English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/195949
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Mapping local and global variability in plant trait distributions

AuthorsButler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc; Chen, Min; Wythers, Kirk R.; Fazayeli, Farideh; Banerjee, Arindam; Atkin, Owen K.; Kattge, Jens; Amiaudi, Bernard; Blonder, Benjamin; Boenisch, Gerhard; Bond-Lamberty, Ben; Brown, Kerry A.; Byun, Chaeho; Campetellan, Giandiego; Cerabolini, Bruno E. L.; Cornelissen, Johannes H.C.; Craine, Joseph M.; Craven, Dylan; Vries, Franciska T. de; Diaz, Sandra; Domingues, Tomas F.; Forey, Estelle; González-Melo, Andrés; Gross, Nicolas; Han, Wenxuan; Hattingh, Wesley N.; Hickler, Thomas; Jansen, Steven; Kramer, Koen; Kraft, Nathan J. B.; Kurokawa, Hiroko; Laughlin, Daniel C.; Meir, Patrick; Minden, Vanessa; Niinemets, Ülo; Onoda, Yusuke; Peñuelas, Josep; Read, Quentin; Sack, Lawren; Schampt, Brandon; Soudzilovskaiau, Nadejda A.; Spasojevic, Marko J.; Sosinsk, Enio; Thornton, Peter E.; Valladares Ros, Fernando ; Van Bodegom, Peter; Williams, Mathew; Wirth, Christian; Reich, Peter B.
KeywordsSpatial statistics
Bayesian modeling
Plant traits
Climate
Global
Issue Date19-Dec-2017
PublisherNational Academy of Sciences (U.S.)
CitationProceedings of the National Academy of Sciences of the United States of America 114(51): E10937-E10946 (2017)
AbstractOur 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.
Publisher version (URL)https://doi.org/10.1073/pnas.1708984114
URIhttp://hdl.handle.net/10261/195949
DOI10.1073/pnas.1708984114
ISSN0027-8424
E-ISSN1091-6490
Appears in Collections:(CREAF) Artículos
(MNCN) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.