Journal of Guangxi Normal University(Natural Science Edition) ›› 2026, Vol. 44 ›› Issue (2): 218-227.doi: 10.16088/j.issn.1001-6600.2025010201

• Ecology and Environmental Science Research • Previous Articles     Next Articles

Non-structural Carbohydrate Content and Allocation Strategies in Branchesand Leaves of Cryptomeria japonica Plantations

JIAN Yi1,2, LI Dongqing3,4,5, LIN Jing1,2*, YOU Chengming3,4,5, TAN Bo3,4,5, XU Zhenfeng3,4,5, XU Lin3,4,5, ZHANG Kexin1,2   

  1. 1. Longmenshan Forest Ecosystem Research Station, National Forestry and Grassland Administration of China, Mianyang Sichuan 622550, China;
    2. Ecological Restoration and Conservation for Forest and Wetland Key Laboratoryof Sichuan Province, Sichuan Academy of Forestry, Chengdu Sichuan 610081, China;
    3. Sichuan Provincial KeyLaboratory of Forest Ecology and Conservation in the Upper Reaches of the Yangtze River (Sichuan AgriculturalUniversity), Chengdu Sichuan 611130, China;
    4. Sichuan Mt. Emei Forest Ecosystem National Observation andResearch Station (Sichuan Agricultural University), Chengdu Sichuan 611130, China;
    5. College of Forestry, Sichuan Agricultural University, Chengdu Sichuan 611130, China
  • Received:2025-01-02 Revised:2025-04-07 Published:2026-02-03

PDF (PC)

2

Abstract: Non-structural carbohydrates (NSC) are key carbon components for plant growth and metabolism. Plants optimize their survival strategies by regulating NSC allocation patterns between branches and leaves. In order to investigate the content characteristics of NSC in the branches and leaves of Cryptomeria japonica plantations of different ages, young (6 a), middle-aged (12 a), near-mature (23 a), mature (27 a, 32 a) and over-mature (46 a, 52 a) were selected as the research objects. The contents of NSC and its components in fresh and litter branches and leaves were determined. The results showed as follows: 1) The contents of soluble sugar, soluble sugar/starch and NSC decreased with the increase of age. The highest starch content was in 23 a. The content of NSC and its components in fresh leaves increased first and then decreased with the increase of age, and the highest content was found at 32 a. The content of soluble sugar/starch was the highest at 23 a. 2) The change trend of NSC and its component content in litter was not obvious with the increase of stand age. The contents of NSC and its components in litter increased first and then decreased with the increase of stand age, and the highest values were found at 23 a or 27 a. The content of soluble sugar/starch was the lowest in 12 a. 3) The contents of non-structural carbohydrate and its components in fresh and litter leaves were higher than those in fresh and litter branches, respectively. The content of starch in leaf litter was positively correlated with the content of nitrogen and phosphorus. There was a significant negative correlation between NSC and leaf nitrogen content in litter. 4) The ratio of the contents of NSC and its components in the fresh branches and leaves of the same age to that in the litters was greater than 1, indicating that C. japonica transfers NSC from senescent tissues to fresh tissues prior to senescence to optimize carbon resource utilization. These findings not only enhance the understanding of the carbon metabolism balance strategies employed by C. japonica stands of varying ages to sustain growth, but also provide a theoretical basis for improving the productivity of C. japonica plantations through rational regulation of stand age structure and optimization of forest stand management.

Key words: Cryptomeria japonica, artificial forest, forest age, non-structural carbohydrates, allocation strategy

