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TRY a Global Database of Plant Traits

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TRY – a global database of plant traits Authors: Jens Kattge, Sandra Díaz, Sandra Lavorel, I. Colin Prentice, Paul Leadley, Gerhard Bönisch, Eric Garnier, Mark Westoby, Peter B. Reich, Ian J. Wright, Johannes H.C. Cornelissen, Cyrille Violle, Sandy P. Harrison, Peter M. van Bodegom, Markus Reichstein, Brian J. Enquist, Nadejda A. Soudzilovskaia, David D. Ackerly, Madhur Anand, Owen Atkin, Michael Bahn, Timothy R. Baker, Dennis Baldocchi, Renée Bekker, Carolina C. Blanco, Benjamin Blonder, William Bond, Ross Bradstock, Daniel E. Bunker, Fernando Casanoves, Jeannine Cavender-Bares, Jeffrey Q. Chambers, F. Stuart Chapin III, Jerome Chave, David Coomes, Will K. Cornwell, Joseph M. Craine, Barbara H. Dobrin, Leandro Duarte, Walter Durka, James Elser, Gerd Esser, Marc Estiarte, William F. Fagan, Jingyun Fang, Fernando Fernández- Méndez, Alessandra Fidelis, Bryan Finegan, Olivier Flores, Henry Ford, Dorothea Frank, Gregoire T. Freschet, Nikolaos M. Fyllas, Rachael Gallagher, Walton A. Green, Alvaro G. Gutierrez, Thomas Hickler, Stephen Higgins, John G. Hodgson, Adel Jalili, Steven Jansen, Carlos Joly, Andrew J. Kerkhoff, Don Kirkup, Kaoru Kitajima, Michael Kleyer, Stefan Klotz, Johannes M. H. Knops, Koen Kramer, Ingolf Kühn, Hiroko Kurokawa, Daniel Laughlin, Tali D. Lee, Michelle Leishman, Frederic Lens, Tanja Lenz, Simon L. Lewis, Jon Lloyd, Joan Llusià, Frédérique Louault, Siyan Ma, Miguel D. Mahecha, Pete Manning, Tara Massad, Belinda Medlyn, Julie Messier, Angela Moles, Sandra C. Müller, Karin Nadrowski, Shahid Naeem, Ülo Niinemets, Stefanie Nöllert, Angela Nüske, Roma Ogaya, Jacek Oleksyn, Vladimir G. Onipchenko, Yusuke Onoda, Jenny Ordoñez, Gerhard Overbeck, Wim Ozinga, Sandra Patiño, Susana Paula, Juli G. Pausas, Josep Peñuelas, Oliver L. Phillips, Valerio Pillar, Hendrik Poorter, Lourens Poorter, Peter Poschlod, Andreas Prinzing, Raphaël Proulx, Anja Rammig, Sabine Reinsch, Björn Reu, Lawren Sack, Beatriz Salgado-Negret, Jordi Sardans, Satomi Shiodera, Bill Shipley, Andrew Siefert, Enio Sosinski, Jean-Francois Soussana, Emily Swaine, Nathan Swenson, Ken Thompson, Peter Thornton, Matthew Waldram, Evan Weiher, Michael White, S. Joseph Wright, Benjamin Yguel, Sönke Zaehle, Amy E. Zanne, Christian Wirth Corresponding Author: Jens Kattge Max-Planck-Institute for Biogeochemistry Hans-Knöll Straße 10, 07745 Jena, Germany phone: +49 (0) 3641 576226 fax: +49 (0) 3641 577200 email: [email protected] Keywords: comparative ecology, database, environmental gradient, functional diversity, global analysis, global change, interspecific variation, intraspecific variation, plant attribute, plant trait, plant functional type, vegetation model 57 Abstract 58 Plant traits – the morphological, anatomical, physiological, biochemical, and 59 phenological characteristics of plants and their organs – determine how primary 60 producers respond to environmental factors, affect other trophic levels, influence 61 ecosystem processes and services, and provide a link from species richness to 62 ecosystem functional diversity. Trait data thus represent the raw material for a wide 63 range of research from evolutionary biology, community and functional ecology to 64 biogeography. Here we present the global database initiative named TRY, which has 65 united a wide range of the plant trait research community worldwide and gained an 66 unprecedented buy-in of trait data: so far 93 trait databases have been contributed. 67 The data repository currently contains almost three million trait entries for 69,000 out 68 of the world's 300,000 plant species, with a focus on 52 groups of traits characterising 69 the vegetative and regeneration stages of the plant life cycle, including growth, 70 dispersal, establishment and persistence. A first data analysis shows that most plant 71 traits are approximately log-normally distributed, with widely differing ranges of 72 variation across traits. Most trait variation is between species (interspecific), but 73 significant intraspecific variation is also documented, up to 40% of the overall 74 variation. Plant functional types (PFTs), as commonly used in vegetation models, 75 capture a substantial fraction of the observed variation - but for several traits most 76 variation occurs within PFTs, up to 75% of the overall variation. In the context of 77 vegetation models these traits would better be represented by state variables rather 78 than fixed parameter values. The improved availability of plant trait data in the 79 unified global database is expected to support a paradigm shift from species to trait- 80 based ecology, offer new opportunities for synthetic plant trait research and enable a 3 81 more realistic and empirically grounded representation of terrestrial vegetation in 82 Earth system models. 