Smart, S., Glanville, H.C., Blanes, M.D.C., Mercado, L.M., Emmett, B.A., Jones, D.L., Cosby, B.J., Marrs, R.H., Butler, A., Marshall, A.R., Reinsch, S., Herrero-Jauregui, C. and Hodgson, J.G.
||We report a novel test of the effect of two of the most widely used Leaf Economics Spectrum traits (Specific Leaf Area and Leaf Dry Matter Content) on finely resolved measurements of above-ground Net Primary Production (aNPP). We show that Leaf Dry Matter Content is the superior predictor of aNPP along a representative gradient of temperate ecosystems. Our results also suggest that for estimation of aNPP, subordinate species may be ignored, and are consistent with the hypothesis that the traits of those species contributing the most biomass are sufficient to predict this ecosystem function. Our results also indicate that inclusion of measured in situ trait values for the dominants significantly improves estimation of aNPP. Introducing intra-specific trait variation by including the effect of replicated trait values from published databases did not improve estimation of aNPP. However, we suggest this is a worthwhile methodological step because it allows intra-specific trait variation, a fraction of which may be ecologically meaningful, to improve the fit between aNPP and abundance-weighted mean trait value.
Given that leaf traits are time-consuming to measure but are needed for large numbers of taxa worldwide we provide evidence that for prediction of aNPP the burden of data collection can be reduced significantly. This is because LDMC is easier to measure than SLA and because little information is lost by focussing on ecosystem dominants only. Both results offer the prospect of greater scientific understanding for less cost.