CLC Number:  S791.31;Q948
[1] 潘庆民, 韩兴国, 白永飞, 等.植物非结构性贮藏碳水化合物的生理生态学研究进展[J].植物学通报, 2002, 37(1):30-38.DOI:10.3969/j.issn.1674-3466.2002.01.004.
[2] 李娜妮, 何念鹏, 于贵瑞.中国东北典型森林生态系统植物叶片的非结构性碳水化合物研究[J].生态学报, 2016, 36(2):430-438.DOI:10.5846/stxb201312182983.
[3] 胡仪, 吴福忠, 吴秋霞, 等.三个亚热带森林优势种凋落物非结构性碳水化合物含量的季节动态[J].生态学报, 2022, 42(5):1901-1910.DOI:10.5846/stxb202103050606.
[4] 王凯, 宋琪, 张日升, 等.科尔沁沙地防护林主要树种的非结构性碳水化合物分布特征[J].林业科学, 2020, 56(12):39-48.DOI:10.11707/j.1001-7488.20201205.
[5] 于丽敏, 王传宽, 王兴昌.三种温带树种非结构性碳水化合物的分配[J].植物生态学报, 2011, 35(12):1245-1255.DOI:10.3724/SP.J.1258.2011.01245.
[6] 张海燕, 王传宽, 王兴昌.温带12个树种新老树枝非结构性碳水化合物浓度比较[J].生态学报, 2013, 33(18):5675-5685.DOI:10.5846/stxb201304210762.
[7] 章异平, 师志强, 竹磊, 等.秦岭东段不同海拔栓皮栎粗根非结构性碳水化合物含量的季节动态[J].生态学杂志, 2021, 40(3):712-720.DOI:10.13292/j.1000-4890.202103.017.
[8] 秦慧君, 焦亮, 周怡, 等.祁连山优势树木碳水化合物资源分配的海拔和树种效应[J].植物生态学报, 2022, 46(2):208-219.DOI:10.17521/cjpe.2021.0262.
[9] 章异平, 海旭莹, 徐军亮, 等.秦岭东段栓皮栎枝条非结构性碳水化合物含量的季节动态[J].植物生态学报, 2019, 43(6):521-531.DOI:10.17521/cjpe.2018.0325.
[10] 王凯, 雷虹, 夏扬, 等.杨树幼苗非结构性碳水化合物对增加降水和氮添加的响应[J].应用生态学报, 2017, 28(2):399-407.DOI:10.13287/j.1001-9332.201702.012.
[11] HARTMANN H, TRUMBORE S.Understanding the roles of nonstructural carbohydrates in forest trees-from what we can measure to what we want to know[J].New Phytologist, 2016, 211(2):386-403.DOI:10.1111/nph.13955.
[12] 杜建会, 邵佳怡, 李升发, 等.树木非结构性碳水化合物含量多时空尺度变化特征及其影响因素研究进展[J].应用生态学报, 2020, 31(4):1378-1388.DOI:10.13287/j.1001-9332.202004.001.
[13] 吴秋霞, 吴福忠, 胡仪, 等.亚热带同质园11个树种新老叶非结构性碳水化合物含量比较[J].植物生态学报, 2021, 45(7):771-779.DOI:10.17521/cjpe.2021.0010.
[14] 王凯, 王欣, 张日升, 等.科尔沁沙地银中杨与白榆非结构性碳水化合物的季节动态比较[J].生态学杂志, 2024, 43(12):3624-3631.DOI:10.13292/j.1000-4890.202412.039.
[15] COTRUFO M F, SOONG J L, HORTON A J, et al.Formation of soil organic matter via biochemical and physical pathways of litter mass loss[J].Nature Geoscience, 2015, 8(10):776-779.DOI:10.1038/ngeo2520.
[16] 刘宁, 刘佩雯, 何浩勇, 等.凋落物对喀斯特檵木土壤微生物生物量和土壤酶活性的影响[J].广西师范大学学报(自然科学版), 2025, 43(1):161-173.DOI:10.16088/j.issn.1001-6600.2024080202.
[17] 周冰倩, 郭志国, 刘隋赟昊, 等.不同发育阶段柽柳沙堆土壤和叶片 C、N、P 化学计量特征及影响因素[J/OL].草业科学, 2024:1-24[2024-12-12].https://doi.org/10.11829/j.issn.1001-0629.2024.0466.
[18] RYAN M G, BINKLEY D, FOWNES J H.Age-related decline in forest productivity:pattern and process[M]//Advances in Ecological Research Volume 27.Amsterdam:Elsevier, 1997:213-262.DOI:10.1016/s0065-2504(08)60009-4.
[19] DENG X X, XIAO W F, ZENG L X, et al.Trade-off of NSC allocation in response to the phosphorus limitation caused by increased stand age in Pinus massoniana[J].Forest Ecology and Management, 2024, 561:121883.DOI:10.1016/j.foreco.2024.121883.
[20] 张瑞香.不同林龄野生南方红豆杉碳氮磷生态化学计量与非结构性碳水化合物特征研究[D].郑州:河南农业大学, 2023.
