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Responses of Nutrients and Mobile Carbohydrates in Quercus variabilis Seedlings to Environmental Variations Using In Situ and Ex Situ Experiments

  • Jing-Pin Lei,

    Affiliation Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, People's Republic of China

  • Wenfa Xiao , (WX); (JFL)

    Affiliation Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, People's Republic of China

  • Jian-Feng Liu , (WX); (JFL)

    Affiliation Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, People's Republic of China

  • Dingpeng Xiong,

    Affiliation College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, China

  • Pengcheng Wang,

    Affiliation College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, China

  • Lei Pan,

    Affiliation Hubei Academy of Forestry, Wuhan, People's Republic of China

  • Yong Jiang,

    Affiliation State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, People's Republic of China

  • Mai-He Li

    Affiliations Swiss Federal Research Institute WSL, Birmensdorf, Switzerland, State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, People's Republic of China

Responses of Nutrients and Mobile Carbohydrates in Quercus variabilis Seedlings to Environmental Variations Using In Situ and Ex Situ Experiments

  • Jing-Pin Lei, 
  • Wenfa Xiao, 
  • Jian-Feng Liu, 
  • Dingpeng Xiong, 
  • Pengcheng Wang, 
  • Lei Pan, 
  • Yong Jiang, 
  • Mai-He Li


Forest tree species distributed across a wide range of geographical areas are subjected to differential climatic and edaphic conditions and long-term selection, leading to genotypes with morphological and physiological adaptation to the local environment. To test the ability of species to cope with changing environmental conditions, we studied the ecophysiological features of Quercus variabilis using seedlings grown in geographically widely isolated populations (Exp. I, in situ) and in a common garden (Exp. II, ex situ) using seedlings originating from those populations. We found that Q. variabilis plants grown in different locations along a south-north gradient had different levels of nutrients (N, P, K) and carbon-physiological performance (photosynthesis, non-structural carbohydrates, such as soluble sugars and starch), and that these physiological differences were not correlated with local soil properties. These geographic variations of plant physiology disappeared when plants from different locations were grown in the same environment. Our results indicate that the physiological performance of Q. variabilis plants is mainly determined by the climatic variations across latitude rather than by their soils or by genetic differentiation. The adaptive ability of Q. variabilis found in the present study suggests that this species has the potential to cope, at least to some extent, with changing environmental conditions.


The global average temperature has increased by approximately 0.6°C (±0.2°C) over the past 100 years and is projected to continue to rise at a rapid rate [1]. Ecologists typically assume that temperature is a primary fitness determinant of plant growth and survival at high-latitudinal and upper elevational limits [2][6]. To predict changes in species' distribution under current and future climate, especially rapid global warming, an understanding of the ecophysiology of plants growing in populations at the northernmost (also uppermost) distribution limit is needed.

Many studies have documented geographic variations in morphology [7][9], phenology [10][12], ecophysiology [13][16], and genetic differentiation [17], [18] among plant populations across geographic ranges. At the ecophysiological level, water use efficiency [19][21], stomata [22][25], photosynthesis [26][28], and nutrients [29][31] in plants have been extensively investigated. Villar et al. [32] found that plants grown in regions with sufficient precipitation allocated more biomass to stem and leaves and less to roots. Miyazawa and Lechowicz [33] studied seedlings of 8 north American Picea species grown in a common garden and found that the relative growth rate and specific leaf area had a positive relationship with latitude, while leaf size and leaf length were negatively correlated with latitude. Ehleringer and Phillips studied the ecophysiological factors contributing to the distributions of several Quercus species and found that leaf size and leaf longevity of Q. macrocarpa Michx. and Q. turbinella Greene were not correlated with summer water shortage [34].

Nitrogen and phosphorus play vital roles in plant functioning, and are among the most important limiting nutrients in terrestrial ecosystems [35], [36]. Patterns of N, P, and K status in plant tissues, especially in leaves, have been studied intensively [36], [37]. Alpine plants often had a higher leaf N concentration in the polar region than in the equatorial region [38]. Reich and Oleksyn [39] found that leaf N and P concentration increased but N/P ratios decreased with increasing latitude together with decreasing temperature.

Studies indicated that mobile carbohydrate concentration of trees increased with elevation during the growing season [29], [40][43], but decreased with increasing elevation up to the alpine treeline during winter [15], [44][46]. Unlike with altitude, however, the availability of mobile carbohydrate in plants across broad latitudinal ranges have received little attention. Concentration of mobile carbohydrates reflect the balance between carbon gain (photosynthesis) and loss (structural growth and respiration) [41], [47], [48].

We studied the ecophysiological characteristics of Quercus variabilis Blume using two experiments, i.e. Q. variabilis seedlings grown over a latitudinal gradient (Exp. I, in situ) and in a common garden (Exp. II, ex situ) using seedlings originating from those locations. Q. variabilis is geographically widely distributed in China, with the northernmost limit in southern Liaoning Province and the southern boundary in Yunnan Province (Fig. 1). Forest tree species distributed across a wide range of geographical areas are subjected to differential climatic and edaphic conditions and long-term natural selection, leading to generating different genotypes with morphological and physiological adaptation to the local environment. Hence, our hypotheses to be tested are that (1) plants grown in northern populations have higher concentration of nutrients and mobile carbohydrates than those grown in southern populations, to adapt to a relatively harsh environment (e.g. low temperature and short growing season in the north), and (2) the adaptation differences remain when they are grown in other environments.

