Mapping local and global variability in plant trait distributions
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...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 51; pp. E10937 - E10946 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| 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 | |
| Online Access | Get full text |
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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. |
|---|---|
| AbstractList | 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 ~50x50-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. 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. 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 ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-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.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 ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-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. 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. 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 further reveal that the most diverse grid cells possess trait variability close to the range of global PFT means. Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which accounts for hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Indeed, critical ecosystem processes tend to depend on the full trait distribution, which therefore needs to be represented accurately. These maps reintroduce substantial local variation and will allow for a more accurate representation of the land surface in ESMs. 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. 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. 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 ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-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. |
| Author | Spasojevic, Marko J. González-Melo, Andrés Laughlin, Daniel C. Schamp, Brandon Hickler, Thomas Chen, Ming Forey, Estelle Han, Wenxuan Craine, Joseph M. de Vries, Franciska T. Williams, Mathew Minden, Vanessa Brown, Kerry A. Domingues, Tomas F. Boenisch, Gerhard Thornton, Peter E. Kramer, Koen Atkin, Owen K. Read, Quentin Onoda, Yusuke Datta, Abhirup Hattingh, Wesley N. Kurokawa, Hiroko Campetella, Giandiego Sosinski, Enio Valladares, Fernando Flores-Moreno, Habacuc Butler, Ethan E. Cornelissen, Johannes H. C. Peñuelas, Josep Wirth, Christian Kattge, Jens Craven, Dylan Bond-Lamberty, Ben Meir, Patrick Reich, Peter B. Sack, Lawren Niinemets, Ülo Fazayeli, Farideh Kraft, Nathan J. B. Byun, Chaeho Gross, Nicolas Blonder, Benjamin Díaz, Sandra Wythers, Kirk R. Banerjee, Arindam Jansen, Steven Amiaud, Bernard Cerabolini, Bruno E. L. van Bodegom, Peter M. Soudzilovskaia, Nadejda A. |
| Author_xml | – sequence: 1 givenname: Ethan E. surname: Butler fullname: Butler, Ethan E. organization: Department of Forest Resources, University of Minnesota, St. Paul, MN 55108 – sequence: 2 givenname: Abhirup surname: Datta fullname: Datta, Abhirup organization: Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205 – sequence: 3 givenname: Habacuc surname: Flores-Moreno fullname: Flores-Moreno, Habacuc organization: Department of Forest Resources, University of Minnesota, St. Paul, MN 55108 – sequence: 4 givenname: Ming surname: Chen fullname: Chen, Ming organization: Department of Forest Resources, University of Minnesota, St. Paul, MN 55108 – sequence: 5 givenname: Kirk R. surname: Wythers fullname: Wythers, Kirk R. organization: Department of Forest Resources, University of Minnesota, St. Paul, MN 55108 – sequence: 6 givenname: Farideh surname: Fazayeli fullname: Fazayeli, Farideh organization: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 – sequence: 7 givenname: Arindam surname: Banerjee fullname: Banerjee, Arindam organization: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 – sequence: 8 givenname: Owen K. surname: Atkin fullname: Atkin, Owen K. organization: Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia – sequence: 9 givenname: Jens surname: Kattge fullname: Kattge, Jens organization: Max Planck Institute for Biogeochemistry, 07745 Jena, Germany – sequence: 10 givenname: Bernard surname: Amiaud fullname: Amiaud, Bernard organization: UMR 1137 Ecologie et Ecophysiologie Forestières, Université de Lorraine–Institut National de la Recherche Agronomique, 54506 Vandoeuvre-lès-Nancy, France – sequence: 11 givenname: Benjamin surname: Blonder fullname: Blonder, Benjamin organization: Environmental Change Institute, University of Oxford, Oxford OX1 3BJ, United Kingdom – sequence: 12 givenname: Gerhard surname: Boenisch fullname: Boenisch, Gerhard organization: Max Planck Institute for Biogeochemistry, 07745 Jena, Germany – sequence: 13 givenname: