This is the accepted version of the article: Flores Moreno, Habacuc; Fazayeli, Farideh; Banerjee, Arindam; [et al.]. «Ro- bustness of trait connections across environmental gradients and growth forms». Global ecology and biogeography, Vol. 28, issue 12 (Dec. 2019), p. 1806-1826. DOI 10.1111/geb.12996 This version is avaible at https://ddd.uab.cat/record/218224 under the terms of the license Appendix S1 List of studies including in the subset of TRY DB used in the current study. Adriaenssens S. (2012). Dry deposition and canopy exchange for temperate tree species under high nitrogen deposition. PhD thesis, Ghent University, Ghent, Belgium, 209p. Auger, S., Shipley, B. (2012). : Interspecific and intraspecific trait variation along short environmental gradients in an old-growth temperate forest.Journal of Vegetation Science. DOI: 1111/j.1654-1103.2012.01473.x B. Sandel, J. D. Corbin, and M. Krupa 2011. Using plant functional traits to guide restoration: A case study in California coastal grassland. Ecosphere 2:art23. http://d Bahn, M., G. Wohlfahrt, E. Haubner, I. Horak, W. Michaeler, K. Rottmar, U. Tappeiner, and A. Cernusca. 1999. Leaf photosynthesis, nitrogen contents and specific leaf area of 30 grassland species in differently managed mountain ecosystems in the Eastern Alps. Pages 247-255 in A. Cernusca, U. Tappeiner, and N. Bayfield, editors. Land-use changes in European mountain ecosystems. ECOMONT- Concept and Results. Blackwell Wissenschaft, Berlin. Bakker, C., J. Rodenburg, and P. Bodegom. 2005. Effects of Ca- and Fe-rich seepage on P availability and plant performance in calcareous dune soils. Plant and Soil 275:111-122. Bakker, C., P. M. Van Bodegom, H. J. M. Nelissen, W. H. O. Ernst, and R. Aerts. 2006. Plant responses to rising water tables and nutrient management in calcareous dune slacks. Plant Ecology 185:19-28. Baraloto, C., C. E. T. Paine, L. Poorter, J. Beauchene, D. Bonal, A.-M. Domenach, B. Herault, S. Patino, J.-C. Roggy, and J. Chave. 2010. Decoupled leaf and stem economics in rainforest trees. Ecology Letters 13:1338-1347 Blonder, B., Buzzard, B., Sloat, L., Simova, I., Lipson, R., Boyle, B., Enquist, B. The shrinkage effect biases estimates of paleoclimate. American Journal of Botany (2012). Blonder, B., Vasseur, F., Violle, C., Shipley, B., Enquist, B., Vile, D. Arabidopsis thaliana rejects theories for the origin of the leaf economics spectrum. (in review, New Phytologist). Blonder, B., Violle, C., Enquist, B. Assessing the causes and scales of the leaf economics spectrum using venation networks in Populus tremuloides. (accepted, Journal of Ecology, 2013). Blonder, B., Violle, C., Patrick, L., Enquist, B. Leaf venation networks and the origin of the leaf economics spectrum. Ecology Letters, 2011. Bond-Lamberty, B., C. Wang, and S. T. Gower (2002), Above- and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba, Can. J. For. Res., 32(8), 1441-1450. Bond-Lamberty, B., C. Wang, and S. T. Gower (2002), Leaf area dynamics of a boreal black spruce fire chronosequence, Tree Physiol., 22(14), 993-1001. Bond-Lamberty, B., C. Wang, and S.T. Gower (2004), Net primary production and net ecosystem production of a boreal black spruce fire chronosequence, Global Change Biol., 10(4), 473-487. Brendan Choat, Steven Jansen, Tim J. Brodribb, Herve Cochard, Sylvain Delzon, Radika Bhaskar, Sandra J. Bucci, Taylor S. Feild, Sean M. Gleason, Uwe G. Hacke, Anna L. Jacobsen, Frederic Lens, Hafiz Maherali, Jordi Martinez-Vilalta, Stefan Mayr, Maurizio Mencuccini, Patrick J. Mitchell, Andrea Nardini, Jarmila Pittermann, R. Brandon Pratt, John S. Sperry, Mark Westoby, Ian J. Wright & Amy E. Zanne (2012) Global convergence in the vulnerability of forests to drought. Nature 491:752-755 doi:10.1038/nature11688 Brown et al. (2011) PLoS ONE 6(9): e24107. doi:10.1371/journal.pone.0024107 Burrascano S., Del Vico E., Fagiani S., Giarrizzo E., Mei M., Mortelliti A., Sabatini F.M., Blasi C., submitted. Environmental filtering of wild boar rooting activity on understorey composition and functional traits. Plant Ecology. Butterfield, B.J. and J.M. Briggs. 2011. Regeneration niche differentiates functional strategies of desert woody plant species. Oecologia, 165:477-487. Campetella, G; Botta-Duk\xe1t, Z; Wellstein, C; Canullo, R; Gatto, S; Chelli, S; Mucina, L; Bartha, S (2011): Patterns of plant trait-environment relationships along a forest succession chronosequence. Agriculture, Ecosystems & Environment, 145(1), 38-48. doi:10.1016/j.agee.2011.06.025 Carswell, F. E., Meir, P., Wandelli, E. V., Bonates, L. C. M., Kruijt, B., Barbosa, E. M., Nobre, A. D. & Jarvis, P. G. 2000 Photosynthetic capacity in a central Amazonian rain forest. Tree physiology. 20, 3, p. 179-186 8 p. Cavender-Bares, J., A. Keen, and B. Miles. 2006. Phylogenetic structure of floridian plant communities depends on taxonomic and spatial scale. Ecology 87:S109-S122. Cavender-Bares, J., L. Sack, and J. Savage. 2007. Atmospheric and soil drought reduce nocturnal conductance in live oaks. Tree Physiology 27:611-620. Cerabolini B.E.L., Brusa G., Ceriani R.M., De Andreis R., Luzzaro A. & Pierce S. 2010. Can CSR classification be generally applied outside Britain? Plant Ecology 210: 253-261 Coomes, D. A., S. Heathcote, E. R. Godfrey, J. J. Shepherd, and L. Sack. 2008. Scaling of xylem vessels and veins within the leaves of oak species. Biology Letters 4:302-306. Cornelissen, J. H. C. 1996. An experimental comparison of leaf decomposition rates in a wide range of temperate plant species and types. Journal of Ecology 84:573-582. Cornelissen, J. H. C., B. Cerabolini, P. Castro-Diez, P. Villar-Salvador, G. Montserrat-Marti, J. P. Puyravaud, M. Maestro, M. J. A. Werger, and R. Aerts. 2003. Functional traits of woody plants: correspondence of species rankings between field adults and laboratory-grown seedlings? Journal of Vegetation Science 14:311-322. Cornelissen, J. H. C., H. M. Quested, D. Gwynn-Jones, R. S. P. Van Logtestijn, M. A. H. De Beus, A. Kondratchuk, T. V. Callaghan, and R. Aerts. 2004. Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. Functional Ecology 18:779-786. Cornelissen, J. H. C., N. Perez-Harguindeguy, S. Diaz, J. P. Grime, B. Marzano, M. Cabido, F. Vendramini, and B. Cerabolini. 1999. Leaf structure and defence control litter decomposition rate across species and life forms in regional floras on two continents. New Phytologist 143:191- 200. Cornelissen, J. H. C., P. C. Diez, and R. Hunt. 1996. Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. Journal of Ecology 84:755-765. Cornwell, W. K., J. H. C. Cornelissen, K. Amatangelo, E. Dorrepaal, V. T. Eviner, O. Godoy, S. E. Hobbie, B. Hoorens, H. Kurokawa, N. P\xe9rez-Harguindeguy, H. M. Quested, L. S. Santiago, D. A. Wardle, I. J. Wright, R. Aerts, S. D. Allison, P. van Bodegom, V. Brovkin, A. Chatain, T. V. Callaghan, S. D\xedaz, E. Garnier, D. E. Gurvich, E. Kazakou, J. A. Klein, J. Read, P. B. Reich, N. A. Soudzilovskaia, M. V. Vaieretti, and M. Westoby. 2008. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecology Letters 11:1065-1071. Cornwell, W. K., R. Bhaskar, L. Sack, S. Cordell, and C. K. Lunch. 2007. Adjustment of structure and function of Hawaiian Metrosideros polymorpha at high vs. low precipitation. Functional Ecology 21:1063-1071. Craine JM, Nippert JB, Towne EG, Tucker S, Kembel SW, Skibbe A, McLauchlan KK (2011) Functional consequences of climate-change induced plant species loss in a tallgrass prairie. Oecologia 165: 1109-1117 Craine JM, Ocheltree TW, Nippert JB, Towne EG, Skibbe AM, Kembel SW, Fargione JE (in press) Global diversity of drought tolerance and grassland climate-change resilience. Nature Climate Change: Craine JM, Towne EG, Ocheltree TW, Nippert JB (2012) Community traitscape of foliar nitrogen isotopes reveals N availability patterns in a tallgrass prairie. Plant Soil 356: 395-403 Craine, J. M., A. J. Elmore, M. P. M. Aidar, M. Bustamante, T. E. Dawson, E. A. Hobbie, A. Kahmen, M. C. Mack, K. K. McLauchlan, A. Michelsen, G. B. Nardoto, L. H. Pardo, J. Penuelas, P. B. Reich, E. A. G. Schuur, W. D. Stock, P. H. Templer, R. A. Virginia, J. M. Welker, and I. J. Wright. 2009. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytologist 183:980-992. D\xedaz, S., J. G. Hodgson, K. Thompson, M. Cabido, J. H. C. Cornelissen, A. Jalili, G. Montserrat-Mart\xed, J. P. Grime, F. Zarrinkamar, Y. Asri, S. R. Band, S. Basconcelo, P. Castro- D\xedez, G. Funes, B. Hamzehee, M. Khoshnevi, N. P\xe9rez-Harguindeguy, M. C. P\xe9rez- Rontom\xe9, F. A. Shirvany, F. Vendramini, S. Yazdani, R. Abbas-Azimi, A. Bogaard, S. Boustani, M. Charles, M. Dehghan, L. de Torres-Espuny, V. Falczuk, J. Guerrero-Campo, A. Hynd, G. Jones, E. Kowsary, F. Kazemi-Saeed, M. Maestro-Mart\xednez, A. Romo-D\xedez, S. Shaw, B. Siavash, P. Villar-Salvador, and M. R. Zak. 2004. The plant traits that drive ecosystems: Evidence from three continents. Journal of Vegetation Science 15:295-304. Demey A, J Staelens, L Baeten, P Boeckx, M Hermy, J Kattge, K Verheyen (2013) Nutrient input from hemiparasitic litter favors plant species with a fast-growth strategy. Plant and Soil DOI 10.1007/s11104-013-1658-4 Domingues TF, Meir P, Feldpausch TR, et al. (2010) Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. Plant, Cell & Environment (33): 959-980. Dunbar-Co, S., M. J. Sporck, and L. Sack. 2009. Leaf Trait Diversification and Design in Seven Rare Taxa of the Hawaiian Plantago Radiation. International Journal of Plant Sciences 170:61- 75. Fonseca, C. R., J. M. Overton, B. Collins, and M. Westoby. 2000. Shifts in trait-combinations along rainfall and phosphorus gradients. Journal of Ecology 88:964-977. Frenette-Dussault, C., Shipley, B., L\xe9ger, J.F., Meziane, D. & Hingrat, Y. (2012). Functional structure of an arid steppe plant community reveals similarities with Grime's C-S-R theory. Journal of Vegetation Science 23: 208-222. Freschet, G. T., J. H. C. Cornelissen, R. S. P. van Logtestijn, and R. Aerts. 2010. Evidence of the \x91plant economics spectrum\x92 in a subarctic flora. Journal of Ecology 98:362-373. Fyllas, N. M., S. Patino, T. R. Baker, G. Bielefeld Nardoto, L. A. Martinelli, C. A. Quesada, R. Paiva, M. Schwarz, V. Horna, L. M. Mercado, A. Santos, L. Arroyo, E. M. Jimenez, F. J. Luizao, D. A. Neill, N. Silva, A. Prieto, A. Rudas, M. Silviera, I. C. G. Vieira, G. Lopez-Gonzale, and J. Lloyd. 2009. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate Biogeosciences 6:2677-2708. Garnier, E., S. Lavorel, P. Ansquer, H. Castro, P. Cruz, J. Dolezal, O. Eriksson, C. Fortunel, H. Freitas, C. Golodets, K. Grigulis, C. Jouany, E. Kazakou, J. Kigel, M. Kleyer, V. Lehsten, J. Lep\x9a, T. Meier, R. Pakeman, M. Papadimitriou, V. P. Papanastasis, H. Quested, F. Qu\xe9tier, M. Robson, C. Roumet, G. Rusch, C. Skarpe, M. Sternberg, J.-P. Theau, A. Th\xe9bault, D. Vile, and M. P. Zarovali. 2007. Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: A standardized methodology and lessons from an application to 11 European sites. Annals of Botany 99:967-985. Givnish T.J., R.A. Montgomery and G. Goldstein. 2004. Adaptive radiation of photosynthetic physiology in the Hawaiian lobeliads: light regimes, static light responses, and whole-plant compensation points. American Journal of Botany 91: 228-246 Guerin G.R., Wen H. & Lowe A.J. (2012) Leaf morphology shift linked to climate change. Biology Letters 8, 882-886. Guti\xe9rrez AG, & Huth A (2012) Successional stages of primary temperate rainforests of Chilo\xe9 Island, Chile. Perspectives in plant ecology, systematics and evolution. 14: 243\x96 256 Guy, A. L., J. M. Mischkolz, and E. G. Lamb. 2013. Limited effects of simulated acidic deposition on seedling survivorship and root morphology of endemic plant taxa of the Athabasca Sand Dunes in well watered greenhouse trials. Botany 91:176-181. http://dx.doi.org/10.1139/cjb- 2012-0162 Han, W. X., J. Y. Fang, D. L. Guo, and Y. Zhang. 2005. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytologist 168:377-385. Hao, G. Y., L. Sack, A. Y. Wang, K. F. Cao, and G. Goldstein. 2010. Differentiation of leaf water flux and drought tolerance traits in hemiepiphytic and non-hemiepiphytic Ficus tree species. Functional Ecology 24:731-740. Hoof, J., L. Sack, D. T. Webb, and E. T. Nilsen. 2008. Contrasting structure and function of pubescent and glabrous varieties of Hawaiian Metrosideros polymorpha (Myrtaceae) at high elevation. Biotropica 40:113-118. Jennifer S. Powers and Peter Tiffin 2012 Plant functional type classifications in tropical dry forests in Costa Rica: leaf habit versus taxonomic approaches. Functional Ecology 2010, 24, 927\x96936doi: 10.1111/j.1365-2435.2010.01701.x Kattge, J., W. Knorr, T. Raddatz, and C. Wirth. 2009. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Global Change Biology 15:976-991. Kazakou, E., D. Vile, B. Shipley, C. Gallet, and E. Garnier. 2006. Co-variations in litter decomposition, leaf traits and plant growth in species from a Mediterranean old-field succession. Functional Ecology 20:21-30. Kichenin et al. 2013. Contrasting effects of plant inter- and intraspecific variation on community-level trait measures along an environmental gradient. Functional Ecology, in press. Kleyer, M., R. M. Bekker, I. C. Knevel, J. P. Bakker, K. Thompson, M. Sonnenschein, P. Poschlod, J. M. van Groenendael, L. Klimes, J. Klimesova, S. Klotz, G. M. Rusch, Hermy, M. , D. Adriaens, G. Boedeltje, B. Bossuyt, A. Dannemann, P. Endels, L. G\xf6tzenberger, J. G. Hodgson, A.-K. Jackel, I. K\xfchn, D. Kunzmann, W. A. Ozinga, C. R\xf6mermann, M. Stadler, J. Schlegelmilch, H. J. Steendam, O. Tackenberg, B. Wilmann, J. H. C. Cornelissen, O. Eriksson, E. Garnier, and B. Peco. 2008. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. Journal of Ecology 96:1266-1274. Kraft, N. J. B., R. Valencia, and D. Ackerly. 2008. Functional traits and niche-based tree community assembly in an Amazonian forest. Science 322:580-582. Kurokawa, H. and T. Nakashizuka. 2008. Leaf herbivory and decomposability in a Malaysian tropical rain forest. Ecology 89:2645-2656. Laughlin, D. C., J. J. Leppert, M. M. Moore, and C. H. Sieg. 2010. A multi-trait test of the leaf- height-seed plant strategy scheme with 133 species from a pine forest flora. Functional Ecology 24:493-501. Laughlin, D.C., P.Z. Ful\xe9, D.W. Huffman, J. Crouse, and E. Laliberte. 2011. Climatic constraints on trait-based forest assembly. Journal of Ecology 99:1489-1499. Louault, F., V. D. Pillar, J. Aufrere, E. Garnier, and J. F. Soussana. 2005. Plant traits and functional types in response to reduced disturbance in a semi-natural grassland. Journal of Vegetation Science 16:151-160. Markesteijn, L., L. Poorter, H. Paz, L. Sack, and F. Bongers. 2011. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant, Cell and Environment 34:137-148. Martin, R. E., G. P. Asner, and L. Sack. 2007. Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden. Oecologia 151:387-400. Medlyn, B. E., F.-W. Badeck, D. G. G. De Pury, C. V. M. Barton, M. Broadmeadow, R. Ceulemans, P. De Angelis, M. Forstreuter, M. E. Jach, S. Kellom\xe4ki, E. Laitat, M. Marek, S. Philippot, A. Rey, J. Strassemeyer, K. Laitinen, R. Liozon, B. Portier, P. Roberntz, K. Wang, and P. G. Jarvis. 1999. Effects of elevated CO2 on photosynthesis in European forest species: a meta- analysis of model parameters. Plant, Cell and Environment 22:1475-1495. Meir, P. & Levy, P. E. 2007 Photosynthetic parameters from two contrasting woody vegetation types in West Africa. Plant Ecology. 192, 2, p. 277-287 11 p. Meir, P., Kruijt, B., Broadmeadow, M., Kull, O., Carswell, F. & Nobre, A. 2002 Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. Plant, Cell and Environment. 25, 3, p. 343-357 15 p. Messier, J., B. J. McGill, and M. J. Lechowicz. 2010. How do traits vary across ecological scales? A case for trait-based ecology. Ecology Letters 13:838-848. Meziane, D. and B. Shipley. 1999. Interacting determinants of specific leaf area in 22 herbaceous species: effects of irradiance and nutrient availability. Plant Cell and Environment 22:447-459. Milla & Reich 2011 Annals of Botany 107: 455\x96465, 2011. Muller, S. C., G. E. Overbeck, J. Pfadenhauer, and V. D. Pillar. 2007. Plant functional types of woody species related to fire disturbance in forest-grassland ecotones. Plant Ecology 189:1-14. Nakahashi, C. D., K. Frole, and L. Sack. 2005. Bacterial leaf nodule symbiosis in Ardisia (Myrsinaceae): Does it contribute to seedling growth capacity? Plant Biology 7:495-500. Niinemets, U. 2001. Global-scale climatic controls of leaf dry mass per area, density, and thickness in trees and shrubs. Ecology 82:453-469. Ogaya, R. and J. Penuelas. 