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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
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.
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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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