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  Mapping local and global variability in plant trait distributions

Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., et al. (2017). Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Sciences of the United States of America, 114(51), E10937-E10946. doi:10.1073/pnas.1708984114.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-95E8-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3F4B-4
Genre: Journal Article

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 Creators:
Butler, Ethan E., Author
Datta, Abhirup, Author
Flores-Moreno, Habacuc, Author
Chen, Ming, Author
Wythers, Kirk R., Author
Fazayeli, Farideh, Author
Banerjee, Arindam, Author
Atkine, Owen K., Author
Kattge, Jens1, Author              
Amiaud, Bernard, Author
Blonder, Benjamin, Author
Boenisch, Gerhard1, Author              
Bond-Lamberty, Ben, Author
Brown, Kerry A., Author
Byun, Chaeho, Author
Campetella, Giandiego, Author
Cerabolini, Bruno E. L., Author
Cornelissen, Johannes H. C., Author
Craine, Joseph M., Author
Craven, Dylan, Author
de Vriess, Franciska T., AuthorDiaz, Sandra, AuthorDomingues, Tomas F., AuthorForey, Estelle, AuthorGonzález-Melox, Andrés , AuthorGrossy, Nicolas, AuthorHan, Wenxuan, AuthorHattingh, Wesley N., AuthorHickler, Thomas, AuthorJansen, Steven, AuthorKramer, Koen, AuthorKraft, Nathan J. B., AuthorKurokawa, Hiroko, AuthorLaughlin, Daniel C., AuthorMeir, Patrick, AuthorMinden, Vanessa, AuthorNiinemets, Ülo, AuthorOnoda, Yusuke, AuthorPenuelas, Josep , AuthorRead, Quentin, AuthorSack, Lawren, AuthorSchamp, Brandon, AuthorSoudzilovskaia, Nadejda A., AuthorSpasojevic, Marko J., AuthorSosinski, Enio, AuthorThornton, Peter E., AuthorValladares, Fernando, Authorvan Bodegom, Peter M., AuthorWilliams, Mathew, AuthorWirth, Christian1, Author              Reich , Peter B., Author more..
Affiliations:
1Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938314              

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Free keywords: In-situ Observations
 Abstract: Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

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 Dates: 2017-12-012017-12-20
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: Other: BGC2777
DOI: 10.1073/pnas.1708984114
 Degree: -

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Project name : BACI
Grant ID : 640176
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Proceedings of the National Academy of Sciences of the United States of America
  Other : Proceedings of the National Academy of Sciences of the USA
  Other : Proc. Acad. Sci. U.S.A.
  Abbreviation : PNAS
Source Genre: Journal
 Creator(s):
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Publ. Info: Washington, D.C. : National Academy of Sciences
Pages: - Volume / Issue: 114 (51) Sequence Number: - Start / End Page: E10937 - E10946 Identifier: ISSN: 0027-8424
CoNE: /journals/resource/954925427230