Nutrient availability regulates Deschampsia antarctica photosynthetic and stress tolerance performance in Antarctica

Abstract

Deschampsia antarctica is one of the only two native vascular plants in Antarctica, mostly located in the ice-free areas of the Peninsula’s coast and adjacent islands. This region is characterized by a short growing season, frequent extreme climatic events, and soils with reduced nutrient availability. However, it is unknown whether its photosynthetic and stress tolerance mechanisms are affected by the availability of nutrients to deal with this particular environment. We studied the photosynthetic, primary metabolic, and stress tolerance performance of D. antarctica plants growing on three close sites (<500 m) with contrasting soil nutrient conditions. Plants from all sites showed similar photosynthetic rates, but mesophyll conductance and photobiochemistry were more limiting (~25%) in plants growing on low-nutrient availability soils. Additionally, these plants showed higher stress levels and larger investments in photoprotection and carbon pools, most probably driven by the need to stabilize proteins and membranes, and remodel cell walls. In contrast, when nutrients were readily available, plants shifted their carbon investment towards amino acids related to osmoprotection, growth, antioxidants, and polyamines, leading to vigorous plants without appreciable levels of stress. Taken together, these findings demonstrate that D. antarctica displays differential physiological performances to cope with adverse conditions depending on resource availability, allowing it to maximize stress tolerance without jeopardizing photosynthetic capacity.

Mesophyll conductance, nutrient mobilization, photobiochemistry, photosynthetic limitations, polyamines, primary metabolism, stomatal conductance

Introduction

Maritime Antarctica and the Antarctic Peninsula (AP) are amongst the world regions that have experienced the strongest warming trends during the last part of the 20th century (Turner et al., 2016; Jones et al., 2019). A small fraction of the territory of the AP is free of ice and snow, and only two vascular species have naturally colonized these ice-free habitats: the Antarctic hair grass Deschampsia antarctica Desv. (Poaceae) and the Antarctic pearlwort Colobanthus quitensis (Kunth) Bartl. (Caryophyllaceae) (Peat et al., 2007).

Climate conditions in the AP, even during the short growing season, are characterized by slightly positive temperatures during the day and frequent sub-zero temperatures during the night, along with strong winds and highly variable solar radiation (Cavieres et al., 2016; Convey et al., 2018). Furthermore, the soils of the AP are characterized by low nutrient availability (Blume et al., 2002; Beyer et al., 2020), imposing an additional stress that plants must deal with in order to survive. Moreover, soil conditions may change over a short distance, in many cases from meter to meter, due to strong influences of cryoturbation and solifluction (Blume et al., 2002) and nutrient enrichments from seabird guano, as well as debris from mosses, lichens, and algae (Campbell and Claridge, 1987; Beyer et al., 1999; Bolter, 2011). Nutrient availability in cold ecosystems strongly depends on soil temperature, soil moisture (SM), and the activity of soil microbiota, which in turn control nitrogen fixation and mineralization, organic matter (OM) decomposition, etc. (Hobbie et al., 2007; Convey et al., 2012; Farrer et al., 2013). In fact, several studies in the Arctic tundra positively relate SM with increased OM decomposition, that would release nutrients to the soil solution (Glanville et al., 2012; Hicks Pries et al., 2013; Scharn et al., 2021), facilitating their availability for the tundra plant’s uptake (Schimel et al., 1996; Semenchuk et al., 2015). Considering the observed global warming trends and the expected higher temperatures for the AP as well as water availability, soil nutrient cycles could be altered, with the potential to influence plant productivity (Wasley et al., 2006; Hill et al., 2011, 2019). In this sense, changes in the availability of nutrients in Antarctic soils could alter the ecophysiological performance of the species by affecting the allocation of resources between traits and physiological processes related to productivity, stress tolerance, or reproduction, that could finally lead to changes in abundance and composition of plant communities as has been observed in arctic (Chapin et al., 1996; Hobbie et al., 2007; Peterson, 2014) and alpine tundras (Bowman et al., 1993; Körner, 2021).

Deschampsia antarctica (hereafter DA) is distributed from the Andes of central Argentina and Chile to sub-Antarctic islands and adjacent islands of the AP, spanning diverse soil types with a wide range of nutrient availability (Komárková et al., 1985; Smith, 2003; Androsiuk et al., 2021). Several authors reported that DA populations in the AP showed higher density in soils containing high levels of P and OM, such as those found on the surroundings of penguin rookeries and zones with elevated moss cover, suggesting that soil nutrients are important drivers of DA abundance (Smykla et al., 2007; Park et al., 2012, 2013). Different physiological adaptations to face soil nutrient scarcity have been described in this species; among them, the capacity of incorporating N in the form of free amino acids and short-chain peptides is remarkable (Hill et al., 2011). Also, under controlled conditions, and in terms of biomass production, a larger and faster growth response was described when exposed to NH₄⁺ than NO₃^–, probably due to the lower energetic cost related to NH₄⁺ assimilation (Rabert et al., 2017).

In any case, in Antarctica, DA needs to obtain a positive carbon balance in an environment characterized by low temperature and limited availability of nutrients. Xiong et al. (1999), measuring short-term photosynthetic temperature response curves, described that DA can sustain up to 30% of its assimilation rates at 0 °C. Moreover, DA showed structural and physiological leaf traits typical of species from xeric environments, such as an elevated leaf mass area (LMA), thick cell walls, and a high Rubisco specificity factor (Sáez et al., 2017). Nutrients such as N and P are directly linked with the carbon assimilation capacity (Walker et al., 2014); N is a major and essential element of the whole photosynthetic machinery, from chlorophylls to Rubisco (Evans and Clarke, 2019), while P is involved in ribulose bisphosphate (RuBP) regeneration, production of energy (ATP) and reduction power (NADPH), synthesis of phosphorylated intermediates of the Calvin–Benson cycle (CBC), and regulation of starch and sucrose synthesis (Lambers et al., 2008). Low temperature environments can exacerbate photosynthetic limitations by constraining enzymatic activity (Hurry et al., 2000). Additionally, the reduced nutrient availability of the Antarctic tundra leads to photoinhibition and reduced carbon assimilation (Ensminger et al., 2006).

When photosynthesis (A) is constrained by one or a combination of these environmental factors (i.e. temperature and nutrient availability), it cannot operate as an energy sink, thus driving an increase in reactive oxygen species (ROS) (Fernández-Marín et al., 2020). Under low temperatures and high radiations, DA showed a higher activation of ROS-scavenging mechanisms, such as the water–water cycle, to protect PSII, and higher levels of superoxide dismutase and ascorbate ­peroxidase compared with other species from the same family (Pérez-Torres et al., 2004a, b, 2007). DA also displays an elevated antioxidant capacity which, in combination with leaf nutrient mobilization, suggests a trade-off between catabolism and maintenance of leaf functionality under stress (Clemente-Moreno et al., 2020). Additionally, DA displays a high activity of anti-freezing proteins, high levels of sugars (which act as osmoprotectants), and an elevated percentage of unsaturated fatty acids, thus ensuring membrane fluidity and functionality under low temperatures (Bravo et al., 2001).

Despite the knowledge obtained on the physiological characteristics of DA, the answers to the following questions remain elusive. Has DA developed differential photosynthetic and stress tolerance mechanisms based on the availability of nutrients? Is there any link between the photosynthetic capacity and the molecular and physiological mechanisms for coordinating stress tolerance? Which are the molecular and physiological mechanisms driving a positive net carbon balance without jeopardizing survival in one of the most extreme environments for vascular plants on Earth? Understanding these processes will allow us to foretell the performance and putative success of DA under the predicted warming scenarios in maritime Antarctica.

