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Article

Compound-Specific Carbon Isotopes and Concentrations of Carbohydrates and Organic Acids as Indicators of Tree Decline in Mountain Pine

by
Olga V. Churakova (Sidorova)
1,2,3,*,
Marco M. Lehmann
4,5,
Matthias Saurer
4,5,
Marina V. Fonti
3,
Rolf T. W. Siegwolf
4,5 and
Christof Bigler
1
1
Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Universitätstrasse 16, 8092 Zürich, Switzerland
2
Climate Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Climatic Change and Climate Impacts, 66 Boulevard Carl Vogt, CH-1205 Geneva, Switzerland
3
Institute of ecology and geography, Siberian Federal University, 660041 Krasnoyarsk, Svobodny pr 79, Russia
4
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
5
Paul Scherrer Institute, 5232 Villigen PSI, Switzerland
*
Author to whom correspondence should be addressed.
Forests 2018, 9(6), 363; https://doi.org/10.3390/f9060363
Submission received: 29 March 2018 / Revised: 24 April 2018 / Accepted: 10 May 2018 / Published: 19 June 2018
(This article belongs to the Special Issue Isotope Application in Forest Growth Assessment)

Abstract

:
We investigated seasonal variations in δ13C values and concentrations of carbohydrates and organic acids in needles of declining and healthy mountain pine (Pinus mugo ssp. uncinata (DC.) Domin) trees from the Swiss National Park (SNP), using compound-specific isotopes analysis (CSIA). Our goal was to study the impact of climatic drivers on the individual compounds and understand the reasons of partial tree declines in relation to healthy mountain pine trees under seasonal weather patterns. We found that temperature is the main climatic driver determining the seasonal carbon dynamics at the needle level. Lower seasonal δ13C variability and lower concentration levels of sucrose in needles suggest less photosynthetic activity and sink carbon demand in declining compared to healthy mountain pine trees. Higher concentration levels of hexose (glucose and fructose) can play a reserve function for surviving mechanisms of mountain pine trees. Seasonal patterns of organic acid (malate and citrate) suggest an increasing investment in maintenance and repair mechanisms. The seasonal course of carbohydrates and organic acids can therefore be considered an indicator for a modified carbon metabolism within the leaves and possibly within the other tree tissues, partially explaining the decline of mountain pine trees.

1. Introduction

Many forest ecosystems are declining globally due to increasing heat and drought-induced tree mortality [1,2,3,4,5]. Several studies have reported on declines in Scots pine (Pinus sylvestris L.) stands in dry inner alpine valleys [6,7,8,9]. In particular, declines of mountain pine (Pinus mugo ssp. uncinata (DC.) Domin) trees in Northern Germany, Poland, Pyrenees, Carpathians, Northern Apennines, Balkan Peninsula [10], and in the Swiss National Park (SNP) have been reported [11,12,13,14]. However, mechanisms of metabolic changes, interactions of different environmental factors, and causes leading to the partial decline and mortality of mountain pine trees in the SNP under recent climatic changes are still unresolved.
Adaptive responses to soil water deficit, pathogens, and insects could change or limit carbohydrate reserves in trees [15]. In recent debates on the possible limitation of carbohydrate reserves, it was stated that limitation rarely occurs [16], possibly only for trees exposed to long lasting drought periods [17]. The transport of photosynthetically derived carbon from leaves to belowground biomass is often reduced under drought hindering optimal development of a functional root system, which can lead to a lack of essential nutrients and to higher mortality rates in the long-term [17,18]. A better understanding of the metabolic processes in declining trees is of great interest for predicting forest response to future warming.
The traditional δ13C bulk isotope analysis of soluble carbohydrates has limitations in determining the response of plants to environmental variations because compounds with slow turnover rates, like pinitol, can strongly dampen the signal [19]. More detailed information can be derived from the individual carbohydrates (sucrose, glucose, and fructose), and sugar alcohols (e.g., pinitol), but also from compounds like organic acids (malate and citrate) that are involved in more “downstream metabolic processes” leading to systematic but varying 13C-enrichment of sink organs (such as stem) in comparison to leaves [20]. Thus far, there have been few investigations analyzing δ13C of individual carbohydrates and organic acids [19,20,21,22,23,24,25,26] using high-performance liquid chromatography (HPLC). However, even fewer investigations were applied in natural forest ecosystems to address questions of tree eco-physiology [19,22,23,26,27]. Therefore, further studies are needed to understand how environmental changes impact δ13C values and concentrations of individual compounds at the leaf level in declining trees.
One of the most dominant non-structural carbohydrates in tree leaves is sucrose, the preferred main transport metabolite in plants [28], which facilitates the carbon transfer from sources to sinks and functions as a precursor for biomarkers such as tree-ring cellulose. Due to its importance, changes in δ13C values and sucrose concentrations are assumed to show strong responses to changing weather conditions [19]. Other soluble carbohydrates, like glucose and fructose, are also synthesized in considerable amounts in almost all plant tissues, often reflecting a carbon pool supplying all ongoing processes in a tissue such as biosynthesis of compounds, maintenance of cell structure, and respiration. Increasing sucrose, glucose, and fructose concentrations can be observed under drought and may protect against frost and water-loss due to their osmotic functionality [28]. Low levels of non-structural carbohydrates could be indicative of low photosynthetic rates and demand by carbon sink, thus potentially inducing tree decline and mortality [4].
In addition, high amounts of sugar alcohols, such as pinitol, are generally found in conifer needles. Sugar alcohols have a very slow turnover compared to other plant sugars and are therefore not expected to reflect seasonal changes [19]. However, some studies suggested that pinitol plays a role in tolerance to low temperature and drought [19,28]. While δ13C values and concentrations of individual sugars may respond to climatic changes, they are actually only an indirect indicator of specific metabolic responses. Organic acids, such as citrate and malate, can also function as carbon storage, however, they are known to support various other physiological functions in plant metabolism such as respiration, stomatal regulation, nutrient transport, pH adjustments in plant and soils, and root-soil interactions [20,25,29]. Thus, δ13C values and concentrations of organic acids might therefore be potential indicators of drought stress [25].
Here, we present a first study of seasonal variations in δ13С values and concentrations of individual carbohydrates (sucrose, glucose, fructose and sugar alcohol—pinitol) and organic acids (malate and citrate) in needles of declining and healthy mountain pine trees from the SNP.
We (1) investigated if δ13C values and concentrations of individual compounds in declining mountain pine trees responded differently over the growing season to infer on potential physiological and metabolic changes; (2) compared the compound responses in declining with healthy mountain pine trees from the same site to better understand which specific compound in needles could be indicative of tree decline; and (3) determined if potential environmental drivers (i.e., air temperature and vapor pressure deficit) are related to changes in δ13C and concentrations in individual compounds.

