DOI: 10.5061/dryad.x0k6djhpz
title: Dataset related to article: Differential effect of climate of origin and cultivation climate on structural and biochemical plant traits

description: 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. 
description: Study species and sampled
 localities for plant material In the experiment, we used
 <em>Festuca</em> <em>rubra</em> ssp.
 <em>rubra</em>, 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
 <em>F. rubra</em> (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 <em>F. rubra</em> ssp. <em>rubra</em>
 (Šurinová et al. 2019). In one of the localities (i.e., 6.5 °C temperature
 and 1300 mm precipitation), <em>F. rubra</em> ssp.
 <em>rubra</em> 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. Cultivation climates
 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 (mm<sup>2</sup>) to dry mass
 (mg). Leaf area was calculated on scanned folded leaves (as
 <em>Festuca</em> 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<sup>-1</sup> 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.