Photorespiration differs among Arabidopsis thaliana ecotypes and is correlated with photosynthesis

Photosynthesis and photorespiration covary, but this relationship is not fully understood. Natural variation among Arabidopsis thaliana ecotypes offers an ideal system for interrogating the physiological co-ordination of photosynthesis and photorespiration.

Abstract

A greater understanding of natural variation in photosynthesis will inform strategies for crop improvement by revealing overlooked opportunities. We use Arabidopsis thaliana ecotypes as a model system to assess (i) how photosynthesis and photorespiration covary and (ii) how mesophyll conductance influences water use efficiency (WUE). Phenotypic variation in photorespiratory CO₂ efflux was correlated with assimilation rates and two metrics of photosynthetic capacity (i.e. V_Cmax and J_max); however, genetic correlations were not detected between photosynthesis and photorespiration. We found standing genetic variation—as broad-sense heritability—for most photosynthetic traits, including photorespiration. Genetic correlation between photosynthetic electron transport and carboxylation capacities indicates that these traits are genetically constrained. Winter ecotypes had greater mesophyll conductance, maximum carboxylation capacity, maximum electron transport capacity, and leaf structural robustness when compared with spring ecotypes. Stomatal conductance varied little in winter ecotypes, leading to a positive correlation between integrated WUE and mesophyll conductance. Thus, variation in mesophyll conductance can modulate WUE among A. thaliana ecotypes without a significant loss in assimilation. Genetic correlations between traits supplying energy and carbon to the Calvin–Benson cycle are consistent with biochemical models, suggesting that selection on either of these traits would improve all of them. Similarly, the lack of a genetic correlation between photosynthesis and photorespiration suggests that the positive scaling of these two traits can be broken.

Introduction

Despite a near complete description of the photorespiratory pathway, availability and characterization of knockout mutants for many of the enzymes in the pathway (Maurino and Peterhansel, 2010), and thorough understanding of the response of photorespiration to environmental stimuli, relatively little is known about natural genetic variation in photorespiration (Nunes-Nesi et al., 2016). Photorespiration reduces the photosynthetic conversion efficiency by almost half (Zhu et al., 2010), providing motivation to modify it to improve crop yields (Long et al., 2015; Ort et al., 2015; Walker et al., 2016). Photorespiration occurs when 2-phosphoglycolate is formed by the oxygenation of ribulose-1,5-bisphosphate (RuBP) by Rubisco. Briefly, two 2-phosphoglycolate molecules are metabolized via the C₂ photorespiratory pathway, resulting in the return of one phosphoglycerate to the Calvin–Benson cycle, but also the release of one CO₂ molecule, and consumption of both ATP and reducing equivalents (Maurino and Peterhansel, 2010; Peterhansel et al., 2010). Additionally, photorespiration is sensitive to environmental change as the ratio of oxygenation to carboxylation reactions by Rubisco increases with temperature (Jordan and Ogren, 1984; Bernacchi et al., 2001), and decreases with increasing [CO₂].

From an ecological and evolutionary perspective, natural genetic variation for traits among habitats and populations may reveal some of the history of differential selection pressures on those populations (Antonovics and Bradshaw, 1968; Geber and Dawson, 1990; Dudley, 1996; Franks et al., 2007; Flood et al., 2016). Natural genetic variation has informed our unraveling of the mechanistic and genetic basis of several ecologically and agronomically important plant traits, but is underused when it comes to enhancing photosynthetic efficiency to improve crop yields (Flood et al., 2011; Driever et al., 2014; Nunes-Nesi et al., 2016; Orr et al., 2016). Natural genetic variation has provided many yield-enhancing traits including pathogen and pest resistance (Foster et al., 1991; Hill et al., 2006), improved heat tolerance (Maestri et al., 2002), yield enhancement through increasing harvest indices (Hay, 1995), and the proliferation of dwarfing genes in cereals (Hedden, 2003), to name a few.

A better understanding of how natural selection has altered net carbon assimilation (A_N) (Flood et al., 2011) and photorespiration in divergent habitats and niches provides a framework for enhancing A_N in crop species because natural populations illustrate the extent of phenotypic space occupied by successful trait combinations (Donovan et al., 2011). Genetic variation exists for a wide array of photosynthetic traits in wild (Geber and Dawson, 1997; Brouillette et al., 2014; Momayyezi and Guy, 2017) and some crop plants (Barbour et al., 2010; Giuliani et al., 2013; Gu et al., 2014; Tomeo and Rosenthal, 2017). In Arabidopsis thaliana, A_N and associated traits differ substantially among wild populations (Brosché et al., 2010; Easlon et al., 2014; van Rooijen et al., 2015). Ecotypes exhibit variability in their stomatal responsiveness to [CO₂] (Takahashi et al., 2015) and other stimuli (Aliniaeifard and Van Meeteren, 2014) which alter A_N. There is standing heritable genetic variation among ecotypes for light use efficiency (i.e. quantum yield of PSII) (van Rooijen et al., 2015), integrated water use efficiency (WUE; i.e. carbon isotope composition of leaves: δ¹³C)(McKay et al., 2003; Easlon et al., 2014), and both stomatal conductance and transpiration efficiency (Easlon et al., 2014). However, the nature and magnitude of variation in photorespiration and covariation with photosynthesis are not fully resolved in crops and hardly explored in wild plants.

The biochemical model of photosynthesis (Farquhar et al., 1980) provides a robust and well-tested system to study photosynthetic variation. Empirical fits to the model demonstrate that photosynthesis is commonly limited by both light and carbon supply under natural (Wullschleger, 1993) and agronomic conditions (Bernacchi et al., 2005; Chen et al., 2005). This co-limitation by carboxylation and RuBP regeneration, referred to as the ‘teeter–totter’ model, is effectively maintained by partitioning resources between carboxylation capacity (V_Cmax) and electron transport capacity (J_max) (Farquhar et al., 2001). A consistent ratio is preserved between V_Cmax and J_max across differing environments (Walker et al., 2014) even when redistribution of nitrogen between the traits would enhance photosynthesis (Evans, 1989; Zhu et al., 2007; Kromdijk and Long, 2016). At least one study has indicated that V_Cmax and J_max are genetically correlated in wild plants (Geber and Dawson, 1997). If a genetic correlation between V_Cmax and J_max is a universal phenomenon, this may constrain independent selection on the traits—away from the observed, consistent ratio—to maximize photosynthesis.