4 83 Introduction 84 Plant traits - morphological, anatomical, biochemical, physiological or phenological 85 features measurable at the individual level (Violle et al. 2007) - reflect the outcome 86 of evolutionary and community assembly processes responding to abiotic and biotic 87 environmental constraints (Valladares et al. 2007). Traits and trait syndromes 88 (consistent associations of plant traits) determine how primary producers respond to 89 environmental factors, affect other trophic levels, and influence ecosystem processes 90 and services (Aerts and Chapin 2000, Grime 2001, Lavorel and Garnier 2002, Díaz et 91 al. 2004, Grime 2006, Garnier and Navas in press.). In addition, they provide a link 92 from species richness to functional diversity in ecosystems (Díaz et al. 2007). A focus 93 on traits and trait syndromes therefore provides a promising basis for a more 94 quantitative and predictive ecology and global change science (McGill et al. 2006, 95 Westoby and Wright 2006). 96 Plant trait data have been used in studies ranging from comparative plant ecology 97 (Grime 1974, Givnish 1988, Peat and Fitter 1994, Grime et al. 1997) and functional 98 ecology (Grime 1977, Reich et al. 1997, Wright et al. 2004) to community ecology 99 (Shipley et al. 2006, Kraft et al. 2008), trait evolution (Moles et al. 2005a), phylogeny 100 reconstruction (Lens et al. 2007), metabolic scaling theory (Enquist et al. 2007), 101 palaeobiology (Royer et al. 2007), biogeochemistry (Garnier et al. 2004, Cornwell et 102 al. 2008), disturbance ecology (Wirth 2005, Paula and Pausas 2008), plant migration 103 and invasion ecology (Schurr et al. 2005), conservation biology (Ozinga et al. 2009, 104 Römermann et al. 2009) and plant geography (Swenson and Weiser 2010). Access to 105 trait data for a large number of species allows testing levels of phylogenetic 106 conservatism, a promising principle in ecology and evolutionary biology (Wiens et al. 5 107 2010). Plant trait data have been used for the estimation of parameter values in 108 vegetation models, but only in a few cases based on systematic analyses of trait 109 spectra (White et al. 2000, Kattge et al. 2009, Wirth and Lichstein 2009, Ziehn et al. 110 accepted). Recently plant trait data have been used for the validation of a global 111 vegetation model as well (Zaehle and Friend 2010). 112 While there have been initiatives to compile data sets at regional scale for a range of 113 traits (e.g. LEDA1, BiolFlor2, EcoFlora3, BROT4) or at global scale focusing on a 114 small number of traits (e.g. GlopNet5, SID6), a unified initiative to compile data for a 115 large set of relevant plant traits at the global scale was lacking. As a consequence 116 studies on trait variation so far have either been focussed on the local to regional scale 117 including a range of different traits (e.g. Baraloto et al. 2010), while studies on global 118 scale were restricted on individual aspects of plant functioning, e.g. the leaf economic 119 spectrum (Wright et al. 2004), the evolution of seed mass (Moles et al. 2005 a,b) or 120 the characterisation of the wood economic spectrum (Chave et al. 2009). Only few 121 analyses on global scale have combined traits from different functional aspects, but 122 for a limited number of plant species (e.g. Diaz et al. 2004). 123 In 2007, the TRY7 initiative started compiling plant trait data from the different 124 aspects of plant functioning on global scale to make the data available in a consistent 125 format through one single portal. Based on a broad acceptance in the plant trait 126 community (so far 93 trait databases have been contributed, Table 1), TRY has 127 accomplished an unprecedented coverage of trait data and is now working towards a 1 LEDA - Life History Traits of the Northwest European Flora: http://www.leda-traitbase.org 2 BiolFlor – Trait Database of the German Flora: http://www.ufz.de/biolflor 3 EcoFlora - The Ecological Flora of the British Isles: www.ecoflora.co.uk 4 BROT – Plant Trait Database for Mediterranean Basin Species: http://www.uv.es/jgpausas/brot.htm 5 GlopNet – Global Plant Trait Network: http://www.bio.mq.edu.au/~iwright/glopian.htm 6 SID - Seed Information Database: data.kew.org/sid/ 7 TRY – Not an acronym, rather an expression of sentiment: http://www.try-db.org 6 128 communal global repository for plant trait data. The new database initiative is 129 expected to contribute to a more realistic and empirically based representation of 130 plant functional diversity on global scale supporting the assessment and modelling of 131 climate change impacts on biogeochemical fluxes and terrestrial biodiversity 132 (McMahon et al. in press). 133 For several traits the data coverage in the TRY database is sufficient to quantify the 134 relative amount of intra- and interspecific variation, as well as variation within and 135 between different functional groups. Thus the dataset allows to examine two basic 136 tenets of comparative ecology and vegetation modelling, which, due to lack of data, 137 had not been quantified so far: 138 (1) On the global scale the aggregation of plant trait data at the species level captures 139 the majority of trait variation. This central assumption of plant comparative ecology 140 implies that, while there is variation within species, this variation is smaller than the 141 differences between species (Garnier et al. 2001, Keddy et al. 2002, Westoby et al. 142 2002, Shipley 2007).
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