Chin J Plant Ecol ›› 2019, Vol. 43 ›› Issue (6): 521-531.doi: 10.17521/cjpe.2018.0325

• Research Articles • Previous Articles     Next Articles

Seasonal dynamics of non-structural carbohydrate content in branch of Quercus variabilis growing in east Qinling Mountain range

ZHANG Yi-Ping1,HAI Xu-Ying1,2,XU Jun-Liang1,*(),WU Wen-Xia1,CAO Peng-He1,3,AN Wen-Jing4   

  1. 1 College of Forestry, Henan University of Science and Technology, Luoyang, Henan 471023, China
    2 College of Forestry, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
    3 College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
    4 Beijing Jingxi Forestry Farm, Beijing 102300, China
  • Received:2018-12-31 Revised:2019-05-30 Online:2019-09-30 Published:2019-06-20
  • Contact: XU Jun-Liang E-mail:xujunliang@haust.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(41401063);Supported by the National Natural Science Foundation of China(41801026);the China Scholarship Council(201808410575);the China Scholarship Council(201908410061)

Abstract:

Aims Measure of non-structural carbohydrate (NSC) reserves indicates tree carbon surplus or shortage stored. Branches connect NSC sources (leaves) and NSC sinks (stemwood, root) of woody plants. Therefore, the seasonal dynamics of NSC concentration in branches will be of important implications for understanding and modeling plant carbon allocation. Methods We conducted a field survey monitoring branch NSC concentrations of Chinese cork oak (Quercus variabilis). We also synchronously observed the leaf phenology of the trees in uneven-aged secondary oak forests at its upper and lower distribution limits (650 m to 970 m) in east Qinling Mountain ranges. Sampling intervals were set semimonthly/monthly during the leaf unfolding period (March to May), and monthly/bimonthly during the tree’s full growing season (June to November) from May 2016 to May 2017. Important findings (1) The NSC measures in the tree branches had weak seasonal changes at both sites. However, the soluble sugar (SS) concentrations at the upper elevation site and the starch (S) concentrations at the lower site had significant seasonal changes. The relative stable NSC levels vs. larger seasonal oscillations of soluble sugar and starch may be explained by the mutual conversion between soluble sugar and starch in the tree branches. (2) Soluble sugar was the major contributor to the total NSC in oak branches, accounting for approximately 61% of it. Here the soluble sugars performed as quick C whereas starch acted mostly as reserved C for future use, it could be inferred that the Q. variabilis, a warm temperate deciduous tree species, developed this feature as its life strategy to survive in warm temperate climate. (3) Soil water availability was positively related to the NSC measures at the high elevation site, while vapor pressure deficit (VPD) was negatively related to the NSC at the low elevation site, indicating the oak may be more drought-susceptible to water stress at lower elevation. (4) The maximum and the minimum concentrations of NSC in tree branches were observed before bud break (late March, approximately 11%) and when full leaf expansion (late April, approximately 11%), respectively. These extremes of NSC could be partly explained by the simultaneous leaf phenological dynamics. Considering the fact of carbon supply for bud break and leaf development via branches rather than by photosynthesis, it was reasonable that the NSC concentration in branches of Q. variabilis reached its maximum before the bud break, and did not change significantly with elevation. Not surprisingly, the significant differences in branches NSC with elevation only occurred during bud break in spring, as a later phenology occurred at higher elevation. In conclusion, this study confirms that the leaf phenology of Q. variabilis directly affects the branches seasonal patterns of NSC, particularly in the spring. The relationship between the carbon budget of the oak branches and its aboveground phenology should be more emphasized for further comprehension on the NSC metabolism.

Key words: non-structural carbohydrate, Quercus variabilis, leaf phenology, altitude, vapor pressure deficit

Table 1

Monthly measured environmental parameters at upper and lower altitudes in Quercus variabilis forests in the east Qinling Mountain range in 2016 and 2017"