2. Materials and Methods

2.1. Study Site

The study was carried out at the site Champlönch (46°40′48″ N, 10°10′20″ E; 1959–1964 m a.s.l.), which is located in the SNP. The SNP has an area of 174.2 km2 and is the largest United Nations Educational, Scientific and Cultural Organization (UNESCO) biosphere reserve. The main tree species at the study site is mountain pine (Pinus mugo ssp. uncinata), which represent ca. 73% of the forests in the SNP, while European larch (Larix decidua Mill.) and Swiss stone pine (Pinus cembra L.) cover ca. 11%. The soil type is a rendzic leptosol. Shallow and rocky soils are found at the study site [30].

2.2. Climate Data

Our study site is characterized by a continental climate with average winter (December–February) temperatures of −9.2 °C, average summer (June–August) temperatures of 9.5 °C, and 910 mm annual precipitation according to the Buffalora weather station (46°39′ N, 10°16′ E, 1968 m a.s.l.) for the period from 1917 to 2014. The weather station is located ca. 11.3 km away from the study site. Average annual and spring temperatures increased by 0.5 °C and 1.0 °C, respectively, for the period 1990–2014 relative to 1917–1989, while average summer temperature increased by 0.6 °C and annual precipitation decreased by 88.9 mm.
Seasonal temperature (Tav—average, Tmin—minimum, Tmax—maximal) and vapor pressure deficit (VPD) was calculated with data from the Buffalora weather station. Sampling dates in 2014 were selected according to the phenology with similar weather conditions as in 2013 (Figure 1a,b). In 2013, the needle samples from healthy mountain pine trees were not collected on 24 May because the new needles had not yet emerged. The first sampling date in 2013 was 6 June, which was similar to the phenological conditions of 24 May, 2014 (Figure 1a,b).

2.3. Selection of Trees and Sampling of Needles

We selected south (S)- and north (N)-facing aspects at the study site with healthy and declining mountain pine trees in May 2013. Averaged height of the healthy and declining mountain pine trees ranged within 13–15 m, while average diameter at 1.3 m tree height was ca. 28 cm for healthy and ca. 25 cm for declining mountain pine trees.
The S-aspect was located at 1964 m a.s.l. with a 35° slope, and is characterized as being sunny and dry. In contrast, the N-aspect was located at 1959 m a.s.l. with a 20° slope, and is characterized as being shady with moister soil, which is covered by denser ground vegetation compared to the S-aspect. During sampling of healthy mountain pine trees in summer 2013 [26], we selected declining mountain pine trees (Pinus mugo ssp. uncinata) based on visual assessment of tree crowns (i.e., presence of reddish, brownish needle coloring) (Figure 2a). Needles (approx. 120 mg per sample) from eight declining mountain pine trees were sampled once per month from late May until October 2014. Needles (approximately 200 mg per sample) from eight healthy mountain pine trees were sampled biweekly from early June until October 2013. To be consistent between two data sets from different years, we provided data for healthy mountain pine trees in a similar manner, once per month (Figure 3b,d,f,h). Average age of the selected mountain pine trees was 150 to 200 years. However, it is worth noting that needles used for the analysis from the declining mountain pine trees were green without visible damage. After collection, all needle samples were heated in a microwave for 90 s at 250 W to stop enzymatic and metabolic activities [31]. Subsequently, the samples were delivered to the laboratory at the Paul Scherrer Institute, where they were dried for 24 h at 60 °C [31] and ground with a steel ball mill to a fine powder, which was used for all further analyses.

