Osmolality and non-structural carbohydrate composition in the secondary phloem of trees across a latitudinal gradient in Europe

ORIGINAL RESEARCH published: 01 June 2016 doi: 10.3389/fpls.2016.00726 Frontiers in Plant Science | www.frontiersin.org 1June 2016 | Volume 7 | Article 726 Edited by: Achim Braeuning, University Erlangen-Nuremberg, Germany Reviewed by: Ivika Ostonen, University of Tartu, Estonia Jürgen Kreuzwieser, University of Freiburg, Germany *Correspondence: Anna Lintunen [email protected] Specialty section: This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science Received: 27 January 2016 Accepted: 11 May 2016 Published: 01 June 2016 Citation: Lintunen A, Paljakka T, Jyske T, Peltoniemi M, Sterck F, von Arx G, Cochard H, Copini P, Caldeira MC, Delzon S, Gebauer R, Grönlund L, Kiorapostolou N, Lechthaler S, Lobo-do-Vale R, Peters RL, Petit G, Prendin AL, Salmon Y, Steppe K, Urban J, Roig Juan S, Robert EMR and Hölttä T (2016) Osmolality and Non-Structural Carbohydrate Composition in the Secondary Phloem of Trees across a Latitudinal Gradient in Europe. Front. Plant Sci. 7:726. doi: 10.3389/fpls.2016.00726 Osmolality and Non-Structural Carbohydrate Composition in the Secondary Phloem of Trees across a Latitudinal Gradient in Europe Anna Lintunen1*, Teemu Paljakka 1, Tuula Jyske2, Mikko Peltoniemi2, Frank Sterck3, Georg von Arx4, Hervé Cochard5, Paul Copini3, 6, Maria C. Caldeira7, Sylvain Delzon8, Roman Gebauer9, Leila Grönlund1, Natasa Kiorapostolou3, Silvia Lechthaler10, Raquel Lobo-do-Vale 7, Richard L. Peters4, Giai Petit10, Angela L. Prendin10, Yann Salmon11, Kathy Steppe12, Josef Urban9, Sílvia Roig Juan2, Elisabeth M. R. Robert13, 14, 15 and Teemu Hölttä 1 1Department of Forest Sciences, University of Helsinki, Helsinki, Finland, 2Natural Resources Institute Finland, Vantaa, Finland, 3Forest Ecology and Forest Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands, 4Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland, 5INRA, UMR 547 PIAF, Université Clermont Auvergne, Clermont-Ferrand, France, 6Alterra, Wageningen University and Research Centre, Wageningen, Netherlands, 7Forest Research Centre, School of Agriculture, University of Lisbon, Lisbon, Portugal, 8INRA, University of Bordeaux, UMR BIOGECO, Talence, France, 9Department of Forest, Botany, Dendrology and Geobiocenology, Mendel University in Brno, Brno, Czech Republic, 10 Department Territorio e Sistemi Agro-Forestali, Legnaro (PD), Università degli Studi di Padova, Padova, Italy, 11 Department of Physics, University of Helsinki, Helsinki, Finland, 12 Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Ghent University, Gent, Belgium, 13 Centre for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès, Spain, 14 Laboratory of Plant Biology and Nature Management (APNA), Vrije Universiteit Brussel, Brussels, Belgium, 15 Laboratory of Wood Biology and Xylarium, Royal Museum for Central Africa (RMCA), Tervuren, Belgium Phloem osmolality and its components are involved in basic cell metabolism, cell growth, and in various physiological processes including the ability of living cells to withstand drought and frost. Osmolality and sugar composition responses to environmental stresses have been extensively studied for leaves, but less for the secondary phloem of plant stems and branches. Leaf osmotic concentration and the share of pinitol and raffinose among soluble sugars increase with increasing drought or cold stress, and osmotic concentration is adjusted with osmoregulation. We hypothesize that similar responses occur in the secondary phloem of branches. We collected living bark samples from branches of adult Pinus sylvestris,Picea abies,Betula pendula and Populus tremula trees across Europe, from boreal Northern Finland to Mediterranean Portugal. In all studied species, the observed variation in phloem osmolality was mainly driven by variation in phloem water content, while tissue solute content was rather constant across regions. Osmoregulation, in which osmolality is controlled by variable tissue solute content, was stronger for Betula and Populus in comparison to the evergreen conifers. Osmolality was lowest in mid-latitude region, and from there increased by 37% toward northern Europe and 38% toward southern Europe due to low phloem water content in these regions. The ratio of raffinose to all soluble sugars was negligible at mid-latitudes and increased toward north and south, reflecting its role in cold and drought tolerance. For pinitol, another sugar known for contributing to stress tolerance, no such latitudinal Lintunen et al. Osmolality and NSC in Branch Phloem pattern