Materials and methods

Experimental sites and environmental conditions

Deschampsia antarctica plants were studied near the Henryk Arctowski Polish Antarctic Station (62°09ʹS, 58°28ʹW) in King George Island (KGI) in the South Shetlands (maritime Antarctica) between 5 and 12 February 2018. Three different sites were selected to investigate the plants’ performance under different soil nutrient regimes: (i) Puchalski hill (PUCH) (62°09ʹ47.11ʹʹS, 58°27ʹ58.15ʹʹW), characterized by a complete vegetation cover dominated by lichens and mosses, such as Sanionia georgicouncinata, Polytrichum piliferum, and Polytrichastrum alpinum (Kozeretska et al., 2010), where DA grows associated with the moss carpet created by these species (Casanova-Katny and Cavieres, 2012; Cavieres et al., 2018); (ii) the surroundings of a penguin colony (PINGU) (62°09ʹ44.46ʹʹS, 58°27ʹ45.15ʹʹW), with soil affected by significant penguin droppings, close to the seashore, characterized by gravel and rocks covered by lichens; and (iii) within the Henryk Arctowski station area (BASE), under a human-altered environment, with no homogeneous plant community established, and usual presence of different birds and marine mammals employing this area as a resting site (62°09ʹ37.32ʹʹS, 58°28ʹ23.83ʹʹW). It is important to note that distances between these sites and the base station are <500 m, and that the individuals were not sheltered and thus shared the climatological conditions prevailing in the area. Plants at PUCH showed small leaves with a mixed pattern of greenish and yellowish leaves, indicating different degrees of leaf senescence within the same individual. However, at PINGU and BASE sites, DA plants were much more vigorous and with a green, homogeneous pattern for all leaves (Fig. 1).

Fig. 1.

Selected locations for the analysis of Deschampsia antarctica (DA) plants. (A) DA growing between mosses and lichens at the top of Puchalski hill (B), (C) DA growing in the vicinity of the penguin rookeries (D), and (E) DA individuals growing in the surroundings of the Henryk Arctowski Research Polish Station (F) at King George Island (South Shetland, maritime Antarctica). We thank Dr Melanie Morales for kindly providing us with the images.

From December 2017 to February 2018, the mean air temperature at PUCH was ~6.9 °C (with an absolute minimum of –4.1 °C in December 2017, and maximum of 16.6 °C in February 2018) (Supplementary Fig. S1). Year-round precipitation falls mostly as snow (~700 mm), and summer days are usually windy and cloudy, although for short periods clear skies produce high irradiance incidences of ~2000 μmol m^–2 s^–1 (Angiel et al., 2010).

Soil characterization

Soil samples were collected at each of the three study sites (three plots per site, 3 × 3 m each), with three independent subsamples per plot in the vicinity of the plant population. The top-soil layer (5–7 cm) was manually removed, and samples were taken from the top 7–15 cm of soil at each plot. The three independent samples were obtained by mixing three subsamples (300 g each) randomly collected within each plot (avoiding large rocks and gravel outcrops) with DA individuals being between 10 cm and 50 cm distant from soil subsampling. After the removal of plant and root remains from the soil samples, they were passed through a 2 mm sieve and dried in the oven at 62 °C up to constant weight.

N and P contents were determined by the Kjeldahl and Mehlich methods, respectively (Mehlich, 1984). The content of exchangeable cations [cation exchange capacity (CEC) for Ca²⁺, Mg²⁺, K⁺, and Na⁺] was analyzed after extraction with ammonium acetate (Metson, 1956). pH, electric conductivity, and OM content were measured following previous protocols (Chapman and Pratt, 1962; Bower and Wilcox, 1965). All the analyses were performed by the soil analysis service of the Agricultural Council of the Balearic Islands Government.

The SM at each site was assessed as volumetric water content measured next to the sampled plants using a humidity probe (WET-2 Sensor/HH2 Moisture Meter, Delta-T Devices, Cambridge, UK).

The three study sites presented a similar texture and can be classified as sandy soils (Supplementary Table S1); in contrast, soil fertility parameters of the three sites significantly differed. While all the samples showed an extremely high exchangeable sodium percentage (ESP; >50%), the soil from PUCH was characterized by low pH (5.15) and electric conductivity (0.12 dS m^–1), with moderate OM content (2.75%) and CEC (3.58 mEq/100 g), compared with the other two sites. The soil from PINGU also showed low pH (5.09) but higher electric conductivity (0.49 dS m^–1) and OM content (6.26%) than PUCH; conversely, the CEC from PINGU was slightly lower (3.37 mEq/100 g) than that from PUCH. On the other hand, the soil from BASE presented higher pH (6.27) and CEC (4.78 mEq/100 g) than the other two sampling sites, a very low OM content (0.62%), and intermediate electric conductivity (0.21 dS m^–1). The differences in CEC among sites were mainly due to Ca²⁺ and Mg²⁺, which were significantly higher at BASE, and to K⁺, which was significantly lower at PUCH than at the other two sites. The soil from PINGU showed the highest contents of N and P, as well as the micronutrient Cu, while BASE soils displayed higher levels of Mn and Zn than the other two sites (Supplementary Table S1). A complete characterization of the soil total element content is presented in Supplementary Table S2, which shows that other elements (such as S, Si, and Mo) displayed higher values at PINGU than at PUCH and BASE. Regarding the SM measurements, PINGU showed significantly higher values than PUCH, whereas BASE presented intermediate values between the other two sites (Supplementary Table S1).

Leaf ionomics, leaf mass per area, and dry matter content

Leaf samples (8–9 biological replicates per site) and soil samples (as described in the previous section) were obtained to analyze the major elements (B, C, Ca, Cu, Fe, K, Mg, Mn, Mo, N, Na, P, S, Tl, and Zn). Approximately, 300 mg FW of completely expanded leaves were sampled per individual. Measurements were carried out in an ICP THERMO ICAP 6500 DUO spectrometer (Thermo Scientific). C and N total contents were analyzed by combustion at 950 °C coupled to individual infrared detection and thermal conductivity. These analyses were performed in the CEBAS-CSIC Ionomic Service in Murcia, Spain. Prior to the element analysis, additional leaves (eight biological replicates per site, the same as those employed for gas exchange and chlorophyll fluorescence analysis) were used to determine the fresh and dry weight to calculate the leaf dry matter content (LDMC), as the ratio between fresh and dry weight, following the recommendations of Vaieretti et al. (2007). The LMA was calculated as the ratio between the leaf area (measured in fresh leaves using the Image J software; https://imagej.nih.gov/ij/index.html) and the dry weight of the same leaves (obtained after drying at 80 °C for 72 h).

Gas exchange and chlorophyll fluorescence

Plants from the three study sites were collected in the field during the early morning from each location for same-day measurements. Turfs with individuals were carefully removed from the ground, ensuring enough soil was kept to minimize root damage, air exposure, or water stress, and transported to the vicinity of the lab’s station for subsequent photosynthesis measurements.

Leaf photosynthesis measurements were conducted on eight different individuals per experimental site using an open gas exchange system with a fluorometer (Li-6400XT; Li-Cor Inc., Lincoln, NE, USA) employing a fluorescence chamber of 2 cm² (Li-6400-40). Leaves were carefully placed in the equipment measurement chamber, avoiding overlaps between them, and ensuring contact with the leaf thermocouple. When leaves did not cover the full chamber, a picture of the leaves inside the chamber’s foam gasket was taken to subsequently re-calculate the area with the image software Image J. This area was later employed for the final gas exchange calculations. CO₂ leakage in the gasket–sample interface was corrected following Flexas et al. (2007). The block temperature was set to 15 °C for all measurements, in order to work at the plant’s optimal photosynthetic temperature (Edwards and Smith, 1988; Xiong et al., 1999; Sáez et al., 2017; Clemente-Moreno et al., 2020).