2003. Comparative field study of Quercus ilex and Phillyrea latifolia: photosynthetic response to experimental drought conditions. Environmental and Experimental Botany 50:137-148. Onoda, Y., M. Westoby, P. B. Adler, A. M. F. Choong, F. J. Clissold, J. H. C. Cornelissen, S. Diaz, N. J. Dominy, A. Elgart, L. Enrico, P. V. A. Fine, J. J. Howard, A. Jalili, K. Kitajima, H. Kurokawa, C. McArthur, P. W. Lucas, L. Markesteijn, N. Perez-Harguindeguy, L. Poorter, L. Richards, L. S. Santiago, Jr. E. Sosinski, S. Van Bael, D. I. Warton, I. J. Wright, S. J. Wright, and N. Yamashita. 2011 . Global patterns of leaf mechanical properties. Ecology Letters 14:301- 312. Ordonez, J. C., P. M. van Bodegom, J. P. M. Witte, R. P. Bartholomeus, J. R. van Hal, and R. Aerts. 2010. Plant Strategies in Relation to Resource Supply in Mesic to Wet Environments: Does Theory Mirror Nature? American Naturalist 175:225-239. Pahl, A.T., Kollmann, J., Mayer, A. & Haider, S. (2013): No evidence for local adaptation in an invasive alien plant: field and greenhouse experiments tracing a colonization sequence. Annals of Botany 112 (9): 1921-1930. DOI: 10.1093/aob/mct246 Peco B., de Pablos I., Traba J. , & Levassor C. (2005) The effect of grazing abandonment on species composition and functional traits: the case of dehesaBasic and Applied Ecology, 6(2): 175-183 Penuelas, J., J. Sardans, J. Llusia, S. Owen, J. Carnicer, T. W. Giambelluca, E. L. Rezende, M. Waite, and \xdc. Niinemets. 2010. Faster returns on \leaf economics\ and different biogeochemical niche in invasive compared with native plant species. Global Change Biology 16:2171-2185. PETER B. ADLER, DANIEL G. MILCHUNAS, WILLIAM K. LAUENROTH, OSVALDO E. SALA and INGRID C. BURKE (2004) Functional traits of graminoids in semi-arid steppes: a test of grazing histories. Journal of Applied Ecology 2004 41, 653\x96663 Pierce S., Brusa G., Sartori M. & Cerabolini B.E.L. 2012. Combined use of leaf size and economics traits allows direct comparison of hydrophyte and terrestrial herbaceous adaptive strategies. Annals of Botany 109(5): 1047-1053 Pierce S., Brusa G., Vagge I. & Cerabolini B.E.L. 2013. Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Functional Ecology (in press) DOI:\xa010.1111/1365-2435.12095. Pierce S., Ceriani R.M., De Andreis R., Luzzaro A. & Cerabolini B. 2007. The leaf economics spectrum of Poaceae reflects variation in survival strategies. Plant Biosystems 141(3): 337-343. Pierce S., Luzzaro A., Caccianiga M., Ceriani R.M. & Cerabolini B. 2007. Disturbance is the principal ?-scale filter determining niche differentiation, coexistence and biodiversity in an alpine community. Journal of Ecology 95: 698-706. Pillar, V. D. and E. E. Sosinski. 2003. An improved method for searching plant functional types by numerical analysis. Journal of Vegetation Science 14:323-332. Prentice, I.C., Meng, T., Wang, H., Harrison, S.P., Ni, J., Wang, G., 2011. Evidence for a universal scaling relationship of leaf CO2 drawdown along a moisture gradient. New Phytologist 190: 169\x96180 Preston, K. A., W. K. Cornwell, and J. L. DeNoyer. 2006. Wood density and vessel traits as distinct correlates of ecological strategy in 51 California coast range angiosperms. New Phytologist 170:807-818. Price, C.A. and B.J. Enquist. Scaling of mass and morphology in Dicotyledonous leaves: an extension of the WBE model. (2007) Ecology 88(5): 1132\x961141. Pyankov, V. I., A. V. Kondratchuk, and B. Shipley. 1999. Leaf structure and specific leaf mass: the alpine desert plants of the Eastern Pamirs, Tadjikistan. New Phytologist 143:131-142. Quero, J. L., R. Villar, T. Maranon, R. Zamora, D. Vega, and L. Sack. 2008. Relating leaf photosynthetic rate to whole-plant growth: drought and shade effects on seedlings of four Quercus species. Functional Plant Biology 35:725-737. Quested, H. M., J. H. C. Cornelissen, M. C. Press, T. V. Callaghan, R. Aerts, F. Trosien, P. Riemann, D. Gwynn-Jones, A. Kondratchuk, and S. E. Jonasson. 2003. Decomposition of sub- arctic plants with differing nitrogen economies: A functional role for hemiparasites. Ecology 84:3209-3221. Reich, P. B., J. Oleksyn, and I. J. Wright. 2009. Leaf phosphorus influences the photosynthesis- nitrogen relation: a cross-biome analysis of 314 species. Oecologia 160:207-212. Reich, P. B., M. G. Tjoelker, K. S. Pregitzer, I. J. Wright, J. Oleksyn, and J. L. Machado. 2008. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecology Letters 11:793-801. Sack, L. 2004. Responses of temperate woody seedlings to shade and drought: do trade-offs limit potential niche differentiation? Oikos 107:110-127. Sack, L. and K. Frole. 2006. Leaf structural diversity is related to hydraulic capacity in tropical rain forest trees. Ecology 87:483-491. Sack, L., M. T. Tyree, and N. M. Holbrook. 2005. Leaf hydraulic architecture correlates with regeneration irradiance in tropical rainforest trees. New Phytologist 167:403-413. Sack, L., P. D. Cowan, N. Jaikumar, and N. M. Holbrook. 2003. The 'hydrology' of leaves: co- ordination of structure and function in temperate woody species. Plant Cell and Environment 26:1343-1356. Sack, L., P. J. Melcher, W. H. Liu, E. Middleton, and T. Pardee. 2006. How strong is intracanopy leaf plasticity in temperate deciduous trees? American Journal of Botany 93:829- 839. Scherer-Lorenzen, M., Schulze, E.-D., Don, A., Schumacher, J. & Weller, E. (2007) Exploring the functional significance of forest diversity: A new long-term experiment with temperate tree species (BIOTREE). Perspectives in Plant Ecology, Evolution and Systematics, 9, 53-70. Schweingruber, F.H., Landolt, W.: The Xylem Database. Swiss Federal Research Institute WSL Updated (2005) Scoffoni, C., A. Pou, K. Aasamaa, and L. Sack. 2008. The rapid light response of leaf hydraulic conductance: new evidence from two experimental methods. Plant Cell and Environment 31:1803-1812. Shipley B., 2002. Trade-offs between net assimilation rate and specific leaf area in determining relative growth rate: relationship with daily irradiance, Functional Ecology(16) 682-689 Shipley, B. 1995. Structured Interspecific Determinants of Specific Leaf-Area in 34 Species of Herbaceous Angiosperms. Functional Ecology 9:312-319. Shipley, B. and M. J. Lechowicz. 2000. The functional co-ordination of leaf morphology, nitrogen concentration, and gas exchange in 40 wetland species. Ecoscience 7:183-194. Shipley, B. and M. Parent. 1991. Germination Responses of 64 Wetland Species in Relation to Seed Size, Minimum Time to Reproduction and Seedling Relative Growth-Rate. Functional Ecology 5:111-118. Shipley, B. and T. T. Vu. 2002. Dry matter content as a measure of dry matter concentration in plants and their parts. New Phytologist 153:359-364. Soudzilovskaia, Nadejda A and Elumeeva, Tatiana G and Onipchenko, Vladimir G and Shidakov, Islam I and Salpagarova, Fatima S and Khubiev, Anzor B and Tekeev, Dzhamal K and Cornelissen, Johannes HC. 2013. Functional traits predict relationship between plant abundance dynamic and long-term climate warming. PNAS. 45:18180-18184 Spasojevic, M. J. and K. N. Suding. 2012. Inferring community assembly mechanisms from functional diversity patterns: the importance of multiple assembly processes. Journal of Ecology 100:652-661. Swaine, E. K. 2007. Ecological and evolutionary drivers of plant community assembly in a Bornean rain forest. PhD Thesis, University of Aberdeen, Aberdeen. Tomas F. Domingues, Luiz A. Martinelli, James R. Ehleringer (2007) Ecophysiological traits of plant functional groups in forest and pasture ecosystems from eastern Amazonia, Brazil. Plant Ecol (2007) 193:101\x96112 DOI 10.1007/s11258-006-9251-z Tucker SS, Craine JM, Nippert JB (2011) Physiological drought tolerance and the structuring of tallgrass assemblages. Ecosphere 2(4): 48 van Bodegom, P. M., B. K. Sorrell, A. Oosthoek, C. Bakke, and R. Aerts. 2008. Separating the effects of partial submergence and soil oxygen demand on plant physiology. Ecology 89:193- 204. van Bodegom, P. M., M. de Kanter, C. Bakker, and R. Aerts. 2005. Radial oxygen loss, a plastic property of dune slack plant species. Plant and Soil 271:351-364. Vanessa Minden, Michael Kleyer (2011): Testing the effect\x96response framework: key response and effect traits determining above-ground biomass ofsalt marshes. Journal of Vegetation Science 22: 387-401 Vanessa Minden, Sandra Andratschke, Janina Spalke, Hanna Timmermann, Michael Kleyer (2012): Plant trait\x96environment relationships in salt marshes: Deviations from predictions by ecological concepts. Perspectives in Plant Ecology, Evolution and Systematics, 14: 183-192 Vergutz, L., S. Manzoni, A. Porporato, R.F. Novais, and R.B. Jackson. 