In this study, we set out to demonstrate the extent of natural phenotypic variation and covariation for leaf-level C₃ photosynthetic physiology and photorespiration and to identify the existence and nature of genetic correlations among these traits. Arabidopsis thaliana ecotypes originating from diverse geographic and climatic regions (Table 1) were selected to represent a range of variation in rosette-level gas exchange parameters (Easlon et al., 2014). Evidence suggests that photosynthesis and photorespiration are co-regulated (Bauwe et al., 2012; Timm et al., 2016), leading to the hypothesis that they should be positively correlated, though even the magnitude of intraspecific variation for photorespiration within and among natural populations is not fully understood. Here we hypothesize that: (i) carbon and energy supplies to the Calvin–Benson cycle are co-ordinated at both genetic and phenotypic levels; and (ii) variation in photorespiration exists and is positively correlated with carbon and energy supplies to the Calvin–Benson cycle. Easlon et al. (2014) suggested that stomatal conductance was of greater consequence to WUE than A_N in A. thaliana ecotypes, but mesophyll conductance was also implicated as driving some of the variation in WUE. Therefore, we test the hypothesis (iii) that WUE—either intrinsic (i.e. the ratio of A_N to stomatal conductance of water vapor: A_N/g_sH) or integrated (δ¹³C)—is correlated negatively with stomatal conductance, but positively with mesophyll conductance and net assimilation.

Table 1.

A list of all of the ecotypes used in this study

Name	Accession	Latitude	Longitude	MAT	MAP	Habit
Ag-0	CS76430	45.0000	1.3000	11.8	887	Winter
Bil-5	CS76709	63.3240	18.4840	2.9	615	Winter
Bur-0	CS76734	54.1000	–6.2000	9.3	953	Spring
Eden-1	CS76826	62.8770	18.1770	3.3	655	Winter
Kas-1	CS903	35.0000	77.0000	–9.1a	74a	Winter
Knox-18	CS76530	41.2816	–86.6210	9.7	975	Spring
Ler-1	CS77021	47.9840	10.8719	8.0	962	Spring
NFA-10	CS77126	51.4108	–0.6383	9.8	701	Spring
Omo2-3	CS77149	56.1400	15.7800	7.9	546	Winter
Sq-8	CS76604	51.4083	–0.6383	9.8	701	Spring
Tamm-2	CS76610	60.0000	23.5000	5.1	611	Winter
Ts-1	CS76615	41.7194	2.9306	16.1	644	Spring
Tsu-1	CS77390	34.4300	136.3100	14.9	2385	Spring
Ws-2	CS76631	52.3000	30.0000	6.7	624	Spring

The names, accession numbers, and latitude/longitude of origin are referenced to the Arabidopsis Biological Resource Center. Mean annual temperature (MAT) and mean annual precipitation (MAP) were taken from the bioclim database (www.worldclim.org, last accessed 7 August 2018; Hijmans et al., 2005) using the given latitude/longitude locations with help from the rgdal (Bivand et al., 2016) and raster (Hijmans, 2016) R packages. Habit refers to the life history strategy of ecotypes: winter habits require vernalization for flowering

^aAvailable latitude and longitude are coarse for this ecotype. Due to the rugged terrain surrounding this location and the high variability in the local climate over small spatial scales, these climate values represent rough approximations.

Materials and methods

Plant material

Seeds of 14 A. thaliana ecotypes (Table 1), chosen to reflect as much photosynthetic trait variance as possible based on previous measurements (Easlon et al., 2014), were obtained from the Arabidopsis Biological Resource Center (abrc.osu.edu; last accessed 7 August 2018). One plant of each ecotype was grown for one generation to collect seed in a common environment, minimizing maternal environmental effects. Seeds were sown in 160 ml pots in a moist 4:1 mixture of Metro-Mix 360 topsoil (Sun Gro Horticulture, Agawam, MA, USA) and fritted clay (Turface, Profile Products LLC, Buffalo Grove, IL, USA). Pots were placed in trays with ~1 cm of water, covered, and incubated in the dark at 4 °C for 7 d. Trays were transferred to a controlled-environment chamber set to a photosynthetic photon flux density (PPFD) at plant height of ~400 µmol photons m^–2 s^–1, day length of 12 h, and day:night temperature of 21:19 °C. At the six-leaf stage, pots were thinned to one plant, pots were rotated within trays weekly, and trays were rotated within the chamber every 3–4 d. Water was replaced as needed and supplemented with 0.5× Hoagland’s solution weekly. At 21 d post-germination, winter-annual ecotypes were transferred to a second growth chamber set to identical conditions, except that the temperature was set to constant 4 °C, where they were kept for 28 d before returning them to the original chamber. As a precautionary measure, 40 cm floral sleeves were placed around the pots before flowering. Once the majority of siliques on a plant reached maturity, the plant was placed in a separate tray without water, allowed to dry for several days, and seeds were harvested.

To facilitate leaf-level gas exchange measurements on rosette leaves, 160 ml pots were overfilled, with the soil mixture used above, and covered with a fiberglass screen to hold it in place. Plants were grown in six replicate blocks with each block contained on a single tray. Two trays (i.e. blocks) were grown simultaneously in a growth chamber set to a PPFD at plant height of ~450 µmol photons m^–2 s^–1 during 12 h days, day:night temperature of 21:18.5 °C, and relative humidity maintained between 60% and 80%. The additional four blocks were grown iteratively. Initially four seeds were sown in each pot and at the six- to eight-leaf stage were thinned to two plants arranged at opposite sides of the pot. Once newly developed leaves reached maximum width for a given ecotype, but always before bolting, one plant from each pot was measured.

Gas exchange and associated measurements

The response of photosynthesis to intercellular [CO₂] was measured at ambient and 1% O₂. The youngest fully expanded leaf was placed in the 6400-40 fluorescence chamber (LI-6400XT Photosynthesis System, LI-COR Environmental, Lincoln, NE, USA) making sure not to damage the leaf, while ensuring contact with the thermocouple and covering as much of the 2 cm² chamber as possible. Leaves were acclimated for ≥25 min to a PPFD of 850 µmol photons m^–2 s^–1 with 10% blue light, a vapor pressure deficit below 1.2 kPa, ambient [CO₂] of 400 µmol CO₂ mol^–1 air, flow rate of 300 µmol air min^–1, and block temperature controlled to hold leaf temperature at 25 °C. Preliminary light response curves indicated that all ecotypes were light saturated at 650 µmol photons m^–2 s^–1. Upon reaching steady-state conditions, a data point was logged and the [CO₂] was changed iteratively along the sequence 400, 325, 250, 175, 100, 50, 400, 400, 500, 650, 950, 1250, and 1600 µmol CO₂ mol^–1 air. After reaching stable conditions at each CO₂ set-point, gas exchange parameters and steady-state fluorescence (F_s) were logged. Before proceeding to the next [CO₂], a multiphase flash chlorophyll fluorescence routine was executed to determine the maximum (F_mʹ) fluorescence following recommended procedures (Loriaux et al., 2013). Once the initial CO₂–response curve at ambient [O₂] was complete, the leaf was allowed to re-acclimate to ambient conditions until A_N and stomatal conductance returned to their initial steady-state values. Air from an N₂ tank with 1% (v/v) O₂ was piped through a humidifying system and connected to the air inlet on the 6400 console. A second CO₂–response curve was performed with only subambient CO₂ concentrations (400, 325, 250, 175, 100, and 50 µmol mol^–1). At each CO₂ set-point, F_s and F_mʹ were again estimated with the multiphase flash routine. As some leaves did not fill the gas exchange cuvette, a digital image of the leaf section in the cuvette and a ruler were captured and used to calculate the area with ImageJ (Schneider et al., 2012), and all gas exchange parameters were recalculated.