2.4. Extraction and Purification of Sugars and Organic Acids

From the milled sample, 60 mg of powder was placed in Eppendorf tubes, 1.5 mL of Milli-Q water was added, and then vortexed until the powder was fully suspended. The tubes were placed in a water bath at 85 °C for 30 min according to the protocol [19,31]. The samples were allowed to cool down for 30 min followed by centrifugation at 10,000× g for 2 min.
For sugar purification, we used Thermo ScientificTM DionexTM OnGuard II CRTG 1CC cartridges (Thermo Fisher Scientific, Waltham, MA, USA): Cation exchange cartridge “H” to remove amino acids, anion exchange cartridge “A” (HCO3) to remove organic acids, and cartridge “P” to remove phenolic compounds present in the plant water extracts [19,32]. After sugar purification, we eluted organic acids from the cartridge “A” with 30 mL 1 M HCl solution (modified after Reference [20]). Purified samples were freeze-dried, dissolved in deionized water, and stored in the freezer until use.

2.5. Compound-Specific δ13C Isotope Analysis

Compound-specific δ13C isotope analysis (CSIA) was performed on-line using a High-Performance Liquid Chromatography (HPLC) coupled via LC Isolink interface to a Delta V Advantage IRMS (both Thermo Fisher Scientific, Bremen, Germany). Soluble carbohydrates were separated on a Dionex CarboPac™ PA 20 3 × 150 mm column (Dionex, Olten, Switzerland) at 22 °C, using a 2 mM NaOH solution as the mobile phase with a flow rate of 250 µL min−1 [19,32]. Organic acids were separated on an Allure Organic Acids 4.6 × 300 mm column (Restek, Bellefonte, PA, USA) at 8 °C, using a 100 mM KH2PO4 (pH 3) solution as the mobile phase with a flow rate of 500 µL min−1. Evaluation of δ13C values and concentrations of individual compounds was performed according to [32].

2.6. Statistical Analysis

Pearson correlation analyses between δ13C values and concentrations of individual needle compounds versus climatic data from the Buffalora weather station were carried out using the STATISTICA 13.3 (TIBCO Software Inc., Palo Alto, CA, USA). We applied unpaired t-test analysis to reveal whether δ13C values and concentrations of carbohydrates and organic acids differ between aspects and between declining and healthy mountain pine trees.

3. Results and Discussion

3.1. δ13C Values and Concentrations of Carbohydrates and Organic Acids as a Potential Indicator of Tree Decline