Light response curves were performed at a CO₂ atmospheric concentration (C_a) of 400 ppm and light intensities from 0 to 2000 μmol photons m^–2 s^–1 through nine consecutive steps (2–3 min per step: 0, 50, 100, 300, 500, 900, 1200, 1500, and 2000 μmol photons m^–2 s^–1) to determine the saturating photosynthetic photon flux density (PPFD) for subsequent measurements (determined as 1200 μmol photons m^–2 s^–1), as previously observed in Sáez et al. (2017) and Clemente-Moreno et al. (2020).

Instantaneous measurements of light-saturated net CO₂ assimilation (A_area), stomatal conductance to CO₂ diffusion (g_sc), substomatal CO₂ concentration (C_i), maximum fluorescence in the light under a saturating pulse (F_mʹ; measuring beam intensity 1 μmol m^–2 s^–1, 8000 μmol quanta m^–2 s^–1, 0.8 s duration), and steady-state yield of fluorescence in the light (F_t) were recorded 20–30 min after clamping.

Leaf chamber conditions were maintained at 400 ppm of C_a, 1200 μmol m^–2 s^–1 of PPFD (90:10% red:blue light), 50–70% relative humidity, and 15 °C block temperature, as in Clemente-Moreno et al. (2020). Afterwards, CO₂ response curves (A–C_i curves) were performed by subjecting the plants to step-by-step changes in C_a (3–4 min each) as follows: 400, 300, 200, 100, 400, 400, 500, 600, 700, 800, 1250, 1500, and 400 μmol CO₂ mol^–1 air. The initial measurement at 400 ppm was employed as reference to compare with the same C_a level steps (within and at the end of the curve), in order to ensure that CO₂ changes did not affect the initial photosynthetic steady state. Finally, leaves were kept for 30 min in darkness to record the CO₂ respiration rate (R_d), dark-adapted fluorescence (Fʹ_m), and maximum fluorescence under a saturating pulse (F_m). Mitochondrial respiration in the light was considered as half of R_d (Niinemets et al., 2005; Gallé et al., 2011).

Non-photochemical quenching (NPQ), the maximum quantum yield of PSII (F_v/F_m), and the quantum yield of PSII (Φ_PSII) were calculated as described in Maxwell and Johnson (2000). From the instantaneous measurements, the electron transport rate (ETR) was calculated as ETR= Φ_PSII×PPFD×αβ (Genty et al., 1989), where αβ is the product of leaf absorptance (α) and the electron partitioning between PSI and PSII (β). The value of α for DA plants from the same location measured by Sáez et al. (2017) was used for all measurements performed in this work, and β was assumed to be 0.5 (Pons et al., 2009). The photorespiratory rate (P_r) was estimated combining gas exchange and chlorophyll fluorescence (Valentini et al., 1995). The model assumed that all the reducing power generated by the electron transport chain is employed for photosynthesis and photorespiration, and that chlorophyll fluorescence gives a reliable estimate of quantum yield of electron transport. According to the known stoichiometries of electron use in photorespiration, it can be solved as follows: P_r=1/12 [ETR–4 (A_area+R_d/2)]. Mesophyll conductance to CO₂ diffusion (g_m) was estimated using the variable J method (g_m-VJ), as g_m-VJ=A_area/{C_i–Γ* [ETR+8 (A_area+R_d/2)]/[ETR–4 (A_area+R_d/2)]} (Harley et al., 1992; Pons et al., 2009), where Γ* is the CO₂ compensation point in the absence of mitochondrial respiration, which was obtained from the Rubisco kinetics at 15 °C measured in Sáez et al. (2017) for DA. The CO₂ concentration inside chloroplasts (C_c) was calculated as C_c=C_i–A_area/g_m. Additionally, mesophyll conductance was estimated from the A–C_i curves using the curve-fitting method (g_m-CF) with the Excel Tool provided in Sharkey (2016), from which the C_c-based maximum velocity of Rubisco carboxylation (V_cmax) and the maximum electron rate (J_max) were also obtained. The Michaelis–Menten constants of Rubisco carboxylation/oxygenation for A–C_i analysis were obtained from Sáez et al. (2017). From the A–C_i curve-derived parameters, photosynthetic limitations were estimated following Grassi and Magnani (2005), including the stomatal (l_s), mesophyll (l_m), and biochemical (l_b) relative limitations, and the contributions of stomata (SL), mesophyll (ML), and biochemistry (BL) to dA_area/A_area, considering the mean A_area value from the ‘BASE’ plants as reference. The product A_area/C_c at C_a=400 μmol mol^–1 was used as a proxy for ∂A/∂C_c. Area-based A_area was converted to mass-based assimilation rate (A_mass) using LMA from the same individual.

Pigment content analysis and primary metabolism profiling

Leaf samples (from 7–8 different individuals) for pigment and metabolism analysis were collected at midday at the different sites, immediately frozen in liquid nitrogen, and stored at –80 °C.

Pigments were extracted from 100 mg of leaf material powdered with liquid nitrogen using a mortar and pestle. A spatula tip of CaCO₃ was added before extracting with 1 ml of 100% HPLC-grade acetone. Pigments were separated and quantified by reversed-phase HPLC. The HPLC system, chromatographic conditions, and quantification of individual pigments were exactly the same as described by Sáez et al. (2013).

Metabolite extraction for GC-MS was carried out as previously described (Lisec et al., 2006), using 50 mg of leaf fresh material. Ribitol (0.2 mg ml⁻¹ in H₂O) was added during extraction as an internal standard. A dry aliquot (150 µl of the polar phase) was re-suspended in methoxyamine hydrochloride (20 mg ml⁻¹ in pyridine) and derivatized using N-methyl-N-[trimethylsilyl]trifluoroacetamide (MSTFA). The GC-TOF-MS system comprises a CTC CombiPAL autosampler, an Agilent 6890N gas chromatograph, and a LECO Pegasus III TOF-MS running in EI+ mode. A volume of 1 μl of derivatized metabolite solution was used for injection. Peaks were annotated to metabolites by comparing mass spectra and GC retention times with database entries and those of authentic standards available in a reference library from the Golm Metabolome Database (Kopka et al., 2005), using Xcalibur^® 2.1 software (Thermo Fisher Scientific).

Data analyses

One-way ANOVA and multiple comparisons Tukey’s HSD test (P<0.05) were employed when data fulfilled the normality and homogeneous variance criteria; otherwise, we used the Kruskal–Wallis analysis with Bonferroni post-hoc comparisons (P<0.05) employing the ‘dplyr’, ‘dlookr’, ‘writexl’, and ‘agricolae’ packages in R software (v. 4.1.1). We also employed the on-line software platform MetaboAnalyst 5.0 (https://www.metaboanalyst.ca) for multivariate analysis and figure plotting of the primary metabolism data. To evaluate the ionomic and primary metabolic profile of the different groups, we used a sparse partial least squares-discriminant analysis (sPLS-DA) to obtain the most important features per component in order to identify the most relevant loadings for each group.

Results

Leaf ion contents show major differences between the studied sites

We analyzed 18 macro- and micronutrients on the leaves of plants from each site (Supplementary Table S3) and, according to the multivariate sPLS-DA considering all the elements, the two major components explained almost 55% of the variance between sites (Fig. 2). PUCH plants were grouped along positive values of principal component 1 (PC1), while the other two groups (PINGU and BASE plants) were located along negative values on this axis, but separated along PC2 (Fig. 2). The mineral elements with higher loading values for PC1 were N, S, Rb, Zn, K, and P, which separated BASE and PINGU (high contents) from PUCH (low contents) (Supplementary Fig. S2). PC2 basically differentiated BASE from PINGU, while PUCH showed an intermediate position; the most relevant mineral elements for PC2 were Pb, Sr, Mo, Ca, Tl, and B.

Fig. 2.

Sparse partial least square discriminant analysis (sPLS-DA) of the leaf ionomic profile for the plants of the three sites studied in this work: PUCH (tundra site), PINGU (vicinity of the penguin rookeries), and BASE (within the base station).

