Dataset related to article: Differential effect of climate of origin and cultivation climate on structural and biochemical plant traits
Thakur, Dinesh et al. (2023), Dataset related to article: Differential effect of climate of origin and cultivation climate on structural and biochemical plant traits , Dryad, Dataset, https://doi.org/10.5061/dryad.x0k6djhpz
Exploring patterns and causes of intraspecific trait variation is crucial for a better understanding of the effects of climate change on plant populations and ecosystems. However, our current understanding of the intraspecific trait variation is mainly based on structural (morphological) traits, and we have limited knowledge on patterns and causes of variation in biochemical traits (e.g., leaf pigments), which are also crucial for plant adaptation. As a result, we also do not know how similar the climatic effects on structural versus biochemical traits are.
Using plant traits from 110 genotypes representing 11 Festuca rubra populations grown in 4 different climates, we studied trait covariation among structural traits (linked to fitness, resource use, gas exchange, and reproduction) and biochemical traits (linked to photosynthesis, photoprotection, and oxidative stress). We also disentangled the relative role of the climate of origin and the climate of cultivation in the structural versus biochemical traits and tested for adaptive plasticity in the traits.
We found that 1) biochemical traits did not covary with structural traits and represent independent ‘photoharvesting – photoprotection’ strategy dimension of functional variation; 2) interactive effects of climate of origin and cultivation were more pronounced for biochemical than structural traits. 3) Trait plasticity was affected by the climate of origin (precipitation and temperature as well as their interaction); 4) F. rubra showed both adaptive and mal-adaptive plasticity, and adaptiveness depended upon trait type, cultivation climate, and climate of origin.
Overall, our results suggest that structural and biochemical plant traits respond differentially to climate and thus the response of one group of traits cannot be predicted from the other. Responses are also strongly determined by interactions between the climate of origin and cultivation. Thus, more studies on variation in biochemical traits, their correspondence to other traits, and their variation with climate are needed.
Study species and sampled localities for plant material
In the experiment, we used Festuca rubra ssp. rubra, a widespread perennial grass species that occurs in temperate grasslands in Europe. This species can reproduce both by seeds and clonally by rhizomes. The experimental plants of this species were collected in 2014 from 12 natural localities representing factorially crossed precipitation and temperature gradients corresponding to the SeedClim Grid (Meineri et al. 2014) in Western Norway. This grid represents an independent combination of three levels of mean summer temperature (4 warmest months, 6.5 °C, 8.5 °C and 10.5 °C) with four levels of annual precipitation (600, 1300, 2000 and 2700 mm).
The plant material used in the current study has been collected for previous studies that have set up the experiments and provided data also for this study (Münzbergová et al. 2017). From each locality, at least 40 plants of F. rubra (at least 1 m apart) were collected in the growing season of 2014 and transported to the experimental garden of the Institute of Botany, Czech Academy of Sciences in Průhonice, Czech Republic (49°59′38.972″N, 14°33′57.637″E) and planted into pots. To ensure that each of the collected plants corresponded to a single genotype, the plants were reduced to a single ramet and planted into 16 × 16 × 16 cm pots in 1:2 soil:sand ratio in August 2014. Later, the plants were checked by flow cytometry and confirmed that all belonged to the hexaploid cytotype of F. rubra ssp. rubra (Šurinová et al. 2019). In one of the localities (i.e., 6.5 °C temperature and 1300 mm precipitation), F. rubra ssp. rubra was absent. So, finally, we worked with plants from only 11 localities.
In November 2014, 25 genotypes from each locality were transferred to a greenhouse with temperatures between 5 and 10 °C. In the greenhouse, the genotypes were multiplied (for details see (Münzbergová et al. 2017)). At the end of February 2015, four ramets of each of the 25 genotypes from all the 11 localities were planted in separate pots, resulting in a total of 1100 ramets. One ramet of each genotype was shifted to one of four different cultivation climates (see next paragraph) so that each genotype is present in each cultivation climate.
The experimental plants were grown in four different growth chambers (Vötsch 1014) under conditions simulating four extreme localities in the SeedClim grid (wettest/driest combined with warmest/coldest) for the spring to summer climate in the field. The temperature and moisture in each of the growth chambers followed the same course as in the simulated natural locality (for details, see Münzbergová et al., 2017). The moisture level in the growth chambers was monitored using TMS dataloggers (Wild et al. 2019) (three inside each chamber) and then adding the necessary amount of water to mimic the soil moisture of the natural localities (also monitored using the TMS dataloggers). Briefly, in the climate chambers with dry conditions, plants were watered with about 20 ml of tap water per plant, applied to the trays if the soil moisture was lower than 15%. In the wet regime, plants were cultivated under full soil saturation with ~1.5 cm water level in the tray. By manipulating the soil moisture in the growth chambers, soil moisture at localities with a certain precipitation level was mimicked. Thus, the moisture conditions in the growth chamber were referred to as precipitation values of the simulated localities. Day length and radiation levels inside the growth chambers were also controlled in a manner to mimic the conditions of the original localities (Münzbergová et al. 2017).