Following gas exchange, leaf absorptance (α) was determined with a spectroradiometer connected to dual integrating spheres built into a leaf clamp (Jaz Spectroclip, Ocean Optics Inc., Dundee, FL, USA). Reflectance and transmittance were measured at three locations on the leaf used for gas exchange. The calculation of α was constrained to ±5 nm surrounding the light-emitting diode (LED) peaks of the 6400-40 light source (i.e. 470 nm and 665 nm), and was weighted to account for the light used during gas exchange, being composed of 10% blue and 90% red. Absorptance was calculated as 1–(reflectance+transmittance).

Leaf thickness (T_L) was measured with digital calipers on the leaf of the rosette directly opposite that used for gas exchange. Then this leaf and 4–5 additional fully expanded leaves were sampled for determination of leaf mass per area (LMA) and leaf dry matter content (LDMC). The total area of these leaves was determined using ImageJ. The leaves were then massed for fresh weight. After drying at 65 °C for >72 h, the leaves were massed again for dry weight. LDMC was calculated as the ratio of dry to fresh mass. LMA was calculated as the ratio of dry mass to total leaf area. Dry leaves were then ground to a fine powder and analyzed for carbon, nitrogen, and ¹³C content at the University of Illinois. The stable carbon isotope ratio (¹³C to ¹²C) of leaf tissue relative to a standard is reported as δ¹³C in units of parts per thousand (‰).

Analysis of gas exchange

Leaks in gas exchange systems can result in systematic biases due to the contrasting diffusional gradients at opposing ends of a CO₂–response curve (Flexas et al., 2007a; Rodeghiero et al., 2007; Gong et al., 2015). As a precaution against these biases, we measured apparent photosynthesis throughout identical CO₂–response curves on heat-inactivated (n=13) leaves with petioles kept in water to maintain hydration. Apparent photosynthesis of these heat-inactivated leaves was then subtracted from all measured CO₂–response curves, followed by updating the estimates of intercellular [CO₂] (C_i) given the new net assimilation rates (Flexas et al., 2007a; Li-Cor Biosystems, 2012). While heat-inactivated leaves do not perfectly match the characteristics of living leaves, they should mimic the characteristics of leaves better than wet filter paper (Flexas et al., 2007a), and many of the diffusional leaks in the 6400 system are probably upstream of the leaf chamber and therefore will be equally accounted for despite the material used to mimic intact leaves.

CO₂–response curves were initially fit to the C₃ biochemical model of photosynthesis (Farquhar et al., 1980) to estimate mitochondrial respiration in the light (R_d). Each curve was fit using the using the R package plantecophys (Duursma, 2015) and the estimate for R_d was extracted. Mean ecotype R_d values (see Supplementary Table 1 at JXB online) were calculated and used in the calculations below.

Electron transport rates through PSII measured combined with gas exchange and chlorophyll fluorescence supports alternative electron sinks, photosynthesis, and the photorespiratory pathway. If corrections for alternative electron sinks are implemented, we can partition the remaining electrons to their respective destinations in photosynthesis and photorespiration. We first quantified the quantum yields of CO₂ fixation (Φ_CO2) and PSII (Φ_PSII) (Valentini et al., 1995; Long and Bernacchi, 2003) as:

$${\Phi }_{\text{CO2}}=\left(A_{\text{N}}+R_{\text{d}}\right)/(\text{aPPFD})$$ (1)

$${\Phi }_{\text{PSII}}=\left(F_{\text{m}}\text{'}–F_{\text{s}}\right)/F_{\text{m}}\text{'}$$ (2)

where A_N is the leak-corrected net assimilation rate, α is the leaf absorptance measured as above; PPFD, F_mʹ, and F_s were taken from the 6400 output. The relationship of Φ_CO2 and Φ_PSII under non-photorespiratory conditions (1% O₂) is linear (Genty et al., 1989) with the intercept representing the share of electrons destined to alternative sinks and the slope indicating the number of electrons required for reducing a CO₂ molecule in vivo. Assuming that this electron partitioning holds under photorespiratory conditions, Φ_PSII was calibrated (Φ_e; Valentini et al., 1995) as:

$$\text{At 1}\%{\text{ O}}_{\text{2}}:{\Phi }_{\text{PSII}}=k{\Phi }_{\text{CO2}}+b$$ (3)

$$\text{At 21}\%{\text{ O}}_{\text{2}}:{\Phi }_{\text{e}}=\text{4}\left({\Phi }_{\text{PSII}}–b\right)/k$$ (4)

where k and b are the slope and intercept of the linear regression, respectively, and four is the theoretical number of electrons required for a single carboxylation (Long and Bernacchi, 2003). Φ_e in Equation 4 represents the quantum efficiency of PSII corrected for any alternative electron sinks. With Φ_e we calculated the total electron flux through PSII used to support both photosynthesis and photorespiration (J_T):

$$J_{\text{T}}={\Phi }_{\text{e}}\text{PPFD}$$ (5)

and

$$J_{\text{T}}=J_{\text{C}}+J_{\text{O}}$$ (6)

where J_C and J_O are the electron flows to carboxylation and oxygenation, respectively. Then assuming that four electrons are required for each CO₂ carboxylation, and eight electrons for each CO₂ release following an oxygenation event:

$$J_{\text{C}}=\text{4}\left(A_{\text{N}}+R_{\text{d}}+\text{PR}\right)$$ (7)

$$J_{\text{O}}=\text{8PR}$$ (8)

where PR is the photorespiratory CO₂ release rate, calculated as:

$$\text{PR}=\left[J_{\text{T}}–\text{4}\left(A_{\text{N}}+R_{\text{d}}\right)\right]/\text{12}$$ (9)

We then insert Equation 9 into Equations 7 and 8 to partition electrons between the two pathways:

$$J_{\text{C}}=\left[J_{\text{T}}+\text{8}\left(A_{\text{N}}+R_{\text{d}}\right)\right]/\text{3}$$ (10)

$$J_{\text{O}}=\text{2}\left[J_{\text{T}}–\text{4}\left(A_{\text{N}}+R_{\text{d}}\right)\right]/\text{3}$$ (11)

Photorespiration can be estimated with other methods, each with assumptions, benefits, and drawbacks (Busch, 2013). We opted for the above method, which combines two independent data sources, gas exchange and fluorescence. Comparison with another model yielded comparable results (Supplementary Fig. S1).