[21] 杨家慧, 谭伟, 冯艳.不同龄组柳杉人工林土壤肥力特征[J].森林与环境学报, 2019, 39(5):505-514.DOI:10.13324/j.cnki.jfcf.2019.05.009.
[22] 覃世杰, 覃德文, 聂珍臻, 等.柳杉人工林经济效益评价[J].湖北农业科学, 2016, 55(1):269-273.DOI:10.14088/j.cnki.issn0439-8114.2016.01.067.
[23] 谢云, 王洪荣, 黄从德.四川洪雅林场森林植被碳储量的空间分布特征研究[J].四川林业科技, 2016, 37(4):14-18.DOI:10.16779/j.cnki.1003-5508.2016.04.003.
[24] JUSTINE M F, YANG W Q, WU F Z, et al.Dynamics of biomass and carbon sequestration across a chronosequence of Masson pine plantations[J].Journal of Geophysical Research:Biogeosciences, 2017, 122(3):578-591.DOI:10.1002/2016JG003619.
[25] 刘宣, 崔宁洁, 谭飞川, 等.华西雨屏区柳杉人工林土壤持水能力及其对土壤有机碳的指示作用[J].应用与环境生物学报, 2023, 29(3):670-679.DOI:10.19675/j.cnki.1006-687x.2022.03034.
[26] CHEN F S, NIKLAS K J, LIU Y, et al.Nitrogen and phosphorus additions alter nutrient dynamics but not resorption efficiencies of Chinese fir leaves and twigs differing in age[J].Tree Physiology, 2015, 35(10):1106-1117.DOI:10.1093/treephys/tpv076.
[27] 魏晨, 张小平, 罗子渝, 等.兰州市南山和北山3种乔木叶片生态化学计量特征的对比研究[J].生态学报, 2021, 41(6):2460-2470.DOI:10.5846/stxb201909061861.
[28] 刁浩宇, 王安志, 袁凤辉, 等.特定化合物同位素分析技术在树木非结构性碳水化合物研究中的应用[J].应用生态学报, 2020, 31(12):4291-4300.DOI:10.13287/j.1001-9332.202012.024.
[29] FAJARDO A, PIPER F I, PFUND L, et al.Variation of mobile carbon reserves in trees at the alpine treeline ecotone is under environmental control[J].New Phytologist, 2012, 195(4):794-802.DOI:10.1111/j.1469-8137.2012.04214.x.
[30] SALA A, WOODRUFF D R, MEINZER F C.Carbon dynamics in trees:feast or famine[J].Tree Physiology, 2012, 32(6):764-775.DOI:10.1093/treephys/tpr143.
[31] 王凯, 逄迎迎, 张日升, 等.不同年龄沙地樟子松非结构性碳水化合物分布特征[J].生态学杂志, 2021, 40(5):1264-1274.DOI:10.13292/j.1000-4890.202105.022.
[32] 郑云普, 王贺新, 娄鑫, 等.木本植物非结构性碳水化合物变化及其影响因子研究进展[J].应用生态学报, 2014, 25(4):1188-1196.DOI:10.13287/j.1001-9332.2014.0110.
[33] CAO J X, WANG X P, TIAN Y, et al.Pattern of carbon allocation across three different stages of stand development of a Chinese pine (Pinus tabulaeformis) forest[J].Ecological Research, 2012, 27(5):883-892.DOI:10.1007/s11284-012-0965-1.
[34] 植可翔, 关欣, 黄苛, 等.杉木不同组织/器官非结构性碳水化合物含量及其对修枝的响应[J].生态学报, 2024, 44(20):9391-9400.DOI:10.20103/j.stxb.202306191305.
[35] LIU Q Q, HUANG Z J, WANG Z N, et al.Responses of leaf morphology, NSCs contents and C:N:P stoichiometry of Cunninghamia lanceolata and Schima superba to shading[J].BMC Plant Biology, 2020, 20(1):354.DOI:10.1186/s12870-020-02556-4.
[36] ZHANG K R, SONG C H, ZHANG Y L, et al.Global-scale patterns of nutrient density and partitioning in forests in relation to climate[J].Global Change Biology, 2018, 24(1):536-551.DOI:10.1111/gcb.13860.
[37] MARTÍNEZ-VILALTA J, SALA A, ASENSIO D, et al.Dynamics of non-structural carbohydrates in terrestrial plants:a global synthesis[J].Ecological Monographs, 2016, 86(4):495-516.DOI:10.1002/ecm.1231.
