Mapping local and global variability in plant trait distributions

Ethan E. Butler; Abhirup Datta; Habacuc Flores-Moreno; Ming Chen; Kirk R. Wythers; Farideh Fazayeli; Arindam Banerjee; Owen K. Atkin; Jens Kattge; Bernard Amiaud; Benjamin Blonder; Gerhard Boenisch; Ben Bond-Lamberty; Kerry A. Brown; Chaeho Byun; Giandiego Campetella; Bruno E.L. Cerabolini; Johannes H.C. Cornelissen; Joseph M. Craine; Dylan Craven; Franciska T. De Vries; Sandra Díaz; Tomas F. Domingues; Estelle Forey; Andrés González-Melo; Nicolas Gross; Wenxuan Han; Wesley N. Hattingh; Thomas Hickler; Steven Jansen; Koen Kramer; Nathan J.B. Kraft; Hiroko Kurokawa; Daniel C. Laughlin; Patrick Meir; Vanessa Minden; Ülo Niinemets; Yusuke Onoda; Josep Peñuelas; Quentin Read; Lawren Sack; Brandon Schamp; Nadejda A. Soudzilovskaia; Marko J. Spasojevic; Enio Sosinski; Peter E. Thornton; Fernando Valladares; Peter M. Van Bodegom; Mathew Williams; Christian Wirth; Peter B. Reich; William H. Schlesinger

doi:10.1073/pnas.1708984114

Mapping local and global variability in plant trait distributions

Ethan E. Butler^*
, Abhirup Datta
, Habacuc Flores-Moreno
, Ming Chen
, Kirk R. Wythers
, Farideh Fazayeli
, Arindam Banerjee
, Owen K. Atkin
, Jens Kattge
, Bernard Amiaud
, Benjamin Blonder
, Gerhard Boenisch
, Ben Bond-Lamberty
, Kerry A. Brown
, Chaeho Byun
, Giandiego Campetella
, Bruno E.L. Cerabolini
, Johannes H.C. Cornelissen
, Joseph M. Craine
, Dylan Craven

Franciska T. De Vries, Sandra Díaz, Tomas F. Domingues, Estelle Forey, Andrés González-Melo, Nicolas Gross, Wenxuan Han, Wesley N. Hattingh, Thomas Hickler, Steven Jansen, Koen Kramer, Nathan J.B. Kraft, Hiroko Kurokawa, Daniel C. Laughlin, Patrick Meir, Vanessa Minden, Ülo Niinemets, Yusuke Onoda, Josep Peñuelas, Quentin Read, Lawren Sack, Brandon Schamp, Nadejda A. Soudzilovskaia, Marko J. Spasojevic, Enio Sosinski, Peter E. Thornton, Fernando Valladares, Peter M. Van Bodegom, Mathew Williams, Christian Wirth, Peter B. Reich, William H. Schlesinger

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

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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 ∼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.

Original language	English
Pages (from-to)	E10937-E10946
Number of pages	10
Journal	Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Volume	114
Issue number	51
Early online date	1 Dec 2017
DOIs	https://doi.org/10.1073/pnas.1708984114
Publication status	Published - 19 Dec 2017

Funding

ACKNOWLEDGMENTS. The authors appreciate the improvements suggested by two anonymous referees, which improved the clarity and depth of the manuscript. This research was supported as part of the Energy Exas-cale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the “Light of West China” Program of the Chinese Academy of Sciences. The authors appreciate the improvements suggested by two anonymous referees, which improved the clarity and depth of the manuscript. This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the "Light of West China" Program of the Chinese Academy of Sciences.

Funders	Funder number
Automotive Research Center
Long-Term Research in Environmental Biology
European Commission
U.S. Department of Energy
NSF Long-Term Ecological Research
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
EU H2020
Chinese Academy of Sciences
Office of Science
Institute on the Environment, University of Minnesota
UK Research and Innovation
Natural Environment Research Council	NE/F002149/1, NE/K002619/1, NE/M019160/1, NE/N012526/1, nceo020005, nceo020004, NE/P018920/1
Biological and Environmental Research	DE-SC0012677
Seventh Framework Programme	1234162, 1242531, 609398, 610028
National Science Foundation	1234162, 1242531, IIS-1563950, DEB-1234162, DEB-1242531
Generalitat de Catalunya	SGR 2014-274
National Natural Science Foundation of China	41473068
European Research Council	CGL2013-48074-P, ERC-SyG-2013-610028 IMBALANCE-P
Horizon 2020 Framework Programme	640176
Japan Society for the Promotion of Science	15KK0255
???publication-publication-funding-organisation-not-added???	06120
Australian Research Council	CE140100008, FT110100457