A reliable estimate of J_T also allows for the calculation of mesophyll conductance (g_m) with the variable-J method (Harley et al., 1992):

$$g_{\text{m}}=A_{\text{N}}/<C_{\text{i}}–\{\text{G}*\left[J_{\text{T}}+\text{8}\left(A_{\text{N}}+R_{\text{d}}\right)\right]/\left[J_{\text{T}}–\text{4}\left(A_{\text{N}}+R_{\text{d}}\right)\right]\}>$$ (12)

where Γ* is the photosynthetic CO₂ compensation point in the absence of R_d. There are several values of Γ* in the literature for A. thaliana, ranging from 3.3 Pa to 5.4 Pa (Kebeish et al., 2007; Flexas et al., 2007b; Cousins et al., 2011; Walker and Cousins, 2013; Walker et al., 2013; Weise et al., 2015). Calculation of mesophyll conductance is sensitive to variation in Γ* (Pons et al., 2009) so we estimated g_m with Γ* values of 3.64 Pa (Walker et al., 2013) and 4.47 Pa (Weise et al., 2015) since these fall within, but span, most of the range in estimates. The absolute value of g_m estimates was greater with Γ* of 4.47 Pa. The correlation of g_m estimates with Γ* of 3.64 Pa and 4.47 Pa was high (r=0.989, data not shown) and no differences in the resulting genetic variance of g_m were detected. Therefore we chose to use the value of Γ* from Walker et al. (2013) since it falls in between the extremes of Γ* for A. thaliana and is closest to the commonly used value from tobacco (Nicotiana tabacum) (Bernacchi et al., 2002). Theoretically, with the variable-J method, g_m can be estimated at any C_i where net assimilation (A_N) is carboxylation limited. However, since g_m is often observed to vary with C_i, we estimated g_m from a common, near-ambient [CO₂] of 325 µmol mol^–1 (C_i range 240–285). Carboxylation should be limiting at this concentration, it should more accurately reflect the C_i of leaves with an intact boundary layer, and we saw no significant difference in estimates at ambient [CO₂]s of 325 µmol mol^–1 or 400 µmol mol^–1 (data not shown). With a known g_m, the [CO₂] in the chloroplast (C_c) was calculated as:

$$C_{\text{c}}=C_{\text{i}}–A_{\text{N}}/g_{\text{m}}$$ (13)

With values of C_c we again fit the biochemical model of C₃ photosynthesis (Farquhar et al., 1980) using plantecophys (Duursma, 2015) to estimate the maximum carboxylation capacity (V_Cmax) and maximum electron transport capacity (J_max). Since there is no way to ensure the PPFD was truly saturating for all A_N–C_i curves, J_max is reported as J₈₅₀ (i.e. subscripted with the measurement PPFD of 850 µmol photons m^–2 s^–1) (Buckley and Diaz-Espejo, 2015).

Statistical analysis

All computations and statistical analyses were performed with the R statistical computing environment (R Core Team, 2015). Models with ecotype and replication block as random effects were fit with the lmer function (Bates et al., 2015) for each trait using restricted maximum likelihood. The significance (P<0.05) of ecotype was determined with likelihood ratio tests between models with and without the ecotype effect. Given the number of traits investigated, we corrected all P-values to account for multiple testing using the false discovery rate (Benjamini and Hochberg, 1995).

For traits with a significant ecotype effect, genetic variation for each was then determined by partitioning variance to ecotype (V_G), replication block (V_E), and residual variation (V_R). Broad-sense heritability (H²) was calculated as H²=V_G/(V_G+V_E/6+V_R), where V_E was divided by six to account for the six replicate blocks in the experimental design. Additionally, for each trait with a significant ecotype effect, we calculated best linear unbiased predictors (BLUPs) as the model-predicted ecotype values. Phenotypic correlations between traits were calculated as Pearson product–moment correlations with all observations of the two traits. Likewise, genetic correlations were calculated as the Pearson product–moment correlation of trait BLUPs. Again, P-values were adjusted to account for the large number of comparisons (Benjamini and Hochberg, 1995). Trait values in ecotypes with spring (n=8) versus winter (n=6) growth habits were compared with Welch’s unequal variance t-tests to account for the unequal sample sizes.

Results

We observed substantial phenotypic variation for leaf-level physiological traits in the 14 A. thaliana ecotypes assessed here. Ecotypes are naturally occurring genetically distinct populations allowing separation of phenotypic variation into its genetic, environmental, and residual components and calculation of broad-sense heritability of traits (Table 3). The ecotypes differed for all traits examined; that is, genetics played a significant role in determining the phenotypic range of a given ecotype. Stomatal conductance to CO₂ (g_sc; see Table 2 for all trait abbreviations and their units) and mesophyll conductance (g_m) differed 2.4- and 2.5-fold, respectively, across all observations (ranging 0.0724–0.259 µmol CO₂ m^–2 s^–1 and 0.356–1.20 µmol CO₂ m^–2 s^-1 Pa^–1), while variation among ecotype BLUPs (i.e. genotypic variation) was 57% and 41%, respectively. Conductance through the full CO₂ diffusion path (g_tot) varied 1.8-fold phenotypically (range: 0.0248–0.0707 µmol CO₂ m^–2 s^-1) and 0.4-fold genotypically. Reductant supply to the photosynthetic reactions varied similarly, though at lower magnitudes. Total, calibrated, linear electron transport (J_T) rates varied 1.4-fold phenotypically (range: 62.7–150 µmol e^– m^–2 s^–1) and 21% genotypically. Total electron transport was partitioned to electrons destined for carboxylation (J_C) and oxygenation (J_O) reactions, which varied 1.4- and 0.80-fold phenotypically (ranging 42.2–99.8 µmol e^– m^–2 s^–1 and 19.0–57.5 µmol e^– m^–2 s^–1, respectively), or 19% and 29% genotypically, respectively. It follows that A_N differed 1.6-fold phenotypically (range: 6.97–18.3 µmol CO₂ m^–2 s^–1) and 21% genotypically. Photorespiration (PR) also varied, >2-fold phenotypically (range: 2.38–7.19 µmol CO₂ m^–2 s^–1) and 29% genotypically. As implied by the presence of genotypic variation, estimates of broad-sense heritability were significant for all traits above (Table 3).

Table 3.

Variance components and heritability estimates for the primary traits investigated

Trait	V G	V E	V R	H 2	P
g sc	3.72e-4	7.19e-5	8.44e-4	0.30	*
g m	0.00733	0.00243	0.0147	0.33	***
g tot	2.35e-5	6.55e-6	5.71e-5	0.29	**
J T	75.8	57.4	218	0.25	**
J C	25.8	20.3	88.3	0.22	*
J O a	16.2	9.27	37.5	0.29	**
V Cmax	21.3	16.7	60.6	0.25	**
J 850	148	55.9	228	0.38	***
A N	0.915	0.552	3.22	0.22	*
PRa	0.253	0.145	0.586	0.29	**
A N/gs	52.7	15.0	55.2	0.48	***
δ13C	0.930	0.0144	0.291	0.76	***
N mass	0.123	0.00357	0.0504	0.71	***
N area	0.0219	0.00110	0.0116	0.65	***
LMA	5.72	0.450	2.88	0.67	***

The genotypic variance (V_G), environmental variance (V_E), residual variance (V_R), broad-sense heritability (H²), and significance (P) are presented for each of the traits. All variance components were estimated with restricted maximum likelihood. V_E represents the portion of phenotypic variance attributable to replication blocks, and V_R is the remaining residual variance in the models. Significance values are from likelihood ratio tests comparing models with and without the random effect of ecotype, where *P<0.05, **P<0.01, and ***P<0.001 after correcting the false discovery rate for multiple testing.

