The worldwide leaf economics spectrum Ian J. Wright1, Peter B. Reich2, Mark Westoby1, David D. Ackerly3, Zdravko Baruch4, Frans Bongers5, Jeannine Cavender-Bares6, Terry Chapin7, Johannes H. C. Cornelissen8, Matthias Diemer9, Jaume Flexas10, Eric Garnier11, Philip K. Groom12, Javier Gulias10, Kouki Hikosaka13, Byron B. Lamont12, Tali Lee14, William Lee15, Christopher Lusk16, Jeremy J. Midgley17, Marie-Laure Navas11, Ulo �� Niinemets18, Jacek Oleksyn2,19, Noriyuki Osada20, Hendrik Poorter21, Pieter Poot22, Lynda Prior23, Vladimir I. Pyankov24, Catherine Roumet11, Sean C. Thomas25, Mark G. Tjoelker26, Erik J. Veneklaas22 & Rafael Villar27 1 Department of Biological Sciences, Macquarie University, New South Wales 2109, Australia 2Department of Forest Resources, University of Minnesota, St Paul, Minnesota 55108, USA 3 Department of Biological Sciences, Stanford University, Stanford, California 94305, USA 4Departamento de Estudios Ambientales, Universidad Simon �� Bolivar, Caracas 1080, Venezuela 5Forest Ecology and Forest Management Group, Department of Environmental Sciences, Wageningen University, PO Box 342, 6700 AH Wageningen, The Netherlands 6Smithsonian Environmental Research Center, PO Box 28, 647 Contees Wharf Road, Edgewater, Maryland 21037, USA 7 Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska 99775, USA 8Institute of Ecological Science, Department of Systems Ecology, Vrije Universiteit, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands 9 Institute fur �� Umweltwissensch, University of Zurich, Zurich, Switzerland 10Departament de Biologia, Laboratori de Fisiologia Vegetal, Universidad de Illes Balears, 07122 Palma de Mallorca, Illes Balears (Spain) 11 Centre d���Ecologie Fonctionnelle et Evolutive, CNRS, UMR 5175, 1919, Route de Mende, 34293 Montpellier cedex 5, France 12Department of Environmental Biology, Curtin University of Technology, Perth, Western Australia 6845, Australia 13 Graduate School of Life Sciences, Tohoku University, Aoba, Sendai 980-8578, Japan 14Department of Biology, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin 54702-4004, USA 15 Landcare Research, Private Bag 1930, Dunedin, New Zealand 16Departamento de Botanica, �� Universidad de Concepcion, �� Casilla 160-C, Concepcion, �� Chile 17 Department of Botany, University of Cape Town, ZA-7701 Rondebosch, South Africa 18 Department of Plant Physiology, University of Tartu, Riia 23, Tartu 51011, Estonia 19Polish Academy of Sciences, Institute of Dendrology, Parkowa 5, 62-035 Kornik, Poland 20 Nikko Botanical Garden, Graduate School of Science, University of Tokyo, 1842 Hanaishi, Nikko, Tochigi 321-1435, Japan 21Plant Ecophysiology, Utrecht University, PO Box 800.84, 3508 TB, Utrecht, The Netherlands 22 School of Plant Biology, University of Western Australia, Crawley, Western Australia 6009, Australia 23Key Centre for Tropical Wildlife Management, Charles Darwin University, Darwin, Northern Territory 0909, Australia 24 Ural State University, Yekaterinburg, Russia 25Faculty of Forestry, University of Toronto, 33 Willcocks St, Toronto, Ontario M5S 3B3, Canada 26 Department of Forest Science, Texas A&M University, 2135 TAMU, College Station, Texas 77843-2135, USA 27Area de Ecolog��a, �� Campus de Rabanales, Universidad de Cordoba, �� 14071 Cordoba, �� Spain ........................................................................................................................................................................................................................... Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate. Green leaves are fundamental for the functioning of terrestrial ecosystems. Their pigments are the predominant signal seen from space. Nitrogen uptake and carbon assimilation by plants and the decomposability of leaves drive biogeochemical cycles. Animals, fungi and other heterotrophs in ecosystems are fuelled by photo- synthate, and their habitats are structured by the stems on which leaves are deployed. Plants invest photosynthate and mineral nutrients in the construction of leaves, which in turn return a revenue stream of photosynthate over their lifetimes. The photo- synthate