was observed. The proportion of sucrose was remarkably low and that of hexoses (i.e., glucose and fructose) high at mid-latitudes. The ratio of starch to all non-structural carbohydrates increased toward the northern latitudes in agreement with the build-up of osmotically inactive C reservoir that can be converted into soluble sugars during winter acclimation in these cold regions. Present results for the secondary phloem of trees suggest that adjustment with tissue water content plays an important role in osmolality dynamics. Furthermore, trees acclimated to dry and cold climate showed high phloem osmolality and raffinose proportion. Keywords: hexose, osmotic concentration, phloem water content, pinitol, raffinose, sucrose, starch INTRODUCTION Plants have to keep osmolality levels sufficiently high in the phloem to maintain basic cell metabolism processes (see Rodríguez-Calcerrada et al., 2015) and cell turgor at levels that allow growth (Kröger et al., 2011). Phloem osmolality levels also have a role in various physiological processes in plants e.g., biomass accumulation (Simard et al., 2013; Steppe et al., 2015), control of transpiration (Schroeder et al., 2001), and maintaining xylem hydraulic integrity (Sala et al., 2012; Sevanto et al., 2014; Pfautsch et al., 2015). In addition, high osmolality decreases the wilting point (Bartlett et al., 2012a,b; Charrier et al., 2013a,b) and the ice nucleation temperature (Burke et al., 1976) of living cells thus affecting their ability to tolerate drought and freezing temperatures. Plants may vary in phloem osmolality because they differ in the control of sugar concentrations (or other osmotic substances), or they differ in phloem water content, i.e., cell osmolality can be increased either by an increase in the amount of solutes or a decrease in the amount of water in the cell. In dry climates, the maintenance of cell turgor may require higher osmolality to compensate for low stem water potentials. In cold climate, such as in the boreal zone, high osmolality and high carbon storage may both be required to avoid symplastic freezing during winters. So far, such processes have been studied for leaves (e.g., O’Neill, 1983; Gross and Koch, 1991; Callister et al., 2008; Bartlett et al., 2014; O’Brien et al., 2014; Maréchaux et al., 2015) but only scarcely for the secondary phloem in plant stems or branches. It has been shown that sucrose concentration in the secondary phloem of Picea abies increases with increasing latitude in Finland (Jyske et al., 2015). Moreover, the comparison of studies suggests that the sugar concentration in the secondary phloem increases with increasing elevation in Larix decidua (Hoch et al., 2003; Streit et al., 2013), but we lack empirical tests over continental scale on the ability of secondary phloem of trees to osmotically adjust to different climates. Studies for the osmolality and non-structural carbohydrate (NSC) concentration in the secondary phloem are needed, because the secondary phloem is structurally different from the primary phloem in leaves. The secondary phloem includes noncollapsed and collapsed tissue. Non-collapsed tissue is typically the youngest part of the phloem, whereas older layers in the outer part of the secondary phloem collapse and become storage tissue (Evert, 2006). Sugars are transported between loading (at C sources) and unloading sites (at C sinks) in sieve elements in the non-collapsed phloem tissue. The transport is driven by a gradient in osmotically established turgor pressure (Münch, 1930; Thompson, 2006; De Schepper et al., 2013), and is coordinated with the axial gradient of water potential developed along the xylem compartments (Hölttä et al., 2006). Secondary phloem also needs to tolerate seasonal drought and cold stresses, whereas these stresses can be avoided in the leaves of deciduous species by shedding. Phloem osmolality is a measure of the moles of solute per kilogram of solvent (mol kg−1), and there are different types of solutes that contribute to it: soluble sugars, ions and amino acids. Sucrose is a soluble sugar that is considered as the most important compound being translocated in phloem elements (Pate, 1976). Hexoses (i.e., glucose and fructose) are present in high amounts in all living cells, and can also be important transport sugars in the phloem for some species (Van Bel and Hess, 2008). Raffinose and pinitol occur in small amounts in phloem, but may contribute to protecting cells against environmental stress, such as drought and low temperatures (Bohnert and Shen, 1999; Zuther et al., 2004; Deslauriers et al., 2014). Starch, which is the most