Comparisons of the leaf element concentrations between sites (Fig. 3; Supplementary Table S3) showed that the C content was slightly lower in PUCH than in BASE, while PINGU showed intermediate levels. Conversely, BASE and PINGU plants showed significantly higher levels of N, P, K, S, and Zn than PUCH (between 1.7- and 4.3-fold). Other mineral elements such as Fe and Mn (more soluble in acidic soils than the previously mentioned elements) did not show statistical differences between sites (Fig. 3).

Fig. 3.

Leaf element content of macro- and micronutrients that showed major differences between the three different sites: BASE (within the base station), PINGU (vicinity of the penguin rookeries), and PUCH (tundra site). Site effect was evaluated by one-way ANOVA, and different letters indicate statistical differences by Tukey’s HSD test (P<0.05).

Nutrient availability affects photosynthetic rates driven by mesophyll conductance and photobiochemistry

The main leaf structural, photosynthetic, and chlorophyll fluorescence parameters are shown in Table 1. There were significant differences between sites for LMA (P=0.01), with PINGU plants showing lower values than plants from the other sites (Table 1), and for LDMC (P<0.001), with PUCH plants showing values ~40% higher than plants from the other two sites. At the photosynthetic level, no differences were observed in the plants between sites on an area basis (A_area, P=0.1); meanwhile marginally significant differences were observed on a mass basis (A_mass, P=0.09). Also, no differences in g_sc, C_i, C_c, and intrinsic water use efficiency (WUE_i) were observed (Table 1). Significant differences were observed for ETR (P<0.001), V_cmax and J_max (P=0.03 for both), but also for g_m-VJ (P=0.04), where BASE and PINGU plants showed higher values than PUCH plants (Table 1). The maximum photochemical quantum efficiency of the PSII (F_v/F_m), a common stress physiological indicator, also showed differences between sites (P<0.001), where PUCH plants obtained significantly lower values (Table 1).

Table 1.

Structural and photosynthetic parameters measured in the different sites

Site	BASE	PINGU	PUCH	F	P-value
LMA (g m–2)	47.44 ± 2.75 a	37.92 ± 1.16 b	45.59 ± 2.26 ab	54.599	0.012*
LDMC	0.27 ± 0.003 a	0.270 ± 0.008 a	0.380 ± 0.010 b	71.665	<0.001***
Aarea (µmol m–2s–1)	13.29 ± 0.75 a	11.48 ± 1.48 a	9.83 ± 0.88 a	25.363	0.103
Amass (nmol g–1s–1)	286.58 ± 23.85 a	307.84 ± 42.19 a	216.13 ± 17.05 a	26.193	0.096
ETR (µmol m–2s–1)	159.42 ± 7.62 a	131.8 ± 7.94 b	121.18 ± 6.29 b	72.705	0.004**
Pr (µmol m–2s–1)	8.49 ± 0.58 a	6.86 ± 0.46 a	6.64 ± 0.58 a	34.345	0.051
gsc (mol m–2s–1)	0.092 ± 0.008 a	0.111 ± 0.022 a	0.083 ± 0.009 a	0.9565	0.400
Ci (µmol mol–1)	237.06 ± 8.92 a	263.43 ± 15.92 a	263.56 ± 11.74 a	14.849	0.249
WUEi (µmol mol–1)	90.88 ± 5.4 a	76.78 ± 9.94 a	78.15 ± 7.11 a	10.154	0.379
gm-VJ (mol m–2s–1)	0.081 ± 0.007 a	0.062 ± 0.01 ab	0.051 ± 0.006 b	36.375	0.044*
Cc (μmol mol–1)	68.26 ± 3.78 a	70.95 ± 6.91 a	66.89 ± 6.31 a	0.1261	0.882
Vcmax (µmol m–2s-1)	71.37 ± 8.98 a	59.41 ± 8.86 ab	40.66 ± 4.89 b	39.279	0.035*
Jmax (µmol m–2s–1)	119.73 ± 5.89 a	105.88 ± 9.87 ab	87.77 ± 7.7 b	40.257	0.033*
Rd (µmol m–2s–1)	2.2 ± 0.46 a	1.79 ± 0.37 a	1.38 ± 0.37 a	10.386	0.371
Fv/Fm	0.77 ± 0.01 a	0.75 ± 0.01 a	0.72 ± 0.01 b	11.283	<0.001***
NPQ	1.72 ± 0.15 a	1.93 ± 0.12 a	1.66 ± 0.07 a	12.781	0.300

Mean ±SE (n=8) of leaf mass per area (LMA), leaf dry matter content (LDMC), area- and mass-based net CO₂ assimilation (A_area and A_mass, respectively), electron transport rate (ETR), photorespiration (P_r), stomatal conductance for CO₂ diffusion (g_sc), substomatal CO₂ concentration (C_i), intrinsic water use efficiency (WUE_i), mesophyll conductance to CO₂ diffusion estimated using the variable J (g_m-VJ), CO₂ concentration inside chloroplasts (C_c), maximum velocity of Rubisco carboxylation (V_cmax) and maximum electron transport rate (J_max) estimated using Cc, respiration in the dark (R_d), maximum quantum yield of PSII (F_v/F_m), and non-photochemical quenching (NPQ). Differences among sites (letters) assessed using one-way ANOVA and Tukey’s HSD test; P-value: <0.001***, <0.01**, <0.05*.

We further explored the relationship between A_area and its physiological and biochemical drivers g_sc, g_m-VJ, ETR, V_cmax, and J_max (Supplementary Fig. S3). The relationship between A_area and g_sc showed a positive saturation curve up to 0.15 mol CO₂ m^–2 s^–1 (polynomial regression P<0.001, Supplementary Fig. S3A). Conversely, linear positive correlations were observed for g_m (P<0.001, Supplementary Fig. S3B) and for ETR, V_cmax, and J_max (Supplementary Fig. S3C–E). Altogether, these data indicated that the highest A_area values were associated with higher g_m and photobiochemistry for BASE and PINGU plants, rather than elevated g_sc values.

The assumptions performed to estimate g_m were tested employing two independent and different methodologies, the curve-fitting method (Sharkey et al., 2007) and the variable J method (Harley et al., 1992). The relationship between both estimations showed a significant agreement (R²=0.67, P<0.001; Supplementary Fig. S4). The photosynthetic response to CO₂ on a C_c basis, where BASE plants showed the highest A_area values, was closely followed by plants from PINGU, while PUCH plants showed the lowest values (Supplementary Fig. S5). In fact, V_cmax and J_max calculations showed that PUCH plants had significantly lower values, PINGU showed an intermediate position between the other two sites, and BASE displayed the highest values for these parameters (Supplementary Fig. S5; Table 1).

We performed a limitation analysis (Grassi and Magnani, 2005) to explore the diffusive and biochemical drivers of A_area, and to quantify the relative photosynthetic limitations in plants from different sites. There were no significant differences between BASE and PINGU plants, but plants from PUCH showed significant limitations by mesophyll conductance and photobiochemistry to their photosynthetic capacity compared with BASE and, to a lesser extent, PINGU plants (Table 2).

Table 2.

Photosynthetical limitation analysis of Deschampsia antarctica plants at each of the three sites studied following Grassi and Magnani (2005)

Site	SL	ML	BL
	(%)	(%)	(%)
BASE	0 a	0 b	0 b
PINGU	–0.2 ± 7.0 a	13.1 ± 6.3 ab	3.2 ± 0.8 ab
PUCH	5.0 ± 4.3 a	19.3 ± 4.3 a	4.3 ± 1.1 a
F	0.295	34.626	51.618
P-value	0.747	0.050	0.015*

Mean ± SE (n=8) of stomatal (SL), mesophyll (ML), and biochemical (BL) contributions to dA/A, considering the ‘BASE’ group as reference (maximum A_area). g_m from curve fitting was used in all cases and A_area/C_c-CF at C_a=400 μmol mol^–1 was used to estimate ∂A_area/∂C_c. Differences among sites (letters) assessed using one-way ANOVA and Tukey’s HSD test; P-value: <0.001***, <0.01**, <0.05*.