The δ13C values and concentrations in needles of declining mountain pine trees did not significantly differ at the lowest p-values between compounds (p > 0.08) between N- and S-facing aspects for the individual compounds. Therefore we merged the data from both aspects for each individual compound separately (n = 8) (Figure 3a,c,e,g). δ13C of individual soluble carbohydrates in needles of declining mountain pine trees (Figure 3a) showed less clear seasonal patterns compared to healthy mountain pine trees (Figure 3b). A strong depletion of sucrose, glucose, and fructose in needles of healthy mountain pine trees by the end of the growing season was detected (Figure 3b). This drop in the δ13C could be caused by rain and cold night temperature on the day before. We found that δ13C values of sucrose in declining mountain pine trees were less negative compared to δ13C values of hexoses (i.e., glucose and fructose), and pinitol by 3 and 5‰ (Figure 3a). This finding was in contrast to the healthy trees, where δ13C values of sucrose were similar to δ13C values of hexoses and varied no more than 1–2‰ between each other during the season (Figure 3b). δ13C of pinitol showed lower seasonal variability compared to other carbohydrates (Figure 3a), which was not the case for healthy mountain pine trees (Figure 3b). A decrease of hexose (fructose and glucose) towards the end of the growing season was detected for both declining (Figure 3a) and healthy (Figure 3b) mountain pine trees. The latter showed much stronger depletion, which may be a response of the healthy mountain pine trees to the development of the cold conditions or of the declining pine trees to desiccation tolerance [26,28]. The 13C-enrichment in sucrose compared to other carbohydrates for declining mountain pine trees might be explained by the decreasing pool size, i.e., the preferential use of 13C-depleted sucrose for biosynthetic and respiratory processes, but might also be a result of physiological adaptations (Figure 3a). Stomatal conductance might have been lower in the declining than in the healthy mountain pine trees given the need to use a more conservative water-use strategy [30].
We found high δ13C of sucrose (Figure 3a) and low sucrose concentration (Figure 3c) in needles of the declining mountain pine trees at a compound-specific level. δ13C increase could be a result of reduced stomatal conductance, supporting the classical model for carbon isotope fractionation [33]. Thereafter, the concentrations showed a decreasing trend. This could be explained by a reduction of carbon supply as a result of needle senescence with a concomitant lower C-demand for tree growth. The opposite pattern for sucrose concentration was found in needles of healthy mountain pine trees, which showed an increasing trend towards the end of the growing season (Figure 3d). Pinitol concentration in needles of healthy mountain pine trees (Figure 3d) is also higher than that in declining mountain pine trees (Figure 3b), which can be explained by higher resistance of healthy compared to declining mountain pine trees towards the frost events [19]. Sudachkova et al. [34] showed that the need for reserve compounds in conifers was stronger at the beginning and at the end of the growing period, when the processes of xylogenesis had ended and when the carbon reserve pools are replenished, while glucose is converted into the starch compound.
In most cases, stomatal limitation is a response to drought whereas increased photosynthesis, often due to enhanced photosynthetically active radiation, under abundant water availability leads to carbon isotope enrichment [33]. Low sucrose concentration could be partially explained by receding export of carbohydrates from the leaves to the phloem for production of reserves under dry conditions [35]. Low carbohydrate concentrations may sometimes occur as a result of limited carbon assimilation and stomatal control under drought [36]. Severe water stress could lead to a continuous or transitory increase of leaf respiration that could also partly cause a decrease in sucrose concentration [34]. Fresh assimilates in declining trees are more likely used for maintenance and respiration than for storage or biomass production (i.e., growth) [21,30]. Lower transport rates into the phloem under water shortage [15] might function as a metabolic feedback and decrease sucrose synthesis [28].
Furthermore, we measured δ13C values and concentrations of organic acids in needles of declining (Figure 3e,g) and healthy (Figure 3f,h) mountain pine trees. Organic acids were clearly more 13C-enriched in needles of declining mountain pine trees compared to carbohydrates and were more variable, e.g., 2.7‰ difference between hexose (Figure 3a) and malate, up to 4‰ with citrate (Figure 3e), and almost 4‰ more enriched compared to healthy mountain pine trees (Figure 3f).
The seasonal pattern of δ13C variability in organic acids is coherent with δ13C variability in carbohydrates (r = 0.84; p < 0.05), however, with an offset up to 4.8‰ over the season. In particular, δ13C of citrate showed the highest seasonal variability of all compounds, with maximum values in mid-summer (Figure 3e) indicating −17.2‰ from the average mean of −23.2‰ during the season. This δ13C increase could be a result of reduced stomatal conductance and enhanced assimilation. The increase in δ13C values of citrate is accompanied by a decrease in citrate concentration (Figure 3g) in needles of declining mountain pine trees. Thus, a preferential use of lighter citrate isotopologes may explain the 13C-enrichment of citrate. The seasonal pattern of organic acid concentrations in needles of healthy mountain pine trees (Figure 3h) is rather similar to that of carbohydrates (Figure 3d). Increase of carbohydrate concentrations in needles of healthy mountain pine trees at the beginning and at the end of the season can be an indicator for stored forms of fixed carbon, which seems to be higher in healthy compared to declining mountain pine trees.

3.2. Averaged Seasonal Patterns

Based on a t-test, we found significant differences (p ≤ 0.05) in seasonally averaged δ13C values of sucrose in needles of declining compared with healthy mountain pine trees (Figure 4a). However, there are no significant differences in δ13C values of other individual carbohydrates (p ≥ 0.071) or organic acids compounds (p > 0.39) (Figure 4b) in needles of declining and healthy mountain pine trees. We found significant differences in averaged concentration of sucrose (p = 0.017), glucose (p = 0.0001), and fructose (p = 0.001) compounds (Figure 4c) in needles of declining compared to healthy mountain pine trees. Variability of sucrose concentration is large compared to glucose and fructose concentrations. We assume that this is a result of differences in assimilation rates and sucrose synthesis between declining and healthy mountain pine trees, indicating that declining trees are less vital than healthy trees and that their metabolic activity to produce carbon reserves is reduced.
Total averaged δ13C values for pinitol, sucrose, glucose, and fructose (PSGF) and carbohydrate concentrations did not differ significantly between healthy and declining mountain pine trees (p ≥ 0.76). Therefore, only analysis of individual compounds can provide detailed information about carbon dynamic at the leaf level.
High hexose (glucose and fructose) concentrations in needles of declining compared with healthy mountain pine trees (Figure 4c) might indicate osmotic regulation to reduce water loss. Particularly, hexose can be more efficient in this case than sucrose to store extra sugars and to form a pool of reserve carbohydrates [37]. Thus, our results emphasize the role of sucrose in tree decline, and the role of hexose as a compound for carbohydrate reserves.
Averaged citrate concentrations differ significantly (p = 0.0092) between declining and healthy mountain pine trees. Citrate may also act as an additional carbon pool (due to reduction of sucrose), supporting maintenance of plant metabolism and respiration [21,29]. However, citrate might also be used as a carbon transport metabolite, which is transported to sinks, such as roots, where it facilitates the transport of nutrients, such as iron [29,38], towards needles.