^aNote that PR and J_O are direct numeric transformations of one another.

Table 2.

List of all trait abbreviations used and their units

Trait	Abbreviation	Units
Ambient CO2 concentration	C a	µmol CO2 mol–1 air
Intercellular CO2 concentration	C i	µmol CO2 mol–1 air
Chloroplast CO2 concentration	C c	µmol CO2 mol–1 air
Stomatal conductance to water vapor	g sH	mol H2O m–2 s–1
Stomatal conductance to CO2	g sc	mol CO2 m–2 s–1
Mesophyll conductance	g m	µmol CO2 m–2 s–1 Pa–1
Total CO2 conductance	g tot	mol CO2 m–2 s–1
Total linear electron transport	J T	µmol e– m–2 s–1
Electron transport to carboxylation	J C	µmol e– m–2 s–1
Electron transport to oxygenation	J O	µmol e– m–2 s–1
Maximum carboxylation capacity	V Cmax	µmol CO2 m–2 s–1
Maximum electron transport capacity	J max (J850a)	µmol e– m–2 s–1
Net assimilation rate	A N	µmol CO2 m–2 s–1
Photorespiratory CO2 efflux rate	PR	µmol CO2 m-2 s-1
Intrinsic water use efficiency	A N/gsH	µmol CO2 mol–1 H2O
Integrated water use efficiency	δ 13C	‰
Leaf nitrogen content	N mass	%
Nitrogen per unit leaf area	N area	g m–2
Leaf dry mass per area	LMA	g m–2
Leaf dry matter content	LDMC	g dry g–1 wet
Leaf thickness	T L	mm

^aThe symbol J₈₅₀ is used throughout to indicate that J_max was estimated at a photon flux density of 850 µmol m^–2 s^–1.

Leaf structural and WUE traits also varied among ecotypes. Nearly half of the variation in both LMA and LDMC was contributed by genetics, with 46% and 39% of variation among ecotype BLUPs for the two traits that varied ~1- and 0.75-fold phenotypically. Intrinsic WUE (A_N/g_sH) differed 1.5-fold across all observations and 42% (i.e. 21 µmol CO₂ mol^–1 H₂O) among ecotypes. Leaf carbon isotope composition (δ¹³C), a metric of integrated WUE, differed 4.8‰ among observations and 2.9‰ among BLUPs. Broad-sense heritability estimates were generally higher for WUE and structural traits than for photosynthetic traits (Table 3).

Physiological leaf traits were by and large correlated with one another both phenotypically (r_p) and genotypically (r_g) (Table 4). Phenotypic correlations signify that the traits are co-ordinated in some way, while genetic correlations signify that some portion of that co-ordination results from genetic differentiation among populations (i.e.ecotypes). Phenotypically the traits supplying the Calvin–Benson cycle with CO₂, g_sc and g_m, were correlated (r_p=0.49, P<0.001), though the genetic correlation was not significant (Table 4; P>0.1). All CO₂ conductance traits (g_sc, g_m, and g_tot) were correlated with A_N, with the sole exception of the genetic correlation between A_N and g_sc (Fig. 1). Phenotypic and genetic correlations between J_T and A_N were both significant (r_p=0.86, r_g=0.79, P<0.001 for both), but were lower than the correlation with A_N after partitioning electrons specifically to carboxylation, namely J_C (r_p=0.96, r_g=0.92, P<0.001 for both). Both g_sc and g_m were phenotypically correlated with J_T (g_sc: r_p=0.37, P<0.01; g_m: r_p=0.67, P<0.001) and J_C (g_sc: r_p=0.49, P<0.001; g_m: r_p=0.81, P<0.001) (Fig. 2 A), but only g_m was genetically correlated with electron transport (J_T: r_g=0.66, P<0.05; J_C: r_g=0.81, P<0.01). Maximum biochemical capacities for carboxylation (V_Cmax) and electron transport (J₈₅₀) were tightly correlated both phenotypically (r_p=0.90, P<0.001) and genetically (r_g=0.92, P<0.001) (Fig. 2B).

Table 4.

Pearson product–moment correlation matrix of physiological traits

	g sc	g m	g tot	J T	J C	J O	V Cmax	J 850	A N	PR	A N/gs	δ13C
g sc	–	0.49	0.79	0.37	0.49	0.17	0.43	0.25	0.61	0.17	–0.69	-0.36
g m	0.43	–	0.92	0.67	0.81	0.40	0.76	0.71	0.91	0.40	0.15	0.28
g tot	0.77	0.91	–	0.63	0.78	0.35	0.73	0.60	0.91	0.35	–0.21	0.02
J T	–0.11	0.66	0.41	–	0.97	0.94	0.72	0.64	0.86	0.94	0.25	0.40
J C	0.10	0.81	0.62	0.97	–	0.83	0.79	0.71	0.96	0.83	0.18	0.35
J O	–0.39	0.38	0.08	0.94	0.82	–	0.53	0.49	0.64	1.0	0.32	0.44
V Cmax	0.29	0.82	0.70	0.65	0.77	0.42	–	0.90	0.82	0.53	0.19	0.26
J 850	0.07	0.68	0.50	0.57	0.64	0.42	0.92	–	0.71	0.49	0.33	0.34
A N	0.44	0.95	0.88	0.79	0.92	0.53	0.81	0.63	–	0.64	0.07	0.23
PR	–0.39	0.38	0.08	0.94	0.82	1.0	0.42	0.42	0.53	–	0.32	0.44
A N/gs	–0.71	0.30	–0.10	0.74	0.62	0.82	0.40	0.48	0.34	0.82	–	0.67
δ13C	–0.71	0.31	–0.10	0.69	0.58	0.76	0.38	0.49	0.31	0.76	0.97	–

The phenotypic correlations are presented above the diagonal, and genotypic correlations are below. Genetic correlations were estimated from ecotype best linear unbiased predictors. Correlations in bold were statistically significant (P<0.05) after correcting for multiple testing. Abbreviations are as in Table 2. Note that since PR and J_O are direct numeric transformations of one another, they are perfectly correlated

Fig. 1.

Relationship between net CO₂ assimilation (A_N) and (A) conductance of CO₂ and (B) electron transport. Conductance of CO₂ is presented as stomatal (g_sc; right most fit line), mesophyll (g_m; middle fit line), and total conductance from the boundary layer to chloroplasts (g_tot; left fit line). Electron transport is presented as total calibrated-linear (J_T; right fit line), and as the partition of electrons utilized as reductant in carboxylation reactions (J_C; left fit line). Phenotypic (r_p) and genetic (r_g) correlations, where significant (P<0.01 for all here), are presented; (ns) is not significant. Each ecotype is represented by a unique symbol. Lines are mixed-model fits with ecotype identity and replication block treated as random effects.