Figure 1. Geographical locations of the study sites in China (Dashed line is the distribution range of Q. variabilis) (ND = northern distribution, Zhuang-He in NE China, MD = middle distribution, He-Feng in central China, SD = southern distribution, An-Ning in SW China. WH = Wu-Han in Hubei province, central China).

Materials and Methods

Study sites and species

The present study included an in situ experiment and an ex situ experiment. The object of this study was 3∼5-year-old seedlings of Q. variabilis grown in different geographical locations (in situ) and in a common garden using seedlings originating from those locations (ex situ) (Fig. 1, Table 1). All necessary permits for the described field study were obtained from the local forestry bureaus at the beginning of the experiment. Five naturally generated old growth stands dominated by Q. variabilis were selected from its southern distribution (SD, An-Ning in SW China, 102.45°E, 24.99°N), middle distribution (MD, He-Feng in central China, 110.21°E, 30.15°N), and northern distribution area (ND, Zhuang-He in NE China, 122.96°E, 39.99°N) (Fig. 1, Table 1), respectively. Before the growing season of 2009, 3–5 experimental gaps (gap diameter ≈1-fold height of surrounding trees) each with 4–6 naturally generated and healthy seedlings (3–5 years old, 1.0∼1.5 cm in base diameter, and 70∼90 cm in height) were created within each of the five stands in ND, MD, and SD, respectively, so that the seedlings can adapt to similar sun exposure across locations prior to treatment or sampling. Simultaneously, 2–3 randomly selected seedlings out of the 4–6 seedlings within each gap were tagged and remained intact for future sampling (in situ), and the other 2–3 seedlings were carefully excavated and transplanted in a common garden (ex situ) in the Jiufeng National Forest Park, Wu-Han (WH, 114.91°E, 30.48°N; Fig. 1, Table 1). The seedlings were planted in a randomized complete block design with five blocks (n = 5) for seedlings originating from ND, MD, and SD, respectively. Six seedlings (2 rows of 3 plants) were planted at a spacing of 40×40 cm with a margin of 30 cm for each block (100 cm×140 cm with a buffer zone of 50 cm between any two blocks).


Samples were taken between August 20 and 28, 2010 (i.e. 2 years after transplanting). Samplings were carried out around noon to minimize the influences of sunlight and temperature on carbohydrate concentration. Each seedling sampled was completely excavated. Leaves (second flush leaves), stem wood (with bark), and fine roots (<0.5 cm in diameter, with bark) were separately collected. A 2-cm long stem segment was taken from the middle part of each stem. Root samples were carefully washed. To obtain a single sample for each tissue for each stand, we mixed the same tissue collected from 5–6 seedlings grown in 3–5 gaps within each stand (in situ in ND, MD, and SD, respectively; n = 5) or each block (ex situ, n = 5), in order to reduce the costs of chemical analyses. All samples were immediately stored in an ice box, and killed in a microwave oven within 6 hours, then dried to constant mass at 65°C. Dried plant material was ground to pass a 0.15 mm sieve.

We randomly selected 3 out of the 5 in situ stands in ND, MD, and SD, respectively, to take soil samples (n = 3). Four to six soil cores each with 3 cm in diameter and 30 cm in depth were taken from the 3–5 gaps within each selected stand, and then pooled to obtain a mixed sample for each stand. All soil samples were air-dried. After removing the stones and plant materials, soils were ground to pass through a 2 mm sieve for chemical analysis.

Photosynthesis parameters

Photosynthetic photon flux density (PPFD) response curves were made with a portable infrared gas analyzer (Licor 6400, Li-Cor, Lincoln, NE). The assimilation rates were measured on fully expanded leaves from 09:00 to 12:00 h on clear, cloudless days (15–30, August, 2010). The air cuvette temperature, the relative humidity, and the air CO2 concentration were maintained at 25±2°C, 50±5%, and 400 µL L−1, respectively. PPFD was decreased from 2000 to 0 µmol m−2 s−1 (2000, 1800, 1600, 1400, 1200, 1000, 800, 600, 400, 200, 100, 80, 50, 20, 0 µmol m−2 s−1). Assimilation was recorded at each light level following a 5 min acclimation time, and three replications were used for each plant. According to Prioul and Chartier [49], apparent quantum efficiency (AQE), maximum photosynthetic rates (Amax), dark respiration (Rd), light compensation points (LCP), and light saturation points (LSP) were calculated from the light response curve data, using the program Photosyn Assistant (Dundee Scientific, Dundee, Scotland).

Analyses of total soluble sugars and starch

The powdered material (0.1 g) was put into a 10 ml centrifuge tube, where 5 ml of 80% ethanol was added. The mixture was incubated at 80°C in a water bath shaker for 30 min, and then centrifuged at 4000 rpm for 5 min. The pellets were extracted two more times with 80% ethanol. Supernatants were retained, combined and stored at −20°C for soluble sugar determinations. The ethanol-insoluble pellet was used for starch extraction. Glucose was used as a standard. Soluble sugars were determined using the anthrone method [50]. The starch concentration was measured spectrophotometrically at 620 nm using anthrone reagent, and was calculated by multiplying glucose concentrations by the conversion factor of 0.9 [51]. Concentration of sugars and starch was described on a dry matter basis (% d.m.).