2012. A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves. Data set. Available on- line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1106 Vile, D. 2005. Significations fonctionnelle et ecologique des traits des especes vegetales: exemple dans une succession post-cultural mediterraneenne et generalisations, PHD Thesis. Von Holle, B. and D. Simberloff. 2004. Testing Fox's assembly rule:Does plant invasion depend on recipient community structure? Oikos 105:551-563. Waite, M. and L. Sack. 2010. How does moss photosynthesis relate to leaf and canopy structure? Trait relationships for 10 Hawaiian species of contrasting light habitats. New Phytologist 185:156-172. Wenxuan Han, Yahan Chen, Fang-Jie Zhao, Luying Tang, Rongfeng Jiang and Fusuo Zhang, 2011, Floral, climatic and soil pH controls on leaf ash content in China\x92s terrestrial plants. Global Ecology and Biogeography, DOI: 10.1111/j.1466-8238.2011.00677.x Williams, M., Y.E. Shimabokuro and E.B. Rastetter. 2012. LBA-ECO CD-09 Soil and Vegetation Characteristics, Tapajos National Forest, Brazil. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1104 WILSON K, D BALDOCCHI, P HANSON (2000) Spatial and seasonal variability of photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest. Tree Physiology 20, 565\x96578 Wirth, C. and J. W. Lichstein. 2009. The Imprint of Species Turnover on Old-Growth Forest Carbon Balances - Insights From a Trait-Based Model of Forest Dynamics. Pages 81-113 in C. Wirth, G. Gleixner, and M. Heimann, editors. Old-Growth Forests: Function, Fate and Value. Springer, New York, Berlin, Heidelberg. Wright, I. J., D. D. Ackerly, F. Bongers, K. E. Harms, G. Ibarra-Manriquez, M. Martinez-Ramos, S. J. Mazer, H. C. Muller-Landau, H. Paz, N. C. A. Pitman, L. Poorter, M. R. Silman, C. F. Vriesendorp, C. O. Webb, M. Westoby, and S. J. Wright. 2007. Relationships among ecologically important dimensions of plant trait variation in seven Neotropical forests. Annals of Botany 99:1003-1015. Wright, I. J., P. B. Reich, M. Westoby, D. D. Ackerly, Z. Baruch, F. Bongers, J. Cavender-Bares, T. Chapin, J. H. C. Cornelissen, M. Diemer, J. Flexas, E. Garnier, P. K. Groom, J. Gulias, K. Hikosaka, B. B. Lamont, T. Lee, W. Lee, C. Lusk, J. J. Midgley, M. L. Navas, U. Niinemets, J. Oleksyn, N. Osada, H. Poorter, P. Poot, L. Prior, V. I. Pyankov, C. Roumet, S. C. Thomas, M. G. Tjoelker, E. J. Veneklaas, and R. Villar. 2004. The worldwide leaf economics spectrum. Nature 428:821-827. Wright, S. J., K. Kitajima, N. J. B. Kraft, P. B. Reich, I. J. Wright, D. E. Bunker, R. Condit, J. W. Dalling, S. J. Davies, S. D\xedaz, B. M. J. Engelbrecht, K. E. Harms, S. P. Hubbell, C. O. Marks, M. C. Ruiz-Jaen, C. M. Salvador, and A. E. Zanne. 2011 . Functional traits and the growth- mortality tradeoff in tropical trees. Ecology 91:3664-3674. Yahan Chen , Wenxuan Han , Luying Tang , Zhiyao Tang and Jingyun Fang 2011 Leaf nitrogen and phosphorus concentrations of woody plants differ in responses to climate, soil and plant growth form. Ecography 34, doi: 10.1111/j.1600-0587.2011.06833.x Yguel B., Bailey R., Tosh N.D., Vialatte A., Vasseur C., Vitrac X., Jean F. & Prinzing A. (2011). Phytophagy on phylogenetically isolated trees: why hosts should escape their relatives. Ecol. Lett., 14, 1117-1124. Appendix S2. Sample size for woody and non-woody species across climate regions, robustness test for the gap filling algorithm and examples of studies focused on multi- organ, multi-trait datasets. Table S2.1. Percentage of observation present by trait in the TRY subset used in this study. Traits in the table are order in ascendant order from the trait with the lowest coverage to the trait with the highest coverage in the dataset. Trait Percentage observation present Leaf lifespan 0.6728772 Leaf phosphorus area 2.5561542 Leaf nitrogen area 5.7611659 Leaf phosphorus mass 6.8018047 Stem specific density 9.8029733 Leaf nitrogen mass 14.3595819 Seed mass 19.8256946 Leaf area 27.8021942 SLA 29.1242534 Plant height 35.6462607 Table S2.3 Number of species by growth form across five climate regions. Climate region Woody Non-woody Tropical 4414 537 Temperate 2695 2352 Arid 1046 873 Cold 752 2050 Polar 146 419 Figure S2.1 Comparison of trait-trait correlations using only gap-filled or only original trait values. Sample sizes (n) of the gap filled and original database are the same to ensure they are comparable. To test whether the gap-filling algorithm had an effect on trait-trait correlation we used a subset of 470769 observations from TRY for five traits (leaf area, SLA, leaf n, plant height and seed mass). We compare the trait-trait correlation for 1000 different gap-filled matrices with the trait-trait correlation of the original data. We did this comparison for trait-trait correlation with low and high sample size. We show that in most cases, the original trait-trait correlation lies inside the 95% CI of the 1000 gap-filled datasets. Sample size for each trait-trait correlation is given on the right top corner of each panel. Figure S2.2 Correlation between Standard deviation and Root mean square error in dataset Table S2.3 Examples of studies focused on multi-organ, multi-trait datasets. Study Growing Spatial Environmental Are Plant groups included Plant groups Number Life Organs conditio scale gradients environmental taken into of stage ns of the gradients consideration? species plants explicitly considered? Ackerly field local Water availability no woody evergreen, woody yes 20 adults leaf 23 2004 deciduous stem 11 seed 2 Baraloto field regional Precipitation, Soil no trees no 668 adults leaf 12 et al. 2010 nutrients stem 5 Cheng et field local Temperature, yes xerophytes, intermediate yes 55 adults leaf 1 al. 2015 Precipitation, Soil xerophytes, intermediate roots 4 nutrients mesophytes, mesophytes shallow vs deep-rooted de la Riva field local Humidity (soil water) yes woody 38 adults leaf 7 et al. 2015 stem 1 roots 5 seed 1 Diaz et al. field regional Precipitation, no eudicotyledons, yes 640 adults leaf 5 2004 Temperature, Altitude monocotyledons; Asteraceae; stem 4 Fabaceae; Poaceae seed 2 other 2 Diaz et al. field global Precipitation, no woody, non-woody; yes 46085 adults 2016 Temperature, Altitude angiosperms, gymnosperms, pteridophyte Fortunel field regional Precipitation, Soil yes trees no 758 adults leaf 11 et al. 2012 nutrients stem 2 root 1 Freschet field local Soil nutrients (C &N), yes woody evergreen, woody yes 40 adults leaf 8 et al. 2010 Temperature (soil deciduous, fern allies, club stem 7 litter), Humidity (soil mosses, monocots, terrestrial roots 7 litter), growing season forbs, aquatic forbs Ishida field no trees, creeping trees, ruderal 32 adults leaf 14 2008 trees, climber, C3 shrubs and stem 1 forbs, CAM, palm Jager et al. field local Terrain slope, Soil yes angiosperm trees, conifer trees, yes 30 > 10 leaf 8 2015 nutrients palm trees, fern trees dbh stem 4 seed 1 Kramer et glasshou local, Soil nutrients yes conifer, eudicot, palm, yes 66 seedli leaf 2 al. 2016 se regional magnoliid, tree fern; ngs stem 2 AM(nodules)*, AM*, Non*, root 5 Dual AM/EM*, EM*, Ericoid* Li and common woody deciduous, woody no 23 1 year leaf 3 Bao 2015 garden xerophythic old stem 1 roots 4 Wright field regional Precipitation no trees, shrubs, lianas no 122 adults leaf 2 2007 stem 2 seed 2 When several plant group classifications were used, a semicolon divides them. The numbers next to the name of the organs included in the study in the ‘Organs’ column refers to the number of traits per organ. * Represent types of mychorrhizal association: arbuscular mychorrizal (AM), no mychorrhizal association (Non), ectomycorrhizal (EM), Dual (association with both arbuscular and ectomychorrizal). Appendix S3. Trait network analyses across plant groups species and climate regions excluding leaf lifespan (LLS) Table S3.1. Trait centrality (i.e. Degree) for all plants, woody, and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of all plants, woody and non- woody species for 1000 bootstrapped trait matrices across 7 traits, excluding LLS. Life form Trait 2.5% 50% 97.5% All Plht 1.00 1.00 1.00 SLA 1.00 1.00 1.00 Sm 0.83 1.00 1.00 SSD 0.83 1.00 1.00 Leaf N 0.67 0.83 1.00 Leaf P 0.67 0.83 0.83 Leaf area 0.67 0.67 0.83 Non-woody Leaf area 0.50 0.83 0.83 Leaf N 0.67 0.83 0.83 Leaf P 0.67 0.67 0.83 Plht 0.67 0.67 0.83 SLA 0.50 0.67 0.68 Sm 0.67 0.67 0.83 SSD 0.00 0.17 0.33 Woody Sm 0.83 1.00 1.00 Leaf area 0.83 0.83 0.83 Leaf P 0.67 0.83 0.83 SLA 0.83 0.83 0.83 