Due to the limitation of resources to take measurements of the biochemical traits from all the grown plants (see below), only 10 genotypes per locality were used for the purpose of this study. The other genotypes along with those considered in this study were used in other studies exploring the growth, fitness, and physiological traits of the plants (Münzbergová et al. 2017; Stojanova et al. 2018; Kosová et al. 2022; Thakur and Münzbergová 2022). The dataset used in this study comprised of samples collected from 440 plants (i.e., 11 localities × 10 genotypes × 4 cultivation climates).
Plant trait measurements
From each genotype in each cultivation climate, we measured 11 biochemical traits and 8 structural (i.e., morphological) traits. The biochemical traits were represented by the content of antheraxanthin, β-carotene, chlorophyll a, chlorophyll b, phenolic substances, lutein, neoxanthin, superoxide dismutase (SOD), violaxanthin and zeaxanthin and xanthophyll cycle de-epoxidation state (DEPS). The considered biochemical traits are related to plant photosynthesis, photoprotection and oxidative stress response (Table 1).
Structural traits included aboveground biomass, plant height, rhizome weight, root weight, stomatal density, stomatal size, and the number of ramets. Additionally, specific leaf area (SLA) was measured from the 10 genotypes originating from each of the four most extreme climates of the climatic grid. All these structural traits have been also used in our previous studies to address totally different hypotheses (Münzbergová et al. 2017; Kosová et al. 2022; Thakur and Münzbergová 2022). The considered structural traits are linked to plant resource use, competitive ability, fitness, growth and gas exchange (Table 1). All the traits were estimated following the standard methodology described in Münzbergová et al. (2017), Münzbergová and Haisel (2019), and Kosová et al. (2022)
During the experiment, in mid-June, the biomass of the plants were cut at 3 cm to simulate biomass removal during regular management in the field sites. This biomass was used for trait measurements as described below. The number of ramets was counted and the length (cm) of the longest ramet (hereafter referred to as plant height) was measured at the end of August 2015 and the plants were subsequently harvested, sorted into aboveground parts, roots and rhizomes, dried to a constant mass at 60 °C and weighed.
The remaining traits were measured during biomass removal in mid-June 2015. Stomatal density and stomatal size (length) were measured on epidermal imprints generated using nail polish (Gitz & Baker, 2009). The impressions were mounted on microscope slides with transparent adhesive tape and stomata were counted from three different regions on a leaf (each 500 × 500μm). From each of these three areas, three stomata were randomly chosen, and their length was measured, resulting in nine measured stomata per sample. SLA was estimated as a ratio of leaf area (mm2) to dry mass (mg). Leaf area was calculated on scanned folded leaves (as Festuca leaves are naturally folded and are often hard to unfold) multiplied by two to acquire the real area of the leaves.
The analyses of chlorophylls and carotenoids followed the protocol described in (Münzbergová and Haisel 2019). The freshly sampled leaves (collected from the plants in mid-June 2015) were frozen in liquid nitrogen, lyophilized for 24 h and stored in a freezer (-80 °C) until the analyses. The contents of chlorophyll (chlorophyll a and chlorophyll b) and carotenoids (b-carotene, lutein, neoxanthin, violaxanthin, antheraxanthin, and zeaxanthin) in all the samples were analyzed by High-performance liquid chromatography (HPLC; ECOM, Prague, Czech Republic) using a reversed-phase column (Watrex Nucleosil 120-5-C18, 5 mm particle size, 125 x 4 mm, ECOM, Prague, Czech Republic). Prior to HPLC injection, pigments were extracted from the leaves with acetone. In HPLC, elution was carried out for 25 min using a gradient solvent system acetonitrile/methanol/water (80:12:6) followed by methanol:ethylacetate (9:1), the gradient was run at the time of 2–5 min. The flow rate was 1 ml min-1 and the detection wavelength was 445 nm. Each sample was analyzed twice, and the results were averaged. The contents of all the pigments were expressed as mg/g of dry weight. The de-epoxidation state (DEPS) was derived from xanthophyll pigments following (Pospíšilová et al. 2000).
Total content of phenolic compounds was quantified spectrophotometrically from 80% methanolic extract of dried leaves using modified Folin-Ciocalteu method (Folin and Ciocalteu 1927). The absorbance was measured in a 1 cm cuvette at 765 nm. The total phenolic compound concentration in the analyzed extract was expressed in gallic acid equivalents.
SOD was extracted from frozen leaves homogenized with mortar and pestle in 2.5 ml buffer (0.1 M Tris-HCl, 1 mM dithiothreitol, 1 mM Na2EDTA, 1% Triton X-100, 5 mM ascorbic acid, pH 7.8) and a small amount of PVP (polyvinylpyrrolidone). Samples were incubated on ice in the dark for 30 min and centrifuged (16,000 g, 10 min, 4 °C). Pellets were discarded, and supernatants were divided into Eppendorf tubes, frozen in liquid nitrogen, and stored at -70 °C (Lubovska et al., 2014). Later, supernatants were used for gel electrophoresis and SOD was stained on the gel (Fridovich 1986). These stained gels were scanned, and densitograms were created and analyzed in the ImageJ program (Schneider et al. 2012). The relative activity of total SOD was estimated as the sum of intensities of bands expressed as peak areas.
Grantová Agentura České Republiky, Award: 19-00522S
Institute of Botany of the Czech Academy of Sciences, Award: 67985939
Ministerstvo Školství, Mládeže a Tělovýchovy