DA plants in BASE and PINGU sites did not show reductions in the physiological stress indicator F_v/F_m; however, the F_v/F_m was significantly lower in PUCH plants (Table 1). Changes in the F_v/F_m performance were not accompanied by correlative changes in the NPQ (Supplementary Table S4), commonly associated with photoprotection and VAZ cycle activation. Similarly, F_v/F_m did not relate to a structural leaf trait such as as LMA, but it was significantly and positively correlated to A_area (P<0.01), A_mass (P=0.017), ETR (P<0.001), P_r (P=0.015), g_sc (P=0.046), g_m-CF (P=0.024), V_cmax (P=0.01), and J_max (P=0.001) (Supplementary Table S4). Altogether, these results additionally indicated that the stress status (reflected by F_v/F_m) was highly related to the plants’ gas exchange performance.

Photosynthetic pigment profile

Characterization of photosynthetic pigments is relevant to understand macro- and micronutrient investments (such as N, Fe, and Mg), as well as light harvesting capacities and photoprotection. Significant site effects were detected by ANOVA for the following pigments: Chl a and b (P=0.03), neoxanthin (P=0.02), violaxanthin (P=0.003), zeaxanthin (P=0.02), and the sum of violoxanthin and anteraxanthin (VA, P=0.02). In general, plants from PUCH showed significantly lower contents of these pigments compared with BASE plants, while PINGU plants presented intermediate values (Table 3).

Table 3.

Photosynthetic pigment composition (mg g DW^–1) of Deschampsia antarctica plants in the three study sites (BASE, PINGU, and PUCH)

Site	Chl a	Chl b	Chl a+b	Neo	Viol	Ant	Lut	Zea	B-car	VA	VAZ
BASE	0.485 ± 0.020 a	0.121 ± 0.005 a	0.606 ± 0.026 a	0.024 ± 0.001 a	0.045 ± 0.002 a	0.012 ± 0.001 a	0.076 ± 0.003 a	0.019 ± 0.001 b	0.034 ± 0.001 a	0.057 ± 0.002 a	0.075 ± 0.002 a
PINGU	0.420 ± 0.030 ab	0.107 ± 0.009 ab	0.528 ± 0.039 ab	0.024 ± 0.003 ab	0.039 ± 0.004 ab	0.014 ± 0.002 a	0.068 ± 0.003 a	0.025 ± 0.005 b	0.030 ± 0.001 a	0.050 ± 0.003 ab	0.072 ± 0.002 a
PUCH	0.368 ± 0.039 b	0.088 ± 0.010 b	0.456 ± 0.049 b	0.018 ± 0.002 b	0.028 ± 0.003 b	0.014 ± 0.001 a	0.062 ± 0.005 a	0.036 ± 0.002 a	0.030 ± 0.002 a	0.042 ± 0.004 b	0.076 ± 0.007 a
F	4.41	4.75	4.49	4.50	8.61	0.58	3.26	14.16	1.61	7.07	0.14
P-value	0.03*	0.02*	0.03*	0.03*	0.003**	0.57	0.06	<0.001***	0.23	<0.01**	0.87

The table also shows the F and P-values from one-way ANOVA. Neo, neoxanthin; Viol, violaxanthin; Ant, antheraxanthin; Lut, lutein; Zea, zeaxanthin; B-car, β-carotenes; V, violaxanthin; A, antheraxanthin; Z, zeaxanthin. Different letters indicate significant differences between sites by the multiple comparison Tukey´s HSD test; P-value: <0.001***, <0.01**, <0.05*.

We also calculated the ratios between Chl a and b, the AZ:VAZ of the xanthophyll cycle, xanthophylls and carotenes normalized per total chlorophyll, and the de-epoxidation state (Table 4). In this case, the ANOVA showed significant site differences for all parameters except for the Neo/Chl ratio. PUCH plants showed significant higher values for the Chl a/b ratio, (AZ)/VAZ, β-carotenes versus the total chlorophyll ratio (B-car/Chl), VAZ/Chl, A/Chl, and Z/Chl compared with BASE plants (Table 4). Interestingly, plants from PINGU displayed intermediate values with respect to the other two sites for AZ/VAZ and VA parameters (Table 3). In this sense, these results indicated a higher photoprotection status in PUCH plants than in those from BASE and PINGU.

Table 4.

Photosynthetic pigment ratios of Deschampsia antarctica plants in the three studied sites (BASE, PINGU, and PUCH)

Site	Chl a/b	(AZ)/VAZ	B- car/Chl	VAZ/Chl	Neo/Chl	Vio/Chl	Ant/Chl	Lut/Chl	Zea/Chl
BASE	4.09 ± 0.03 ab	0.43 ± 0.01 b	93.81 ± 1.9 b	188.49 ± 4.7 b	59.81 ± 0.75 a	110.07 ± 2.7 a	33.12 ± 1.1 b	196.79 ± 3.0 a	50.176 ± 2.37 b
PINGU	4.01 ± 0.09 b	0.506 ± 0.05 b	93.0 ± 2.3 b	209.8 ± 18.7 b	61.03 ± 1.00 a	105.06 ± 3.6 ab	38.34 ± 5.3 ab	202.51 ± 9.1 a	70.82 ± 14.6 ab
PUCH	4.29 ± 0.05 a	0.63 ± 0.03 a	112.54 ± 4.2 a	249.79 ± 4.67 a	60.03 ± 0.41 a	93.43 ± 4.3 b	47.49 ± 3.2 a	215.80 ± 6.0 a	117.77 ± 11.61 a
F	5.06	13.14	8.15	11.67	0.68	6.33	6.06	3.12	14.78
P-value	0.020*	<0.001***	0.003**	<0.001***	0.518	<0.01**	0.01*	0.07	>0.001***

The table shows the F and P-values from one-way ANOVA. Violaxanthin (V); antheraxanthin (A); zeaxanthin (Z); β-carotenes versus total chlorophyll ratio (B-car/Chl); violaxanthin plus antheraxanthin plus zeaxanthin versus total chlorophyll ratio (VAZ/Chl); neoxanthin versus total chlorophyll ratio (Neo/Chl); violaxanthin versus total chlorophyll ratio (Vio/Chl); antheraxanthin versus total chlorophyll ratio (Ant/Chl); lutein versus total chlorophyll ratio (Lut/Chl); zeaxanthin versus total chlorophyll ratio (Zea/Chl). Different letters indicate significant differences between sites by the multiple comparison Tukey´s HSD test; P-value: <0.001***, <0.01**, <0.05*.

Primary metabolism strongly differs between sites

A total of 69 metabolites were annotated and quantified in the leaves of DA (Supplementary Table S5). We first performed a sPLS-DA to compare the leaf metabolome depending on the different sites. The two major components explained 52.2% of the data variance (Fig. 4). PC1 (43.7%) separated PUCH plants from PINGU and BASE populations, while the PC2 (8.5%) separated plants from PINGU and BASE (Fig. 4). The major loadings for PC1 were related to N metabolism, osmoprotection, and polyamine synthesis, including serine, glutamate, proline, putrescine, and glutamine (Supplementary Fig. S6). The products of glutathione and ascorbate degradation (pyroglutamate and threonate, respectively) were higher in plants from BASE and PINGU than in those from PUCH. Interestingly, only one metabolite was relevant for the PC1, and was higher in PUCH plants than in those from the other sites: the non-reducing disaccharide trehalose, known to act as an osmoprotectant. PC2 explained a minor part of the variance and mainly separated plants from sites with enriched soils. The most important loadings in this component were intermediates of the tricarboxylic acid (TCA) cycle (citrate and succinate), lipid metabolism (glycerol-3P), S metabolism (O-acetyl-serine; OAS), secondary metabolism (3-caffeoylquinic acid; 3-CQA), and the hemicellulose component xylose (Supplementary Fig. S6).

