Dataset: Equations for predicting leaf area and leaf mass for woody plant species in the SERC chronosequence
<p dir="ltr">This project sought coefficients of allometric equations for estimating leaf area and leaf mass for major woody plant species in the vicinity of the Smithsonian Environmental Research Center (SERC; 38.9 N, 76.6 W). Unlike the commonly used destructive analysis, this approach inferred the coefficients from field data on stem and leaf litter production. The derived relations are independent of interannual variation and applicable to stands of many ages. They may be used to estimate stand Leaf Area Index (LAI).</p><p dir="ltr">The method was to find coefficients which, when applied to all living stems in a plot, produced an estimate of leaf area (or mass) equal to the leaf area (or mass) measured in the plot. There were two sorts of equations. For the typical power law (leaf area or mass = a ∙ DBH<sup>b</sup>) we used non-linear regression. For the linear relation between leaf area and stand basal area (the ‘Support Ratio’) we used ordinary least squares. Equations were validated by applying the predictive equations produced from a training dataset (80% of the full set) to a testing dataset (the remaining 20%).</p><p dir="ltr">The sources of these cases were field data of leaf area, leaf mass, and stems for many sites of the same forest type (‘tulip poplar”) where both litterfall and stems were measured in the same year and plot. The categories of species or species groups were the same in both the stems and litter datasets.</p><p dir="ltr">We successfully fitted the power and Support Ratio relations, yielding equations for both leaf area and leaf mass for 21 species groups and three habits (trees, shrubs and vines).</p>
