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Journal Articles Proceedings of the National Academy of Sciences of the United States of America Year : 2017

Mapping local and global variability in plant trait distributions

1 Department of Forest Resources, University of Minnesota, St. Paul, MN, USA
2 Department of Biostatistics [Baltimore]
3 UMN - University of Minnesota [Twin Cities]
4 Guangzhou Institute of Geochemistry
5 University of Minnesota, Department of Computer Science and Engineering
6 ANU - Australian National University
7 German Centre for Integrative Biodiversity Research
8 EEF - Ecologie et Ecophysiologie Forestières [devient SILVA en 2018]
9 Environmental Change Institute
10 MPI-BGC - Max Planck Institute for Biogeochemistry
11 Joint Global Change Research Institute
12 Department of Geography and Geology, Kingston University, Kingston upon Thames, UK
13 SNU - Seoul National University [Seoul]
14 UNICAM - Università degli Studi di Camerino = University of Camerino
15 Department of Theoretical and Applied Sciences [Insubria]
16 Jonah Ventures
17 SEES - School of Earth and Environmental Sciences [Manchester]
18 Insituto Multidisciplinario de Biologia Vegetal
19 Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto
20 ECODIV - Étude et compréhension de la biodiversité
21 Programa de Biología, Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario
22 CEBC - Centre d'Études Biologiques de Chizé - UMR 7372
23 College of Resources and Environmental Sciences, Ministry of Agriculture, Key Laboratory of Arable Land Conservation (North China)
24 Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography
25 School of Animal, Plant & Environmental Sciences
26 Department of Physical Geography and Ecosystem Science [Lund]
27 Universität Ulm - Ulm University [Ulm, Allemagne]
28 Alterra - Green World Research
29 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California
30 Tohoku University [Sendai]
31 Department of Botany, University of Wyoming, Laramie, WY, USA
32 Research School of Biology
33 Institute of Biology and Environmental Science, Carl von Ossietzky University Oldenburg
34 Kyoto University
35 Global Ecology Unit CREAF-CEAB-CSIC
36 Department of Forestry, Michigan State University, East Lansing
37 Department of Ecology and Evolutionary Biology
38 Department of Biology, Algoma University, Marie, OA, Canada
39 CML - Institute of Environmental Sciences [Leiden]
40 Laboratorio de Planejamento Ambiental
41 ORNL - Oak Ridge National Laboratory [Oak Ridge]
42 CCSI - Climate Change Science Institute [Oak Ridge]
43 Escuela Superior de Ciencias Experimentales y Tecnológicas, Departamento de Biología y Geología
44 School of Geosciences [Edinburgh]
45 Department of Systematic Botany and Functional Biodiversity
46 Department of Forest Resources
Owen Atkin
Chaeho Byun
Bruno Cerabolini
Josep Craine
  • Function : Author
Dylan Craven
Estelle Forey
Yusuke Onoda
Josep Peñuelas
Lawren Sack
  • Function : Author
  • PersonId : 975865
Peter 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 (N-m) and phosphorus (P-m), we characterize how traits vary within and among over 50,000 similar to 50 x 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 and versions

hal-01852904 , version 1 (26-05-2020)





Ethan Butler, Abhirup Datta, Habacuc Flores-Moreno, Ming Chen, Kirk Wythers, et al.. Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114 (51), pp.E10937 - E10946. ⟨10.1073/pnas.1708984114⟩. ⟨hal-01852904⟩
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