Fig. 2.

The relationship between (A) electron transport driving carboxylation (J_C) with stomatal (g_sc; right fit line) and mesophyll (g_m; left fit line) conductance to CO₂, and (B) maximum electron transport (J₈₅₀) and carboxylation (V_Cmax) capacities. Each ecotype is designated by a unique open symbol. Filled symbols (solid triangles and squares in A; solid squares in B) represent ecotype best linear unbiased predictors. Lines are mixed-model fits to the bivariate relationships with ecotype identity and replication as random effects. When significant, phenotypic (r_p) and genetic (r_g) correlations are presented (P<0.01 for all); (ns) is not significant.

To estimate photorespiration (PR), we partitioned the portion of J_T used to support PR (J_O), and then calculated PR from J_O. While J_O and PR represent different aspects of physiology, they are calculated from the same combination of traits (i.e. A_N, R_d, and J_T), only differing with respect to the stoichiometric coefficients used in their calculation such that J_O and PR are directly proportional to one another. Therefore, comparisons with both traits yield equivalent statistical results—albeit with differing parameter estimates. We present results for PR only since we are interested specifically in carbon losses associated with photorespiratory CO₂ efflux. We detected a significant correlation between A_N and PR phenotypically (r_p=0.64, P<0.001; Fig. 3A), though the genetic correlation was not significant after controlling for multiple testing (r_g=0.53, 0.1>P>0.05). Since A_N is an input parameter in the calculation of PR, some correlation of A_N and PR is expected. We further test the physiological correlation of photosynthesis with photorespiration by comparing the biochemical parameters that underlie photosynthesis, namely V_Cmax and J₈₅₀, with PR. The phenotypic correlations of PR with V_Cmax (r_p=0.53, P<0.001) and J₈₅₀ (r_p=0.49, P<0.001) were lower than with A_N directly, but a clear positive relationship remained (Fig. 3B, C). No genetic correlations were detected between PR and V_Cmax or J₈₅₀ (Table 4).

Fig. 3.

Photorespiratory CO₂ efflux (PR) as a function of (A) net assimilation (A_N), (B) the maximum carboxylation capacity (V_Cmax), and (C) maximum electron transport capacity (J₈₅₀). Phenotypic correlations (r_p) are presented (P<0.001 for all); no genetic correlations were significant after correcting for multiple testing. Ecotypes are represented by unique symbols and ecotype means are overplotted as solid squares. Fit lines are drawn from mixed-models with ecotype and replication block as random effects.

Intrinsic WUE (A_N/g_sH) is determined by the interaction of A_N and g_sH, and therefore by any trait influencing A_N or g_sH (e.g. g_m and V_Cmax). Therefore, we assessed the extent to which each of these traits contributes individually to A_N/g_sH and/or δ¹³C. Both metrics of WUE were phenotypically correlated (r_p=0.67, P<0.001) and demonstrated an even stronger genetic correlation with each other (r_g=0.971, P<0.001)(Fig. 4A). The phenotypic (r_p= –0.67, P<0.001) and genetic (r_g= –0.96, P<0.001) correlations of δ¹³C with the ratio of intercellular to ambient [CO₂] (C_i/C_a) mirrored those with A_N/g_sH (Fig. 4B). Net assimilation rates were not correlated with A_N/g_sH, and only a weak phenotypic correlation was found with δ¹³C (r_p=0.276, P<0.05) (Table 4; Fig. 5A, D). Evidence of g_sH influencing WUE was stronger: phenotypic and genetic correlations were observed between g_sH and A_N/g_sH (r_p= –0.69, P<0.001; and r_g= –0.71, P<0.05) and δ¹³C (r_p= –0.36, P<0.01; and r_g= –0.71, P<0.05) (Table 4; Fig. 5B, E). The influence of g_m mirrored that of A_N in that it was only weakly correlated phenotypically with δ¹³C (r_p=0.276, P<0.05) (Table 4; Fig. 5C, G). The full path of CO₂ diffusional conductance, g_tot, was not associated with either metric of WUE (Table 4). Ecotype growth habit (spring or winter annual type) altered relationships with WUE. The correlation of δ¹³C with A_N, or g_m, was only significant for winter annual types (Fig. 5D, F), and with g_sH only for spring types (Fig. 5E).

Fig. 4.

Leaf-level integrated water use efficiency (δ¹³C) by (A) intrinsic water use efficiency (A_N/g_sH) and (B) the ratio of intercellular to atmospheric [CO₂] (C_i/C_a). Phenotypic and genetic correlations were significant (P<0.001) for both. Each ecotype is represented by a unique symbol, and solid squares are ecotype best linear unbiased predictors. Fit lines represent mixed-model fits with replication and ecotype as random effects.

Fig. 5.

Relationships between water use efficiency metrics and components effecting water use efficiency. Correlations between intrinsic water use efficiency (A_N/g_sH) and (A) A_N, (B) stomatal conductance to water vapor (g_sH), and (C) mesophyll conductance (g_m). Integrated water use efficiency (δ¹³C) is likewise correlated with (D) A_N, (E) g_sH, and (F) g_m. Ecotypes are represented by unique symbols and are shaded according to their life history strategy. Fit lines, presented when significant (P<0.05), are linear regressions for spring (B, E) or winter (B, D, F) subsets of the data.

The ecotypes used in this study originate from areas spanning a large latitudinal range, and thus areas differing widely in variables that covary with latitude (e.g. temperature and precipitation; Table 1). Ecotypes were also split between those exhibiting winter and spring annual life history strategies, and, with the exception of Kas-1, winter annual types tend toward higher latitudes (Table 1). Winter annual types had higher g_m (t=2.7, P<0.05; not shown), N_area (t=6.1, P<0.001), V_Cmax (t=3.1, P<0.01), J₈₅₀ (t=3.1, P<0.001), LMA (t=7.7, P<0.001), WUE (A_N/g_sH: t=4.3, P<0.001; δ¹³C: t=5.7, P<0.001) (Fig. 6), T_L (t=4.3, P<0.001), and LDMC (t=7.1, P<0.001) (Supplementary Fig. S1).

Fig. 6.

Comparisons of trait values in ecotypes exhibiting spring or winter annual life history strategies. The traits are (A) nitrogen content per unit leaf area (N_area), (B) maximum carboxylation capacity (V_Cmax), (C) maximum electron transport capacity (J₈₅₀), (D) intrinsic water use efficiency (A_N/g_sH), (E) integrated water use efficiency (δ¹³C), and (F) leaf mass per area (LMA). Winter annuals (n=6) have significantly (P<0.01) greater trait values than spring annuals (n=8) following unequal variance t-tests. Eight of the 14 ecotypes exhibit the spring habit. Box width is scaled to the relative sample size in each group (n=46 for spring, n=34 for winter).