common storage form of non-soluble carbohydrates, contributes only marginally to the value of osmolality due to its high molar mass. Non-structural carbohydrates (i.e., soluble sugars and starch) are constantly transformed from one form to another. Starch, for example, is formed when high levels of soluble sugars occur, and is transformed to sugars if sugar content is low (EscobarGutiérrez et al., 1998). Amount and composition of NSC in phloem tissue show a seasonal behavior in temperate and boreal regions (Hoch et al., 2003; Simard et al., 2013; Jyske et al., 2015), and have an important role in the development of cold hardiness: starch is converted into sugars during cold acclimation (Zwieniecki et al., 2015). Furthermore, a fraction of NSCs can be converted to defensive chemicals in some species (Kozlowski, 1992). In this study, we aimed at showing variation in osmolality and non-structural carbohydrate composition in secondary branch phloem across a large geographical and climatic gradient. We hypothesize that (i) variability in osmolality is mainly controlled by solute content over large geographical scale, (ii) osmolality and solute content increase from the mid-latitudes toward the more drought-prone lower latitudes as well as to more coldstressed higher latitudes, and locally from moist to dry soil sites, Frontiers in Plant Science | www.frontiersin.org 2June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem and (iii) the share of raffinose and pinitol among soluble sugars increase from the mid-latitudes toward south and north given their role in tolerating drought and cold stress. To test these hypotheses we collected branches from four widely distributed species Pinus sylvestris,Picea abies,Betula pendula and Populus tremula from moist and dry soil sites, from boreal, temperate and Mediterranean regions across Europe. For phloem samples of those branches, we analyzed osmolality, concentrations of different sugars and water content, and discuss patterns across the studied geographic/climatic gradient. MATERIALS AND METHODS Plant Material We conducted a European wide study on Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), silver birch (Betula pendula Roth.) and common aspen (Populus tremula L.). These four species have a wide distribution and cover both deciduous angiosperm species and evergreen coniferous species. In total, we studied trees in seven regions along a climate gradient across Europe from northern Finland (67◦N 29◦E) to Portugal (40◦N 7◦W) (Figure 1,Table 1), and selected one moist soil site and one dry soil site per region based on soil type, ground vegetation, and soil moisture measurements. Measurements on needle lengths of Pinus and Picea showed that the needles from the moist soil sites were longer than the ones from the dry soil sites within each region with the exception of Pinus at the northern Finland (Figure S1 in the Supplementary Material). The climate gradient runs from cold and slow growth conditions in the north, through higher growth conditions at mid-latitudes, to drier and slower growth conditions in the south (Table 1). For each region, we selected five trees per species and per moist and dry soil site (Table 1). We selected healthy trees more than 5 m in height to harvest one 0.7-m-long branch (linear distance from tip) that was fully exposed to light in order to avoid shading effects. Fixed distance from the branch tip was selected for sampling to fix the transport distance from the C source to the sampling location. Branches were cut at 1 pm or later in the afternoon to minimize the impact of confounding diurnal trends in osmolality. Two 5-cm-long branch segments between distances 70 and 60 cm from the branch tip were cut. The basipetal segment was put in 50% ethanol for anatomical analysis of phloem area. The acropetal segment was sealed in a plastic FIGURE 1 | Studied regions and their vegetation zones. Each region is numbered by their northern latitude. The European map is based on CORINE Land Cover data with forests in green, and USGS digital elevation model. Frontiers in Plant Science | www.frontiersin.org 3June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem TABLE 1 | Information on sampling regions, climate conditions (annual mean temperature; mean temperature for January; annual sum of precipitation; sum of precipitation for June, July and August; annual sum of potential evapotranspiration (PET) based on the Jensen-Haise method; sum of PET for June, July and August in years 1950–2000) and on sampled trees. Region (latitude code) Coordinates Elevation, m.a.s.l. Annual T, ◦C Jan T, ◦C Annual Precip.mm