Journal articles

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 S 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 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, 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

Document type :

Journal articles

Domain :

Environmental Sciences

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Submitted on : Tuesday, May 26, 2020 - 11:12:00 AM
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