Fig. 4.

Sparse partial least square discriminant analysis (sPLS-DA) of the metabolic profile of Deschampsia antarctica plants at the three different sites (BASE, PINGU, and PUCH) (n=6–8 individuals per site).

A heatmap of the metabolites from plants growing at the different sites (Fig. 5) showed that PUCH plants had marked metabolic differences compared with plants from BASE and PINGU. The majority (58%) of the analyzed metabolites displayed significant changes; of these, 14 (20%) were increased and 26 (38%) were decreased. Differences between BASE and PINGU plants were less pronounced: only six metabolites were present at significantly different levels (9% of the analyzed metabolites), with an equal number of increased and reduced metabolites. In general, compared wuth PUCH plants, those from BASE and PINGU showed increased levels of compounds related to N metabolism, such as Glu, Gln, Asp, and Asn, as well as Pro and putrescine, which are known to act as osmoprotectants and cold tolerance drivers (Fig. 5). Strong, significant reductions were observed in BASE and PINGU plants for metabolites related to sugar metabolism (fructose, glucose, and maltose), particularly those related to membrane stabilization, such as trehalose and raffinose, as well as the precursors of raffinose synthesis, myo-inositol and galactinol (Fig. 5). Similarly, the precursors of lignin synthesis (p-coumarate, caffeate, and chlorogenate) and the products of cell wall degradation (galactose and xylose) were reduced in plants from BASE and PINGU (Fig. 5). The intermediates of the TCA cycle showed different patterns: citrate and fumarate were reduced and 2-oxoglutarate was increased in BASE and PINGU plants, whereas succinate and malate levels were similar in all studied sites (Fig. 5).

Fig. 5.

Fold change heatmap of the leaf primary metabolism comparing Deschampsia antarctica plants at the tundra site (PUCH) with respect to the plants living in the surroundings of the base station (BASE) and penguin rookeries (PINGU). Dots indicate significant differences between the sites compared by Tukey’s HSD test (P<0.05) (black dots) and (P<0.1) (white dots).

Discussion

Deschampsia antarctica plants show low genetic diversity but an important phenotypic plasticity in response to the environment, such as, for example, the soil nutrient availability along the maritime Antarctica (Smykla et al., 2007; Chwedorzewska et al., 2008; Park et al., 2012, 2013; Holderegger et al., 2018; Androsiuk et al., 2021). In accordance, we observed contrasting vigor levels of DA in the surroundings of Henryk Arctowski Polish Antarctic Research Station (KGI, South Shetlands), depending on whether they were located in the tundra habitat (PUCH), close to the penguin rookeries (PINGU), or in human disturbed habitat, such as the buildings of the station (BASE). The photosynthetic performance, growth, and reproduction of several arctic and alpine tundra plant species are highly dependent on the availability of soil nutrients (Bowman et al., 1993; Hobbie, 2007; Peterson, 2014; Körner, 2021). Experiments using nutrient addition (mostly N) in the arctic tundra have shown that, in general, grasses are the most responsive species, followed by forbs, deciduous shrubs, and evergreen shrubs (Shaver and Chapin, 1986; Chapin et al., 1996). Moreover, increasing soil moisture in field experiments showed higher OM decomposition rates, significantly leading to higher nutrient availability, which in turn strongly affected the species community composition (Glanville et al., 2012; Hicks Pries et al., 2013; Semenchuk et al., 2015; Scharn et al., 2021).

However, to date, there are a limited number of molecular and ecophysiological studies dealing with plant species from extreme environments under field conditions; particularly those including the main photosynthetic physiological drivers (g_sc, g_m, and photobiochemistry) combined with primary metabolic profiling, as has been highlighted in recent reviews and meta-analyses (Dussarrat et al., 2018; Fernández-Marín et al., 2020; Knauer et al., 2022). This topic remains relatively unexplored due to the inherent difficulties associated with working in these environments. Besides this, during the last decade, both vascular Antarctic species were the center of several ecophysiological works, mainly focused on their capacity for nutrient usage and association with endophyte funghi (Hill et al., 2011, 2019; Park et al., 2012, 2013; Rabert et al., 2017; Santiago et al., 2017). Additionally, their photosynthetic performances were thoroughly characterized by Sáez et al. (2017, 2018a, b), who showed that DA leaves displayed the anatomical traits typically observed in plants from xerophytic environments. Moreover, Rubisco kinetics showed significantly higher affinity for CO₂ under low temperature, in agreement with previous studies about the response of Rubisco activase to temperature (Salvucci et al., 2004). Altogether, these data highlight the plasticity of DA for adapting to an extremely dry, cold, and nutrient-poor environment, but the main mechanisms coordinating photosynthetic capacity and stress tolerance under field conditions remain mostly unknown.

Interestingly, here we found that DA plants from sites differing in soil nutrient content showed no A_area differences and only marginal differences on a mass basis (P=0.096) (Table 1), although differences in the parameters limiting their photosynthesis were found (Table 2). These reduced differences in the carbon assimilation capacity, even for plants living in an extremely poor soil, could be explained by the previously described nutrient mobilization capacity from source to sink tissues, specifically in relation to N metabolism (Clemente-Moreno et al., 2020). DA plants living in the tundra (PUCH) showed a significant physiological stress, whereas plants at BASE and PINGU (with higher availability of soil nutrients) were under optimal values (Table 1; Supplementary Table S4). Thus, despite similar levels of net carbon assimilation, central metabolism strongly differed between sites (Fig. 5), suggesting a differential molecular and physiological performance depending on the availability of soil nutrients, and not directly related to carbon resource limitations. In order to facilitate an integrative discussion, the major roles and physiological responses of the main altered metabolic routes observed in this study are shown in Fig. 6.

Fig. 6.

Integrative metabolic pathway based into the relationship between BASE and PINGU plants (available nutrient environment) versus PUCH plants (limited nutrient environment). Boxes with blue and red colors indicate that metabolites are reduced or accumulated significantly comparing BASE/PINGU with respect to the PUCH metabolome. The orange and green shapes indicate the major metabolic pathways altered preferentially in the limited nutrient environment versus the available nutrient environment, respectively. Major roles and physiological responses of the main metabolic routes are indicated.

The relationship of major nutrients, such as N and P, with the photobiochemistry and, subsequently, with the photosynthetic capacity, are well established in the literature (Walker et al., 2014; Evans and Clarke, 2019; Knauer et al., 2022), not only for model species but also for plants from extreme environments (Fernández-Marín et al., 2020). It is thus surprising how DA, even in nutrient-limited soils (in terms of N or P), can still sustain substantial photosynthetic rates compared with species growing without nutrient limitations (Walker et al., 2014; Gago et al., 2019; Clemente-Moreno et al., 2020). One possible explanation, however, is that DA possesses a highly efficient route for mobilizing N-rich metabolites from source to sink tissues when leaves cross a certain stress threshold, to be later employed when conditions again become favorable (Clemente-Moreno et al., 2020).

The major photosynthetic limitation in DA from the nutrient-deprived soils was driven by g_m, being larger than that imposed by photobiochemistry (Table 2). How the availability of major nutrients affects g_m has seldom been addressed in the literature, which is surprising considering the multiple essential biochemical processes controlled by major nutrients in the leaf. For instance, g_m is frequently related to the activity of both aquaporins and carbonic anhydrases due to their roles in the transport of CO₂ through the membranes and through the cytosol and stroma, respectively. However, the mechanisms behind the relationship between N, P, and K, and g_m are still far from being completely understood (Xiong et al., 2015; Barbour and Kaiser, 2016; Lundgren and Fleming, 2020; Evans, 2021; Knauer et al., 2022).

