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Mapping local and global variability in plant trait distributions

Ethan Butler Abhirup Datta Habacuc Flores-Moreno Ming Chen 1 Kirk Wythers Farideh Fazayeli Arindam Banerjee Owen Atkin 2 Jens Kattge 3 Bernard Amiaud 4 Ben Blonder Gerhard Boenisch Ben Bond-Lamberty 5 Kerry Brown Chaeho Byun Giandiego Campetella 6 Bruno Cerabolini Johannes Cornelissen Josep Craine Dylan Craven Franciska de Vries Sandra Diaz 7 Tomas Domingues Estelle Forey 8 Andrés González-Melo Nicolas Gross 9, 10 Wenxuan Han Wesley Hattingh Thomas Hickler 11 Steven Jansen 12 Koen Kramer 13 Nathan Kraft Hiroko Kurokawa 14 Daniel Laughlin Patrick Meir 15 Vanessa Minden Yusuke Onoda 16 Josep Peñuelas 17 Quentin Read Lawren Sack 18 Brandon Schamp Nadejda Soudzilovskaia Marko Spasojevic Enio Sosinski Peter Thornton 19, 20 Fernando Valladares 21 Peter van Bodegom Mathew Williams 22 Christian Wirth 23 Peter Reich 24
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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Déposant : Martine Lacalle <>
Soumis le : mardi 26 mai 2020 - 11:12:00
Dernière modification le : vendredi 11 septembre 2020 - 09:04:05

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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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