Mapping local and global variability in plant trait distributions

Ethan E. Butler, Abhirup Datta, Habacuc Flores-Moreno, Ming Chen, Kirk R. Wythers, Farideh Fazayeli, Arindam Banerjee, Owen K. Atkin, Jens Kattge, Bernard Amiaud, Benjamin Blonder, Gerhard Boenisch, Ben Bond-Lamberty, Kerry A. Brown, Chaeho Byun, Giandiego Campetella, Bruno E.L. Cerabolini, Johannes H.C. Cornelissen, Joseph M. Craine, Dylan CravenFranciska T. De Vries, Sandra Díaz, Tomas F. Domingues, Estelle Forey, Andrés González-Melo, Nicolas Gross, Wenxuan Han, Wesley N. Hattingh, Thomas Hickler, Steven Jansen, Koen Kramer, Nathan J.B. Kraft, Hiroko Kurokawa, Daniel C. Laughlin, Patrick Meir, Vanessa Minden, Ülo Niinemets, Yusuke Onoda, Josep Peñuelas, Quentin Read, Lawren Sack, Brandon Schamp, Nadejda A. Soudzilovskaia, Marko J. Spasojevic, Enio Sosinski, Peter E. Thornton, Fernando Valladares, Peter M. Van Bodegom, Mathew Williams, Christian Wirth, Peter B. Reich, William H. Schlesinger

Research output: Contribution to journalArticlepeer-review

78 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)E10937-E10946
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number51
DOIs
StatePublished - Dec 19 2017

Bibliographical note

Funding Information:
The authors appreciate the improvements suggested by two anonymous referees, which improved the clarity and depth of the manuscript. This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the "Light of West China" Program of the Chinese Academy of Sciences.

Funding Information:
ACKNOWLEDGMENTS. The authors appreciate the improvements suggested by two anonymous referees, which improved the clarity and depth of the manuscript. This research was supported as part of the Energy Exas-cale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the “Light of West China” Program of the Chinese Academy of Sciences.

Keywords

  • Bayesian modeling
  • Climate
  • Global
  • Plant traits
  • Spatial statistics

Fingerprint

Dive into the research topics of 'Mapping local and global variability in plant trait distributions'. Together they form a unique fingerprint.

Cite this