3.3. Correlations between δ13C and Concentrations of Individual Compounds in Declining Trees with Climate Data

The correlation analysis showed significant relationships (p < 0.05) between δ13C of sucrose in needles of declining mountain pine trees and average air temperature (Tav) (r = 0.90, p = 0.014; Figure 5a). Minimum air temperature (Tmin) only influenced δ13C in sucrose (r = 0.82, p = 0.048) (Figure 5b). Tmin could lead to a reduction in stomatal conductance and cause relatively low intercellular versus ambient CO2 changes [39], thus affecting δ13C of the carbohydrates. Only the δ13C in sucrose in needles of declining pine trees correlated positively with VPD (r = 0.89, p = 0.01) (Figure 5d), which may be a result of drought due to increases in temperature and evaporation.
δ13C in pinitol, sucrose, glucose, total carbohydrates, and organic acids (malate and citrate) in needles of declining mountain pine trees correlated significantly (p < 0.05) with Tmax (r = 0.85, p = 0.035; 0.81, p = 0.049; 0.83, p = 0.040, 0.84, p = 0.034 and 0.84, p = 0.034), respectively (Figure 5c). δ13C of pinitol in needles of healthy mountain pine trees showed significant correlations with Tmin (r = 0.81; p = 0.049), while glucose positively correlated with Tmax (r = 0.88, p = 0.021) and total carbohydrate concentration (r = 0.82; p = 0.046). Sugar alcohols, like pinitol, are potentially involved in cryoprotection, playing a role in low temperature (frost) and drought resistance, and may also be important to endure drought stress [28]. Yet, impact of Tmax on glucose and organic acids can be an indicator for protection against water loss under developing drought conditions. δ13C of carbohydrates in needles can indicate the impact of water availability, because higher δ13C occurs under water limitation. Therefore, water deficit and elevated temperature can give rise to significant changes in leaf biochemistry.
While δ13C values were strongly affected by site-specific temperature conditions, concentrations of individual compounds were not climate sensitive. Interpretations of physiological adaptions to temperature in carbon isotopes might ultimately transfer from assimilates to tree rings [30]. Declining trees might be more sensitive to water shortage compared to healthy trees on the same site. This might cause earlier stomatal closure, according to the classical model for carbon isotope fractionation [33], and can result in an increase in δ13C of sucrose as a response to drought, leading to a higher water-use efficiency in declining compared with healthy mountain pine trees. Higher concentration levels of hexose (glucose and fructose) can play a reserve function for survival mechanisms of declining compared to healthy mountain pine trees. Seasonal patterns of organic acid (malate and citrate) suggest increasing investment in maintenance and repair mechanisms. The seasonal cycle of carbohydrates and organic acids may be considered as an indicator for a modified carbon metabolism within the leaves and possibly within the other tree tissues, partially explaining the decline of mountain pine trees.
Our findings show differences in the seasonal variability of individual carbohydrates and organic acids in needles of declining compared to healthy mountain pine trees at the compound-specific level. We found that temperature plays a significant role in determining the seasonal carbon dynamics at the needle level. Lower seasonal δ13C variability and lower concentration levels of sucrose in needles suggest less photosynthetic activity and sink carbon demand in declining compared to healthy mountain pine trees. Higher concentration levels of hexose (glucose and fructose) can play a reserve function for surviving mechanisms of mountain pine trees. Seasonal patterns of organic acid (malate and citrate) suggest increasing investment in maintenance and repair mechanisms. Therefore, the seasonal cycle of carbohydrates and organic acids can be considered to be an indicator of a modified carbon metabolism within the leaves and possibly within the other tree tissues, partially explaining the decline of mountain pine trees.
Further field studies with different tree species and direct measurements of leaf gas-exchange as well as measurements of starch concentrations are urgently needed to complete the eco-physiological explanation of tree-decline processes. Detailed information can be derived from the analysis of each individual compound by CSIA only, while using traditional bulk δ13C analysis does not provide significant differences in carbon dynamic at the leaf level.

Author Contributions

O.V.C.(S)., C.B., M.S. and R.T.W.S. planned and designed the research. O.V.C.(S)., C.B., M.V.F., M.S. and R.T.W.S. performed experiments, O.V.C.(S)., C.B., M.V.F. conducted fieldwork, O.V.C.(S)., C.B., M.V.F., M.M.L analyzed data and O.V.C.(S)., M.M.L., M.S., R.T.W.S. and C.B. wrote the manuscript.