Leaf N 0.50 0.50 0.67 Plht 0.50 0.50 0.50 SSD 0.50 0.50 0.67 Table S3.2 Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for seven traits. We ran the trait centrality analyses for the trait of all plant species, woody and non-woody species across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. All plants Woody Non-woody Trait-pairs 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.17 0.00 -0.17 0.00 -0.17 0.33 Leaf area-Leaf N 0.17 0.33 0.17 0.33 -0.33 0.17 Leaf area-Leaf P 0.00 0.17 0.00 0.17 -0.33 0.17 Leaf area-Plht 0.33 0.33 0.33 0.33 -0.33 0.17 Leaf area-SSD 0.00 0.33 0.00 0.33 0.17 0.67 Leaf area-Sm -0.17 0.00 -0.17 0.00 -0.33 0.17 SLA-Leaf N 0.17 0.33 0.17 0.33 -0.50 0.17 SLA-Leaf P 0.00 0.17 0.00 0.17 -0.33 0.17 SLA-Plht 0.33 0.50 0.33 0.50 -0.17 0.00 SLA-SSD 0.17 0.33 0.17 0.33 0.17 0.83 SLA-Sm -0.17 0.00 -0.17 0.00 -0.33 0.17 Leaf N-Leaf P -0.33 0.00 -0.33 0.00 -0.17 0.17 Leaf N-Plht 0.00 0.17 0.00 0.17 -0.17 0.33 Leaf N-SSD 0.00 0.00 0.00 0.00 0.33 0.83 Leaf N-Sm -0.50 -0.17 -0.50 -0.17 -0.17 0.17 Leaf P-Plht 0.17 0.33 0.17 0.33 -0.17 0.33 Leaf P-SSD 0.00 0.33 0.00 0.33 0.33 0.83 Leaf P-Sm -0.17 -0.17 -0.17 -0.17 -0.17 0.17 Plht-SSD -0.17 0.00 -0.17 0.00 0.17 0.83 Plht-Sm -0.50 -0.33 -0.50 -0.33 -0.17 0.17 SSD-Sm -0.50 -0.17 -0.50 -0.17 -0.83 -0.33 Table S3.3 Edge density for woody and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of woody and non-woody species across climate regions for 1000 bootstrapped trait matrices. Life-form Climate 2.5% 50% 97.5% Woody Arid 0.52 0.57 0.67 Cold 0.43 0.52 0.57 Polar 0.29 0.38 0.48 Temperate 0.52 0.57 0.62 Tropical 0.62 0.67 0.71 Non-woody Arid 0.38 0.48 0.57 Cold 0.52 0.62 0.67 Polar 0.33 0.43 0.52 Temperate 0.52 0.62 0.71 Tropical 0.29 0.38 0.52 Table S3.4 Multiple-comparison adjusted 95% confidence interval (CI) from the edge density analyses among climate regions for woody and non-woody species. We ran the edge density comparison for woody and non-woody species across five climate regions for 1000 trait matrices. Differences in edge density between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Woody Non-woody Region-pairs 2.5% 97.5% 2.5% 97.5% Arid-Cold -0.05 0.19 -0.29 0.00 Arid-Polar 0.05 0.38 -0.19 0.19 Arid-Temperate -0.14 0.14 -0.33 0.05 Arid-Tropical -0.19 0.05 -0.14 0.24 Cold-Polar 0.00 0.29 0.00 0.33 Cold-Temperate -0.19 0.05 -0.19 0.14 Cold-Tropical -0.24 -0.05 0.00 0.38 Polar-Temperate -0.38 -0.05 -0.33 0.05 Polar-Tropical -0.43 -0.14 -0.19 0.24 Temperate-Tropical -0.19 0.05 0.00 0.38 Table S3.5 Modularity for woody and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of woody and non-woody species across climate regions for 1000 bootstrapped trait matrices. Life-form Climate 2.5% 50% 97.5% Woody Arid 0.06 0.15 0.22 Cold 0.15 0.21 0.28 Polar 0.17 0.27 0.37 Temperate 0.10 0.15 0.21 Tropical 0.02 0.06 0.10 Non-woody Arid 0.10 0.20 0.31 Cold 0.02 0.05 0.13 Polar 0.13 0.20 0.32 Temperate 0.02 0.05 0.13 Tropical 0.09 0.22 0.32 Table S3.6 Multiple-comparison adjusted 95% confidence interval (CI) from the modularity analyses among climate regions for woody and non-woody species. We ran the modularity comparison for woody and non-woody species across five climate regions for 1000 trait matrices. Differences in modularity between regions is significant when the 95% CI for the differences between regions pairs does not overlap with zero. Woody Non-woody Region pairs 2.5% 97.5% 2.5% 97.5% Arid-Cold -0.21 0.11 -0.02 0.33 Arid-Polar -0.31 0.08 -0.24 0.22 Arid-Temperate -0.15 0.16 0.00 0.34 Arid-Tropical -0.04 0.20 -0.22 0.23 Cold-Polar -0.23 0.13 -0.33 0.05 Cold-Temperate -0.06 0.18 -0.11 0.16 Cold-Tropical 0.00 0.26 -0.30 0.06 Polar-Temperate -0.06 0.30 0.00 0.34 Polar-Tropical 0.03 0.35 -0.19 0.21 Temperate-Tropical -0.04 0.19 -0.32 0.00 Table S3.7. Trait centrality (i.e. Degree) for woody species across five climate regions and 95% confidence interval (CI). We ran this analysis over 1000 bootstrapped trait matrices. Climate Trait 2.50% 50% 97.50% Tropical Sm 0.83 1.00 1.00 SLA 0.83 0.83 0.83 Leaf area 0.67 0.67 0.67 Leaf P 0.50 0.67 0.67 Leaf N 0.50 0.50 0.67 Plht 0.50 0.50 0.67 SSD 0.50 0.50 0.67 Temperate Leaf area 0.67 0.67 0.67 SLA 0.67 0.67 0.83 Sm 0.50 0.67 0.67 Leaf N 0.33 0.50 0.50 Leaf P 0.50 0.50 0.67 Plht 0.50 0.50 0.67 SSD 0.50 0.50 0.67 Arid Leaf area 0.67 0.67 0.83 SLA 0.67 0.67 1.00 SSD 0.50 0.67 0.83 Leaf P 0.50 0.50 0.67 Plht 0.33 0.50 0.67 Sm 0.50 0.50 0.67 Leaf N 0.33 0.33 0.50 Cold Plht 0.33 0.67 0.83 SLA 0.50 0.67 0.67 Leaf area 0.33 0.50 0.50 Leaf P 0.50 0.50 0.67 Sm 0.50 0.50 0.50 SSD 0.33 0.50 0.50 Leaf N 0.33 0.33 0.33 Polar Leaf area 0.17 0.50 0.67 Leaf P 0.17 0.50 0.67 SLA 0.33 0.50 0.67 Sm 0.33 0.50 0.50 Leaf N 0.33 0.33 0.50 Plht 0.17 0.33 0.50 SSD 0.17 0.33 0.34 Table S3.8. Trait centrality (i.e. Degree) for non-woody species across five climate regions and 95% confidence interval (CI). We ran this analysis over 1000 bootstrapped trait matrices. Climate Trait 2.50% 50% 97.50% Tropical Leaf area 0.33 0.50 0.67 Leaf N 0.50 0.50 0.67 Sm 0.33 0.50 0.83 Leaf P 0.17 0.33 0.67 Plht 0.17 0.33 0.50 SLA 0.17 0.33 0.50 SSD 0.00 0.17 0.50 Temperate Leaf N 0.67 0.83 1.00 Leaf area 0.50 0.67 0.83 Leaf P 0.33 0.67 0.83 Plht 0.50 0.67 0.83 Sm 0.67 0.67 0.83 SLA 0.50 0.50 0.50 SSD 0.00 0.17 0.33 Arid Leaf area 0.50 0.50 0.67 Leaf N 0.33 0.50 0.67 SLA 0.33 0.50 0.67 Sm 0.33 0.50 0.83 Leaf P 0.33 0.33 0.67 Plht 0.17 0.33 0.50 SSD 0.17 0.17 0.33 Cold Leaf area 0.67 0.83 0.83 Leaf N 0.50 0.67 0.83 SLA 0.50 0.67 0.83 Sm 0.50 0.67 0.67 Leaf P 0.50 0.50 0.67 Plht 0.17 0.50 0.67 SSD 0.17 0.33 0.50 Polar Sm 0.50 0.67 0.83 Leaf area 0.33 0.50 0.50 Leaf N 0.50 0.50 0.67 Leaf P 0.33 0.50 0.67 SLA 0.33 0.50 0.50 Plht 0.33 0.33 0.50 SSD 0.00 0.17 0.33 Table S3.9 Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for seven traits of woody species across five climate regions. We ran the trait centrality analyses for the trait of woody species across five climate regions across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Tropical Temperate Arid Cold Polar Trait-pairs 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.33 -0.17 -0.17 0.17 -0.33 0.33 -0.33 0.17 -0.50 0.33 Leaf area-Leaf N 0.00 0.17 0.17 0.33 0.17 0.50 0.00 0.33 -0.17 0.33 Leaf area-Leaf P -0.17 0.17 0.00 0.33 0.00 0.17 -0.33 0.17 -0.50 0.33 Leaf area-Plht 0.00 0.17 0.00 0.33 0.00 0.50 -0.33 0.17 -0.17 0.50 Leaf area-SSD -0.17 0.17 -0.17 0.33 -0.17 0.33 -0.17 0.17 -0.17 0.50 Leaf area-Sm -0.33 -0.17 0.00 0.33 -0.17 0.33 -0.17 0.17 -0.33 0.33 SLA-Leaf N 0.17 0.50 0.17 0.50 0.00 0.67 0.00 0.50 -0.17 0.33 SLA-Leaf P 0.00 0.50 0.00 0.33 -0.17 0.50 -0.17 0.33 -0.33 0.33 SLA-Plht 0.17 0.50 0.00 0.33 0.00 0.50 -0.17 0.17 -0.17 0.50 SLA-SSD 0.17 0.33 0.00 0.33 -0.17 0.33 0.00 0.33 -0.17 0.50 SLA-Sm -0.17 0.17 0.00 0.33 -0.17 0.33 0.00 0.33 -0.17 0.33 Leaf N-Leaf P -0.33 0.17 -0.33 0.17 -0.50 0.00 -0.33 0.00 -0.33 0.33 Leaf N-Plht -0.17 0.17 -0.33 0.17 -0.33 0.17 -0.50 0.00 -0.17 0.33 Leaf N-SSD -0.17 0.00 -0.33 0.00 -0.50 0.00 -0.33 0.00 -0.17 0.33 Leaf N-Sm -0.50 -0.17 -0.33 0.00 -0.33 -0.17 -0.33 0.00 -0.33 0.17 Leaf P-Plht 0.00 0.17 -0.17 0.17 -0.17 0.33 -0.33 0.17 -0.33 0.50 Leaf P-SSD -0.33 0.33 -0.33 0.17 -0.33 0.17 -0.17 0.33 -0.17 0.50 Leaf P-Sm -0.33 -0.17 -0.17 0.17 -0.33 0.33 -0.17 0.17 -0.33 0.50 Plht-SSD -0.33 0.17 -0.17 0.17 -0.33 0.17 -0.17 0.50 -0.33 0.50 Plht-Sm -0.50 -0.17 -0.33 0.17 -0.50 0.17 -0.17 0.33 -0.50 0.17 SSD-Sm -0.50 0.00 -0.17 0.33 -0.17 0.33 -0.17 0.17 -0.50 0.17 Table S3.10. Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for seven traits of non-woody species across five climate regions. We ran the trait centrality analyses for the trait of non-woody species across five climate regions across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Tropical Temperate Arid Cold Polar Trait-pairs 2.5% 99.9% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.17 0.50 -0.17 0.33 -0.17 0.33 0.00 0.17 -0.17 0.33 Leaf area-Leaf N -0.17 0.17 -0.50 0.00 -0.33 0.33 -0.17 0.33 -0.33 0.17 Leaf area-Leaf P -0.33 0.50 -0.33 0.33 -0.17 0.33 0.00 0.33 -0.17 0.17 Leaf area-Plht -0.17 0.50 -0.33 0.33 0.00 0.50 0.00 0.67 -0.17 0.33 Leaf area-SSD 0.00 0.50 0.17 0.67 0.17 0.67 0.17 0.67 0.00 0.67 Leaf area-Sm -0.50 0.50 -0.33 0.17 -0.33 0.50 0.00 0.33 -0.50 0.17 SLA-Leaf N -0.50 0.17 -0.50 0.00 -0.33 0.50 -0.33 0.33 -0.33 0.17 SLA-Leaf P -0.50 0.33 -0.33 0.17 -0.17 0.50 -0.17 0.33 -0.33 0.17 SLA-Plht -0.17 0.33 -0.33 0.00 -0.17 0.67 -0.17 0.50 -0.17 0.33 SLA-SSD -0.33 0.50 0.17 0.50 0.17 0.50 0.17 0.50 0.00 0.50 SLA-Sm -0.50 0.17 -0.33 -0.17 -0.33 0.50 -0.17 0.33 -0.50 0.00 Leaf N-Leaf P -0.33 0.50 -0.17 0.67 -0.17 0.50 -0.17 0.33 -0.17 0.33 Leaf N-Plht 0.00 0.50 -0.17 0.50 -0.17 0.50 0.00 0.50 0.00 0.33 Leaf N-SSD 0.00 0.67 0.33 0.83 0.00 0.50 0.17 0.50 0.00 0.67 Leaf N-Sm -0.33 0.33 -0.17 0.33 -0.33 0.33 -0.17 0.33 -0.33 0.33 Leaf P-Plht -0.17 0.50 -0.33 0.33 -0.17 0.33 0.00 0.33 0.00 0.50 Leaf P-SSD -0.17 0.67 0.00 0.83 0.00 0.50 0.00 0.50 0.17 0.50 Leaf P-Sm -0.50 0.33 -0.33 0.00 -0.50 0.33 -0.33 0.17 -0.50 0.17 Plht-SSD -0.33 0.50 0.17 0.67 -0.17 0.50 -0.17 0.50 -0.17 0.50 Plht-Sm -0.67 0.33 -0.33 0.17 -0.50 0.17 -0.33 0.00 -0.50 0.00 SSD-Sm -0.67 0.00 -0.83 -0.33 -0.67 0.17 -0.50 0.00 -0.67 -0.17 Appendix S4 Conditional dependency and its determination through the calculation of precision matrix and its confidence interval. Conditional Independency Two random variables ! and " are independent if the occurrence of one variable does not affect the probability of the other or formally if their joint distribution equals the product of their probabilities: #(!, ") = #(!)#(") Similarly, we say two random variables ! and " are conditionally independent given a third variable ( if the knowledge of one variable does not provide any information on the likelihood of the other after observing variable (,or formally if their conditional joint distribution equals the product of their conditional probabilities: #(!, " | () = #(!|()#("|() and the conditional mutual information of ! and " given (, +(!, "|(), is zero (i.e. the “amount of information” obtained about ! through " after observing ( is zero): #(! = /, " = 0|( = -) + (!, " | () = , #( ( = - ) . #(! = /, " = 0) log 9∈: #(! = /|( = -)#(" = 0|( = -) 4∈6,7∈8 #(! = /|( = -)#(" = 0|( = -) =, #( ( = - ) . #(! = /, " = 0) log 9∈: #(! = /|( = -)#(" = 0|( = -) 4∈6,7∈8 =0 Intuitively, if ! and " are conditionally independent given (, then any relationship that may exist between ! and " can be explained by (. In other words, ! and " may appear to be correlated if ( is not considered, but if ( is controlled by holding it constant then any relationship between ! and "would disappear. Probabilistic Graphical Models A graph G = (V, E) is a data structure consisting of a set of nodes V = {XA , XB , … , XD } and a set of edges E that connect pairs of nodes. A pair of nodes XF and XG can be connected by either a directed edge XF → XG or an undirected edge XF − XG . If all edges of the graph are directed edges, we say that the graph is directed. If all edges of graph are undirected edges, we say that the graph is undirected. Graphs are an intuitive way of representing and visualizing the relationships between random variables, where nodes correspond to random variables, and edge represents dependency or relationship between variables. For example, correlation graphs (also called correlation networks) represent the pairwise correlation between variables. In a correlation graph, a pair of nodes !J and !G are connected if the pairwise correlation of the data associated with !J and !G is beyond a threshold. Probabilistic graphical models (PGMs) use a graph-based representation as the basis for encoding a complex distribution over a high-dimensional space. In this representation, the edges correspond to direct probabilistic interactions between nodes. The graph is a compact representation of a set of conditional independencies that hold in the distribution. In particular, if there is no edge between two nodes XF and XG , then the random variable XF is conditionally independent of the random variable XG given all other variables. Two approaches for graphically representing the distributions are commonly used: (i) Bayesian networks that uses a directed graph (where the edges have a source and a target) and (ii) Markov networks which uses an undirected graph representation. Precision Matrix One special example of PGMs is Gaussian graphical models which gives an efficient and compact representation for Gaussian multivariate data. In particular, let K = {XA , XB , … , XD } be a set of random variables with a multivariate Gaussian design ensemble (i.e., K ∼ M(0, Σ); for simplicity and without loss of generality, here we assume that the mean of O is zero). The precision matrix (the inverse of covariance matrix; Ω = Σ QA ) reveals the statistical conditional independence structure among features i.e., if ΩFG = 0, then feature XF is conditionally independent of XG given all other variables and there is no edge in the undirected graph structure associated with X (Figure 1). To illustrate the relationship between the precision matrix and the graph structure, we consider the probabilistic graphical models as a skeleton for compactly representing a high- dimensional distribution P. We “break” the distribution into smaller factors, each over a much smaller space of possibilities. Then, we can define the overall joint distribution as a product of these factors. The graph structure defines the factorization of a distribution P associated with it — the set of factors and the variables that they encompass. In the Gaussian graphical models, the set of factors can be represented in terms of elements of the precision matrix S. Mainly, the density function of O, is written as 1 1 T (O ) = exp ^− O_ Z QA O` V(2X)Y |Z| 2 1 1 = exp ^− O_ SO` V(2X)Y |Z| 2 Y 1 1 = exp a− b !J !c SJc e V(2X)Y |Z| 2 J,cdA Y 1 1 = f exp ^− !!S ` V(2X)Y |Z| 2 J c Jc J,cdA Y 1 = f gJ (!J ) f gJc (!J , !c ) V(2X)Y |Z| JdA (J,c)∈h where |Z| is determinant of Z, the second equality uses the fact that S = Z QA , and the third A equality is the expansion of matrix multiplication, ψF (XF ) = exp j− B !JB SJJ k can be viewed A as the node potential and ψFG lXF , XG m = exp j− B !J !c SJc k can be viewed as the edge potential. As shown in the above equation, the density function of O is factorized to a set A A factors written as exp j− B !J !c SJc k. If SJc = 0, then the factor exp j− B !J !c SJc k = 1 with no effect in the density of O. In other words, if the edge potential TJc l!J , !c m = 0, and there is no edge between nodes XF and XG in the graph structure, then we say that the feature XF is conditionally independent of XG given all other variables. Interestingly, the elements of a precision matrix are related to the partial correlation. In particular, the partial correlation coefficient between variables XF and XG measures their conditional correlation after removing the effect of the variables !QJc = ! − {!J , !c }. The partial correlation values can be calculated by normalizing the off-diagonal entries of the precision matrix (Lauritzen 1996) as opql!J , !c r!QJc ) SJc nJc = = − sqtnl!J r!QJc mqtnl!c r!QJc ) VSJJ Scc Why are we not using sample covariance matrix and instead using Precision matrix? Consider an independent and identically distributed random sample {KA , K B , … , K u } from the distribution of O. In the classical situation where number samples v → ∞, the sample covariance matrix Zxy = ∑yJdA OJ O_J is a consistent and well behaved estimator of Z = {[OJ O_J ] and Sxy = ZxyQA is a natural and good estimator of S. However, if the number of samples is not sufficient, then Zxy is no longer a consistent estimate of Z and in some cases (e.g., # > v), Zxy is not invertible, thus Sxy is not well-defined. Unlike the covariance matrix, the precision matrix is usually sparse (it contains many zero values). This characterization yields to several efficient and consistent algorithms for estimating S. Thus, in this paper, we focus on estimating S directly. Difference between Correlation and Dependency Graphs Correlation graphs capture the similarity between variables. Two variables are similar if their pairwise correlation exceeds a threshold. However, the similarity between two variables may come from a third source (e.g., another variable). Thus, the correlation graph captures indirect similarities. The covariance matrix essentially indicates the correlation between variables; if an element in the covariance matrix is zero, that means that two variables are not correlated or marginally independent without observing other variables. However, this kind of independency is rarely expected to happen in real world applications, because it reveals that two random variables have no active relationship even without observing other nodes. Precision matrices are related to the partial correlations, as they can calculate the correlation