Quantifying leaf-trait covariation and its controls across climates and biomes

Yanzheng Yang*, Han Wang, Sandy P. Harrison, I. Colin Prentice, Ian J. Wright, Changhui Peng, Guanghui Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

15 Citations (Scopus)


Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea)), and photosynthetic capacities (Vcmax, Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Original languageEnglish
Pages (from-to)155-168
Number of pages14
JournalNew Phytologist
Issue number1
Early online date11 Sep 2018
Publication statusPublished - Jan 2019


  • climate
  • leaf economics spectrum
  • multivariate analysis
  • photosynthetic capacity
  • phylogeny
  • plant functional traits
  • vegetation modelling

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