1. Quantifying leaf-trait covariation and its controls across climates and biomes
- Author
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I. Colin Prentice, Hang Wang, Changhui Peng, Sandy P. Harrison, Ian J. Wright, Yanzheng Yang, Guanghui Lin, and AXA Research Fund
- Subjects
0106 biological sciences ,0301 basic medicine ,China ,Multivariate analysis ,Specific leaf area ,Nitrogen ,Physiology ,ADAPTIVE VARIATION ,Climate ,Plant Biology & Botany ,Biome ,Plant Science ,Biology ,phylogeny ,CARBON-ISOTOPE DISCRIMINATION ,01 natural sciences ,03 medical and health sciences ,LEADING DIMENSIONS ,Photosynthesis ,Ecosystem ,vegetation modelling ,2. Zero hunger ,Principal Component Analysis ,Functional ecology ,Science & Technology ,plant functional traits ,Ecology ,leaf economics spectrum ,Plant Sciences ,BIOCHEMICAL-MODEL ,Vegetation ,06 Biological Sciences ,15. Life on land ,Photosynthetic capacity ,Plant Leaves ,multivariate analysis ,030104 developmental biology ,13. Climate action ,Principal component analysis ,Trait ,VEGETATION ,07 Agricultural And Veterinary Sciences ,COMMUNITIES ,Life Sciences & Biomedicine ,PHOTOSYNTHETIC CAPACITY ,RESPONSES ,010606 plant biology & botany - Abstract
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.
- Published
- 2018