Plant Functional Diversity and Traits

Biodiversity has two main components at the species level, the richness and the composition. As such, it is generally agreed that human impacts on the environment from local to global scales cause not only a general decline in diversity, but also predictable functional shifts, as sets of species with particular traits are replaced by other sets with different traits (Loreau and al. 2001). Recent studies that were aimed at quantifying leaf economics across the plant species of the World lead to the formation of a global plant trait network.. They have revealed a universal spectrum of correlated leaf traits that affect global patterns of nutrient cycling and primary productivity and can thus be used to calibrate vegetation-climate models. The spectrum runs from a quick return to a slow return of nutrients and dry mass on leaves. The correlation patterns are seen in species from the arctic to the tropics, and they are largely independent of growth form or phylogeny. This generality suggests that unidentified fundamental constrains control the return of photosynthates on investments of nutrients and dry mass on leaves (Wright and al. 2004; Shipley and al. 2006a).

On the basis of their traits, plants can be grouped into functional types as non-phylogenetic groups of species that show close similarities in their responses to environmental and biotic constraints. They are considered as reflecting adaptations to variations in the physical environment, and trade-offs (ecophysiological and/or evolutionary) among different functions within a plant (Diaz and Cabido 1997; Duckworth and al. 2000; Lavorel and al. 2007). These can aid in our understanding of the ecological processes, such as the assembly and stability of communities, and the succession, and facilitate the detection and prediction of responses to environmental change that occur on a wide range of scales. The members of a plant functional type share similar morphological, physiological and/or life-

history traits, while the differences between members within one functional type are smaller than those between functional types (Duckworth and al. 2000). Measuring functional diversity is therefore about measuring the diversity of functional traits, where these functional traits are the components of an organism phenotype that influence the landscape of an ecosystem (Figure 2) (Diaz and Cabido 1997; Diaz and al. 1998; Petchey and Gaston 2006). This approach of grouping species according to their function within an ecosystem has been used both to investigate fundamental ecological issues for the prediction of the functional consequences of biotic change caused by human or other types of disturbance (Walker and al. 1999, Diaz and Cabido 1997; Diaz and al. 1998; Regvar and al. 2006) and to investigate remediation (van der Putten and al. 2000).

Specific knowledge about how particular organisms interact with their environment, with each other, and how traits vary over environmental gradients, are essential to determine specific traits to use. This selection, however, also depends on the importance of a particular trait in the function of interest, thus giving it an informative value within the hypotheses that need to be tested. The critical points in the development of predictive measures of functional diversity are: (i) the choice of functional traits with which organisms are distinguished; (ii) how the diversity of that trait information is summarized to provide a measure of functional diversity; and (iii) the validation of these measures of functional diversity through quantitative analyses and experimental tests. One of the ways for the selection of traits is to select those that maximize the explanatory power of the functional diversity (Petchey and Gaston 2006).

In a synthesis of empirical and theoretical studies, it was therefore proposed that at least four axes of plant specialization should be considered in studies of functional trait diversity (Figure 2). These four axes are: (i) the specific leaf area (SLA = the ratio between leaf area and leaf biomass), as the leaf life-span trade-off is associated with the turnover time of plant parts; (ii) the trade-off between fecundity and seed mass, which addresses the opportunities for plant establishment and success in the face of hazards (seed mass and fecundity are negatively correlated, even after correcting for plant size); (iii) the potential plant height, that carries several trade-offs and adaptive elements, but that captures multiple constraints, such as the density and height of the shade-producing competitors, the water economy, and the response to disturbance; and (iv) the coupled variation between twig size and leaf size, which determines the texture of the canopy (Westoby 1998; Westoby and al. 2002; Lavorel and al. 2007). Recent studies that have focused on the functions of root traits have indicated how the trade-offs among them can be used as proxies for below-ground function, and how these trade-offs relate to above-ground traits. In general, low specific root length (SRL = the ratio between root length and root biomass) is associated with thick, dense roots with low nitrogen and high lignin concentrations (Lavorel and al. 2007; Biondini 2007) and should be therefore considered in functional classifications (Figure 2). In addition, the incorporation of mycorrhizal associations into current functional-type classifications (Figure 2) is a valuable tool for the assessment of plant-mediated control of carbon and nutrient cycling. Indeed, a study of the existence of possible links between plant carbon cycling traits among 83 British plant species of known mycorrhizal type revealed that plant species forming ericoid mycorrhiza have consistently low inherent relative growth rates, low foliar N and P, and poor litter decomposability. In contrast, plant species forming ectomycorrhiza had intermediate relative growth rates, higher foliar N and P, and intermediate to poor litter decomposability, while those forming arbuscular mycorrhiza have comparatively high relative growth rates, high foliar N and P, and rapid litter decomposition (Cornelissen and al. 2001).

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