JJA Precip.mm Annual PET, mm JJA PET, mm Species Site No. of trees Tree H, m Mean sample age Finland North 67◦N 29◦E 380 −2−13 545 205 380 315 Pinus sylvestris Moist 5 5–10 20 (67) Dry 5 7–11 18 67◦N 29◦E 405 −2−13 545 205 380 315 Picea abies Moist 5 6–11 17 Dry 5 6–11 16 67◦N 29◦E 370 −2−13 545 205 380 315 Populus tremula Moist 5 7–11 11 Dry 5 5–9 13 Finland South 61◦N 24◦E 140 3 -−610 210 585 405 Pinus sylvestris Moist 5 10–13 10 (61) Dry 5 6–18 12 61◦N 24◦E 140 3 −9 610 210 585 405 Picea abies Moist 5 5–9 7 Dry 4 9–11 9 60◦N 24◦E 60 4 −7 645 200 645 425 Betula pendula Moist 3 7–15 4 Dry 3 6–7 3 61◦N 24◦E 150 3 −9 610 210 585 405 Populus tremula Moist 3 8–10 4 Dry 5 6–9 6 Netherlands 52◦N 05◦E 5 9 2 765 220 885 460 Pinus sylvestris Moist 5 8–11 3 (52) Dry 5 9–17 4 52◦N 05◦E 5 9 2 765 220 885 460 Picea abies Moist 5 14–17 5 Dry 5 16–21 7 52◦N 05◦E 5 9 2 765 220 885 460 Betula pendula Moist 4 17 8 Dry 4 11–23 4 52◦N 05◦E 5 9 2 765 220 885 460 Populus tremula Moist 5 18–22 7 Dry 5 8–24 6 Czech Republic 49◦N 16◦E 416 8 −3 575 230 890 485 Pinus sylvestris Moist 5 5–6 2 (49) Dry 5 7–9 4 49◦N 16◦E 416 8 −3 575 230 890 485 Picea abies Moist 5 6–9 2 Dry 5 7–9 3 49◦N 16◦E 416 8 −3 575 230 890 485 Betula pendula Moist 3 7–8 1 Dry 5 5–9 3 49◦N 16◦E 416 8 −3 575 230 890 485 Populus tremula Moist 5 8 4 Dry 5 8–12 4 Italy 46◦N 12◦E 1075 7 −4 1070 350 765 430 Pinus sylvestris Moist 5 6–10 4 (46a) Dry 5 5–6 27 46◦N 12◦E 1075 7 −4 1070 350 765 430 Picea abies Moist 5 5–14 5 Dry 5 7–10 8 (Continued) Frontiers in Plant Science | www.frontiersin.org 4June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem TABLE 1 | Continued Region (latitude code) Coordinates Elevation, m.a.s.l. Annual T, ◦C Jan T, ◦C Annual Precip.mm JJA Precip.mm Annual PET, mm JJA PET, mm Species Site No. of trees Tree H, m Mean sample age Switzerland 46◦N 08◦E 645 9 0 660 170 910 480 Pinus sylvestris Moist 5 18–21 12 (46b) Dry 5 8–11 13 46◦N 08◦E 1340 5 −3 1315 390 610 380 Picea abies Moist 4 20–30 8 Dry 5 14–21 13 Portugal 40◦N 07◦W 1450 8 2 1740 125 820 450 Pinus sylvestris Moist – – – (40) Dry 5 10–14 14 40◦N 07◦W 1500 8 2 1725 125 885 455 Betula pendula Moist 4 8–9 20 Dry 5 8–14 14 Climate data is from WorldClim original 30-s data (http://www.worldclim.org/bioclim) downscaled to 100-m resolution (Zimmermann and Roberts, 2001) for all but the Finnish sites; the Italian sites and the Swiss pine site have their precipitation data from a nearby weather stations at San Vito di Cadore (Centre of Studies of Alpine Environment) and Sierre (http://www.meteoswiss.ch), respectively, due to highly varying topography. Mean sample age refers to the number of growth rings in the xylem tissue at the fixed 0.7-m-sampling distance from branch tip. Especially in the case of Betula pendula, some of the five sampled trees per species and per site were removed from the dataset due to inadequate sampling material for osmolality measurements. tube and frozen immediately in the field in liquid nitrogen or dry ice for osmolality and water content measurements. Sampling was performed in late summer of 2014 after the end of seasonal secondary growth but before leaf senescence in the different regions. Phloem Osmolality Phloem osmolality measurements were conducted in the laboratory at the University of Helsinki. Frozen samples were brought to room temperature for 15 min to thaw. Freezing and thawing the samples rapidly breaks the cell membranes and releases symplastic contents to the apoplast (Kikuta and Richter, 1992; Callister et al., 2006). The outer bark was scraped away with a razor blade and each sample cut in two 2 cm pieces in order to have two subsamples of each branch. The inner bark (including cambium and all the tissues from cambium to the innermost periderm) was separated from xylem on the basis of the hardness and color differences between the tissues and weighted for fresh mass (FM). The samples were set in silicabased membrane collection tubes (GeneJET Plasmid Miniprep Kit, Thermo Scientific, Massachusetts, USA) into a centrifuge (Heraeus Fresco 17, Thermo Scientific, Massachusetts, USA) at 14,000 g for 10 min (Devaux et al., 2009). The liquid was collected in osmometer tubes and the osmolality of the liquid was measured with a freezing-point osmometer (Osmomat-030 Freezing point osmometer, Gonotec, Berlin, DE). We assumed that the ratio of phloem tissue volume to whole inner bark tissue volume is large enough that it is justified to refer to the collected inner bark sap as phloem sap. Amount of Solutes and Water Content In order to get comparable information of the accumulation of cellular solutes in secondary phloem in