fed conditions in a tropical savanna climate. Photosynt. 2018; 56:1398-413. https://doi.org/10.1007/s11099-018-0849-x

105. Evans JR, Loreto F. Acquisition and diffusion of CO2 in higher plant leaves. In: Govindjee, Amesz J, Aro E-M, Barber J, Blankenship RE, et al., editors. Photosynthesis. Dordrecht: Springer Netherlands; 2000. pp. 321-51.

106. Taub DR, Lerdau MT. Relationship between leaf nitrogen and photosynthetic rate for three NAD-ME and three NADP-ME C4 grasses. Am J Bot. 2000; 87:412-7. https://doi.org/10.2307/2656637 PMID: 10719002

107. Sage RF, Pearcy RW. The nitrogen use efficiency of C(3) and C(4) Plants: II. Leaf nitrogen effects on the gas exchange characteristics of Chenopodium album (L.) and Amaranthus retroflexus (L.). Plant Physiol. 1987; 84:959-63. https://doi.org/10.1104/pp.84.3.959 PMID: 16665551.

108. Niinemets Ü. Adjustment of foliage structure and function to a canopy light gradient in two co-existing deciduous trees. Variability in leaf inclination angles in relation to petiole morphology. Trees. 1998; 12:446. https://doi.org/10.1007/s004680050173

109. Posada JM, Sievänen R, Messier C, Perttunen J, Nikinmaa E, Lechowicz MJ. Contributions of leaf photosynthetic capacity, leaf angle and self-shading to the maximization of net photosynthesis in Acer saccharum: a modelling assessment. Ann Bot. 2012; 110:731-41. Epub 2012/06/04. https://doi.org/10.1093/aob/mcs106 PMID: 22665700.

110. Koyama K, Kikuzawa K. Geometrical similarity analysis of photosynthetic light response curves, light saturation and light use efficiency. Oecologia. 2010; 164:53-63. Epub 2010/04/28. https://doi.org/10.1007/s00442-010-1638-9 PMID: 20425123.