Mapping local and global variability in plant trait distributions

Ethan E. Butler, Abhirup Datta, Habacuc Flores-moreno, Ming Cheng, 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 de Vries, Sandra Diaz, Tomas Domingues, Steven Forey, Andres Gonzalez, 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 Penuelas, Quentin Read, Fernando Valladares Ros, Lawren Sack, Brandon Schamp, Nadejda A. Soudzilovskaia, Marko J. Spasojevic, Enio Sosinski, Peter Thornton, Peter M. van Bodegom, Mathew Williams, Christian Wirth, Peter B. Reich

Research output: Contribution to journalArticlepeer-review

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
Article number51
Pages (from-to)E10937-E10946
JournalProceedings of the National Academy of Sciences
Volume114
Early online date1 Dec 2017
DOIs
Publication statusPublished - 19 Dec 2017

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