Year

Month
空气温度 Air temperature (℃) 土壤温度 Soil temperature (℃) 相对湿度 Relative humidity (%) 土壤体积含水量 SWC (%)
高海拔 Upper 低海拔 Lower 高海拔 Upper 低海拔 Lower 高海拔 Upper 低海拔 Lower 高海拔 Upper 低海拔 Lower
2016 3 8.2 8.9 5.6 7.0 47 51 16.6 15.4
4 14.7 15.1 10.8 13.2 62 69 17.9 15.9
5 16.5 16.8 13.5 15.6 65 71 14.8 11.9
6 22.0 22.2 18.1 20.4 72 77 17.8 14.2
7 23.5 24.0 20.4 23.2 84 86 16.7 13.1
8 22.6 23.3 20.9 23.6 87 90 16.5 13.2
9 18.6 19.5 18.1 20.1 77 80 12.0 11.0
10 11.4 12.6 13.6 14.9 89 90 13.8 12.5
11 6.1 6.7 7.6 8.8 74 79 20.6 17.4
12 2.0 3.3 2.6 6.0 74 76 21.6 NA
2017 1 -0.7 0.1 1.0 1.0 73 70 21.2 12.8
2 3.1 3.9 NA NA 67 67 NA NA
3 6.3 7.2 5.3 6.4 61 64 20.5 11.9
4 14.1 14.5 10.3 11.7 59 63 20.7 12.9
5 18.7 19.1 13.9 15.7 57 63 18.4 10.0

Table 2

Physical (altitude, slope and aspect) and forest (canopy closure, average tree age, average tree height and average DBH) measures of sample plots in Quercus variabilis forest in the east Qinling Mountain range"

样地
Plot
海拔
Altitude
(m)
坡度
Slope
(°)
坡向
Aspect
林分郁闭度
Canopy density
平均树龄
Average tree age
(a)
平均树高
Average tree height
(m)
平均胸径
Average diameter at breast height
(cm)
低海拔
Lower altitude
650 25 阳坡 South-facing slope 0.9 26 16 16
高海拔
Upper altitude
970 28 阳坡 South-facing slope 0.8 30 15 16

Fig. 1

Chronological changes of non-structural carbohydrate (NSC) and its components in the branches of Quercus variabilis at upper altitude in east Qinling Mountain (mean ± SD). Shaded area represent the non-growing season of Q. variabilis based on leaf phenology."

Fig. 2

Chronological changes of non-structural carbohydrate (NSC) and its components in the branches of Quercus variabilis at lower altitude in east Qinling Mountain (mean ± SD). Shaded area represent the non-growing season of Q. variabilis based on leaf phenology."

Fig. 3

A comparison of non-structural carbohydrate (NSC) and its components in branches of Quercus variabilis at different altitudes in east Qinling Mountain (mean ± SD). Different lowercase letters indicate significant differences between two altitudes based on Paired-sample t test (p < 0 .05). Letters a and b refer to NSC, letters c and d refer to starch, and letters e and f refer to soluble sugars."

Table 3

Pearson correlation between measured environmental variables and branch NSC contents of Quercus variabilis at upper and lower altitudes in east Qinling Mountain"

样地
Plot
成分
Compound
空气温度
Ta (℃)
土壤温度
Ts (℃)
相对湿度
RH (%)
土壤体积含水量
SWC (%)
降水量 P (mm) 饱和水汽压差
VPD (kPa)
高海拔
Upper altitude
SS -0.578 -0.589 -0.164 0.717* -0.190 -0.278
S -0.190 0.008 0.121 -0.537 0.049 -0.185
NSC -0.590 -0.472 -0.055 0.235 -0.123 -0.349
SS/S -0.178 -0.194 0.121 0.644* -0.261 -0.297
低海拔
Lower altitude
SS -0.591 -0.453 0.378 0.315 -0.051 -0.552
S -0.343 -0.250 0.722* 0.302 -0.470 -0.788**
NSC -0.564 -0.423 0.703* 0.382 -0.349 -0.845**
SS/S 0.296 0.174 -0.546 -0.246 0.821** 0.702**

Fig. 4

A comparison between measured and modeling values extracted from Gompertz fitting curve of leaf phenology of uppnr and lower altitude Quercus variabilis in east Qinling Mountain. ▲, measured value of leaf phenology at upper altitude; ■, measured value of leaf phenology at lower altitude; ┅, gompertz fitting curve of leaf phenology of upper altitude; ━, gompertz fitting curve of leaf phenology of lower altitude."

Table 4

The dates of main leaf phenology phase of Quercus variabilis based on Gompertz fitting values in east Qinling Mountain"

样地 Plot 展叶初期 Onset leaf expansion 展叶中期 Mid-stage leaf expansion 展叶末期 Leaf expansion completed
高海拔 Upper altitude 04-12 04-22 05-10
低海拔 Lower altitude 04-09 04-19 05-07
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