different tree individuals growing in different regions, phloem osmolality measurements either need to be analyzed at full tissue saturation (Rosner et al., 2001) or be connected with tissue water content measurements. To determine the amount of solutes (n) and water content (WC) in the inner bark tissue, we randomly selected three of the five phloem osmolality samples per species and site. The samples were dried at 80◦C in an oven for 72 h to obtain their dry mass (DM). WC (g g−1DM) was calculated as the difference between FM and DM divided by the DM, and n (mol kg−1DM) was calculated as osmolality multiplied by WC. Non-Structural Carbohydrate Composition To avoid methodological artifacts (Quentin et al., 2015), all NSC measurements were done at the Natural Resources Institute Finland. Furthermore, to standardize our results, we focused on the ratio between starch and the total NSC content, and the ratio between the target sugar and the total soluble sugar content. We analyzed NSC composition of evergreen conifer phloem by using a sub-sample (ca. 2 cm in length) of the segment collected for the measurements of osmolality and water content. NSC measurements were performed according to Jyske et al. (2015). Briefly, samples of inner bark were cut into matchsticksized pieces, freeze-dried for 72 h, and milled with a ballmill while kept frozen. About 20 mg of powder was weighed Frontiers in Plant Science | www.frontiersin.org 5June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem into glass test tubes and heated to 100◦C to deactivate the enzymes. The soluble sugars were extracted twice (at 100◦C) by using 80% ethanol to which m-erythtrit (Calbiochem, Merck KGaA, Darmstadt, Germany) was added as an internal standard. The sugar extracts were evaporated to dryness with nitrogen flow, silylated with 20% TMSI-pyridine mixture (i.e., 1trimethylsilyl-imidazole; Sigma-Aldrich, Darmstadt, Germany), and analyzed with gas chromatography–mass spectrometry (GC-MS; Agilent Hewlett-Packard 6890 GC, equipped with a Zebron ZB-SemiVolatiles column (30 m ×0.25 mm i.d ×0.25 µm df) and Hewlett-Packard 5973 MSD, EI-MS 70 eV), in which helium was used as a carrier gas (flow 1.5 ml/min). The chromatographic conditions were as follows: initial temperature 110◦C; rate of temperature increase 10◦C min−1; final temperature 320◦C maintained for 14 min; injector temperature 260◦C, and split ratio 1:20. The MS-interface temperature was 300◦C and ion source temperature was 230◦C. In the analysis, the compounds were identified on the basis of their mass spectra and retention times as verified by using the following authentic compounds (i.e., external standards): Dfructose (Merck, Darmstadt, Germany), myo-inositol (Merck), D-glucose (BDH AnalaR, VWR International Ltd, Poole, UK), sorbitol (Fluka, Sigma-Aldrich), sucrose (BDH AnalaR), Draffinose pentahydrate (Fluka). For pinitol, fructose was used as a standard. The results were calculated using an internal standard and the external standards. The soluble-sugar-free samples obtained after extraction were used for starch analyses with a commercial starch assay kit (Total Starch Assay Procedure, Megazyme International, Wicklow, Ireland). Briefly, starch in residual pellets was hydrolyzed into maltodextrins by adding α-amylase (in MOPSbuffer, pH 7) and incubated for 6 min at 100.5◦C. Next, the samples were suspended in acetate buffer (pH 4.5) and amyloglugosidase was added to hydrolyze maltodextrins into dglucose by incubating for 30 min at 50.5◦C. The absorbance of the samples was measured colorimetrically (Shimadzu UV2401 spectrometer at 510 nm) using glucose oxidase and peroxidase. The standard curve was made with D-glucose (BDH AnalaR). Phloem Area and Sample Age Measurements The most basipetal branch segment was used for anatomical analysis. Each segment infiltrated in 50% ethanol was cut with a hand saw to have a 5–8 mm thick disk. Disks were then dehydrated with immersions in ascending ethanol concentrations until absolute ethanol, infiltrated with liquid paraffin, and embedded into paraffin blocks (Anderson and Bancroft, 2002). The blocks were trimmed and moistened with cold water for at least 2 h to soften the woody tissue and then cut with a rotary microtome (RM2245, Leica). Sections (10–15 µm in thickness) were then stained with a solution of safranine and Astra blue (1 and 0.5% in distilled water, respectively), dehydrated with alcohol (50 and 96%), rinsed with xylol and permanently fixed by mounting a cover glass with Eukitt (Bioptica, Milan, Italy). Digital images were captured at 40×magnifications with a camera mounted on a light microscope (Eclipse80i, Nikon) to cover the whole cross-sectional area and then stitched with PTGui v8.3.10 (New House Internet Services B.V., Rotterdam, The Netherlands). Stitched images were analyzed with ROXAS v2.1 (von Arx and Dietz, 2005; von Arx and Carrer, 2014) along a wedge of known angle centered at the pith to identify tree-ring boundaries and determine branch age. Proxy for the growth rate (cm year−1) of 70cm-long branches could be calculated from the branch age. In addition, the non-collapsed phloem area was determined from the wedge and upscaled to the total cross-section (Zhang et al., 2015). Collapsed phloem was identified as the phloem older than 1 year, characterized by bigger and stretched cells. Statistical Analysis We first analyzed the effect of water content (WC) on osmolality. A two-level mixed-effect model for explaining osmolality was created. The fixed term of the model included the explanatory variables 1/WC, species and their interaction. In addition, the model had random intercepts for levels describing the nested structure of the data: regions, and sites within regions. Random intercepts followed normal distribution. These models, and mixed-effect models described below, were fitted with the function lme of the R package nlme (Pinheiro et al., 2013). All statistical analyses were performed with R version 3.2.2 (R Core Team, 2013). Solute content—osmolality regression was fitted but the significance of the fit was not analyzed because the osmolality was used in the calculation of the solute content, thus creating dependency of response and explanatory variables. Nonetheless fitted curves are given to guide the reader’s eye. Curves were estimated with the function nls of the R package stats (Bates and Chambers, 1992) assuming a relationship y=a+x∧bbetween ordinate (y) and abscissa (x). Parameters aand bwere fitted by species. Secondly, we compared the differences of osmolality, n, WC, and NSC composition between species and regions. Therefore, the fixed term of the mixed-effect model included species and region, and several covariates [sample age, non-collapsed phloem area, tree height, site moisture status (moist/dry)]. The random term included sites, and, in the case of osmolality, observations within tree as we had two repetitions per tree. First, we performed the selection of covariates using AIC criterion and step AICfunction of the R package MASS (Venables and Ripley, 2002) when the site moisture status, species, and region were always in the model. Second, also the site moisture status and species were removed from the model if they did not improve AIC. ANOVA results of the model in the Result section are shown for the marginal effects, i.e., for the effects, when all other variables are already in the model. Pairwise differences between regions and species were tested with Tukey’s range test (R function glht—R package multcomp; Hothorn et al., 2008), except for NSC-related variables, where ANOVA results were used to test whether the conifers significantly differ from each other. Frontiers in Plant Science | www.frontiersin.org 6June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem RESULTS Phloem osmolality decreased with increasing water content per tissue dry mass in all studied species (Table 2,Figure 2A). In addition to tissue water content, phloem osmolality increased with increasing tissue solute content (calculated from osmolality and water content measurements) in Populus and Betula, but such a trend was either weaker (Picea) or absent (Pinus) for both evergreen conifers (Figure 2B). Phloem osmolality (mol kg−1) varied across regions between 0.38–0.60 (Pinus), 0.44–0.69 (Picea), 0.53–0.69 (Populus), and 0.49–0.64 (Betula). Phloem osmolality was on average 15% lower in Pinus than in the other species (Table 3,Figure 3A). Among the studied regions, osmolality was lowest at midlatitude (Czech Republic), increased toward the north and the south with the highest average values being measured in Southern Finland and Italy, respectively, and then decreased again in Northern Finland and Portugal (Table 3,Figure 3B). The difference in the average osmolality between the Czech Republic and Southern Finland was 37%, and between the Czech Republic and Italy 38%. There was no significant difference in phloem osmolality between dry and moist soil sites within the regions. Tree height, sample age or, the area of non-collapsed phloem were not related to phloem osmolality. The latitudinal trends were visible in all species (Figure S2 in the Supplementary Material). Water content per dry mass was on average 91% higher in Pinus and 57% higher in Picea