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Abstract
The leaf economics spectrum (LES) describes strong relationships between multiple functional leaf traits that determine resource fluxes in vascular plants. Five models have been proposed to explain these patterns: two based on patterns of structural allocation, two on venation networks and one on resource allocation to cell walls and cell contents. Here we test these models using data for leaf and whole-plant functional traits. We use structural equation modelling applied to multiple ecotypes, recombinant inbred lines, near isogenic lines and vascular patterning mutants of Arabidopsis thaliana that express LES trait variation. We show that a wide variation in multiple functional traits recapitulates the LES at the whole-plant scale. The Wright et al. (2004) model and the Blonder et al. (2013) venation network model cannot be rejected by data, while two simple models and the Shipley et al. (2006) allocation model are rejected. Venation networks remain a key hypothesis for the origin of the LES, but simpler explanations also cannot be ruled out.
Highlights
The leaf economics spectrum (LES) describes correlations between multiple leaf traits, including carbon assimilation rate (Am), leaf lifespan (LL), leaf mass-per-area (LMA) and nitrogen content (Nm) (Wright et al 2004)
Consistent with a previous demonstration (Vasseur et al 2012), LES correlations between Am, LL, LMA and Nm were found across genotypes (Fig. 2)
All pairwise correlations between these genotype-mean values were significant, with signs the same as those described by the interspecific LES (Wright et al 2004)
Summary
The leaf economics spectrum (LES) describes correlations between multiple leaf traits, including carbon assimilation rate (Am), leaf lifespan (LL), leaf mass-per-area (LMA) and nitrogen content (Nm) (Wright et al 2004). These patterns are found globally and across all vascular plant taxa (Reich et al 1997; Wright et al 2005). The trait correlations that define the LES describes a gradient of leaf and plant strategies between ‘live fast, die young’ and ‘live slow, die old’ (Wright et al 2004; Reich 2014).
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