between two variables after removing the effect of other variables. Thus, the precision matrix provides information about the direct similarity between two variables. In particular, two variables that are connected in correlation graphs, may or may not be connected in a precision graph and vice versa. Below, we provide some examples to illustrate the difference between correlation graph and precision graph. a. Example 1 Consider three binary variables: • Rain? (yes/no) i.e., set to yes if it was rainy today. • Wet Grass? (yes/no) i.e., set to yes if it the grass is wet. • Wet Street? (yes/no) i.e., set to yes if it the street is wet. When it is raining there is a high chance that both the grass and the street get wet, yielding a high correlation between Rain-Wet Street, and Rain-Wet Grass. At the same time, if we calculate the correlation between Wet Grass -Wet Street, they will likely be highly correlated, however, neither Wet grass, nor Wet street is causing the occurrence of the other variable (Figure 1 a). Rain is the main reason that Wet Street and Wet Grass are correlated, and if we remove the effect of Rain from both variables, they will no longer be correlated. As a result, Wet Street and Wet Grass are not connected in the dependency graph i.e., they are conditionally independent given Rain (Figure 1 b). a. Correlation Graph b. Dependency Graph Figure 1. Rain Example. Wet Street and Wet Grass are connected in the correlation graph (highly correlated) but are independent given Rain in the dependency graph. b. Example 2 Consider three variables: • Lung Cancer? (yes/no) i.e., set to yes if the person has lung cancer. • Age i.e., indicates the age of a person. • Smoking? (yes/no) i.e., set to yes if it the person is smoking. Whether a person gets lung cancer depends on several factors such as age and whether the person is smoking or not. As a result, in the correlation graph there is an edge between Lung Cancer-Age and Lung Cancer-Smoking. However, for the population considered in this example, Age and Smoking are not correlated i.e., the age of a person does not significantly impact whether he/she is smoking (Figure 2a). On the other hand, if a person with lung cancer diagnosis is young, this raises the chances that the person is smoking, as lung cancer patients often have at least one of the two major risk factors, increased age or are smokers. As a result, there is an edge between Age-Smoking in the dependency graph (Figure 2b). a. Correlation Graph b. Dependency Graph Figure 2. Lung Cancer Example. Age and Smoking are not connected in the correlation graph (not correlated) but are dependent given Lung Cancer in the dependency graph. Analysis We estimated the precision matrix for each plant group (see methods section) separately. To estimate the precision matrix the glasso algorithm assumes a multi-variate Gaussian distribution and estimates the precision matrix by minimizing the negative log-likelihood among all measurements (in this case traits). The log-likelihood of a Gaussian multivariate distribution is: 1 1 log T(/) = log ( exp ^− O_ SO` ) V(2X)Y |Z| 2 1 1 = − log V(2X)Y Ä – log|Z| − Çn(O_ OS) 2 2 1 1 = − log V(2X)Y Ä + log|S| − Çn(Zx S) 2 2 A A where the last equality is from the determinant property: |Z| = |Ñ ÖÜ | = |á| . Thus, ignoring the constants, glasso finds the precision matrix S, by solving the following optimization problem: arg min − log|S| + ÇnlZx Sm + ç ∥ S ∥A á where ∥ S ∥A is the èA norm of S. Selecting penalty parameter ç Since the precision matrix is usually sparse (i.e. most of the precision matrix elements are zero), glasso adds a penalty term (èA norm) to the objective function which encourages to have a sparse precision matrix. The glasso algorithm includes a penalty parameter λ, which controls the sparsity level of the precision matrix. If ç = 0, then all of the elements of the precision matrix are non-zero. In contrast, with a very large value of ç, all the elements become zero. Following Jankova and van de Geer (2015), we accounted for differences in sample size across precision matrices and their effect on λ as follows: λ = 2 √(log p/ n), where n refers to the sample size and p denotes the number of variables, in this case traits (Appendix S4). Significance of trait-trait connections in the trait network Given a data sample matrix ! ∈ ℜy×Y and estimated precision matrix S ∈ ℜY×Y , the test statistic is defined as Ç = S + çS(S where ( ∈ ℜY×Y is the sub-gradient of norm ∥ S ∥A , and its variance íJcB = SJJ Scc + SJc B . Then under this scenario, thresholding ÇJc at level ì QA (1 − î ï ñ ) óò for all ö, õ will remove all zero entries (i.e., non-significant trait-trait interactions) Y(YQA) √y with probability 1 − ú asymptotically (Jankova & van de Geer, 2015). Specifically, considering the data for a particular group represented in a matrix ! ∈ ℜy×Y which contains the trait information for # traits. First, we fix ç at a particular value and estimate the precision matrix S ∈ ℜY×Y by running glasso. Next, we estimate the (1 − ú )% asymptotic confidence interval of all the obtained edges as ú í°Jc ú í°Jc +Jc = ûÇJc − ì QA ü1 − † , ÇJc + ì QA (1 − ) ¢ #( # − 1 ) √ v #(# − 1) √v References Friedman J, Hastie T, Tibshirani R. 2008. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3): 432-441. Jankova J, van de Geer S. 2015. Confidence intervals for high-dimensional inverse covariance estimation. Electronic Journal of Statistics 9(1): 1205-1229. Lauritzen SL. 1996. Graphical models: Clarendon Press. Appendix S5. Analysis of trait network across plant growth forms and climate regions. Table S5.1. Trait centrality (i.e. Degree) for all plants, woody, and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of all plants, woody and non-woody species for 1000 bootstrapped trait matrices. Life form Trait 2.50% 50% 97.50% All LLS 1 1 1 SSD 1 1 1 SLA 0.86 1 1 Seed mass 0.86 0.86 0.86 Plant height 0.71 0.86 0.86 Leaf area 0.71 0.71 0.86 Leaf p 0.71 0.71 0.86 Leaf n 0.71 0.71 0.71 Non-woody Leaf n 0.71 0.71 0.86 Leaf area 0.57 0.71 0.86 Leaf p 0.57 0.71 0.86 LLS 0.57 0.71 0.86 Plant height 0.57 0.57 0.86 Seed mass 0.57 0.57 0.86 SLA 0.43 0.57 0.57 SSD 0.29 0.29 0.43 Woody LLS 0.86 0.86 1 Leaf area 0.86 0.86 0.86 Seed mass 0.71 0.86 0.86 Leaf n 0.71 0.71 0.71 Leaf p 0.71 0.71 0.71 SLA 0.57 0.71 0.71 Plant height 0.57 0.57 0.57 SSD 0.43 0.43 0.57 Table S5.2. Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for eight traits. We ran the trait centrality analyses for the trait of all plant species, woody and non-woody species across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. All Woody Non-woody Trait-pairs 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.29 0 0 0.29 -0.14 0.29 Leaf area-Leaf n 0 0.14 0 0.29 -0.29 0.14 Leaf area-LLS -0.29 -0.14 -0.14 0 -0.29 0.29 Leaf area-Leaf p -0.14 0 0.14 0.14 -0.29 0.29 Leaf area-Plant height -0.14 0.14 0.14 0.43 -0.29 0.29 Leaf area-SSD -0.29 -0.14 0.14 0.43 0.29 0.43 Leaf area-Seed mass -0.29 0 0 0.14 -0.29 0.29 SLA-Leaf n 0.14 0.29 -0.29 0.14 -0.43 0 SLA-LLS -0.14 0 -0.43 0 -0.43 0.14 SLA-Leaf p 0 0.29 -0.14 0.14 -0.43 0.14 SLA-Plant height 0.14 0.14 0 0.29 -0.29 0.14 SLA-SSD -0.14 0 -0.14 0.43 0 0.43 SLA-Seed mass 0 0.14 -0.14 0 -0.29 0.14 Leaf n-LLS -0.29 -0.29 -0.29 0 -0.14 0.29 Leaf n-Leaf p -0.29 0 -0.14 0.14 -0.14 0.29 Leaf n-Plant height -0.14 0 0 0.29 0 0.14 Leaf n-SSD -0.29 -0.29 0 0.29 0.29 0.57 Leaf n-Seed mass -0.29 -0.14 -0.29 0.14 -0.14 0.29 LLS-Leaf p 0 0.29 0.14 0.29 -0.14 0.29 LLS-Plant height 0.14 0.29 0.14 0.57 -0.14 0.29 LLS-SSD 0 0 0.14 0.43 0.14 0.71 LLS-Seed mass 0 0.14 0 0.29 -0.14 0.29 Leaf p-Plant height -0.14 0.14 0 0.29 -0.14 0.29 Leaf p-SSD -0.29 0 0 0.29 0.14 0.57 Leaf p-Seed mass -0.14 0 -0.14 0 -0.14 0.14 Plant height-SSD -0.29 -0.14 -0.14 0.29 0.14 0.57 Plant height-Seed mass -0.14 0 -0.43 0 -0.29 0.29 SSD-Seed mass 0 0.14 -0.43 0 -0.57 -0.14 Table S5.3. Edge density for woody and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of woody and non-woody species across climate regions for 1000 bootstrapped trait matrices. Life form Climate 2.50% 50% 97.50% Woody Tropical 0.61 0.64 0.68 Temperate 0.61 0.68 0.71 Arid 0.61 0.64 0.68 Cold 0.53 0.57 0.64 Polar 0.29 0.39 0.46 Non-woody Tropical 0.29 0.36 0.46 Temperate 0.5 0.57 0.64 Arid 0.39 0.46 0.57 Cold 0.57 0.61 0.68 Polar 0.36 0.43 0.5 Table S5.4. Multiple-comparison adjusted 95% confidence interval (CI) from the edge density analyses among climate regions for woody