Journal of Resources and Ecology ›› 2021, Vol. 12 ›› Issue (3): 332-345.DOI: 10.5814/j.issn.1674-764x.2021.03.003

• Forest and Grassland Ecosystem • Previous Articles     Next Articles

Predictability of Functional Diversity Depends on the Number of Traits

ZHANG Zihao1, HOU Jihua1,*(), HE Nianpeng2,3,4,*()   

  1. 1.Key Laboratory of Forest Resources and Ecosystem Process, Beijing Forestry University, Beijing 100083, China
    2.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    3.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    4.Key Laboratory of Vegetation Ecology of Ministry of Education, Institute of Grassland Science, Northeast Normal University, Changchun 130024, China
  • Received:2020-10-27 Accepted:2021-01-04 Online:2021-05-30 Published:2021-07-30
  • Contact: HOU Jihua,HE Nianpeng
  • Supported by:
    The National Natural Science Foundation of China(31872683);The National Natural Science Foundation of China(31800368);The National Key Research and Development Program of China(2017YFA0604803)

Abstract:

Analysis of functional diversity, based on plant traits and community structure, provides a promising approach for exploration of the adaptive strategies of plants and the relationship between plant traits and ecosystem functioning. However, it is unclear how the number of plant traits included influences functional diversity, and whether or not there are quantitatively dependent traits. This information is fundamental to the correct use of functional diversity metrics. Here, we measured 34 traits of 366 plant species in nine forests from the tropical to boreal zones in China. These traits were used to calculate seven functional diversity metrics: functional richness (functional attribute diversity (FAD), modified FAD (MFAD), convex hull hypervolume (FRic)), functional evenness (FEve), and functional divergence (functional divergence (FDiv), functional dispersion (FDis), quadratic entropy (RaoQ)). Functional richness metrics increased with an increase in trait number, whereas the relationships between the trait divergence indexes (FDiv and FDis) and trait number were inconsistent. Four of the seven functional diversity indexes (FAD, MFAD, FRic, and RaoQ) were comparable with those in previous studies, showing predictable trends with a change in trait number. We verified our hypothesis that the number of traits strongly influences functional diversity. The relationships between these predictable functional diversity metrics and the number of traits facilitated the development of a standard protocol to enhance comparability across different studies. These findings can support integration of functional diversity index data from different studies at the site to the regional scale, and they focus attention on the influence of quantitative selection of traits on functional diversity analysis.

Key words: trait, functional diversity, richness, evenness, divergence, stability, predictability