Mapping local and global variability in plant trait distributions

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 51; pp. E10937 - E10946
• Main Authors: Butler, Ethan E., Datta, Abhirup, Flores-Moreno, Habacuc, Chen, Ming, Wythers, Kirk R., Fazayeli, Farideh, Banerjee, Arindam, Atkin, Owen K., Kattge, Jens, Amiaud, Bernard, Blonder, Benjamin, Boenisch, Gerhard, Bond-Lamberty, Ben, Brown, Kerry A., Byun, Chaeho, Campetella, Giandiego, Cerabolini, Bruno E. L., Cornelissen, Johannes H. C., Craine, Joseph M., Craven, Dylan, de Vries, Franciska T., Díaz, Sandra, Domingues, Tomas F., Forey, Estelle, González-Melo, Andrés, Gross, Nicolas, Han, Wenxuan, Hattingh, Wesley N., Hickler, Thomas, Jansen, Steven, Kramer, Koen, Kraft, Nathan J. B., Kurokawa, Hiroko, Laughlin, Daniel C., Meir, Patrick, Minden, Vanessa, Niinemets, Ülo, Onoda, Yusuke, Peñuelas, Josep, Read, Quentin, Sack, Lawren, Schamp, Brandon, Soudzilovskaia, Nadejda A., Spasojevic, Marko J., Sosinski, Enio, Thornton, Peter E., Valladares, Fernando, van Bodegom, Peter M., Williams, Mathew, Wirth, Christian, Reich, Peter B.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 19.12.2017; National Academy of Sciences, Washington, DC (United States)
• Series: PNAS Plus
• Subjects: 60 APPLIED LIFE SCIENCES; Bayesian analysis; Bayesian modeling; Biodiversity; Biological Sciences; Cells; Climate; Ecosystem; Environment; Environmental changes; ENVIRONMENTAL SCIENCES; Geography; Global; global climate; Leaf area; Leaves; Mapping; Mathematical models; Modelling; Models, Statistical; Nitrogen; Phosphorus; Photosynthesis; Physical Sciences; Plant Dispersal; Plant diversity; Plant traits; Plants; PNAS Plus; Quantitative Trait, Heritable; Spatial Analysis; Spatial statistics; Statistical models; Vegetation; global; spatial statistics; climate; Bayesian modeling; plant traits
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1708984114

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 ∼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.