Mapping local and global variability in plant trait distributions

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Ethan Butler
  • Abhirup Datta
  • Habacuc Flores-Moreno
  • Ming Chen
  • Kirk R. Wythers
  • Farideh Fazayeli
  • Arindam 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 Craven
  • Sandra Díaz
  • Tomas Domingues
  • Estelle Jansen
  • Koen Kramer
  • Nathan J.B. Kraft
  • Hiroko Kurokawa
  • Daniel C. Laughlin
  • Patrick Meir
  • Vanessa Minden
  • Yusuke Onoda
  • Josep Peñuelas
  • Quentin Read
  • Fernando Valladares Ros
  • Lawren Sack
  • Nadejda A. Soudzilovskaia
  • Marko J. Spasojevic
  • Enio Sosinski
  • Peter Thornton
  • Peter M. van Bodegom
  • Mathew Williams
  • Christian Wirth
  • Peter B. Reich


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 (NmNm) and phosphorus (PmPm), we characterize how traits vary within and among over 50,000 ∼50×50∼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.

Bibliographical metadata

Original languageEnglish
JournalProceedings of the National Academy of Sciences
Publication statusPublished - 19 Dec 2017