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The leaf economic spectrum drives litter decomposition within regional floras worldwide

AuthorsCornwell, William K.; Godoy, Óscar ; Westoby, Mark
KeywordsBiogeochemical cycles
Biosphere/atmosphere interactions
Carbon cycling
Data sets
Plant ecology
Issue Date2008
PublisherJohn Wiley & Sons
CitationEcology Letters 11: 1065-1071 (2008)
AbstractLeaves are the green machines that drive terrestrial oxygen production, carbon assimilation and primary productivity worldwide. Some green machines are fast but short-lived producers, while others are durable enough for steady carbon gain over the long term. The nuts and bolts of the green machinery to support fast production versus durability are the chemical, physiological, and structural traits of the plant species that together shape the globally operating "leaf economics spectrum". These green-leaf traits have crucial implications not only for the production of biomass in biogeochemical cycles, but also for its subsequent fate: the litter traits inherited from the green leaves together with the litter environment drive the rate of litter decomposition. From a global synthesis of 818 species in 66 decomposition experiments on six continents, we quantified the degree to which functional differentiation among species affects their litter decomposition rates. For the first time at global scale, we show that: (1) the magnitude of species-driven differences in decomposition within biomes is surprisingly large--often greater than 10-fold (2) the decomposability of a species' litter is consistently correlated with that species' position on the green-leaf economics spectrum: species designed to achieve a fast return on carbon invested in leaves produce faster decomposing litter compared to those species with a slower-return strategy. These results suggest that a shift in relative abundance of particular species traits within a biome could strongly impact overall decomposition rates. Correctly predicting the abundance and distribution of particular plant traits will be crucial for accurate forecasts of future carbon pools and their feedbacks to further climate change.
Publisher version (URL)http://dx.doi.org/10.111/j.1461-0248.2008.01219.x.
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