4.7 Article

Variation in leaf chlorophyll concentration from tropical to cold-temperate forests: Association with gross primary productivity

期刊

ECOLOGICAL INDICATORS
卷 85, 期 -, 页码 383-389

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecolind.2017.10.025

关键词

Chlorophyll; CM a/b; Community; Functional traits; GPP; Nutrients; Photosynthesis; Pigments; Spatial variation; Trait; Forest

资金

  1. Key Project of National Key Research and Development Plan [2017YFC0504004, 2016YFC0500202]
  2. National Natural Science Foundation of China [31000263, 611606015, 31290221]
  3. STS of Chinese Academy of Sciences [KFJ-SW-STS-167]
  4. program of Youth Innovation Research Team Project [LENOM2016Q0005]

向作者/读者索取更多资源

Leaf chlorophyll is essential to harvest light energy to drive photosynthesis. Yet, most of studies of leaf chlorophyll concentration (Chl) have typically been limited to few species or sites, and there has been little understanding of its variation among species of contrasting environments, or across forests differing in gross primary productivity (GPP). We measured mass-based foliar Chl concentrations (Chl a, Chl b, and total Chl) for 937 species of nine Chinese forests ranging from tropical to cold-temperate regions. Total Chl varied by over ten-fold from 1.45 to 19.2 mg g(-1) (mean: 5.54 mg g(-1)), and differed among plant functional groups. At the community level, total Chl decreased with increasing latitude. The ratio of Chl a/b decreased from trees to shrubs to herbs, consistent with adaptation to contrasting irradiance, i.e., overstory vs. understory. Total Chl was positively related to leaf N and P concentrations, as would be expected to optimize photosynthesis and carbon fixation. Across forests, GPP was positively correlated with community-averaged Chl (r = 0.57; P < 0.01), a new case of a linkage between leaf traits and ecosystem function at regional to continental scale. Our findings revealed variation in Chl from tropical to cold-temperate forests, and the linkage of leaf Chl with ecosystem function, enhancing our ability to parameterize vegetation models.

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