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1.  Is leaf dry matter content a better predictor of soil fertility than specific leaf area? 
Annals of Botany  2011;108(7):1337-1345.
Background and Aims
Specific leaf area (SLA), a key element of the ‘worldwide leaf economics spectrum’, is the preferred ‘soft’ plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC?
SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described.
Key Results
Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility.
Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended.
PMCID: PMC3197453  PMID: 21948627
Ellenberg numbers; functional traits; leaf density; leaf nitrogen; leaf size; leaf thickness; relative growth rate (RGR); shade tolerance; variation in trait expression
2.  A Modern Tool for Classical Plant Growth Analysis 
Annals of Botany  2002;90(4):485-488.
We present an all‐inclusive software tool for dealing with the essential core of mathematical and statistical calculations in plant growth analysis. The tool calculates up to six of the most fundamental growth parameters according to a purely ‘classical’ approach across one harvest‐interval. All of the estimates carry standard errors and 95 % confidence limits. The tool is written in Microsoft® Excel 2000 and is available free of charge for use in teaching and research from article supplementary data (
PMCID: PMC4240380  PMID: 12324272
Relative growth rate; unit leaf rate; net assimilation rate; specific leaf area; leaf weight fraction; leaf area ratio; allometry
3.  TRY – a global database of plant traits 
Kattge, J | Díaz, S | Lavorel, S | Prentice, I C | Leadley, P | Bönisch, G | Garnier, E | Westoby, M | Reich, P B | Wright, I J | Cornelissen, J H C | Violle, C | Harrison, S P | Van Bodegom, P M | Reichstein, M | Enquist, B J | Soudzilovskaia, N A | Ackerly, D D | Anand, M | Atkin, O | Bahn, M | Baker, T R | Baldocchi, D | Bekker, R | Blanco, C C | Blonder, B | Bond, W J | Bradstock, R | Bunker, D E | Casanoves, F | Cavender-Bares, J | Chambers, J Q | Chapin, F S | Chave, J | Coomes, D | Cornwell, W K | Craine, J M | Dobrin, B H | Duarte, L | Durka, W | Elser, J | Esser, G | Estiarte, M | Fagan, W F | Fang, J | Fernández-Méndez, F | Fidelis, A | Finegan, B | Flores, O | Ford, H | Frank, D | Freschet, G T | Fyllas, N M | Gallagher, R V | Green, W A | Gutierrez, A G | Hickler, T | Higgins, S I | Hodgson, J G | Jalili, A | Jansen, S | Joly, C A | Kerkhoff, A J | Kirkup, D | Kitajima, K | Kleyer, M | Klotz, S | Knops, J M H | Kramer, K | Kühn, I | Kurokawa, H | Laughlin, D | Lee, T D | Leishman, M | Lens, F | Lenz, T | Lewis, S L | Lloyd, J | Llusià, J | Louault, F | Ma, S | Mahecha, M D | Manning, P | Massad, T | Medlyn, B E | Messier, J | Moles, A T | Müller, S C | Nadrowski, K | Naeem, S | Niinemets, Ü | Nöllert, S | Nüske, A | Ogaya, R | Oleksyn, J | Onipchenko, V G | Onoda, Y | Ordoñez, J | Overbeck, G | Ozinga, W A | Patiño, S | Paula, S | Pausas, J G | Peñuelas, J | Phillips, O L | Pillar, V | Poorter, H | Poorter, L | Poschlod, P | Prinzing, A | Proulx, R | Rammig, A | Reinsch, S | Reu, B | Sack, L | Salgado-Negret, B | Sardans, J | Shiodera, S | Shipley, B | Siefert, A | Sosinski, E | Soussana, J-F | Swaine, E | Swenson, N | Thompson, K | Thornton, P | Waldram, M | Weiher, E | White, M | White, S | Wright, S J | Yguel, B | Zaehle, S | Zanne, A E | Wirth, C
Global Change Biology  2011;17(9):2905-2935.
Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.
PMCID: PMC3627314
comparative ecology; database; environmental gradient; functional diversity; global analysis; global change; interspecific variation; intraspecific variation; plant attribute; plant functional type; plant trait; vegetation model

Results 1-3 (3)