植物生态学报 ›› 2018, Vol. 42 ›› Issue (2): 209-219.doi: 10.17521/cjpe.2017.0132

• 研究论文 • 上一篇    下一篇

基于叶干质量比的杉木比叶面积估算模型的构建

彭曦,闫文德,王凤琪,王光军,玉昉永,赵梅芳()   

  1. 中南林业科技大学生命科学与技术学院, 长沙 410004; 湖南会同杉木林国家重点野外科学观测研究站, 湖南会同 418307; 南方林业生态应用技术国家工程实验室, 长沙 410004
  • 出版日期:2018-02-20 发布日期:2018-04-16
  • 通讯作者: 赵梅芳 E-mail:zhao_mei_fang.2017@aliyun.com
  • 基金资助:
    国家林业公益性行业科研专项(201404316);国家自然科学基金(31600355)

Specific leaf area estimation model building based on leaf dry matter content of Cunninghamia lanceolata

PENG Xi,YAN Wen-De,WANG Feng-Qi,WANG Guang-Jun,YU Fang-Yong,ZHAO Mei-Fang()   

  1. Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China; Huitong National Field Station for Scientific Observation and Research of Chinese Fir Plantation Ecosystem in Hunan Province, Huitong, Hunan 418307, China; and National Engineering Laboratory for Applied Forest Ecological Technology in Southern China, Changsha 410004, China
  • Online:2018-02-20 Published:2018-04-16
  • Contact: Mei-Fang ZHAO E-mail:zhao_mei_fang.2017@aliyun.com
  • Supported by:
    Supported by the National Forestry Industry Research Special Funds for Public Welfare Projects(201404316);the National Natural Science Foundation of China.(31600355)

摘要:

随着叶片功能性状研究的不断深入, 通过简单易测量的叶片指标, 同时探究植物生活史权衡对策和估算林分生产力的研究需求日益增长, 例如叶干质量比(LDMC)和比叶面积(SLA)的相互转换。杉木(Cunninghamia lanceolata)是亚热带重要的常绿针叶树种, 基于LDMC对杉木SLA进行估算, 能够为核算SLA提供途径, 为机理解释和生产估算构建连接途径, 为小区域到大尺度、精算到估算搭建桥梁。该研究在湖南会同和河南信阳两个杉木生长区, 对处于不同小生境(坡向、坡位和冠层深度)以及不同生活史(林龄和叶龄)的叶片进行抽样和采集, 通过测得不同叶龄的单叶LDMCSLA, 初步探究在不同因子下两个性状值的分布差异, 进一步基于LDMC构建SLA估算模型并讨论以叶龄为差分因子对模型的影响。结果表明: 1)杉木SLA平均值为(103.15 ± 69.54) cm 2·g -1, LDMC为0.39 ± 0.11; 2)杉木LDMCSLA可用非线性模型进行估算, 模型符合估算要求; 3)其中一年生叶的拟合效果最好, 老叶(大于二年生叶)的拟合优度较低, 老叶较低的SLA (52.28-75.74 cm 2·g -1)可能暗示LDMC的变化保持相对独立性。该研究基于杉木LDMCSLA估算模型可信且有效, 且不同叶龄对LDMCSLA的影响可能预示着杉木叶片的响应敏感性和生活史权衡策略。

关键词: 杉木, 比叶面积, 叶干质量比, 模型估算, 叶片功能性状

Abstract:
Aims With progresses of leaf functional traits study, there is an increasing demand to explore the life history strategy and trade-offs in plants, as well as estimate stand productivity, by employing easy and simple leaf parameters. For instance, the interconversion between leaf dry matter content (LDMC) and specific leaf area (SLA) just fit the bill. Cunninghamia lanceolata serves as one of the most important afforestation evergreen needle species in subtropical zone. Building the SLA estimation model based on LDMC could provide a new approach to estimate SLA, and establish a connection path between mechanism explanation and productivity evaluation. Moreover, it could also build a bridge between individual level and large-scale, as well as between actuarial and estimation. Methods Leaf samples were collected from two sampling sites located in C. lanceolata growing region: Huitong County of Hunan Province and Xinyang City of Henan Province. The samples covered fundamentally different niches (aspect, slope position, and canopy depth), and different life history (stand age and leaf age). SLA and LDMC were determined along leaf age gradients, and their value distributions in linkage to different factors were discussed. A general model based on LDMC of C. lanceolata was built to estimate SLA, and the impact of leaf age on the model was explored. Important findings The SLA of C. lanceolata was (103.15 ± 69.54) cm 2·g -1, while LDMC was 0.39 ± 0.11. The LDMC and SLA of C. lanceolata can be estimated by nonlinear model (R 2 = 0.718β4, p < 0.001), which meets the estimation requirements. One-year-old leaves showed the best fitting model (R 2 = 0.889, p < 0.001), while old leaves (more than 2-year-old) showed the worst (R 2 = 0.100β1, p < 0.001). Old leaves with a lower SLA (52.28-75.74 cm 2·g -1) might imply the relative independence among the variation of LDMC. The model based on LDMC to evaluate SLA is credible and effective. The effects on LDMC and SLA along leaf age gradients indicate leaf sensitivity, life history strategies and trade-offs.

Key words: Cunninghamia lanceolata, specific leaf area, leaf dry matter content, model estimation, leaf functional traits