is used to acquire mineral nutrients, to support metabo- lism and to re-invest in leaves, their supporting stems and other plant parts. There are more than 250,000 vascular plant species, all engaging in the same processes of investment and reinvestment of carbon and mineral nutrients, and all making enough surplus to ensure con- tinuity to future generations. These processes of investment and re-investment are inherently economic in nature1���3. Understanding how these processes vary between species, plant functional types and the vegetation of different biomes is a major goal for plant ecology and crucial for modelling how nutrient fluxes and veg- etation boundaries will shift with land-use and climate change. Data set and parameters We formed a global plant trait network (Glopnet) to quantify leaf economics across the world���s plant species. The Glopnet data set spans 2,548 species from 219 families at 175 sites (approximately 1% of the extant vascular plant species). The coverage of traits, species and sites is at least tenfold greater than previous data compilations4���11, extends to all vegetated continents, and represents a wide range of vegetation types, from arctic tundra to tropical rainforest, from hot to cold deserts, from boreal forest to grasslands. Site elevation ranges from below sea level (Death Valley, USA) to 4,800 m. Mean annual temperature (MAT) ranges from 216.5 8C to 27.5 8C mean annual rainfall (MAR) ranges from 133 to 5,300 mm per year. This covers most of the range of MAT���MAR space in which higher plants occur12 (Fig. 1). The broad coverage of the data set has articles NATURE | VOL 428 | 22 APRIL 2004 | www.nature.com/nature 821 �� 2004 Nature Publishing Group

allowed us to quantify the relationships of leaf economics to climate at a scale not previously possible. Here we report some global outcomes from our analyses. We focus on six key features of leaves that together capture many essentials of leaf economics. (1) Leaf mass per area (LMA) measures the leaf dry-mass investment per unit of light-intercepting leaf area deployed. Species with high LMA have a thicker leaf blade or denser tissue, or both. (2) Photosynthetic assimilation rates measured under high light, ample soil moisture and ambient CO2 are here called photosynthetic capacity (A mass) for brevity. Photosynthetic capacity is influenced both by stomatal conductance and by the drawdown of CO2 concentration inside the leaf (carboxylation capacity). (3) Leaf nitrogen (N) is integral to the proteins of photosynthetic machinery, especially Rubisco8,13. The photosyn- thetic machinery is responsible for drawdown of CO2 inside the leaf, a process also affected by leaf structure14,15. (4) Leaf phosphorus (P) is found in nucleic acids, lipid membranes and bioenergetic mol- ecules such as ATP. Phosphorus derives from weathering of soil minerals at a site, in contrast to nitrogen, much of which may be fixed from the atmosphere by plants. (5) Dark respiration rate (R mass) reflects metabolic expenditure of photosynthate in the leaf, especially protein turnover and phloem-loading of photo- synthates16. (6) Leaf lifespan (LL) describes the average duration of the revenue stream from each leaf constructed. Long LL requires Figure 1 Mean annual rainfall (MAR) and mean annual temperature (MAT). Results for the 175 sites from where leaf data were compiled (a), in relation to major biome types of the world (b), following ref. 12. Biome boundaries are only approximate. Figure 2 Three-way trait relationships among the six leaf traits with reference to LMA, one of the key traits in the leaf economics spectrum. The direction of the data cloud in three-dimensional space can be ascertained from the shadows projected on the floor and walls of the three-dimensional space. Sample sizes for three-way relationships are necessarily a subset of those for each of the bivariate relationships. a, A mass, LMA and N mass 706 species. b, LL, R mass and LMA 217 species. c, N mass, P mass and LMA 733 species. d, A area, LMA and N area 706 species. articles NATURE | VOL 428 | 22 APRIL 2004 | www.nature.com/nature 822 �� 2004 Nature Publishing Group