Discussion

We found a strong co-ordination between assimilation and photorespiration as we show that photorespiration was phenotypically correlated with net carbon assimilation (A_N), and with the maximum capacities for carboxylation and electron transport (i.e. V_Cmax and J₈₅₀; Fig. 3). Molecular crosstalk between the Calvin–Benson cycle and the photorespiratory pathway indicates that the two processes should manifest at the physiological scale as co-ordination between photorespiratory CO₂ efflux (PR) and A_N. Indeed, the Calvin–Benson cycle and photorespiratory pathway are interlinked to such an extent at the molecular level that they are referred to holistically as a ‘supercycle’ (Timm et al., 2016). If flux through the photorespiratory pathway is too slow, the concentration of 2-phosphoglycolate will rise until it inhibits regeneration of RuBP through the Calvin–Benson cycle, ultimately suppressing carbon fixation (Igamberdiev et al., 2004; Bauwe et al., 2012). In addition, the photorespiratory intermediates glyoxylate (Chastain and Ogren, 1989) and glycerate (Schimkat et al., 1990) act as signals to the Calvin–Benson cycle, allowing rapid co-regulation of the two pathways (Timm et al., 2016). Recent work now suggests that the scaling of A_N and PR is also maintained as a result of photorespiratory nitrate assimilation providing a secondary carbon sink in the form of amino acid production (Busch et al., 2018).

Genetic variation for photosynthetic traits is commonly observed in wild species (Comstock and Ehleringer, 1992; Geber and Dawson, 1997; Arntz and Delph, 2001; Geber and Griffen, 2003; Brouillette et al., 2014), crops (Reynolds et al., 2000; Gu et al., 2012; Liu et al., 2012; Tomeo and Rosenthal, 2017), and specifically in A. thaliana (McKay et al., 2003; Aliniaeifard and Van Meeteren, 2014; Easlon et al., 2014; Takahashi et al., 2015; van Rooijen et al., 2015). Natural variation in photorespiration has been reported in a few crops including tobacco (Zelitch and Day, 1973), alfalfa (Peterschmidt, 1980), and wheat (Aliyev, 2012). We demonstrate that heritable genetic variation for photorespiration also exists for A. thaliana (Table 3). Aliyev (2012) found diurnal and seasonal co-ordination between A_N and PR when contrasting high- and low-yielding wheat (Triticum aestivum) varieties, and that greater A_N and PR were associated with higher yielding varieties. It seems likely that genetic variation for PR exists in most crops. If and how natural variation in photorespiration exists in wild plants and whether this is associated with fitness is an interesting and an open question. If the significant broad-sense heritability for PR exists across plants, the implication is that natural selection could alter this trait if there are measurable fitness effects of PR. We could not detect a genetic correlation between PR and A_N (or V_Cmax, or J₈₅₀) here, but additional analyses are needed to assess the nature and extent of linkage between A_N and PR more broadly and if or how selection could act on either trait independently.

Co-ordination of photosynthetic drivers

Across the A. thaliana ecotypes, there was broad co-ordination among photosynthetic traits. We expected to see phenotypic correlations among traits supporting photosynthesis because V_Cmax and J_max are co-ordinated at various scales reflecting optimization of nitrogen distribution among photosynthetic proteins to balance photosynthetic limitation between carboxylation and RuBP regeneration (Wullschleger, 1993; Geber and Dawson, 1997; Walker et al., 2014). Co-ordination should also be expected between traits influencing CO₂ diffusion from the intercellular air spaces to Rubisco (i.e. g_sc, g_m, and g_tot), the rates of electron transport through the light-dependent reactions of photosynthesis (i.e. J_T and J_C), and carboxylation capacity (i.e. V_Cmax). Indeed, g_sc, g_m, J_T, J_C, J₈₅₀, and V_Cmax were all phenotypically intercorrelated (Table 3). Both g_m versus J_C and V_Cmax versus J₈₅₀ also exhibited strong genetic correlations. This indicates that the essential co-ordination of the photosynthetic light-dependent and light-independent reactions supporting the co-limitation of photosynthesis by RuBP carboxylation and regeneration is not simply a product of optimal allocation (Evans, 1989; Hikosaka and Terashima, 1995), but rather is constrained by genetics, or alternatively has been constrained by selection (Donovan et al., 2011). Genetic correlations result from pleiotropy or linkage disequilibrium and, while we cannot disentangle the contribution of each of these mechanisms here, this experimental system provides a path for doing so in future studies. Regardless of the mechanism, the strength of the genetic correlation between V_Cmax and J₈₅₀ here, and in at least one other study (Geber and Dawson, 1997), indicates that selection on either trait should simultaneously alter the other.

Drivers of variation in water use efficiency (WUE)

Variation in leaf-level WUE emerges from the complex interaction of a suite of traits. We expected to see g_m positively correlated with intrinsic WUE (A_N/g_sH) due to the positive relationship between A_N and g_m, and because g_m should increases A_N without increasing g_sH. A strong case has been made for greater g_m driving greater intrinsic WUE (Flexas et al., 2013), though this idea depends on A_N being a strong driver of variation in A_N/g_sH. A previous study in A. thaliana (Easlon et al., 2014) and one in soybean (Glycine max) (Tomeo and Rosenthal, 2017) indicate that in herbaceous annual species with relatively high g_sH, A_N/g_sH is determined to a greater extent by variation in g_sH than A_N—at least under conditions where water is not limiting.

Several complications arise when interpreting A_N/g_sH as WUE because it is a ratio of traits that covary; these complications are partially avoided by also considering integrated WUE (i.e. δ¹³C, the stable carbon isotope ratio of leaf tissue relative to a standard source). With leaves grown in the same environment, leaf δ¹³C is indicative of discrimination against ¹³C, which relates to the ratio of intercellular to ambient [CO₂] (C_i/C_a) over the lifetime of carbon accumulation in the leaf (Farquhar et al., 1982; Dawson et al., 2002; Seibt et al., 2008). Any change in either A_N or g_sH will alter C_i independently of C_a, so the C_i/C_a ratio is related to A_N/g_sH, leading to the interpretation of δ¹³C as proportional to the mean leaf lifetime WUE. Rubisco discrimination against ¹³C is strictly speaking responsive to the [CO₂] in the chloroplast (C_c), and therefore is also dependent on g_m (Seibt et al., 2008). All else being equal, higher g_m leads to greater C_c and ¹³C discrimination (and more negative δ¹³C). Variation for δ¹³C in A. thaliana is well documented in the literature among ecotypes (Nienhuis et al., 1994; McKay et al., 2003; Easlon et al., 2014) and inbred lines (Nienhuis et al., 1994; Juenger et al., 2005). As in other studies (Schuster et al., 1992; Easlon et al., 2014), we show that broad-sense heritability for δ¹³C was high among the ecotypes (Table 3) and δ¹³C was correlated closely with both A_N/g_sH and C_i/C_a (Fig. 4).