Analyses of plant and soil nutrients

The finely ground plant samples were firstly digested through the Kjeldahl procedure, using H2SO4 and H2O2 for digestion, and then the total nitrogen and phosphorus concentrations were determined using the flow injection method, and potassium was determined by applying the flame photometry method [52]. Soil pH was determined by the acidimetry method (soil∶water = 1∶5). Total soil N concentration [11] was measured with the Kjeldahl procedure, total soil P (TP) with Perchloric acid digestion followed by the molybdate colorimetric test, and total soil K (TK) with the flame photometry method. Soil hydrolyzable N (HN) was determined by using alkaline hydrolysis diffusion method, soil available P and K (AP and AK) by Mo-Sb anti-spetrophotography method and the flame photometry method, respectively.

Data analysis

NSC is defined as the sum of the starch plus the total soluble sugars for each sample. Data (NSC, starch, total soluble sugars, and nutrient concentration) were confirmed for normality by Kolmogorov-Smirnov-Tests. Two-way analysis of variance (ANOVA) was performed for each parameter within each tissue type, using experiments (in situ vs. ex situ) and origin (ND, MD, and SD) as factors, and found that the responses of most parameters differed with experiments (data not shown). Hence, we analyzed the data for each parameter within each tissue type for each experiment separately, using one-way ANOVA, and followed by multiple comparisons. Pearson's correlation analysis was performed to detect the relationships between physiological parameters and the soil chemical properties across geographic locations where plants grown in situ. Differences were considered significant if p<0.05. All statistical analyses were conducted using SPSS 17.0 version (SPSS, Chicago, Illinois, USA).


Plant nutrients

In situ experiment.

Seedlings grown in ND had significantly higher tissue N concentration than those grown in MD and SD (p<0.05, Table 2). Nitrogen concentration in leaves and roots of seedlings in ND were 30–39% and 130–188% higher than those in MD and SD, respectively. Tissue P and K concentration did not vary among different geographical locations (Table 2), except that K in roots in MD was 36% and 38% lower than those in ND and SD, respectively.

Table 2. Nutrients concentrations (mean ± SD; mg g−1, n = 5) in Quercus variabilis seedlings grown in different geographical locations and in a common garden in China.

Ex situ experiment.

No difference was found in N, P, and K concentration in seedlings grown in the common garden for 2 years after transplanting from different geographical locations (Table 2).

Photosynthetic responses

In situ experiment.

Seedlings grown in ND, MD and ND showed non-significant difference in AQE (Table 3). SD plants had significantly higher Amax compared to plants in ND and MD (p<0.05, Table 3). Rd was found to be the smallest in MD plants, while LCP was the least in SD plants (p<0.05, Table 3). LSP did not vary among plants grown in SD, MD, and ND (Table 3).

Table 3. Photosynthetic parameters (mean ± SD, n = 5) of Quercus variabilis seedlings grown in different geographical locations and in a common garden in China.

Ex situ experiment.

Like plants grown in situ, the highest Amax was found in plants originating from SD (p<0.05, Table 3). AQE, Rd, LCP and LSP did not vary among plants originating from ND, MD, and SD in the common garden (Table 3). The statistically significant differences in Rd and LCP found in plants grown in situ were not found in plants grown ex situ (Table 3).

Responses of mobile carbohydrates

In situ experiment.

Concentration of soluble sugars in stems of SD plants were much less than those in ND and MD plants (p<0.05, Table 4). But roots of MD plants had significantly lower soluble sugar concentration compared to SD and ND plant roots (p<0.05, Table 4). Both leaves and roots of ND plants showed significantly higher starch contents compared to those of MD and SD plants (p<0.05, Table 4). Concentration of NSC in stem and roots were found to be significantly higher in ND plants than in MD and SD plants (p<0.05, Table 4).

Table 4. Results of ANOVA analyses for mobile carbohydrates (sugars, starch, NSC) in Quercus variabilis seedlings grown in different geographical locations and in a common garden in China.

Ex situ experiment.

Two years after transplanting seedlings into the common garden, concentration of mobile carbohydrates in tissues did not differ among plants originating from ND, MD, and SD (Table 4), except for the starch concentration in leaves of plants originating from SD which was significantly lower than that in plants originating from ND (increased by +61%) and MD (+72%) (p<0.05, Table 4).

Allocation of nutrients and carbohydrates within the plant

In situ experiment.

Only P allocation to roots and K allocation to leaves differed significantly among ND, MD, and SD plants grown in situ (p<0.05, Table 5). From north to south, plants invested more P into roots, but less K into leaves (Table 5). The allocation of soluble sugars and NSC to stem decreased but to roots increased in plants grown in situ from north to south (Table 5).

Table 5. Allocation (mean % ± SD, n = 5) of nutrients and mobile carbohydrates within a Quercus variabilis seedling grown in different geographical locations and in a common garden in China.

Ex situ experiment.

Plants originating from the north tended to allocate more N and P to stem, as well as more P to leaves, but less P and K to roots compared to plants originating from the south (Table 5). Differences in allocation of mobile carbohydrates were detected only for stem in plants originating from different locations grown in the common garden, showing a decreased trend for mobile carbohydrates (sugars, starch, and NSC) from ND, to MD and SD plants (Table 5).

Relationship between physiological parameters and soil nutrients

Soils in the 3 populations in situ were acid soil with pH values ranging from 4.7 to 5.6 (Fig. 2). MD showed higher concentration of total N, hydrolyzable N, and available P and K (Fig. 2). Results of Pearson's correlation analysis indicated that plant nutrients, photosynthetic parameters and mobile carbohydrates all were not correlated with soil nutrients for Q. variabilis grown across scales in situ (data not shown).

