2024-03-29T08:44:48Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1959492022-11-18T14:06:30Zcom_10261_77com_10261_8com_10261_63617col_10261_330col_10261_63619
00925njm 22002777a 4500
dc
Butler, Ethan E.
author
Datta, Abhirup
author
Flores-Moreno, Habacuc
author
Chen, Min
author
Wythers, Kirk R.
author
Fazayeli, Farideh
author
Banerjee, Arindam
author
Atkin, Owen K.
author
Kattge, Jens
author
Amiaudi, Bernard
author
Blonder, Benjamin
author
Boenisch, Gerhard
author
Bond-Lamberty, Ben
author
Brown, Kerry A.
author
Byun, Chaeho
author
Campetellan, Giandiego
author
Cerabolini, Bruno E. L.
author
Cornelissen, Johannes H.C.
author
Craine, Joseph M.
author
Craven, Dylan
author
Vries, Franciska T. de
author
Díaz, Sandra
author
Domingues, Tomas F.
author
Forey, Estelle
author
González-Melo, Andrés
author
Gross, Nicolas
author
Han, Wenxuan
author
Hattingh, Wesley N.
author
Hickler, Thomas
author
Jansen, Steven
author
Kramer, Koen
author
Kraft, Nathan J. B.
author
Kurokawa, Hiroko
author
Laughlin, Daniel C.
author
Meir, Patrick
author
Minden, Vanessa
author
Niinemets, Ülo
author
Onoda, Yusuke
author
Peñuelas, Josep
author
Read, Quentin
author
Sack, Lawren
author
Schampt, Brandon
author
Soudzilovskaia, Nadejda A.
author
Spasojevic, Marko J.
author
Sosinsk, Enio
author
Thornton, Peter E.
author
Valladares Ros, Fernando
author
Van Bodegom, Peter
author
Williams, Mathew
author
Wirth, Christian
author
Reich, Peter B.
author
2017-12-19
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 (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-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.
Proceedings of the National Academy of Sciences of the United States of America 114(51): E10937-E10946 (2017)
0027-8424
http://hdl.handle.net/10261/195949
10.1073/pnas.1708984114
1091-6490
http://dx.doi.org/10.13039/100000001
http://dx.doi.org/10.13039/100007249
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100000270
http://dx.doi.org/10.13039/501100002809
http://dx.doi.org/10.13039/501100001830
http://dx.doi.org/10.13039/501100001809
http://dx.doi.org/10.13039/501100000923
http://dx.doi.org/10.13039/100000015
29196525
Spatial statistics
Bayesian modelling
Plant traits
Climate
Global
Mapping local and global variability in plant trait distributions