Acknowledgments

This work was supported by the Swiss National Science Foundation, the Marie Heim-Vögtlin Programm PMPD2-145507 granted to Olga V. Churakova (Sidorova). Rolf T.W. Siegwolf acknowledges the financial support for the acquisition of the compound-specific isotope analysis (CSIA) instrumentation by the Swiss National Science Foundation (grant 206021_128761). We are grateful to Ruedi Haller, Thomas Scheurer and Samuel Wiesmann from the Swiss National Park for their help. We would also like to acknowledge the research committee of the Swiss National Park for sampling permission in the protected area. We thank our colleagues from ETH Zurich and Paul Scherrer Institute, who helped with the field sampling and in the laboratory. We acknowledge Natalie C. Ceperley for her English-native proofreading.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  2. Fischer, E.M.; Knutti, R.; Lustenberger, A.; Schär, C. Expected changes in extremes. In Swiss Climate Change Scenarios CH 2011; C2SM, Meteo Swiss, ETH, NCCR Climate, and OcCC: Zürich, Switzerland, 2011; 88p. [Google Scholar]
  3. Anderegg, W.R.; Kane, J.M.; Anderegg, L.D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang. 2013, 3, 30–36. [Google Scholar] [CrossRef]
  4. McDowell, N.; Allen, C.D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Chang. 2015, 5, 669–672. [Google Scholar] [CrossRef]
  5. IPCC; Pachauri, R.K.; Meyer, L.A. (Eds.) Climate Change 2014: Synthesis Report Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014; 151p. [Google Scholar]
  6. Bigler, C.; Braker, O.U.; Bugmann, H.; Dobbertin, M.; Rigling, A. Drought as an inciting mortality factor in Scots pine stands of the Valais. Ecosystems 2006, 9, 330–343. [Google Scholar] [CrossRef]
  7. Zweifel, R.; Rigling, A.; Dobbertin, M. Species-specific stomatal response of trees to drought—A link to vegetation dynamics? J. Veg. Sci. 2009, 20, 442–454. [Google Scholar] [CrossRef]
  8. Eilmann, B.; Zweifel, R.; Buchmann, N.; Graf Pannatier, E.; Rigling, A. Drought alters timing, quantity, and quality of wood formation in Scots pine. J. Exp. Bot. 2011, 62, 2763–2771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Timofeeva, G.; Treydte, K.; Bugmann, H.; Rigling, A.; Schaub, M.; Siegwolf, R.; Saurer, M. Long-term effects of drought on tree-ring growth and carbon isotope variability in Scots pine in a dry environment. Tree Physiol. 2017, 37, 1028–1041. [Google Scholar] [CrossRef] [PubMed]
  10. Solar, J. Effect of climate change on mountain pine distribution in western Tatra Mountains. In Climate Change—Realities, Impacts Over Ice Cap, Sea level and Risks; Singh, B.R., Ed.; Intech: Rijeka, Croatia, 2013. [Google Scholar] [CrossRef]
  11. Brang, P. Decline of mountain pine (Pinus mugo ssp. uncinata) stands in the Swiss National Park: A dendrochronological approach. Dendrochronologia 1988, 6, 151–162. [Google Scholar]
  12. Dobbertin, M.; Baltensweiler, A.; Rigling, D. Tree mortality in an unmanaged mountain pine (Pinus mugo var. uncinata) stand in the Swiss National Park impacted by root fungi. For. Ecol. Manag. 2001, 145, 79–89. [Google Scholar] [CrossRef]
  13. Cherubini, P.; Fontana, G.; Rigling, D.; Dobbertin, M.; Brang, P.; Innes, J.L. Tree-life history prior to death: Two fungal root pathogens affect tree-ring growth differently. J. Ecol. 2002, 90, 839–850. [Google Scholar] [CrossRef]
  14. Bendel, M. The Spread of Root Rot Fungi in Mountain Pine Stands in the Swiss National Park: A Case Study of Its Influence on Forest Dynamic. Ph.D. Thesis, ETH, Zurich, Switzerland, 2006; 100p. [Google Scholar]
  15. Magel, E.; Einig, W.; Hampp, R. Carbohydrates in trees. In Carbohydrate Reserves in Plant—Synthesis and Regulation; Guppa, A.K., Kaur, N., Eds.; Elsevier Science BV: Amsterdam, The Netherlands, 2000; pp. 317–336. [Google Scholar]
  16. Körner, C. Carbon limitation in trees. J. Ecol. 2003, 91, 4–17. [Google Scholar] [CrossRef] [Green Version]
  17. Hartmann, H.; Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees—From what we can measure to what we want to know. New Phytologist. 2016, 211, 386–403. [Google Scholar] [CrossRef] [PubMed]
  18. Sevanto, S.; Dickman, L.T. Where does the carbon go?—Plant carbon allocation under climate change. Tree Physiol. 2015, 35, 581–584. [Google Scholar] [CrossRef] [PubMed]
  19. Rinne, K.T.; Saurer, M.; Kirdyanov, A.V.; Bryukhanova, M.V.; Prokushkin, A.S.; Churakova (Sidorova), O.V.; Siegwolf, R.T.W. Examining the response of needles carbohydrates from Siberian larch trees to climate using compound-specific δ13C and concentration analyses. Plant Cell Environ. 2015, 38, 2340–2352. [Google Scholar] [CrossRef] [PubMed]
  20. Lehmann, M.M.; Wegener, F.; Werner, R.A.; Werner, C. Diel variations in carbon isotopic composition and concentration of organic acids and their impact on plant dark respiration in different species. Plant Biol. 2016, 18, 776–784. [Google Scholar] [CrossRef] [PubMed]
  21. Lehmann, M.M.; Rinne, K.T.; Blessing, C.; Siegwolf, R.T.W.; Buchmann, N.; Werner, R.A. Malate as a key carbon source of leaf dark-respired CO2 across different environmental conditions in potato plants. Exp. Bot. 2015, 66, 5769–5781. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Simard, S.; Giovanelli, A.; Treydte, K.; Traversi, M.L.; King, G.M.; Frank, D.; Fonti, P. Intra-annual dynamics of non-structural carbohydrates in the cambium of mature conifer trees reflects radial growth demands. Tree Physiol. 2013, 33, 913–923. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Streit, K.; Siegwolf, R.; Hagerdon, F.; Schaub, M.; Buchmann, N. Lack of photosynthetic or stomatal regulation after 9 years of elevated [CO2] and 4 years of soil warming in two conifer species at the alpine treeline. Plant Cell Environ. 2014, 37, 315–326. [Google Scholar] [CrossRef] [PubMed]
  24. Wild, B.; Wanek, W.; Postl, W.; Richter, A. Contribution of carbon fixed by Rubisco and PEPC to phloem export in the Crassulacean acid metabolism plant Kalanchoe daigremontiana. J. Exp. Biol. 2010, 61, 1375–1383. [Google Scholar] [CrossRef] [PubMed]
  25. Hettmann, E.; Gleixner, G.; Juchelka, D. irm-LC/MS. δ13C Analysis of Organic Acids in Plants; Application Note 30075; Thermo Fischer Scientific: Ashville, NC, USA, 2005. [Google Scholar]
  26. Balicka, A. Untersuchung von Bergföhren im Schweizerischen Nationalpark mit Analyse Stabiler Isotope. Bachelor Thesis, ETH, Zürich, Switzerland, 2014. 31s. [Google Scholar]
