Building a better foundation [electronic resource] : improving root-trait measurements to understand and model plant and ecosystem processes
- Washington, D.C. : United States. Dept. of Energy. Office of Science, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 27-37 : digital, PDF file
- Additional Creators:
- Oak Ridge National Laboratory
United States. Department of Energy. Office of Science
United States. Department of Energy. Office of Scientific and Technical Information
- Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual roots to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.
- Published through SciTech Connect.
New Phytologist 215 1 ISSN 0028-646X AM
M. Luke McCormack; Dali Guo; Colleen M. Iversen; Weile Chen; David M. Eissenstat; Christopher W. Fernandez; Le Li; Chengen Ma; Zeqing Ma; Hendrik Poorter; Peter B. Reich; Marcin Zadworny; Amy Zanne.
- Funding Information:
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