[38] LI N N, HE N P, YU G R, et al.Leaf non-structural carbohydrates regulated by plant functional groups and climate:Evidences from a tropical to cold-temperate forest transect[J].Ecological Indicators, 2016, 62:22-31.DOI:10.1016/j.ecolind.2015.11.017.
[39] 王轶浩, 周建岗, 符裕红.林龄对重庆武陵山区马尾松天然次生林C、N、P生态化学计量特征的影响[J].生态学报, 2022, 42(23):9537-9547.DOI:10.5846/stxb202201100093.
[40] 王维奇, 徐玲琳, 曾从盛, 等.河口湿地植物活体-枯落物-土壤的碳氮磷生态化学计量特征[J].生态学报, 2011, 31(23):134-139.DOI:10.20103/j.stxb.2011.23.016.
[41] 王强, 杨鸿淋, 徐春晖, 等.涪江中游4种造林树种碳氮磷化学计量及养分重吸收特征[J].四川农业大学学报, 2023, 41(3):437-445.DOI:10.16036/j.issn.1000-2650.202211191.
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] TIAN Sheng, ZHAO Kailong, MIAO Jialin. Research on Automatic Driving Road Traffic Detection Algorithm Based on Improved YOLO11n Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 1 -9 .
[2] XU Xiuhong, ZHANG Jinyan, LU Yuling, LIANG Xiaoping, LIAO Guangfeng, LU Rumei. Research Progress of New C21 Steroids in Medicinal Plants of Asclepiadaceae(Ⅱ)[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 1 -16 .
[3] WANG Xingyu, ZHENG Haonan, LIU Xiao, CUI Shilong, CAI Jinjun. Research Progress on Preparation of Chitosan-based Adsorbents andTheir Applications Towards Adsorptive Removal of Pollutants From Water[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 17 -30 .
[4] TIAN Sheng, FENG Shuaitao, LI Jia. A Framework for Enhanced Vehicle Trajectory Extraction in Urban Road Scenes[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 31 -51 .
[5] LÜ Hui, SI Ke. Photovoltaic Panel Defect Detection Based on Improved RT-DETR[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 52 -64 .
[6] SONG Guanwu, LI Jianjun. Semantic Segmentation of Remote Sensing Images Basedon Self-distillation Edge Refinement[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 65 -76 .
[7] WANG Xuyang, LIANG Yuhang. Multi-scale Asymmetric Attention Transformer for Remote Sensing Image Dehazing[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 77 -89 .
[8] ZHANG Shengwei, CAO Jie. Detection Algorithm of Tiny Defects on Steel Surface Based onFourier Convolution and Difference Perception[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 90 -102 .
[9] WANG Wei, LI Zhiwei, ZHANG Zhaoyang, ZHANG Hong, ZHOU Li, WANG Zhen, HUANG Fang, WANG Can. Carbon Emission Prediction of Substation Based on IFA-BP Neural Network Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 103 -114 .
[10] LUO Yuan, ZHU Wenzhong, WANG Wen, WU Yuhao. A Multi-step Water Quality Prediction Model Based on Improved PatchTST[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 115 -131 .
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd