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Mapping local and global variability in plant trait distributions

Ethan Butler 1 Abhirup Datta 2 Habacuc Flores-Moreno 3 Ming Chen 4 Kirk Wythers 1 Farideh Fazayeli 5 Arindam Banerjee 5 Owen Atkin 6 Jens Kattge 7 Bernard Amiaud 8 Ben Blonder 9 Gerhard Boenisch 10 Ben Bond-Lamberty 11 Kerry Brown 12 Chaeho Byun 13 Giandiego Campetella 14 Bruno Cerabolini Johannes Cornelissen 15 Josep Craine Dylan Craven 16 Franciska de Vries 17 Sandra Diaz 18 Tomas Domingues 19 Estelle Forey 20 Andrés González-Melo 21 Nicolas Gross 22, 23 Wenxuan Han 24, 25 Wesley Hattingh 26 Thomas Hickler 27 Steven Jansen 28 Koen Kramer 29 Nathan Kraft 30 Hiroko Kurokawa 31 Daniel Laughlin 32 Patrick Meir 33 Vanessa Minden 34 Yusuke Onoda 35 Josep Peñuelas 36 Quentin Read 37 Lawren Sack 38 Brandon Schamp 39 Nadejda Soudzilovskaia 40 Marko Spasojevic 30 Enio Sosinski 41 Peter Thornton 42, 43 Fernando Valladares 44 Peter van Bodegom 40 Mathew Williams 45 Christian Wirth 46 Peter Reich 47
1 Department of Forest Resources, University of Minnesota, St. Paul, MN, USA
2 Department of Biostatistics [Baltimore]
3 Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, USA
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, University of Oxford, Oxford, UK
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 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 USC 1339 INRA - Centre d'Etudes Biologiques de Chizé [France]
24 College of Resources and Environmental Sciences, Ministry of Agriculture, Key Laboratory of Arable Land Conservation (North China)
25 Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography
26 School of Animal, Plant & Environmental Sciences
27 Department of Physical Geography and Ecosystem Science [Lund]
28 Universität Ulm - Ulm University [Ulm, Allemagne]
29 Alterra - Green World Research
30 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California
31 Tohoku University [Sendai]
32 Department of Botany, University of Wyoming, Laramie, WY, USA
33 Research School of Biology
34 Institute of Biology and Environmental Science, Carl von Ossietzky University Oldenburg
35 Kyoto University [Kyoto]
36 Global Ecology Unit CREAF-CEAB-CSIC
37 Department of Forestry, Michigan State University, East Lansing
38 Department of Ecology and Evolutionary Biology
39 Department of Biology, Algoma University, Marie, OA, Canada
40 CML - Institute of Environmental Sciences [Leiden]
41 Laboratorio de Planejamento Ambiental
42 ORNL - Oak Ridge National Laboratory [Oak Ridge]
43 CCSI - Climate Change Science Institute [Oak Ridge]
44 Escuela Superior de Ciencias Experimentales y Tecnológicas, Departamento de Biología y Geología
45 School of Geosciences [Edinburgh]
46 Department of Systematic Botany and Functional Biodiversity
47 Department of Forest Resources
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 (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.
Keywords : Bayesian modeling global climate
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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 , National Academy of Sciences, 2017, 114 (51), pp.E10937 - E10946. ⟨10.1073/pnas.1708984114⟩. ⟨hal-01852904⟩

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