Also, the cell wall is an important sink of C and N, and significantly contributes to the total cell dry mass (Onoda et al., 2017). The higher LDMC shown by PUCH plants could be achieved through smaller cells and/or increased cell wall investment (Garnier and Laurent, 1994; Pyankov et al., 1999; Castro-Díez et al., 2000), ultimately affecting leaf functionality and its response to stress (Gago et al., 2014, 2020). For instance, the proportion of their main components such as cellulose, hemicelluloses, pectins, lignin, phenols, and structural proteins would determine the size of the free spaces within the cell wall that the CO₂ needs to cross to reach the carboxylation sites in the chloroplast, thus affecting g_m and subsequently A_n (Ellsworth et al., 2018; Clemente-Moreno et al., 2019) in a mechanistic manner that is not fully understood yet (Gago et al., 2020). Further, cell walls are dynamic structures that promptly respond to environmental changes, such as drought, where hydrophilic compounds, structural proteins, and lignification frequently increase to retain water and maintain the cell wall flexibility, to avoid the collapse by cell water losses (Moore et al., 2013; Tenhaken, 2015). However, those changes have been suggested to decrease cell wall conductance to CO₂ due to the occlusion of pores and changes in its physico-chemical properties, interacting with the CO₂ diffusion pathway, reducing g_m and therefore A_n, and leading to ROS generation and oxidative stress (Monties, 1989; Niinemets et al., 2009; Clemente-Moreno et al., 2019; Flexas et al., 2021). In addition, we observed changes in several mineral elements (e.g. Ca, B, and Cu, Supplementary Table S3) that are directly related to cell wall properties (Nari et al., 1991; Fry et al., 2002; Bascom et al., 2018), and thus could be linked with cell wall composition and porosity, affecting g_m and the diffusive limitations imposed by the cell walls. In this sense, PUCH plants, compared with BASE and PINGU, showed a higher content of hydrophilic cell wall sugars such as as xylose (a major component of hemicelluloses) and galactose (mainly found in pectins), along with phenolic compounds such as trans-cinnamate and p-coumarate (Figs 5, 6), indicating significant cell wall remodeling, which in turn could sustain cell integrity but constrain g_m and photosynthetical capacity (Fig. 6).

A differential response of central metabolism: do what you can, with what you have, where you are

Overall, our results indicate that plants growing in a regular Antarctic tundra (PUCH), where the soil contains lower levels of macro- and micronutrients than in the other two sites, invest more carbon in compounds related to photoprotection such as carotenes and xanthophylls per chlorophyll, a higher activation of the VAZ cycle, protein and membrane stabilization, and structure remodeling (Table 4; Figs 5, 6). In this sense, a meta-analysis performed with 809 different species from green algae to mosses and vascular plants indicated that the VAZ cycle was highly responsive to the environment, including nutrient deficiency, showing the high phenotypic plasticity of VAZ driving photoprotection (Esteban et al., 2015).

While PUCH plants drive their investments in direct efforts to provide photoprotection and structure stabilization, BASE and PINGU plants direct their metabolism to the synthesis of different amino acids, which is known to lead to the promotion of antioxidant activity and osmoprotection, besides their role as the building blocks for protein synthesis and thus growth promotion (Ishihara et al., 2015, 2017; Li et al., 2017; Duncan and Millar, 2022). BASE and PINGU plants showed lower levels of non-structural carbohydrates (glucose, fructose, maltose, raffinose, and trehalose) than those from PUCH (Figs 5, 6). The accumulation of trehalose and raffinose in PUCH plants could indicate a divergence in carbon metabolism to investments related to known stress tolerance responses. For instance, it is known that trehalose drives autophagy to remove cellular toxins, recycling of nutrients, and delaying senescence in the resurrection grass Tripogon loliiformis (Williams et al., 2015), thus becoming an interesting biotechnological target for cereal improvement (Havé et al., 2017).

Raffinose metabolism is universal in the plant kingdom and its accumulation has been implicated in cold tolerance and other abiotic stresses (Sengupta et al., 2015). It has also been reported that raffinose is present in chloroplasts, where it could protect PSII during freeze–thaw cycles (Schneider and Keller, 2009; Knaupp et al., 2011). In fact, the contents of certain amino acids (i.e. asparagine, aspartate, glutamine, and glutamate) were increased in BASE and PINGU plants compared with those from PUCH (Figs 5, 6). It seems that, due to the higher N availability in BASE and PINGU soils (Supplementary Table S1), plants growing in these sites divert more carbon to the synthesis of amino acids at the expense of sucrose synthesis, which may explain the reduced levels of soluble sugars (glucose, fructose, and raffinose) observed in these plants (Fig. 5) (Paul et al., 1978; Larsen et al., 1981). We consider that increased levels of these amino acids are not related to the reduction of protein synthesis by stress (Naidu et al., 1991), as plants from BASE and PINGU were vigorous, actively growing, and without stress symptoms, as shown by the measured physiological indicators (Fig. 1; Table 1; Supplementary Table S4). Interestingly, such amino acids were previously reported to confer cold stress tolerance by lowering the levels of reducing equivalents through the malate–aspartate shuttle operating between plastids, mitochondria, and peroxisomes (Klotke et al., 2004; Liepman and Olsen, 2004; Bocian et al., 2015; Hoermiller et al., 2017, and references therein) (Fig. 6).

DA plants can incorporate short peptides three times faster than free amino acids, nitrate, or ammonium as N source from the soils (Hill et al., 2011). Moreover, Rabert et al. (2017) observed that DA plants showed higher biomass accumulation when fertilized with NH₄⁺ rather than with NO₃⁻, pointing to a preferential use of ammonium as N source. Thus, the relatively higher levels of aspartate, asparagine, glutamate, and glutamine in BASE and PINGU plants (Figs 5, 6) could be explained by the increased content of organic N and NH₄⁺ in those locations.

It is important to note that aspartate and glutamate directly derive from TCA cycle intermediates (Fig. 6), and their synthesis ultimately depends on the available carbohydrate pools (Bocian et al., 2015). In this sense, avoiding the accumulation of carbohydrates by diverting carbon to the synthesis of these amino acids could prevent the feedback inhibition of photosynthesis by its own products (Lambers et al., 2008; Bocian et al., 2015). Moreover, such inhibition might be exacerbated in P-limited environments (as observed in PUCH, Fig. 3), where the accumulation of cytosolic phosphorylated intermediates could sequester the phosphate needed for the regeneration of the CBC intermediates (Groot et al., 2003; Lambers et al., 2008). This is in agreement with the results of the photosynthetic limitation analysis, where PUCH plants (which displayed higher levels of sugars than plants from the other two sites) were partially limited by biochemistry. Moreover, avoiding the feedback inhibition of photosynthesis by sugar accumulation also reduces photoinhibition and ROS production, and thus oxidative stress, by sustaining electron flow to the major sink of photosynthesis (Strand et al., 1999; Distelbarth et al., 2013), while accumulation of these amino acids ensures a valuable source of nitrogen and carbon skeletons to sustain plant performance in the harsh Antarctic environment.

Aspartate is the precursor of methionine, a key metabolite for the production of S-adenosylmethionine (SAM), which serves for multiple methylation reactions and the synthesis of ethylene, polyamines (such as spermidine and spermine), and the metal-chelating agent nicotianamine (Watanabe et al., 2021). Glutamine and glutamate are essential not only for NH₄⁺ fixation (Miflin and Habash, 2002), but also for the synthesis of other amino acids (such as aspartate and asparagine) and metabolites recognized to increase tolerance to low temperatures, such as proline and putrescine (the precursor of the polyamines spermidine and spermine) (Bocian et al., 2015). All of them are significantly exacerbated in BASE plants compared with those from PUCH, indicating that these routes might play a major role driving their stress tolerance (Fig. 6).