Figure 2. Soil pH and nutrients in different geographical locations (ND = northern distribution, Zhuang-He in NE China, MD = middle distribution , He-Feng in central China, SD = southern distribution, An-Ning in SW China). TN, TP, and TK were total N, P, and K contents in g kg−1 soil (+1 SD), respectively. HN, AP, and AK were hydrolyzable N, available P and K in mg kg−1 soil (+1 SD), respectively.

Different letters indicate significant difference (p<0.05) within each parameter among the three locations.


Plant nutrients

Geographic locations significantly affected N but not P and K concentration in Q. variabilies plants (in situ, Table 2). Discrepancy of nutrient concentration in a plant species or functional type across large distribution range has been observed [53][56]. Reich and Oleksyn [39] found that leaf N and P concentration declined towards the equator as the average temperature and the growing season length increase. A meta-analysis with 753 terrestrial plant species in China found that leaf N and P concentration increased with increasing latitude [57]. Similarly, leaf N, P and K of Q. liaotungensis Koidz. [58] and Celtis australis L. [59] were found to increase with the increase of elevation (i.e. decrease of temperature). However, Kerkhoff et al. [60] reported that leaf N and P were not correlated with latitude.

Different Q. variabilies provenances grown under the same conditions did not show any differences in nutrient concentration (ex situ, Table 2). This may imply that the nutrient concentration of Q. variabilies is mainly determined by its growing environment. The same climate conditions (temperature and precipitation) and soil nutrient availability led to similar concentration of nutrients in tissues (ex situ, Table 2). However, previous studies of Pinus sylvestris L.found that N and P concentration in needles were higher [56], [61], and K concentration were lower in northern provenance than in southern provenances grown in a common garden [62]. Leaf N of Populus trichocarpa Torr. & A. Gray ex Hook. was also found to be higher in northern provenance than in southern provenance in a common garden [63].

Patterns of nutrient allocation did not differ among ND, MD, and SD Q. variabilis plants, except for northern plants invested less P to roots but more K to leaves compared to southern plants (in situ, Table 5). Domisch et al. [64] found that soil temperature did not affect the allocation patterns of N or P between shoots and roots in P. sylvestris seedlings. But Xu et al. [65] found that higher temperature induced Populus cathayana Rehd. cuttings to allocate more N to the aboveground organs.

Previous studies indicated that leaf N increased with increasing latitude as a result of decreasing mean annual temperature [39], [56], [57], [66], and our results gained from plants grown in situ (Table 2) were consistent with this. Temperature-related plant physiological stoichiometry and cold temperature effects on biogeochemistry associated with soil nutrient supply may contribute to such trend [39], [67]. The results from Weih and Karlsson [67] suggested that increased leaf N concentration with increasing latitude and/or altitude was not only a passive consequence of weaker N dilution by declined growth rate, but also a physiological acclimation to lower growth temperature. Hence, it may also be possible that such a trend is resulted from the adaptation strategy of plants to their growing conditions, reflecting the metabolic adaptation of leaves producing more protein to acclimate to the cold environment, because N is integral to proteins involved in photosynthesis process.

Photosynthesis and non-structural carbohydrates

Q. variabilis plants grown both in situ and ex situ showed significantly higher assimilation rates in southern than in northern plants (Table 3). Other in situ experiments indicated that the maximum photosynthetic rate was highest in plants grown in the middle part of the distribution area for P. sylvestris [68] and Eucryphia cordifolia Cav. [69], and decreased northwards and southwards. Significant increases in photosynthesis rate were found in red alder (Alnus rubra Bong.) grown along a geographic gradient from southeast to northwest in China [70]. A common garden experiment using Clarkia unguiculata Lindl. plants from 16 populations across latitudes found that the maximum photosynthesis rates decreased with increasing latitude of plant origin [71]. However, photosynthetic rate was found to increase with increasing latitude of origin in five provenances of black cottonwood [63]. Such increasing trends of photosynthesis were also observed in other species, e.g. for Populus balsamifera L. [72], [73], Picea abies (L.) Karst. [74], Alnus sinuate (Regel) Rydb. and Betula papyrifera Marsh. [75] grown in a common garden. The populations/provenances from locations with lower temperature and shorter growing season had higher maximum photosynthetic rates, which may reflect plants' adaptation to produce more carbohydrates within the short growing season.

No differences in leaf dark respiration were found in different provenances of Q. variabilis grown in the common garden (Table 3). In line with our finding, previous common garden studies also showed little evidence for differences in leaf dark respiration rates in geographically contrasting sources of Pinus taeda L. [76], P. banksiana Lamb. [77], Quercus alba L., Q. rubra L.[78], Acer rubrum L. [78], [79], and A. saccharum Marsh. [80].

Q. variabilis plants grown in north tended to have higher concentrations of mobile carbohydrates (NSC, sugars, and starch) than plants grown in south in situ (Table 4). But when plants originating from different geographic locations grown in the common garden, those differences disappeared except for starch in leaves (Table 4). However, P. sylvestris seedlings [81] and Alcantarea imperialis Rubra plants [82] were found to have higher concentration levels of mobile carbohydrates under higher soil temperature compared to lower temperature. Oleksyn et al. [83] found that total non-structural carbohydrate concentrations were significantly higher in roots and needles of P. sylvestris originating from 50° than 60°N. But for the same species, it was also reported that concentration of mobile carbohydrates decreased in needles but increased in roots with latitude of origin [84].

