Chin J Plant Ecol ›› 2020, Vol. 44 ›› Issue (8): 828-841.DOI: 10.17521/cjpe.2019.0146

Special Issue: 植物功能性状

• Research Articles • Previous Articles     Next Articles

Variation and correlation in functional traits of main woody plants in the Cyclobalanopsis glauca community in the karst hills of Guilin, southwest China

LIU Run-Hong1, BAI Jin-Lian2, BAO Han1,3, NONG Juan-Li1,3, ZHAO Jia-Jia1,3, JIANG Yong1,3,*(), LIANG Shi-Chu1,3, LI Yue-Juan1,3   

  1. 1Key Laboratory of Wild Animal and Plant Ecology of Guangxi Colleges and Universities, Guangxi Normal University, Guilin, Guangxi 541006, China
    2Wangfeng Experimental School, Zhongshan, Guangxi 542699, China
    3College of Life Science, Guangxi Normal University, Guilin, Guangxi 541006, China
  • Received:2019-06-13 Accepted:2020-04-13 Online:2020-08-20 Published:2020-07-09
  • Contact: JIANG Yong
  • Supported by:
    National Natural Science Foundation of China(31860124);Guangxi Natural Science Foundation(2016GXNSFBA380030)

Abstract:

Aims Exploring the variation and the relationship between different functional traits of different growth forms and life forms woody species is helpful to understand the adaptation strategies of plants to the external environment, and is of great significance for understanding community assembly and biodiversity maintenance mechanisms.
Methods We measured leaf chlorophyll content (CHL), leaf thickness (LTH), leaf area (LA), leaf dry mass (LDM), specific leaf area (SLA), leaf dry matter content (LDMC), leaf tissue density (LTD), twig dry matter content (TDMC) and twig tissue density (TTD) of 18 main woody species from the Cyclobalanopsis glauca community in karst hills of Guilin, southwest China. Traits variations among different plant functional types (growth form and life form) of woody species were analyzed by a series of methods, including the one-way analysis of variance (one-way ANOVA), and the linear mixed-effects model. In addition, the relationships between nine functional traits on individual and species levels were assessed by the Pearson’s correlation test and principal component analysis (PCA).
Important findings The results showed that: (1) The nine functional traits had different degrees of variation. Specifically, LA and LDM had the maximum coefficient of intraspecific and interspecific variation, while the intraspecific and interspecific variation coefficients of TDMC and TTD were the lowest. (2) For different growth forms, there were significant differences in most functional traits between trees, shrubs and woody lianas. (3) For different life forms, except that the deciduous species showed significantly higher LA and SLA values than evergreen species, and for the other seven functional traits, evergreen species showed significantly higher values than those of deciduous species. (4) There were differences in the intraspecific and interspecific variation of functional traits between different growth forms and life forms plants. Except for some plant functional traits showing the intraspecific variation higher than interspecific variation, most of the other functional traits showed the interspecific variation was higher than intraspecific variation. (5) The relationships between nine functional traits are roughly the same at the individual level and the species level, while the significant correlation ratio at the individual level is higher than the species level. In conclusion, the interspecific variation of plant functional traits is basically higher than the intraspecific variation, but the intraspecific variation cannot be ignored. In addition, species with different growth forms and life forms adopt different ecological strategies to adapt to the karst habitat. Future research should be based on sampling at the individual level, and in combination with environmental factors to explore the variation and correlation in functional traits of different plant functional types at different scales.

Key words: karst hill, Cyclobalanopsis glauca community, plant functional traits, interspecific variability, intraspecific variability