in comparison to the deciduous angiosperm species (Figure 3A). The highest tissue water content was measured at mid-latitudes, from where it decreased on average by 21% toward northern latitudes and 28% toward southern latitudes (Figure 3B). Tissue water content variability was high at the intermediate latitudes (Figure 3B). In addition to species and region, increasing non-collapsed phloem area increased water content per tissue dry mass indicating that non-collapsed phloem contains more water in comparison to collapsed phloem (Table 3). Water content per tissue dry mass TABLE 2 | Mixed-effect model result for testing the effect of species, water content (WC) and their interaction on phloem osmolality. Dependent variable Independent Class Estimate ±SE variables Osmolality, mol kg−1Intercept*** (Betula pendula) 0.15 ±0.09 Species*** Pinus sylvestris 0.19 ±0.09 Picea abies 0.04 ±0.10 Populus tremula 0.12 ±0.12 1 WC−1*** (Betula pendula) 0.36 ±0.07*** Species ×1 WC−1*Pinus sylvestris −0.13 ±0.09 Picea abies 0.12 ±0.10 Populus tremula −0.07 ±0.10 Betula pendula is used as reference for the model estimates for the class variable species. Sample size is 208. *P<0.05, ***P<0.001. was higher in younger samples in comparison to older samples, and higher in shorter trees in comparison to taller trees (Table 3). Phloem water content was slightly higher in moist soil sites TABLE 3 | Mixed-effect model results for testing the influence of species and region on osmolality, water content (WC), and solute content (n). Dependent variable Covariates and fixed effects Class Estimate ±SE Osmolality, mol kg−1 Intercept*** (dry site, Betula pendula, 40◦N) 0.64 ±0.03*** N=342 Site Moist site −0.02 ±0.02 Species*** Pinus sylvestris −0.081 ±0.028** Picea abies 0.002 ±0.029 Populus tremula 0.067 ±0.031* Region*** 46a◦N0.03 ±0.05 46b◦N−0.04 ±0.05 49◦N−0.17 ±0.04*** 52◦N−0.09 ±0.04* 61◦N0.02 ±0.04 67◦N−0.10 ±0.04* WC, g g−1DM Intercept*** (dry site, Betula pendula, 40◦N) 0.90 ±0.14*** N=208 Sample age, y*−0.0074 ±0.0030* Non-c. phloem area, mm2** 0.016 ±0.005** Tree height, m*−0.01 ±0.005* Site Moist site 0.13 ±0.07 Species*** Pinus sylvestris 0.85 ±0.11*** Picea abies 0.55 ±0.11*** Populus tremula −0.01 ±0.12 Region** 46a◦N−0.33 ±0.18 46b◦N−0.03 ±0.18 49◦N0.12 ±0.16 52◦N0.23 ±0.16 61◦N−0.20 ±0.16 67◦N−0.11 ±0.17 n, mol kg−1DM Intercept*** (Betula pendula, 40◦N) 0.57 ±0.05*** N=208 Sample age, y*** −0.005 ±0.001*** Species*** Pinus sylvestris 0.31 ±0.04*** Picea abies 0.25 ±0.04*** Populus tremula 0.01 ±0.04 Region*46a◦N−0.030 ±0.06 46b◦N−0.030 ±0.06 49◦N−0.092 ±0.05 52◦N0.023 ±0.05 61◦N−0.033 ±0.05 67◦N−0.090 ±0.05 Potential covariates in the model were site moisture status, tree height, sample age and non-collapsed phloem area; covariates and their order in the final model were selected with AIC. Dry site, Betula pendula and Portugal (40◦N) are used as references for the model estimates for the class variables site, species and region, respectively, in the model output. N is sample size. *P<0.05, **P<0.01, ***P<0.001. Frontiers in Plant Science | www.frontiersin.org 7June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem FIGURE 2 | Phloem osmolality is shown against tissue water content (A) and solute content (B) per tissue dry mass (DM) for each species. Species-specific model fits are drawn in a based on a mixed-effect model (Table 2). In (B), power fits and 95% confidence intervals are drawn for each species based on the raw data to guide the eye although statistical tests are not justified (nis not independent from osmolality). in comparison to dry soil sites, but this difference was not statistically significant (Table 3). Solute content per phloem dry mass was on average 51% higher in the studied evergreen conifers in comparison to the deciduous angiosperm species (Figure 3A). Solute content showed only few statistically significant differences between the regions (Table 3,Figure 3B), and a decreasing trend with increasing sample age (Table 3). The ratio of starch to total NSC increased approximately 60% from Portugal to the Finnish regions (Table 4,Figure 4A). The ratio of disaccharide sucrose to total soluble sugars was the lowest at mid-latitudes in Switzerland and the Czech Republic (Table 4,Figure 4B), whereas the ratio of monosaccharides glucose and fructose (i.e., hexoses) to total soluble sugars was the highest in these regions (Table 4,Figure 4C). Raffinose content was negligible at mid-latitudes in Czech Republic, and increased with both increasing and decreasing latitudes (Table 4, Figure 4D). The ratio of raffinose to all soluble sugars was the only soluble sugar that had