Ben surname: Bond-Lamberty fullname: Bond-Lamberty, Ben organization: Joint Global Change Research Institute, Department of Energy Pacific Northwest National Laboratory, College Park, MD 20740 – sequence: 14 givenname: Kerry A. surname: Brown fullname: Brown, Kerry A. organization: Department of Geography and Geology, Kingston University London, Surrey KT1 2EE, United Kingdom – sequence: 15 givenname: Chaeho surname: Byun fullname: Byun, Chaeho organization: School of Biological Sciences, Seoul National University, Seoul 08826, South Korea – sequence: 16 givenname: Giandiego surname: Campetella fullname: Campetella, Giandiego organization: Plant Diversity and Ecosystems Management Unit, School of Biosciences & Veterinary Medicine, University of Camerino, 62032 Camerino, Italy – sequence: 17 givenname: Bruno E. L. surname: Cerabolini fullname: Cerabolini, Bruno E. L. organization: Department of Theoretical and Applied Sciences, University of Insubria, I-21100 Varese, Italy – sequence: 18 givenname: Johannes H. C. surname: Cornelissen fullname: Cornelissen, Johannes H. C. organization: Systems Ecology, Department of Ecological Science, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands – sequence: 19 givenname: Joseph M. surname: Craine fullname: Craine, Joseph M. organization: Jonah Ventures, Manhattan, KS 66502 – sequence: 20 givenname: Dylan surname: Craven fullname: Craven, Dylan organization: German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, 04103 Leipzig, Germany – sequence: 21 givenname: Franciska T. surname: de Vries fullname: de Vries, Franciska T. organization: School of Earth and Environmental Sciences, The University of Manchester, Manchester M13 9PT, United Kingdom – sequence: 22 givenname: Sandra surname: Díaz fullname: Díaz, Sandra organization: Instituto Multidisciplinario de Biología Vegetal (Consejo Nacional de Invetigaciones Cientificas y Técnicas), Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, CC 495 Córdoba, Argentina – sequence: 23 givenname: Tomas F. surname: Domingues fullname: Domingues, Tomas F. organization: Faculdade de Filosofia Ciencias e Letras de Ribeirao Preto, Universidade de Sao Paulo, CEP 14040-901 Bairro Monte Alegre, Ribeirao Preto, Sao Paulo, Brazil – sequence: 24 givenname: Estelle surname: Forey fullname: Forey, Estelle organization: Laboratory of Ecology Ecodiv, Institut National de Recherche en Sciences et Technologies pour l’Environnement et l’Agriculture, Normandie Université, 76821 Mont-Saint-Aignan, France – sequence: 25 givenname: Andrés surname: González-Melo fullname: González-Melo, Andrés organization: Facultad de Ciencias Naturales y Matematicas, Universidad del Rosario, Bogota 110111, Colombia – sequence: 26 givenname: Nicolas surname: Gross fullname: Gross, Nicolas organization: Institut National de la Recherche Agronomique, Unité Sous Contrat 1339, Centre d’Etude Biologique de Chizé, F 79360 Villiers en Bois, France – sequence: 27 givenname: Wenxuan surname: Han fullname: Han, Wenxuan organization: Key Laboratory of Biogeography and Bio-Resource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China – sequence: 28 givenname: Wesley N. surname: Hattingh fullname: Hattingh, Wesley N. organization: School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, WITS 2050, Johannesburg, South Africa – sequence: 29 givenname: Thomas surname: Hickler fullname: Hickler, Thomas organization: Senckenberg Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt/Main, Germany – sequence: 30 givenname: Steven surname: Jansen fullname: Jansen, Steven organization: Institute of Systematic Botany and Ecology, Ulm University, 89081 Ulm, Germany – sequence: 31 givenname: Koen surname: Kramer fullname: Kramer, Koen organization: Team Vegetation, Forest and Landscape Ecology, Wageningen Environmental Research, 6708 PB Wageningen, The Netherlands – sequence: 32 givenname: Nathan J. B. surname: Kraft fullname: Kraft, Nathan J. B. organization: Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 – sequence: 33 givenname: Hiroko surname: Kurokawa fullname: Kurokawa, Hiroko organization: Department of Forest Vegetation, Forestry and Forest Products Research Institute, Tsukuba 305-8687, Japan – sequence: 34 givenname: Daniel C. surname: Laughlin fullname: Laughlin, Daniel C. organization: Department of Botany, University of Wyoming, Laramie, WY 82071 – sequence: 35 givenname: Patrick surname: Meir fullname: Meir, Patrick organization: School of Geosciences, University of Edinburgh, Edinburgh EH9 3FF, United Kingdom – sequence: 36 givenname: Vanessa surname: Minden fullname: Minden, Vanessa organization: Institute of Biology and Environmental Science, University of Oldenburg, 26111 Oldenburg, Germany – sequence: 37 givenname: Ülo surname: Niinemets fullname: Niinemets, Ülo organization: Department of Plant Physiology, Estonian University of Life Sciences, 51014 Tartu, Estonia – sequence: 38 givenname: Yusuke surname: Onoda fullname: Onoda, Yusuke organization: Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan – sequence: 39 givenname: Josep surname: Peñuelas fullname: Peñuelas, Josep organization: CSIC, Unitat d’Ecologia Global CREAF-CSIC-UAB, Bellaterra 08193, Barcelona, Catalonia, Spain – sequence: 40 givenname: Quentin surname: Read fullname: Read, Quentin organization: Department of Forestry, Michigan State University, East Lansing, MI 48824 – sequence: 41 givenname: Lawren surname: Sack fullname: Sack, Lawren organization: Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 – sequence: 42 givenname: Brandon surname: Schamp fullname: Schamp, Brandon organization: Department of Biology, Algoma University, Sault Ste. Marie, ON P6A 2G4, Canada – sequence: 43 givenname: Nadejda A. surname: Soudzilovskaia fullname: Soudzilovskaia, Nadejda A. organization: Institute of Environmental Sciences, Leiden University, 2333 CC Leiden, The Netherlands – sequence: 44 givenname: Marko J. surname: Spasojevic fullname: Spasojevic, Marko J. organization: Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, CA 92521 – sequence: 45 givenname: Enio surname: Sosinski fullname: Sosinski, Enio organization: Laboratório de Planejamento Ambiental, Embrapa Clima Temperado, Pelotas, RS, Brazil 96010-971 – sequence: 46 givenname: Peter E. surname: Thornton fullname: Thornton, Peter E. organization: Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831 – sequence: 47 givenname: Fernando surname: Valladares fullname: Valladares, Fernando organization: Museo Nacional de Ciencias Naturales, CSIC, E-28006 Madrid Spain – sequence: 48 givenname: Peter M. surname: van Bodegom fullname: van Bodegom, Peter M. organization: Institute of Environmental Sciences, Leiden University, 2333 CC Leiden, The Netherlands – sequence: 49 givenname: Mathew surname: Williams fullname: Williams, Mathew organization: School of Geosciences, University of Edinburgh, Edinburgh EH9 3FF, United Kingdom – sequence: 50 givenname: Christian surname: Wirth fullname: Wirth, Christian organization: Max Planck Institute for Biogeochemistry, 07745 Jena, Germany – sequence: 51 givenname: Peter B. surname: Reich fullname: Reich, Peter B. organization: Hawkesbury Institute for the Environment, Western Sydney University, Penrith NSW 2751, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29196525$$D View this record in MEDLINE/PubMed https://hal.science/hal-01852904$$DView record in HAL https://www.osti.gov/biblio/1410892$$D View this record in Osti.gov |
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| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 Chinese Academy of Sciences USDOE Office of Science (SC), Biological and Environmental Research (BER) PNNL-SA-121738 Univ. of Leipzig (Germany) National Science Foundation (NSF) National Natural Science Foundation of China (NSFC) AC05-00OR22725; AC05-76RL01830; SC0012677; CE140100008; DEB-1234162; DEB-1242531; IIS-1563950; 640176; NE/M019160/1; ERC-SyG-2013-610028 IMBALANCE-P; CGL2013-48074-P; SGR 2014-274; FT110100457; NE/F002149/1; 41473068 Spanish Government Australian Research Council Univ. of Minnesota, Minneapolis, MN (United States) Natural Environment Research Council (NERC) European Research Council (ERC) Wageningen Univ. and Research (Netherlands) Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry Catalan Government Author contributions: E.E.B., A.D., H.F.-M., M.C., K.R.W., F.F., A.B., O.K.A., J.K., and P.B.R. designed research; E.E.B. and A.D. performed research; E.E.B., A.D., H.F.-M., and J.K. analyzed data; and E.E.B., A.D., H.F.-M., M.C., K.R.W., A.B., O.K.A., J.K., B.A., B.B., G.B., B.B.-L., K.A.B., C.B., G.C., B.E.L.C., J.H.C.C., J.M.C., D.C., F.T.d.V., S.D., T.F.D., E.F., A.G.-M., N.G., W.H., W.N.H., T.H., S.J., K.K., N.J.B.K., H.K., D.C.L., P.M., V.M., Ü.N., Y.O., J.P., Q.R., L.S., B.S., N.A.S., M.J.S., E.S., P.E.T., F.V., P.M.v.B., M.W., C.W., and P.B.R. wrote the paper. 1E.E.B. and A.D. contributed equally to this work. Edited by William H. Schlesinger, Cary Institute of Ecosystem Studies, Millbrook, NY, and approved October 18, 2017 (received for review May 31, 2017) |
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| Snippet | Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However,... Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which... |
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| SubjectTerms | 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 |
| Title | Mapping local and global variability in plant trait distributions |
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