Leaf Epicuticular Waxes and the Functional Ecology of Prairie Grasses
Land plants have a waxy layer covering their leaf cuticle that minimizes water loss and contributes to many other functions, such as pathogen defense and energy balance. These epicuticular waxes are made up of various hydrocarbons ranging from alkanes, alkanoic acids, and alcohols to terpenoids. This wax layer is critically important for all land plants, but wax composition and function have been well-studied for relatively few plant taxa and ecosystems. For example, the variable investment in leaf waxes has not been integrated into broader theory regarding axes of leaf economics variation or specific functional traits related to plant strategies. We hypothesized that total leaf wax allocation per unit leaf area might correlate with overall leaf investment (LMA; Leaf Mass per Area). Alternatively or in addition to this, leaf wax might be more associated with LDMC (Leaf Dry Matter Content), an important functional trait associated often with tolerance of drought and other stresses. Here, we conducted a phylogenetically controlled study of leaf wax content and functional traits of thirty-three tallgrass species at Konza Prairie Biological Station, a remnant prairie located near Manhattan, Kansas, USA. Epicuticular waxes were quantified gravimetrically following extraction with an organic solvent (9:1 hexane:ethyl acetate) and other traits were measured using standard methods. The results indicate that important traits like LMA and leaf thickness (LT; p > 0.05), do not relate to wax amounts across these grasses. Instead, there was a strong correlation between the amount of waxes of a leaf and LDMC (p = 0.008, R2 = 0.22). Thus, the relationship with LDMC supports the idea that drought-tolerant species allocate more resources to waxes. We suggest that further study of total wax content as a functional trait, across landscapes (e.g., with imaging spectroscopy) and across the plant evolutionary tree, could provide insight as to how and why plant traits differ across environmental conditions and could prove useful for predicting ecosystem responses in the face of global change.