Variation in trait co-ordination: winter versus spring ecotypes

Considering all ecotypes, regardless of growth habit, we confirm earlier results (Easlon et al., 2014) that δ¹³C is more closely correlated with g_sH than with A_N. However, when we consider each growth habit separately, this alters the interpretation of which traits drive variation in δ¹³C. Specifically, among spring annual ecotypes, only g_sH was significantly related to δ¹³C. In contrast, for those with winter annual habits, g_sH was not correlated with δ¹³C, but g_m and A_N were positively correlated with δ¹³C (Fig. 5). Thus, higher g_m, and not lower g_sH, led to higher integrated WUE in winter annuals. The latter result is interesting as it provides some evidence that the influence of g_m on A_N can increase WUE (e.g. Flexas et al., 2013, 2016).

WUE is well described as a trade-off with respect to the time required to transition from vegetative to reproductive growth in annual weedy plants including A. thaliana (McKay et al., 2003) and Polygonum arenastrum (Geber, 1990; Geber and Dawson, 1990). In both species there is a continuum of traits conferring a drought avoidance to tolerance trade-off: fast developing genotypes have lower WUE and reach the reproductive transition quickly to avoid drought, whereas slower growing genotypes have greater WUE allowing them to tolerate drought more effectively (Geber, 1990; Geber and Dawson, 1990; McKay et al., 2003). In our study, ecotypes with the winter habit exhibited a greater phenotypic and genotypic range of WUE (as A_N/g_sH or δ¹³C; Figs 5, 6), therefore sampling the phenotypic space of the drought avoidance versus tolerance axis more completely than spring habit ecotypes. The added variance in the WUE traits was modulated by roughly equal ranges of A_N and g_m for winter and spring ecotypes, but winter ecotypes presented approximately half the range in g_sH. Several studies have suggested that selection to improve WUE by enhancing g_m is contingent upon maintaining g_sH relatively constant (Flexas et al., 2013, 2016), with a report on soybean cultivars indicating that a strong correlation between g_sH and g_m impedes our ability to alter WUE through selection on g_m (Tomeo and Rosenthal, 2017). However, here we show that natural selection may have already followed this proposed route (i.e. modulating g_m to alter WUE) in an array of genotypes with a range of WUEs.

Winter habit ecotypes possess a suite of traits that differ from spring ecotypes, including greater structural robustness (i.e. T_L, LMA, and LDMC), instantaneous and integrated WUE A_N/g_sH and δ¹³C, leaf N (N_area), mesophyll conductance (g_m), and photosynthetic capacity (V_Cmax and J₈₅₀). We do not know if winter ecotypes have higher Rubisco content or activation, but one could predict that selection would favor this in plants adapted to shorter days. We hypothesize that thicker leaves (i.e. greater T_L) in A. thaliana arise due to a thicker palisade mesophyll layer which allows for a larger area of mesophyll cell surface exposed to intercellular airspace. Greater LMA is consistent with a greater number of mesophyll cells per unit leaf area (Milla-Moreno et al., 2016) enabling higher N_area, V_Cmax, and J₈₅₀. Taken together, this suggests that a greater number of parallel diffusion paths exist for CO₂ into the mesophyll cells (e.g. Milla-Moreno et al., 2016) of winter ecotypes, explaining their greater g_m. Likewise, plants with greater g_m have a higher [CO₂] in their chloroplasts, slightly altering the ratio of CO₂ to O₂, potentially driving some of the variation in photorespiration and V_Cmax detected here.

The winter habit ecotypes tend toward colder, more northerly latitudes than spring types, though the small number of ecotypes, coupled with imprecise geographic locations (Table 1), prevent accurate correlation of trait values and climate or latitude of origin. However, trait–latitude relationships suggested here were observed in Populus balsamifera where greater assimilation rates, electron transport, and mesophyll conductance were observed in genotypes from more northern latitudes, and a positive correlation was found between latitude and WUE (Soolanayakanahally et al., 2009). Interestingly, LMA also acclimates in colder climates. Greater A_N and LMA are reported for lowland Plantago species compared with an alpine congener when grown at low temperatures (Atkin et al., 2006). LMA increased at colder temperatures, effectively enhancing photosynthetic capacity per unit leaf area (Atkin et al., 2006), allowing for positive carbon balance despite the absolute decline in photosynthesis with decreasing temperatures. Finally, we illustrate that photosynthetic trait–latitude responses were similar in Arabidopsis to that reported in Populus (Soolanayakanahally et al., 2009) by modeling the response of assimilation to temperature in winter and spring ecotypes (Fig. 7). Not surprisingly, winter ecotypes are predicted to have higher A_N than spring ecotypes at all temperatures when grown and measured under identical conditions (i.e. at 25 °C) because both V_Cmax and J₈₅₀ are greater in winter ecotypes.

Fig. 7.

The photosynthetic temperature response for an average spring (lower curves) and winter (upper curves) ecotype. The dotted lines represent the Rubisco-limited photosynthetic rate, the dashed lines represent the RuBP-limited photosynthetic rate, and the solid lines represent the minimum of the two across the temperature range of 1–35 °C. The temperature response is modeled using the average V_Cmax and J₈₅₀ measured at 25 °C for spring and winter habit ecotypes. The Rubisco-limited function was modeled according to a simple temperature response function (Equation 10; Bernacchi et al., 2013) using a standard energy of activation constant for V_Cmax (Bernacchi et al., 2001). The RuBP-limited function was modeled using a complete temperature response function (Equation 12, Bernacchi et al., 2013), assuming an optimum temperature for J_max of 25 °C and utilizing the heat of activation, deactivation, and entropy constants of Bernacchi et al. (2003). All other Rubisco parameters (K_C, K_O, and Γ*) and R_d were modeled using the simple temperature response function (Equation 10; Bernacchi et al., 2013), and heat of activation constants were taken from Bernacchi et al. (2001).

Conclusion

Arabidopsis thaliana ecotypes provide a powerful system for dissecting the mechanistic and genetic determinants of complex traits. In vivo estimates of photorespiration indicate that the molecular co-ordination of the Calvin–Benson cycle with the photorespiratory pathway scales to the physiological level as a correlation between photorespiratory CO₂ efflux and CO₂ assimilation. The absence of genetic correlation between photosynthesis and photorespiration is perhaps surprising given the strong physiological links between these processes (Busch et al., 2018). Strong genetic correlations between mesophyll conductance and electron transport supporting carboxylation, and between maximum carboxylation and electron transport capacities, point to shared inheritance for traits underlying variation in photosynthesis among ecotypes. Structural and physiological traits were differentiated by ecotype life history strategy, with winter annuals generally exhibiting greater structural robustness, physiological capacity, and WUE. Integrated WUE was positively correlated with assimilation rate and mesophyll conductance in winter, but not spring, ecotypes, largely resulting from a lack of variance for g_sH in winter ecotypes and demonstrating that if g_sH is held relatively constant, WUE is responsive to mesophyll conductance.

Supplementary Data

Supplementary data are available at JXB online.

Table S1. Mean (± SE) ecotype trait values

Fig. S1. A comparison of photorespiration estimated by two methods.

Fig. S2. Leaf thickness (T_L) and leaf dry matter content (LDMC) of spring and winter annual ecotypes.