  27. Churakova (Sidorova), O.V.; Lehmann, M.M.; Siegwolf, R.T.W.; Saurer, M.; Fonti, M.V.; Schmid, L.; Timofeeva, G.; Rinne-Garmston (Rinne), K.T.; Bigler, C. Compound-specific carbon isotope patterns in needles of conifer tree species from the Swiss National Park under recent climate change. Clim. Chang. 2018. under review. [Google Scholar]
  28. Moing, A. Sugar alcohols as carbohydrate reserves in some higher plants. In Carbohydrate Reserves in Plant—Synthesis and Regulation; Guppa, A.K., Kaur, N., Eds.; Elsevier Science BV: Amsterdam, The Netherlands, 2000; pp. 337–358. [Google Scholar]
  29. Igamberdiev, A.U.; Eprintsev, A.T. Organic acids: The pools of fixed carbon involved in redox regulation and energy balance in higher plants. Front. Plant Sci. 2016, 7, 1042. [Google Scholar] [CrossRef] [PubMed]
  30. Churakova (Sidorova), O.V.; Saurer, M.; Bryukhanova, M.; Siegwolf, R.; Bigler, C. Site-specific water-use strategies of mountain pine and larch to cope with recent climate change. Tree Physiol. 2016, 36, 942–953. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Wanek, W.; Heintel, S.; Richter, A. Preparation of starch and other carbon fractions from higher plan leaves for stable carbon isotope analysis. Rapid Commun. Mass Spectrom. 2001, 15, 1136–1140. [Google Scholar] [CrossRef] [PubMed]
  32. Rinne, K.T.; Saurer, M.; Streit, K.; Siegwolf, R.T.W. Evaluation of a liquid chromatography method for compound-specific δ13C analysis of plant carbohydrates in alkane media. Rapid Commun. Mass Spectrom. 2012, 26, 2173–2185. [Google Scholar] [CrossRef] [PubMed]
  33. Farquhar, G.D.; Ehleringer, J.R.; Hubick, K.T. Carbon Isotope Discrimination and Photosynthesis. Ann. Rev. Plant Physiol. Plant Mol. Biol. 1989, 40, 503–537. [Google Scholar] [CrossRef]
  34. Sudachkova, N.E.; Mulyutina, I.L.; Romanova, L.I.; Semenova, G.P. The annual dynamics of reserve compounds and hydrolitic enzymes activity in the tissues of Pinus sylvestris L. and Larix sibirica Ledeb. The metabolism of reserve compounds in the tissues of Siberian conifers. Eur. J. For. Res. 2004, 7, 1–10. [Google Scholar]
  35. Ainsworth, E.A.; Bush, D.R. Carbohydrate export from the leaf: A highly regulated process and target to enhance photosynthesis and productivity. Plant Physiol. 2011, 155, 64–69. [Google Scholar] [CrossRef] [PubMed]
  36. Atkin, O.K.; Macherel, D. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann. Bot. 2009, 103, 581–597. [Google Scholar] [CrossRef] [PubMed]
  37. Granot, D.; David-Schwartz, R.; Kelly, G. Hexose kinases and their role in sugar-sensing and plant development. Front. Plant Sci. 2013, 4, 44. [Google Scholar] [CrossRef] [PubMed]
  38. Ryan, P.R.; Delhaize, E.; Jones, D.L. Function and mechanism of organic anion exudation from plant roots. Plant Mol. Biol. 2001, 52, 527–560. [Google Scholar]
  39. Hu, J.; Moore, D.J.P.; Monson, R.K. Weather and climate controls over the seasonal carbon isotope dynamics of sugars from subalpine forest trees. Plant Cell Environ. 2010, 33, 35–47. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Variability of air temperatures: Average (Tav), minimum (Tmin), maximum (Tmax), and vapor pressure deficit (VPD) over the sampling season of 2013 (a) and 2014 (b).
Figure 1. Variability of air temperatures: Average (Tav), minimum (Tmin), maximum (Tmax), and vapor pressure deficit (VPD) over the sampling season of 2013 (a) and 2014 (b).
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Figure 2. Declining(a) and healthy (b) mountain pine trees from the study site at the Swiss National Park.
Figure 2. Declining(a) and healthy (b) mountain pine trees from the study site at the Swiss National Park.
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Figure 3. (a,b) δ13C values of pinitol (P), sucrose (S), glucose (G), and fructose (F); (c,d) concentrations (Pc, Sc, Gc, Fc); (e,g) δ13C values of organic acids (malate, M; citrate, C); (f,h) concentrations (Mc, Cc) in needles of declining (d) and healthy (h) mountain pine trees. Error bars are based on ± 1 SE (standard error).
Figure 3. (a,b) δ13C values of pinitol (P), sucrose (S), glucose (G), and fructose (F); (c,d) concentrations (Pc, Sc, Gc, Fc); (e,g) δ13C values of organic acids (malate, M; citrate, C); (f,h) concentrations (Mc, Cc) in needles of declining (d) and healthy (h) mountain pine trees. Error bars are based on ± 1 SE (standard error).
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Figure 4. Averaged seasonal δ13C values (a) of carbohydrates: pinitol (P), sucrose (S), glucose (G), and fructose (F); and (b) organic acids: malate (M) and citrate (C); and their concentrations (c,d), and average of total carbohydrates (PSGF) in needles of declining (d) and healthy (h) mountain pine trees. Error bars are based on ± 1 SE (standard error). Statistically significant differences in δ13C values and concentrations of carbohydrates and organic acids in needles of healthy and declining mountain pine trees are marked on the top as P-values.
Figure 4. Averaged seasonal δ13C values (a) of carbohydrates: pinitol (P), sucrose (S), glucose (G), and fructose (F); and (b) organic acids: malate (M) and citrate (C); and their concentrations (c,d), and average of total carbohydrates (PSGF) in needles of declining (d) and healthy (h) mountain pine trees. Error bars are based on ± 1 SE (standard error). Statistically significant differences in δ13C values and concentrations of carbohydrates and organic acids in needles of healthy and declining mountain pine trees are marked on the top as P-values.
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Figure 5. Pearson correlation coefficients were calculated between Tav—average (a); Tmin—minimum (b); Tmax—maximum (c) temperatures and vapor pressure deficit (VPD) (d) versus δ13C for pinitol (P), sucrose (S), glucose (G), fructose (F), total carbohydrates (PSGF) and their concentrations Pc, Sc, Gc, Fc, and organic acid compounds (δ13C in malate (M), citrate (C) and their concentrations Mc, Cc) in declining mountain pines. Horizontal black lines on the top and bottom present significance levels of p < 0.05.
Figure 5. Pearson correlation coefficients were calculated between Tav—average (a); Tmin—minimum (b); Tmax—maximum (c) temperatures and vapor pressure deficit (VPD) (d) versus δ13C for pinitol (P), sucrose (S), glucose (G), fructose (F), total carbohydrates (PSGF) and their concentrations Pc, Sc, Gc, Fc, and organic acid compounds (δ13C in malate (M), citrate (C) and their concentrations Mc, Cc) in declining mountain pines. Horizontal black lines on the top and bottom present significance levels of p < 0.05.
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MDPI and ACS Style