More than 60% of the mobile carbohydrates (sugars, starch, NSC) were invested into roots, and south plants allocated more carbohydrates to roots than north plants did (Table 5). The percentage of carbohydrates stored in roots gained in the present study was consistent with the results reported by Canham et al. [85]. Allocation pattern of carbohydrates was found to be affected by temperature (e.g. along elevational or latitudinal gradients) [81], [82], [86], and nutrients available [87][89]. The present study found that the north plants allocated more NSC to the stem but less NSC to the roots compared to the south plants (Table 5).

The lack of clear relationships between plant physiological parameters and soil nutrients across scales found in the present study may suggest that climate discrepancy is the major contributor to the differences in physiology of Q. variabilis plants growing in different geographic populations. Although soil nutrients are essential for plant growth, there was no correlation between the supply of nutrients and the concentration of mineral nutrients in plant tissues, indicating that plant nutrition may be mainly determined by plants' absorption and utilization rather than the pool size of nutrients in soil [90], [91].


Today's plant communities are the result of long-term adaptation to their growth environment including climatic impacts. Plant distribution is largely determined by climatic conditions [4], [92]. Plant species distributed across a wide range of environmental conditions, may differentiate genetically, leading to generating ecotypes with different functional traits. Inconsistent with our hypotheses, the differences in nutrient and carbon physiology found among plants grown across geographic locations disappeared when they were transplanted to grow in the same environment (Tables 2, 3, 4). Our results showed that the physiological performance of Q. variabilis plants may be mainly determined by the climate variations across scales but not by different soil conditions, indicating that this species has a high degree of plasticity and is highly flexible in terms of its physiology, and can adapt readily to a range of sites. This adaptation ability of Q. variabilis found in the present study suggests that Q. variabilis has the potential to cope, at least to some extent, with changing environmental conditions, as proposed recently by Zhu et al. [16] and Li et al. [93] for other Quercus species facing to climate changes.


We thank Dr. Yongyu Sun and Dr. Fangyan Liu for help with sampling in SD.

Author Contributions

Conceived and designed the experiments: JPL JFL WX. Performed the experiments: JPL JFL DX PW LP. Analyzed the data: JPL JFL MHL. Contributed reagents/materials/analysis tools: JPL WX JFL YJ. Wrote the paper: JPL JFL MHL.