and non-woody species. We ran the edge density comparison for woody and non-woody species across five climate regions for 1000 trait matrices. Differences in edge density between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Woody Non-woody Regions-pairs 2.5% 97.5% 2.5% 97.5% Arid-Cold -0.04 0.18 -0.29 0.04 Arid-Polar 0.14 0.39 -0.11 0.21 Arid-Temperate -0.11 0.07 -0.29 0.07 Arid-Tropical -0.07 0.11 -0.07 0.29 Cold-Polar 0.04 0.36 0.07 0.32 Cold-Temperate -0.21 0.04 -0.11 0.18 Cold-Tropical -0.18 0.07 0.07 0.39 Polar-Temperate -0.43 -0.14 -0.29 0.04 Polar-Tropical -0.39 -0.11 -0.14 0.21 Temperate-Tropical -0.07 0.14 0.04 0.39 Table S5.5. Modularity for woody and non-woody species and 95% confidence interval (CI). We ran this analysis for each network of woody and non-woody species across climate regions for 1000 bootstrapped trait matrices. Life form Climate 2.5% 50% 97.5% Woody Tropical 0.05 0.08 0.11 Temperate 0.05 0.08 0.11 Arid 0.08 0.11 0.15 Cold 0.10 0.14 0.19 Polar 0.16 0.25 0.39 Non-woody Tropical 0.12 0.22 0.34 Temperate 0.07 0.12 0.19 Arid 0.09 0.19 0.28 Cold 0.04 0.09 0.13 Polar 0.13 0.21 0.34 Table S5.6. Multiple-comparison adjusted 95% confidence interval (CI) from the modularity analyses among climate regions for woody and non-woody species. We ran the modularity comparison for woody and non-woody species across five climate regions for 1000 trait matrices. Differences in modularity between regions is significant when the 95% CI for the differences between regions pairs does not overlap with zero. Woody Non-woody Regions-pairs 2.5% 97.5% 2.5% 97.5% Arid-Cold -0.14 0.06 -0.04 0.25 Arid-Polar -0.32 0.01 -0.25 0.17 Arid-Temperate -0.07 0.11 -0.08 0.25 Arid-Tropical -0.05 0.09 -0.21 0.16 Cold-Polar -0.29 0.03 -0.33 0.03 Cold-Temperate -0.02 0.17 -0.14 0.07 Cold-Tropical -0.02 0.15 -0.3 0.02 Polar-Temperate 0.03 0.34 -0.06 0.32 Polar-Tropical 0.04 0.32 -0.2 0.22 Temperate-Tropical -0.08 0.07 -0.28 0.07 Table S5.7. Trait centrality (i.e. Degree) for woody species across five climate regions and 95% confidence interval (CI). We ran this analysis over 1000 bootstrapped trait matrices. Climate Trait 2.5% 50% 97.5% Tropical Sm 0.86 0.86 0.86 Leaf area 0.57 0.71 0.71 LLS 0.57 0.71 0.86 SLA 0.71 0.71 0.86 Leaf N 0.57 0.57 0.71 Leaf P 0.57 0.57 0.57 Plht 0.57 0.57 0.71 SSD 0.43 0.43 0.57 Temperate LLS 0.71 0.86 1.00 Leaf area 0.71 0.71 0.86 Leaf P 0.43 0.71 0.86 SLA 0.57 0.71 0.71 Sm 0.71 0.71 0.71 Leaf N 0.57 0.57 0.71 Plht 0.43 0.57 0.71 SSD 0.43 0.57 0.71 Arid LLS 0.71 0.86 0.86 Leaf area 0.71 0.71 0.86 SLA 0.57 0.71 0.71 Sm 0.57 0.71 0.71 Leaf N 0.43 0.57 0.57 Leaf P 0.43 0.57 0.71 SSD 0.43 0.57 0.71 Plht 0.43 0.43 0.57 Cold LLS 0.57 0.71 1.00 Plht 0.57 0.71 0.86 Leaf area 0.57 0.57 0.57 Leaf P 0.43 0.57 0.71 SLA 0.57 0.57 0.71 Sm 0.43 0.57 0.57 Leaf N 0.43 0.43 0.57 SSD 0.29 0.43 0.57 Polar Leaf area 0.29 0.43 0.57 Leaf N 0.29 0.43 0.43 Leaf P 0.14 0.43 0.57 LLS 0.29 0.43 0.57 SLA 0.43 0.43 0.57 Sm 0.29 0.43 0.43 Plht 0.14 0.29 0.43 SSD 0.14 0.29 0.29 Table S5.8. Trait centrality (i.e. Degree) for non-woody species across five climate regions and 95% confidence interval (CI). We ran this analysis over 1000 bootstrapped trait matrices. Climate Trait 2.5% 50% 97.5% Tropical Leaf area 0.29 0.43 0.57 Leaf N 0.43 0.43 0.71 LLS 0.14 0.43 0.57 Sm 0.29 0.43 0.71 Leaf P 0.14 0.29 0.71 Plht 0.29 0.29 0.57 SLA 0.14 0.29 0.57 SSD 0.00 0.14 0.43 Temperate Leaf N 0.57 0.71 0.86 LLS 0.57 0.71 0.86 Sm 0.57 0.71 0.86 Leaf area 0.43 0.57 0.57 Leaf P 0.43 0.57 0.86 Plht 0.43 0.57 0.71 SLA 0.43 0.43 0.43 SSD 0.14 0.29 0.43 Arid Leaf area 0.43 0.57 0.71 Leaf N 0.43 0.57 0.86 LLS 0.43 0.57 0.71 SLA 0.29 0.57 0.71 Sm 0.29 0.43 0.71 Leaf P 0.29 0.29 0.57 Plht 0.14 0.29 0.57 SSD 0.29 0.29 0.43 Cold Leaf area 0.71 0.86 0.86 Leaf N 0.71 0.71 0.86 LLS 0.57 0.71 0.86 Sm 0.57 0.71 0.71 Leaf P 0.43 0.57 0.71 SLA 0.43 0.57 0.71 Plht 0.29 0.43 0.57 SSD 0.29 0.43 0.57 Polar Leaf N 0.57 0.57 0.71 Sm 0.43 0.57 0.71 Leaf area 0.29 0.43 0.43 Leaf P 0.29 0.43 0.57 LLS 0.29 0.43 0.57 Plht 0.29 0.43 0.57 SLA 0.29 0.43 0.57 SSD 0.00 0.14 0.43 Table S5.9. Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for eight traits of woody species across five climate regions. We ran the trait centrality analyses for the trait of woody species across five climate regions across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Tropical Temperate Arid Cold Polar Trait-pairs 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.29 0.14 -0.14 0.29 -0.14 0.29 -0.14 0.14 -0.43 0.43 Leaf area-Leaf N -0.14 0.14 0 0.29 0 0.43 0 0.14 -0.29 0.43 Leaf area-LLS -0.14 0.14 -0.29 0.14 -0.29 0.14 -0.43 0 -0.29 0.29 Leaf area-Leaf P -0.14 0.14 0 0.29 0 0.29 -0.14 0.29 -0.29 0.57 Leaf area-Plht -0.14 0.29 0 0.43 0.14 0.43 -0.3 0.14 -0.14 0.43 Leaf area-SSD -0.14 0.29 0 0.43 -0.01 0.43 0 0.29 -0.14 0.57 Leaf area-Sm -0.43 -0.14 0 0.14 -0.14 0.29 -0.14 0.14 -0.29 0.43 SLA-Leaf N 0 0.43 -0.14 0.29 -0.01 0.29 0 0.29 -0.14 0.43 SLA-LLS -0.14 0.29 -0.43 0 -0.29 0 -0.43 0.14 -0.29 0.43 SLA-Leaf P 0 0.43 -0.29 0.29 -0.14 0.29 -0.16 0.43 -0.29 0.43 SLA-Plht 0 0.43 -0.14 0.29 0 0.29 -0.29 0.14 -0.14 0.57 SLA-SSD 0 0.43 0 0.29 0 0.29 0 0.43 0 0.57 SLA-Sm -0.29 0.14 -0.14 0.14 -0.14 0.14 -0.14 0.29 -0.29 0.43 Leaf N-LLS -0.29 0.14 -0.43 0 -0.43 -0.14 -0.57 0 -0.29 0.29 Leaf N-Leaf P -0.14 0.14 -0.29 0.29 -0.29 0.14 -0.29 0.14 -0.43 0.29 Leaf N-Plht -0.14 0.14 -0.14 0.29 -0.14 0.14 -0.43 0 -0.14 0.43 Leaf N-SSD 0 0.29 -0.14 0.29 -0.29 0.29 -0.14 0.14 -0.14 0.43 Leaf N-Sm -0.43 -0.14 -0.14 0.14 -0.14 0 -0.29 0.14 -0.29 0.29 LLS-Leaf P -0.14 0.29 0 0.43 0.13 0.43 0 0.57 -0.29 0.43 LLS-Plht 0.14 0.57 -0.29 0.43 -0.29 0.43 0 0.57 -0.14 0.29 LLS-SSD 0 0.43 0.14 0.57 -0.14 0.57 0.14 0.43 -0.14 0.43 LLS-Sm 0 0.3 0 0.43 -0.29 0.43 0 0.29 -0.43 0 Leaf P-Plht 0 0.29 -0.43 0.01 -0.29 0.43 -0.14 0.29 -0.14 0.14 Leaf P-SSD -0.16 0.29 -0.14 0.3 -0.14 0.43 -0.29 0.43 -0.14 0.29 Leaf P-Sm -0.29 0.14 -0.29 0.29 -0.29 0.43 -0.29 0.14 -0.29 -0.14 Plht-SSD -0.16 0.14 0 0.57 -0.14 0.43 -0.29 0.29 -0.14 0.29 Plht-Sm -0.29 0 -0.14 0.43 -0.43 0.29 -0.29 0.14 -0.43 -0.14 SSD-Sm -0.29 0.14 -0.29 0.14 -0.43 0.14 -0.29 0.14 -0.57 -0.14 Table S5.10. Multiple-comparison adjusted 95% confidence interval (CI) from the trait centrality (i.e. degree) analyses for eight traits of non-woody species across five climate regions. We ran the trait centrality analyses for the trait of non-woody species across five climate regions across 1000 trait matrices. Differences in centrality between trait pairs significant when the 95% CI for the differences between trait pairs does not overlap with zero. Tropical Temperate Arid Cold Polar Trait-pairs 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% 2.5% 97.5% Leaf area-SLA -0.29 0.57 -0.14 0.29 -0.14 0.29 0 0.29 -0.29 0.29 Leaf area-Leaf N -0.29 0.14 -0.43 0.14 -0.43 0.29 -0.14 0.29 -0.43 0 Leaf area-LLS -0.29 0.43 -0.43 0.14 -0.29 0.29 -0.29 0.43 -0.29 0.29 Leaf area-Leaf P -0.43 0.57 -0.29 0.29 -0.14 0.43 0 0.43 -0.29 0.14 Leaf area-Plht -0.29 0.43 -0.29 0.29 0 0.57 0.14 0.71 -0.29 0.29 Leaf area-SSD -0.14 0.57 0 0.57 0 0.57 0.14 0.57 -0.14 0.43 Leaf area-Sm -0.43 0.43 -0.43 0.14 -0.29 0.57 -0.14 0.43 -0.43 0.14 SLA-Leaf N -0.57 0.29 -0.57 0 -0.43 0.29 -0.43 0.14 -0.43 0.14 SLA-LLS -0.29 0.43 -0.43 0 -0.43 0.29 -0.43 0.14 -0.29 0.29 SLA-Leaf P -0.43 0.43 -0.43 0.14 -0.29 0.57 -0.29 0.29 -0.43 0.29 SLA-Plht -0.29 0.29 -0.43 0 -0.14 0.57 -0.14 0.43 -0.29 0.29 SLA-SSD -0.29 0.71 -0.14 0.57 -0.14 0.43 0 0.43 -0.14 0.57 SLA-Sm -0.43 0.29 -0.43 0 -0.29 0.43 -0.29 0.14 -0.43 0.14 Leaf N-LLS -0.14 0.43 -0.29 0.29 -0.29 0.43 -0.29 0.29 0 0.57 Leaf N-Leaf P -0.43 0.57 -0.29 0.57 -0.14 0.57 0 0.43 0 0.43 Leaf N-Plht -0.14 0.43 -0.14 0.43 -0.14 0.57 0.14 0.57 0 0.43 Leaf N-SSD -0.14 0.57 0.14 0.71 0.14 0.43 0.14 0.57 0 0.71 Leaf N-Sm -0.29 0.43 -0.29 0.29 -0.14 0.57 -0.14 0.29 -0.14 0.43 LLS-Leaf P -0.43 0.29 -0.29 0.43 -0.14 0.57 -0.14 0.29 -0.43 0.29 LLS-Plht -0.29 0.29 -0.29 0.43 -0.14 0.57 0 0.57 -0.29 0.14 LLS-SSD -0.29 0.43 0.14 0.71 -0.14 0.57 0 0.57 -0.14 0.43 LLS-Sm -0.57 0.29 -0.29 0.29 -0.29 0.57 -0.14 0.29 -0.43 0.14 Leaf P-Plht -0.29 0.43 -0.29 0.29 -0.29 0.43 -0.14 0.43 -0.29 0.43 Leaf P-SSD -0.29 0.57 -0.14 0.86 -0.14 0.43 -0.14 0.29 -0.14 0.43 Leaf P-Sm -0.43 0.43 -0.43 0.14 -0.43 0.43 -0.29 0.14 -0.43 0.43 Plht-SSD -0.14 0.57 0 0.57 -0.29 0.29 -0.29 0.29 -0.14 0.57 Plht-Sm -0.43 0.29 -0.43 0.29 -0.43 0.29 -0.43 0 -0.43 0.14 SSD-Sm -0.57 0.14 -0.86 -0.14 -0.43 0.29 -0.43 0 -0.57 0