Keywords

Bayesian modeling
Climate
Global
Plant traits
Spatial statistics

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10.1073/pnas.1708984114

butler-et-al-2017-mapping-local-and-global-variability-in-plant-trait-distributionsFinal published version, 2.61 MBLicence: Article 25fa Dutch Copyright Act

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Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., Banerjee, A., Atkin, O. K., Kattge, J., Amiaud, B., Blonder, B., Boenisch, G., Bond-Lamberty, B., Brown, K. A., Byun, C., Campetella, G., Cerabolini, B. E. L., Cornelissen, J. H. C., Craine, J. M., ... Schlesinger, W. H. (2017). Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 114(51), E10937-E10946. https://doi.org/10.1073/pnas.1708984114

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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 (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.",

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doi = "10.1073/pnas.1708984114",

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Butler, EE, Datta, A, Flores-Moreno, H, Chen, M, Wythers, KR, Fazayeli, F, Banerjee, A, Atkin, OK, Kattge, J, Amiaud, B, Blonder, B, Boenisch, G, Bond-Lamberty, B, Brown, KA, Byun, C, Campetella, G, Cerabolini, BEL, Cornelissen, JHC, Craine, JM, Craven, D, De Vries, FT, Díaz, S, Domingues, TF, Forey, E, González-Melo, A, Gross, N, Han, W, Hattingh, WN, Hickler, T, Jansen, S, Kramer, K, Kraft, NJB, Kurokawa, H, Laughlin, DC, Meir, P, Minden, V, Niinemets, Ü, Onoda, Y, Peñuelas, J, Read, Q, Sack, L, Schamp, B, Soudzilovskaia, NA, Spasojevic, MJ, Sosinski, E, Thornton, PE, Valladares, F, Van Bodegom, PM, Williams, M, Wirth, C, Reich, PB & Schlesinger, WH 2017, 'Mapping local and global variability in plant trait distributions', Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 114, no. 51, pp. E10937-E10946. https://doi.org/10.1073/pnas.1708984114

TY - JOUR

T1 - Mapping local and global variability in plant trait distributions

AU - Butler, Ethan E.

AU - Datta, Abhirup

AU - Flores-Moreno, Habacuc

AU - Chen, Ming

AU - Wythers, Kirk R.

AU - Fazayeli, Farideh

AU - Banerjee, Arindam

AU - Atkin, Owen K.

AU - Kattge, Jens

AU - Amiaud, Bernard

AU - Blonder, Benjamin

AU - Boenisch, Gerhard

AU - Bond-Lamberty, Ben

AU - Brown, Kerry A.

AU - Byun, Chaeho

AU - Campetella, Giandiego

AU - Cerabolini, Bruno E.L.

AU - Cornelissen, Johannes H.C.

AU - Craine, Joseph M.

AU - Craven, Dylan

AU - De Vries, Franciska T.

AU - Díaz, Sandra

AU - Domingues, Tomas F.

AU - Forey, Estelle

AU - González-Melo, Andrés

AU - Gross, Nicolas

AU - Han, Wenxuan

AU - Hattingh, Wesley N.

AU - Hickler, Thomas

AU - Jansen, Steven

AU - Kramer, Koen

AU - Kraft, Nathan J.B.

AU - Kurokawa, Hiroko

AU - Laughlin, Daniel C.

AU - Meir, Patrick

AU - Minden, Vanessa

AU - Niinemets, Ülo

AU - Onoda, Yusuke

AU - Peñuelas, Josep

AU - Read, Quentin

AU - Sack, Lawren

AU - Schamp, Brandon

AU - Soudzilovskaia, Nadejda A.

AU - Spasojevic, Marko J.

AU - Sosinski, Enio

AU - Thornton, Peter E.

AU - Valladares, Fernando

AU - Van Bodegom, Peter M.

AU - Williams, Mathew

AU - Wirth, Christian

AU - Reich, Peter B.

AU - Schlesinger, William H.

PY - 2017/12/19

Y1 - 2017/12/19

N2 - 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.

AB - 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.

KW - Bayesian modeling

KW - Climate

KW - Global

KW - Plant traits

KW - Spatial statistics

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DO - 10.1073/pnas.1708984114

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JO - Proceedings of the National Academy of Sciences of the United States of America (PNAS)

JF - Proceedings of the National Academy of Sciences of the United States of America (PNAS)

IS - 51

ER -

Mapping local and global variability in plant trait distributions

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