  1. 1. IPCC editor (2007) Climate Change 2007: Synthesis Report. Geneva: Intergovernmental Panel on Climate Change. 104 p.
  2. 2. Crescente MF, Gratani L, Larcher W (2002) Shoot growth efficiency and production of Quercus ilex L. in different climates. Flora 197: 2–9.
  3. 3. Fang J, Lechowicz MJ (2006) Climatic limits for the present distribution of beech (Fagus L.) species in the world. Journal of Biogeography 33: 1804–1819.
  4. 4. Li MH, Kräuchi N, Gao SP (2006) Global warming: Can existing reserves really preserve current levels of biological diversity? Journal of Integrative Plant Biology 48: 255–259.
  5. 5. Li MH, Kräuchi N, Dobbertin M (2006) Biomass distribution of different-aged needles in young and old Pinus cembra trees at highland and lowland sites. Trees-Structure and Function 20: 611–618.
  6. 6. Fang KY, Gou XH, Chen FH, Peng JF, D'Arrigo R, et al. (2009) Response of regional tree-line forests to climate change: evidence from the northeastern Tibetan Plateau. Trees-Structure and Function 23: 1321–1329.
  7. 7. Turna I (2004) Variation of morphological characters of oriental spruce (Picea orientalis) in Turkey. Biologia 59: 519–526.
  8. 8. Wahid N, Gonzalez-Martinez SC, El Hadrami I, Boulli A (2006) Variation of morphological traits in natural populations of maritime pine (Pinus pinaster Ait.) in Morocco. Annals of Forest Science 63: 83–92.
  9. 9. Ito M (2009) Variation in leaf morphology of Quercus crispula and Quercus dentata assemblages among contact zones: a method for detection of probable hybridization. Journal of Forest Research 14: 240–244.
  10. 10. Weinstein A (1989) Geographic variation and phenology of Pinus halepensis, Pinus Brutia and Pinus eldarica in Israel. Forest Ecology and Management 27: 99–108.
  11. 11. Ceulemans R, Scarasciamugnozza G, Wiard BM, Braatne JH, Hinckley TM, et al. (1992) Production physiology and morphology of Populus species and their hybrids grown under short rotation.I. Clonal comparisons of 4-year growth and phenology Canadian Journal of Forest Research 22: 1937–1948.
  12. 12. Nielsen CN, Jorgensen FV (2003) Phenology and diameter increment in seedlings of European beech (Fagus sylvatica L.) as affected by different soil water contents: variation between and within provenances. Forest Ecology and Management 174: 233–249.
  13. 13. Reich PB, Ellsworth DS, Walters MB (1998) Leaf structure (specific leaf area) modulates photosynthesis-nitrogen relations: evidence from within and across species and functional groups. Functional Ecology 12: 948–958.
  14. 14. Scarano F, Duarte H, Franco A, Gessler A, de Mattos EA, et al. (2005) Ecophysiology of selected tree species in different plant communities at the periphery of the Atlantic Forest of SE Brazil. I. Performance of three different species of Clusia in an array of plant communities. Trees-Structure and Function 19: 497–509.
  15. 15. Li MH, Xiao WF, Shi PL, Wang SG, Zhong YD, et al. (2008) Nitrogen and carbon source-sink relationships in trees at the Himalayan treelines compared with lower elevations. Plant Cell and Environment 31: 1377–1387.
  16. 16. Zhu WZ, Cao M, Wang SG, Xiao WF, Li MH (2012) Seasonal dynamics of mobile carbon supply in Quercus aquifolioides at the upper elevational limit. Plos One 7: e34213.
  17. 17. Newton AC, Gow J, Robertson A, Williams-Linera G, Ramirez-Marcial N, et al. (2008) Genetic variation in two rare endemic Mexican trees, Magnolia sharpii and Magnolia schiedeana. Silvae Genetica 57: 348–356.
  18. 18. Klumpp R, Dhar A (2011) Genetic variation of Taxus baccata L. populations in the Eastern Alps and its implications for conservation management. Scandinavian Journal of Forest Research 26: 294–304.
  19. 19. Tognetti R, Michelozzi M, Lauteri M, Brugnoli E, Giannini R (2000) Geographic variation in growth, carbon isotope discrimination, and monoterpene composition in Pinus pinaster Ait. provenances. Canadian Journal of Forest Research 30: 1682–1690.
  20. 20. Grossnickle SC, Fan SH, Russell JH (2005) Variation in gas exchange and water use efficiency patterns among populations of western redcedar. Trees-Structure and Function 19: 32–42.
  21. 21. Gornall JL, Guy RD (2007) Geographic variation in ecophysiological traits of black cottonwood (Populus trichocarpa). Canadian Journal of Botany 85: 1202–1213.
  22. 22. Acherar M, Rambal S, Lepart J (1991) Influence of soil drying on leaf water potential and stomatal conductance in 4 meditrranean oak species. Annales Des Sciences Forestieres 48: 561–573.
  23. 23. Karenlampi L, Metsarinne S, Paakkonen E (1998) Stomatal conductance of birch leaves - plenty of variation in the variable which determines the ozone dose. Chemosphere 36: 675–678.
  24. 24. St Hilaire R, Graves WR (1999) Foliar traits of sugar maples and black maples near 43°N latitude in the eastern and central United States. Journal of the American Society for Horticultural Science 124: 605–611.
  25. 25. Cregg BM, Olivas-Garcia JM, Hennessey TC (2000) Provenance variation in carbon isotope discrimination of mature ponderosa pine trees at two locations in the Great Plains. Canadian Journal of Forest Research 30: 428–439.
  26. 26. Swenson JJ, Waring RH (2006) Modelled photosynthesis predicts woody plant richness at three geographic scales across the north-western United States. Global Ecology and Biogeography 15: 470–485.
  27. 27. Lopez R, Rodriguez-Calcerrada J, Gil L (2009) Physiological and morphological response to water deficit in seedlings of five provenances of Pinus canariensis: potential to detect variation in drought-tolerance. Trees-Structure and Function 23: 509–519.
  28. 28. Coops NC, Waring RH (2011) Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America. Ecological Modelling 222: 2119–2129.
  29. 29. Hoch G, Popp M, Körner C (2002) Altitudinal increase of mobile carbon pools in Pinus cembra suggests sink limitation of growth at the Swiss treeline. Oikos 98: 361–374.
  30. 30. Gusewell S (2004) N∶P ratios in terrestrial plants: variation and functional significance. New Phytologist 164: 243–266.
