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 photosynthate, 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 photosynthate is used to acquire mineral nutrients, to support metabolism 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 continuity to future generations. These processes of investment and re-investment are inherently economic in nature [1][2][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 vegetation boundaries will shift with land-use and climate change. Data set and parametersWe 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 compilations [4][5][6][7][8][9][10][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 cove...

AimOur aim was to quantify climatic influences on key leaf traits and relationships at the global scale. This knowledge provides insight into how plants have adapted to different environmental pressures, and will lead to better calibration of future vegetation-climate models.Location The data set represents vegetation from 175 sites around the world.Methods For more than 2500 vascular plant species, we compiled data on leaf mass per area (LMA), leaf life span (LL), nitrogen concentration (N mass ) and photosynthetic capacity (A mass ). Site climate was described with several standard indices. Correlation and regression analyses were used for quantifying relationships between single leaf traits and climate. Standardized major axis (SMA) analyses were used for assessing the effect of climate on bivariate relationships between leaf traits. Principal components analysis (PCA) was used to summarize multidimensional trait variation.Results At hotter, drier and higher irradiance sites, (1) mean LMA and leaf N per area were higher; (2) average LL was shorter at a given LMA, or the increase in LL was less for a given increase in LMA (LL-LMA relationships became less positive); and (3) A mass was lower at a given N mass , or the increase in A mass was less for a given increase in N mass . Considering all traits simultaneously, 18% of variation along the principal multivariate trait axis was explained by climate.Main conclusions Trait-shifts with climate were of sufficient magnitude to have major implications for plant dry mass and nutrient economics, and represent substantial selective pressures associated with adaptation to different climatic regimes.

Summary 1.Leaf morphology at the site/species level should reflect environmental constraints on plant growth. One of the oldest controversies in ecology is the environmental basis for sclerophylly. The dominant view (Beadle's theory) is that it has a nutritional, rather than a drought, basis, especially low phosphorus. 2. Using leaf mass per area (LMA) as an index of sclerophylly, we assessed its relationship with leaf phosphorus (P) and nitrogen (N) along extensive rainfall gradients in southwestern Australia and the Cape of South Africa. Leaf 13 C/ 12 C discrimination (∆ 13 C), as an index of intrinsic water-use efficiency, was also examined in the Cape. All Hakea species (Proteaceae) were sampled at 10 sites in Australia (96 species), and all Proteaceae at 14 sites in the Cape (82 species). All were evergreen shrubs with iso(bi)-lateral leaves. 3. In each region there was a strong (inverse) curvilinear relationship between mean LMA per site and mean annual rainfall and ∆ 13 C, but none with mean P or N on a mass basis (although P and N on an area basis declined with rainfall). The Cape study was a particularly good test of Beadle's theory, as P varied as much between sites as rainfall, and more between sites than within sites. 4. Leaf thickness and dry density were not as well correlated with rainfall as LMA, and leaf area and mass showed no relationship with rainfall. Area and mass had much greater variation within sites than between sites, limiting their value in plant-environment studies, while LMA was the most site-stable of the eight leaf attributes measured, except for ∆ 13 C. 5. For all species considered individually in each region, there was a similar pattern as the site level, with LMA most strongly correlated (negatively) with rainfall and ∆ 13 C and (positively) with leaf thickness, but no consistent relationship with P, N or density. 6. We conclude that when water and nutrient supply vary independently in the field, rainfall (as an index of water status) and ∆ 13 C may be more closely correlated (inversely) with level of sclerophylly than nutrient status among evergreens, so that the role of sclerophylly as a drought adaptation warrants further consideration.

Leaf trait data were compiled for 258 Australian plant species from several habitat types dominated by woody perennials. Specific leaf area (SLA), photosynthetic capacity, dark respiration rate and leaf nitrogen (N) and phosphorus (P) concentrations were positively correlated with one another and negatively correlated with average leaf lifespan. These trait relationships were consistent with previous results from global datasets. Together, these traits form a spectrum of variation running from species with cheap but frequently replaced leaves to those with strategies more attuned to a nutrient-conserving lifestyle. Australian species tended to have SLAs at the lower end of the spectrum, as expected in a dataset dominated by sclerophyllous species from low fertility or low rainfall sites. The existence of broad-scale, 'global' relationships does not imply that the same trait relationships will always be observed in small datasets. In particular, the probability of observing concordant patterns depends on the range of trait variation in a dataset, which, itself, may vary with sample size or species-sampling properties such as the range of growth forms, plant functional 'types', or taxa included in a particular study. The considerable scatter seen in these broad-scale trait relationships may be associated with climate, physiology and phylogeny.