significantly different values in the two studied evergreen conifers: the share of raffinose was 10% higher in Pinus in comparison to Picea (Table 4). In contrast, the share of pinitol to total soluble sugars showed only a few statistically significant differences across regions and there was no difference between the two conifers (Table 4, Figure 4E). No significant differences were observed in any soluble sugars between moist and dry soil sites (Table 4). Similar latitudinal trends were visible in NSC composition in absolute concentrations (Figure S3 in the Supplementary Material). In addition to region, the ratios of starch to total NSC, and the ratios of sucrose and raffinose to all soluble sugars were positively linked to tree height (Table 4). The ratio of starch to total NSC increased with increasing sample age (Table 4). Similarly, sample age affected the share of sucrose positively, as did the area of non-collapsed phloem (Table 4). Share of hexoses, on contrary, decreased with increasing tree height, and was the lower the higher the area of non-collapsed phloem (Table 4). The share of pinitol decreased with increasing sample age (Table 4). Branch growth rate was highest at mid-latitudes and decreased toward north and south (Table 1). Also needle length in the studied conifers showed similar trend (Figure S1 in the Supplementary Material). DISCUSSION Latitudinal Trends and Species Differences in Osmolality The results showed that the major determinant of observed variation in phloem osmolality across Europe was tissue water content instead of solute content, in contrary to what we expected. Solute content played a role in explaining the variation in phloem osmolality for both deciduous angiosperm species (Betula pendula and Populus tremula), but its effect was weak for the two evergreen conifers (Pinus sylvestris and Picea abies). We thus found support for active osmoregulation, by adjusting sugar contents, for deciduous angiosperms, but not for evergreen conifers. Phloem transport, conversion of NSC from one form to another, or unloading of sugars with the xylem may contribute to such osmoregulation. The study confirmed our hypothesis that phloem osmolality increases from mid-latitudes toward the extreme ends of the latitudinal gradient following decreasing branch growth rate (see Table 1). A higher osmolality of phloem sap was expected in the driest conditions as it contributes to maintaining turgor when tree water potential is low, and in cold conditions because it decreases the freezing point of living tissue (Charrier et al., 2013a) and maintains sufficient metabolism as the metabolic Frontiers in Plant Science | www.frontiersin.org 8June 2016 | Volume 7 | Article 726 Lintunen et al. Osmolality and NSC in Branch Phloem FIGURE 3 | Solute content (n) and water content (WC) per tissue dry mass, and osmolality of the tissue are shown for each (A) species and (B) region. The latitudes in (B) represent countries as shown in Table 1 and Figure 1. Error bars indicate standard deviation. Significant differences between species and regions were analyzed with a mixed-effect model for n, WC and osmolality (Table 3), and are shown with different Roman numbers, Arabic numbers and letters, respectively. efficiency decreases at lower temperatures (see e.g., Piper et al., 2006). Moreover, high osmolality may enable refilling of xylem conducting elements embolised during freezing and thawing, as has been shown for Juglans regia (Charrier et al., 2013b) and Picea abies in alpine timberline (Mayr et al., 2014). Local soil properties and/or topography had no effect on phloem osmolality, implying that climate rather than soil water supply affected phloem osmolality and its components. The sites where selected subjectively, but the contrast between two moisture statuses was strong enough to induce differences in needle length of Pinus and Picea (see Figure S1 in the Supplementary Material). Although site moisture status did not have a direct effect on phloem osmolality or its components, it played a role via sample age and non-collapsed phloem area as these variables varied between moist and dry soil sites. These sources of variation were controlled in the analyses with statistical tests. The latitudinal trends observed in phloem osmolality followed the latitudinal trends observed in the level of tissue water content, whereas the level of phloem solute content was surprisingly similar across Europe. Low phloem water content per tissue dry mass in the extreme ends of the latitudinal gradient can Frontiers in Plant Science | www.frontiersin.org 9June 2016 | Volume 7 | Article 726