  31. 31. Soethe N, Lehmann J, Engels C (2008) Nutrient availability at different altitudes in a tropical montane forest in Ecuador. Journal of Tropical Ecology 24: 397–406.
  32. 32. Villar R, Veneklaas EJ, Jordano P, Lambers H (1998) Relative growth rate and biomass allocation in 20 Aegilops (Poaceae) species. New Phytologist 140: 425–437.
  33. 33. Miyazawa K, Lechowicz MJ (2004) Comparative seedling ecology of eight north American spruce (Picea) species in relation to their geographic ranges. Annals of Botany 94: 635–644.
  34. 34. Ehleringer J, Phillips S (1996) Ecophysiological factors contributing to the distributions of several Quercus species in the intermountain west. Annales Des Sciences Forestieres 53: 291–302.
  35. 35. Chapin FS (1980) The mineral nutrition of wild plants. Annual Review of Ecology and Systematics 11: 233–260.
  36. 36. Reich PB, Grigal DF, Aber JD, Gower ST (1997) Nitrogen mineralization and productivity in 50 hardwood and conifer stands on diverse soils. Ecology 78: 335–347.
  37. 37. Foulds W (1993) Nutrient concentrations of foliage and soil in south-westrn Australia. New Phytologist 125: 529–546.
  38. 38. Körner C (1989) The nutritional status of plants from high altitudes- a worldwide comparison. Oecologia 81: 379–391.
  39. 39. Reich PB, Oleksyn J (2004) Global patterns of plant leaf N and P in relation to temperature and latitude. Proceedings of the National Academy of Sciences of the United States of America 101: 11001–11006.
  40. 40. Hoch G, Körner C (2003) The carbon charging of pines at the climatic treeline: a global comparison. Oecologia 135: 10–21.
  41. 41. Körner C (2003) Carbon limitation in trees. Journal of Ecology 91: 4–17.
  42. 42. Shi P, Körner C, Hoch G (2006) End of season carbon supply status of woody species near the treeline in western China. Basic and Applied Ecology 7: 370–377.
  43. 43. Shi P, Korner C, Hoch G (2008) A test of the growth-limitation theory for alpine tree line formation in evergreen and deciduous taxa of the eastern Himalayas. Functional Ecology 22: 213–220.
  44. 44. Genet M, Li MC, Luo TX, Fourcaud T, Clement-Vidal A, et al. (2011) Linking carbon supply to root cell-wall chemistry and mechanics at high altitudes in Abies georgei. Annals of Botany 107: 311–320.
  45. 45. Li MH, Xiao WF, Wang SG, Cheng GW, Cherubini P, et al. (2008) Mobile carbohydrates in Himalayan treeline trees I. Evidence for carbon gain limitation but not for growth limitation. Tree Physiology 28: 1287–1296.
  46. 46. Zhu WZ, Cao M, Wang SG, Xiao WF, Li MH (2012) Seasonal Dynamics of Mobile Carbon Supply in Quercus aquifolioides at the Upper Elevational Limit. Plos One 7.
  47. 47. Hoch G, Richter A, Körner C (2003) Non-structural carbon compounds in temperate forest trees. Plant Cell and Environment 26: 1067–1081.
  48. 48. Li MH, Hoch G, Körner C (2002) Source/sink removal affects mobile carbohydrates in Pinus cembra at the Swiss treeline. Trees 16: 331–337.
  49. 49. Prioul JL, Chartier P (1977) Paritioning of transfer and carboxylation components of intracellular resistance to photosynthetic CO2 fixation-critical analysis of methods used. Annals of Botany 41: 789–800.
  50. 50. Seifter S, Dayton S, Novic B, Muntwyler E (1950) The Estimation of Glycogen with the Anthrone Reagent. Archives of Biochemistry 25: 191–200.
  51. 51. Osaki M, Shinano T, Tadano T (1991) Redistribution of carbon and nitrogen compounds from the shoot to the harvesting organs during maturation in field crops. Soil Science and Plant Nutrition 37: 117–128.
  52. 52. Grimshaw H, Allen S, Parkinson J, Allen E (1989) Chemical Analysis of Ecological Materials. Blackwell Scientific Publications, London.
  53. 53. Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, et al. (2004) The worldwide leaf economics spectrum. Nature 428: 821–827.
  54. 54. Wright IJ, Reich PB, Cornelissen JHC, Falster DS, Garnier E, et al. (2005) Assessing the generality of global leaf trait relationships. New Phytologist 166: 485–496.
  55. 55. Castro-Díez P, Villar-Salvador P, Pérez-Rontomé C, Maestro-Martínez M, Montserrat-Martí G (1997) Leaf morphology and leaf chemical composition in three Quercus (Fagaceae) species along a rainfall gradient in NE Spain. Trees-Structure and Function 11: 127–134.
  56. 56. Oleksyn J, Reich P, Zytkowiak R, Karolewski P, Tjoelker M (2003) Nutrient conservation increases with latitude of origin in European Pinus sylvestris populations. Oecologia 136: 220–235.
  57. 57. Han W, Fang J, Guo D, Zhang Y (2005) Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytologist 168: 377–385.
  58. 58. Qi J, Ma K, Zhang Y (2009) Leaf-trait relationships of Quercus liaotungensis along an altitudinal gradient in Dongling Mountain, Beijing. Ecological Research 24: 1243–1250.
  59. 59. Singh B, Bhatt BP, Prasad P (2010) Altitudinal variation in nutritive value of adult-juvenile foliage of Celtis australis L.: A promising fodder tree species of Central Himalaya, India. Journal of American Science 6: 108–112.
  60. 60. Kerkhoff AJ, Enquist BJ, Elser JJ, Fagan WF (2005) Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecology and Biogeography 14: 585–598.
  61. 61. Reich PB, Oleksyn J, Tjoelker MG (1996) Needle respiration and nitrogen concentration in Scots pine populations from a broad latitudinal range: a common garden test with field-grown trees. Functional Ecology 10: 768–776.
  62. 62. Oleksyn J, Reich PB, Zytkowiak R, Karolewski P, Tjoelker MG (2002) Needle nutrients in geographically diverse Pinus sylvestris L. populations. Annals of Forest Science 59: 1–18.
  63. 63. Guy RD, Gornall JL (2007) Geographic variation in ecophysiological traits of black cottonwood (Populus trichocarpa). Canadian Journal of Botany 85: 1202–1213.
  64. 64. Domisch T, Finér L, Lehto T, Smolander A (2002) Effect of soil temperature on nutrient allocation and mycorrhizas in Scots pine seedlings. Plant and Soil 239: 173–185.