Interestingly, the levels of both proline and putrescine in plants from BASE and PINGU were higher than in those from PUCH (Fig. 5). It has been described that proline has a double role as an osmolyte and anti-oxidant compound, thus driving low temperature stress tolerance (Smirnoff and Cumbes, 1989; Bravo et al., 2001; Pérez-Torres et al., 2007; Van den Ende, 2013; Fernández-Marín et al., 2020). Similarly, polyamines are involved in numerous physiological processes, including stress tolerance. The levels of polyamines usually increase upon exposure to abiotic stress conditions. Moreover, overexpression of SAM decarboxylase (involved in polyamine biosynthesis) in economically important species (such as rice, tobacco, and tomato) increased the tolerance to various types of abiotic stress (Watanabe et al., 2021).

The whole dataset indicates strong divergences in central metabolism, driven mainly by nutrient availability (Figs 4, 5). PUCH plants invest carbon in structural (cell wall and membrane) components and photoprotection, which could be related to the observed photosynthetical limitations imposed mostly by g_m and photobiochemistry, respectively. Conversely, PINGU and BASE plants seemed to invest carbon in amino acids (particularly proline) and polyamines (not only putrescine, but also spermidine and spermine), which might contribute to avoid oxidative stress when nutrients are available (Figs 5, 6). Among the different primary metabolic routes driving carbon to secondary metabolism, we only observed accumulation of phenylalanine in BASE and PINGU plants compared with PUCH (Fig. 5). This metabolite is the primary precursor of lignin, but also of several antioxidant secondary metabolites, such as phenylpropanoids and flavonoids (Tohge et al., 2013). Thus, we speculate that carbon flux towards secondary antioxidant compounds via phenylalanine was increased in plants growing in BASE and PINGU compared with those from PUCH. Altogether, this could increase the antioxidant capacity while avoiding structural rearrangements, which in turn could constrain the CO₂ flow to the carboxylation sites in the chloroplast, and ultimately also the A_n (Fig. 6).

Conclusions

Overall, our results suggest that DA plants employ different photosynthetic and stress tolerance mechanisms depending on the soil nutrient availability. When nutrients are available, costly compounds in terms of mineral elements and energetic requirements (such as amino acids, secondary metabolites, and polyamines) are increased to keep oxidative stress under control. Conversely, plants growing in nutrient-limited environments (where N and P levels are considerably low) divert carbon to metabolites related to membrane, protein, and cell wall stabilization (such as raffinose, galactose, xylose trehalose, and p-coumarate, all of them cheaper compounds in terms of nutrients and ATP requirements), to avoid structure collapse under the higher levels of oxidative stress. These rearrangements could lead to the observed increased mesophyll and photobiochemical limitations. This response seems to be an attempt to sustain cell integrity at significant oxidative stress levels, when antioxidant (nutrient-costly) metabolism is constrained by the scarcity of resources.

Increases in temperature due to climate change in cold regions may increase nutrient cycling, generating more fertile soils for plant colonization (Chapin et al., 1996). Global warming models for the AP and adjacent islands predict temperature increments and increases in the frequency and intensity of heat waves (Carrasco et al., 2021; González-Herrero et al., 2022). Warmer temperatures may facilitate nutrient cycling processes, therefore increasing their availability and allowing the subsequent proliferation of angiosperms (Hill et al., 2019). Based on our results, namely the wide physiological and metabolic responses displayed by DA to nutrient availability, we hypothesize that this species could become more dominant in the terrestrial Antarctic communities under the predicted climatic change scenarios. This possible expansion of this species could be mediated by its differential employment of the available nutrients in osmoprotection and antioxidant capacity, thus sustaining stress tolerance without penalizing their photosynthetical capacity.

Supplementary data

The following supplementary data are available at JXB online.

Fig. S1. Deschampsia antarctica leaf and air temperature 1 November 2017 to 1 May 2018 at Puchalski Hill, near Henryk Arctowski Polish Research Station in King George Island (South Shetlands).

Fig. S2. Major loadings from sPLS-DA showing the values of each element for component 1 and 2.

Fig. S3. Relationships between area-based net CO₂ assimilation (A_area) and the different estimated photosynthetic parameters across individuals from the different locations.

Fig. S4. Comparison of mesophyll conductance to CO₂ diffusion estimated from the variable J method (g_m-VJ) and the curve-fitting method (g_m-CF).

Fig. S5. Relationship between area-based net CO₂ assimilation (A_area) and CO₂ concentration inside chloroplasts estimated using the curve-fitting method (C_c-CF) from A–C_i curves.

Fig. S6. Major loadings from sPLS-DA showing the values of each metabolite for component 1 and 2.

Table S1. Soil fertility analysis from three different sites in the vicinity of the Henryk Arctowski Antarctic Research Polish Station.

Table S2. Soil ion content from the three different sites in the vicinity of the Henryk Arctowski Antarctic Research Polish Station.

Table S3. Leaf ion content from plants from the three different sites in the vicinity of the Henryk Arctowski Antarctic Research Polish Station.

Table S4. Pearson’s correlation of F_v/F_m versus LMA, A_mass, A_area, ETR, P_r, g_sc, C_i, WUE_i, g_m-VJ, C_c, V_cmax, and J_max estimated using C_c, R_d, and NPQ.

Table S5. Average metabolite level ±SE (n=8) measured in the different sites.

Acknowledgements

The authors wish to publicly thank MCIN/AEI for the national research projects Proyectos I+D+i -Modalidades ‘Retos Investigación’ y ‘Generación de Conocimiento’ for 2015–2018 and 2019–2020. JG and JF are grateful to the national research project CTM2014-53902-C2-1-P funded by MCIN/AEI/10.13039/501100011033, and by ‘ESF Investing in your future’ and by ‘European Union NextGenerationEU/PRTR’. MN was supported by the MINECO (Spain) and the European Social Fund (pre-doctoral fellowship BES-2015-072578), and acknowledges his postdoctoral contract Juan de la Cierva-Formación (FJC2020-043902-I), financed by MCIN/AEI/10.13039/501100011033 (Spain) and the European Union. MJC-M acknowledges her postdoctoral contract RYC2020-029602-I funded by MCIN/AEI/10.13039/501100011033. CMF is funded by the Max-Planck-Gesellschaft (Partner Group for Plant Biochemistry). LB and JG acknowledge financial support from FONDECYT (11130332; 1171640) and NEXER-UFRO (NXR17-002; Chile). MJC-M, LAC, LB, and JG also acknowledge REDES-CONICYT 170102 (Chile) for the Chile–Spain researcher exchange. LAC acknowledges funding from projects ACT210038 and FB21006. The authors also wish to thank the Instituto Antártico Chileno (INACH) for the support provided [including for the permits to sample the Antarctic base station Henryk Arctowski (Polska Akademia Nauk, Poland)], the ships Lautaro and Aquiles of the Armada de Chile (Chile) that make the work in Antarctica possible, and Dr Melanie Morales, who kindly provided pictures of Deschampsia turfs in King George Island close to Henryk Arctowski Antarctic Polish station.

Author contributions

MN, LB, and JGM: planning design; MN, LB, MJCM, DBM, NCR, and KL: performing the experiments, conducting fieldwork, and data analysis; MN, JG, JF, AF, CMF, MJCM, LAC, LB, and JGM: writing the manuscript.

Conflict of interest

The authors have no conflicts to declare.

Funding

This work was supported by the Ministerio de Ciencia, Innovación y Universidades (MCIN/AEI/10.13039/501100011033), by the Regional Development Fund (ERDF) ‘A way of making Europe’ [grant no. PGC2018-093824-B-C41], and MCIN/AEI/10.13039/501100011033 [grant no. PID2019-107434GA-I00].

Data availability

All data are available on request from the authors.

Author notes

These authors contributed equally to this work.