Churakova, O.V.; Lehmann, M.M.; Saurer, M.; Fonti, M.V.; Siegwolf, R.T.W.; Bigler, C. Compound-Specific Carbon Isotopes and Concentrations of Carbohydrates and Organic Acids as Indicators of Tree Decline in Mountain Pine. Forests 2018, 9, 363. https://doi.org/10.3390/f9060363

AMA Style

Churakova OV, Lehmann MM, Saurer M, Fonti MV, Siegwolf RTW, Bigler C. Compound-Specific Carbon Isotopes and Concentrations of Carbohydrates and Organic Acids as Indicators of Tree Decline in Mountain Pine. Forests. 2018; 9(6):363. https://doi.org/10.3390/f9060363

Chicago/Turabian Style

Churakova (Sidorova), Olga V., Marco M. Lehmann, Matthias Saurer, Marina V. Fonti, Rolf T. W. Siegwolf, and Christof Bigler. 2018. "Compound-Specific Carbon Isotopes and Concentrations of Carbohydrates and Organic Acids as Indicators of Tree Decline in Mountain Pine" Forests 9, no. 6: 363. https://doi.org/10.3390/f9060363

APA Style

Churakova, O. V., Lehmann, M. M., Saurer, M., Fonti, M. V., Siegwolf, R. T. W., & Bigler, C. (2018). Compound-Specific Carbon Isotopes and Concentrations of Carbohydrates and Organic Acids as Indicators of Tree Decline in Mountain Pine. Forests, 9(6), 363. https://doi.org/10.3390/f9060363

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