  65. 65. Xu X, Peng G, Wu C, Han Q (2010) Global warming induces female cuttings of Populus cathayana to allocate more biomass, C and N to aboveground organs than do male cuttings. Australian Journal of Botany 58: 519–526.
  66. 66. Han W, Fang J, Reich P, Ian Woodward F, Wang Z (2011) Biogeography and variability of eleven mineral elements in plant leaves across gradients of climate, soil and plant functional type in China. Ecology Letters 14: 788–796.
  67. 67. Weih M, Karlsson P (2001) Growth response of Mountain birch to air and soil temperature: is increasing leaf-nitrogen content an acclimation to lower air temperature? New Phytologist 150: 147–155.
  68. 68. Luoma S (1997) Geographical pattern in photosynthetic light response of Pinus sylvestris in Europe. Functional Ecology 11: 273–281.
  69. 69. Figueroa JA, Cabrera HM, Queirolo C, Hinojosa LF (2010) Variability of water relations and photosynthesis in Eucryphia cordifolia Cav.(Cunoniaceae) over the range of its latitudinal and altitudinal distribution in Chile. Tree Physiology 30: 574–585.
  70. 70. Dang Q, Xie CY, Ying C, Guy R (1994) Genetic variation of ecophysiological traits in red alder (Alnus rubra Bong.). Canadian Journal of Forest Research 24: 2150–2156.
  71. 71. Jonas CS, Geber MA (1999) Variation among populations of Clarkia unguiculata (Onagraceae) along altitudinal and latitudinal gradients. American Journal of Botany 86: 333–343.
  72. 72. Soolanayakanahally RY, Guy RD, Silim SN, Drewes EC, Schroeder WR (2009) Enhanced assimilation rate and water use efficiency with latitude through increased photosynthetic capacity and internal conductance in balsam poplar (Populus balsamifera L.). Plant, Cell and Environment 32: 1821–1832.
  73. 73. Keller SR, Soolanayakanahally RY, Guy RD, Silim SN, Olson MS, et al. (2011) Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L.(Salicaceae). American Journal of Botany 98: 99–108.
  74. 74. Oleksyn J, Modrzýnski J, Tjoelker MG, Z·ytkowiak R, Reich PB, et al. (1998) Growth and physiology of Picea abies populations from elevational transects: common garden evidence for altitudinal ecotypes and cold adaptation. Functional Ecology 12: 573–590.
  75. 75. Benowicz A, Guy RD, El-Kassaby YA (2000) Geographic pattern of genetic variation in photosynthetic capacity and growth in two hardwood species from British Columbia. Oecologia 123: 168–174.
  76. 76. Teskey RO, Will RE (1999) Acclimation of loblolly pine (Pinus taeda) seedlings to high temperatures. Tree Physiology 19: 519–525.
  77. 77. Tjoelker MG, Oleksyn J, Reich PB, Żytkowiak R (2008) Coupling of respiration, nitrogen, and sugars underlies convergent temperature acclimation in Pinus banksiana across wide-ranging sites and populations. Global Change Biology 14: 782–797.
  78. 78. Bolstad PV, Reich P, Lee T (2003) Rapid temperature acclimation of leaf respiration rates in Quercus alba and Quercus rubra. Tree Physiology 23: 969–976.
  79. 79. Lee T, Reich P, Bolstad P (2005) Acclimation of leaf respiration to temperature is rapid and related to specific leaf area, soluble sugars and leaf nitrogen across three temperate deciduous tree species. Functional Ecology 19: 640–647.
  80. 80. Gunderson CA, Norby RJ, Wullschleger SD (2000) Acclimation of photosynthesis and respiration to simulated climatic warming in northern and southern populations of Acer saccharum: laboratory and field evidence. Tree Physiology 20: 87–96.
  81. 81. Domisch T, Finér L, Lehto T (2002) Growth, carbohydrate and nutrient allocation of Scots pine seedlings after exposure to simulated low soil temperature in spring. Plant and Soil 246: 75–86.
  82. 82. Mollo L, Martins M, Oliveira V, Nievola C, L. Figueiredo-Ribeiro Rd (2011) Effects of low temperature on growth and non-structural carbohydrates of the imperial bromeliad Alcantarea imperialis cultured in vitro. Plant Cell, Tissue and Organ Culture 107: 141–149.
  83. 83. Oleksyn J, Tjoelker M, Reich P (1992) Whole-plant CO2 exchange of seedlings of two Pinus sylvestris L. provenances grown under simulated photoperiodic conditions of 50° and 60° N. Trees-Structure and Function 6: 225–231.
  84. 84. Oleksyn J, Zytkowiak R, Karolewski P, Reich P, Tjoelker M (2000) Genetic and environmental control of seasonal carbohydrate dynamics in trees of diverse Pinus sylvestris populations. Tree Physiology 20: 837–847.
  85. 85. Canham CD, Kobe RK, Latty EF, Chazdon RL (1999) Interspecific and intraspecific variation in tree seedling survival: effects of allocation to roots versus carbohydrate reserves. Oecologia 121: 1–11.
  86. 86. Repo T, Leinonen I, Ryyppö A, Finér L (2004) The effect of soil temperature on the bud phenology, chlorophyll fluorescence, carbohydrate content and cold hardiness of Norway spruce seedlings. Physiologia Plantarum 121: 93–100.
  87. 87. Knox K, Clarke P (2005) Nutrient availability induces contrasting allocation and starch formation in resprouting and obligate seeding shrubs. Functional Ecology 19: 690–698.
  88. 88. Sanz Pérez V, Castro Díez P, Valladares F (2007) Growth versus storage: responses of Mediterranean oak seedlings to changes in nutrient and water availabilities. Annals of Forest Science 64: 201–210.
  89. 89. Kobe RK, Iyer M, Walters MB (2010) Optimal partitioning theory revisited: Nonstructural carbohydrates dominate root mass responses to nitrogen. Ecology 91: 166–179.
  90. 90. Schulze ED, Kelliher FM, Korner C, Lloyd J, Leuning R (1994) Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate, and plant nitrogen nutrition: a global ecology scaling exercise. Annual Review of Ecology and Systematics 629–660.
  91. 91. Rennenberg H, Dannenmann M, Gessler A, Kreuzwieser J, Simon J, et al. (2009) Nitrogen balance in forest soils: nutritional limitation of plants under climate change stresses. Plant Biology 11: 4–23.
  92. 92. Woodward FI (1987) Climate and plant distribution. New York: Cambridge University Press. xi, 174 p.
  93. 93. Li MH, Cherubini P, Dobbertin M, Arend M, Xiao WF, et al. (2013) Responses of leaf nitrogen and mobile carbohydrates in different Quercus species/provenances to moderate climate changes. Plant Biology 15: 177–184.