Tall shrub biomass estimates

<p>Applications that scale-up from the individual to the plot-level and beyond require methods that reduce propagated error. Here we present a field protocol that minimizes individual shrub uncertainty as measured by the range in 95%PI, while increasing the precision and the accuracy of plot-level biomass estimates. In particular and by example, we show a substantial increase in precision over sample plot estimates using single-component allometry.</p> <p>Given diameter D, single-component allometric equations describe woody biomass M as a power function M = aD^p, with p &gt;1; uncertainty also scales as a power function of D. We present a field method that increases accuracy and precision of plot-level biomass estimates over single-component models. The method treats shrubs with two-component allometry: terminal aerial tips and stem internodes, each modeled as log-log linear regressions with lognormally distributed prediction intervals. The following field-sampling algorithm reduces uncertainty in estimated biomass of large (DRC &gt;D_max) shrubs, where diameter D_max offers the greatest acceptable uncertainty for M(D) = aD^p. Step-1: Identify root collar. Step-2: Record diameter D&lt;sub&gt;1&lt;/sub&gt; there. Step-3: If D₁≤ D_max, stop; aerial tips with D≤ D_max have acceptably low uncertainty. If D₁&gt; D_max, identify stem internode above D₁ as a conic frustrum. Record its length L and end diameters D₁&gt; D_max and D₂ (where D₂ is measured just below the upper node swelling). Step-4: return to Step-2 for stems above the node, treating each stem diameter as D_1.The individual shrub biomass estimate is the sum of biomass estimates for frustra and aerial tips with associated uncertainties. The uncertainty in each sample-plot is calculated using Monte Carlo sampling of internodes and tips from lognormal distributions with parameters estimated from log-log allometry. For individual shrubs uncertainty was halved by two-component method and accuracy increased. At the plot level, we found that among 1,430 individual Salix and Alnus shrubs (2.5 ≤DRC ≤30.4 cm) measured in 17 plots (169m²), we found that the uncertainty in total sample-plot biomass estimation using the two-component method was 40% less than the single-component method; this difference depends on shrub count with DRC &gt;D_max. Reducing field-sample prediction error increases precision in multi-stage modeling because additional measures efficiently improve plot-level biomass precision, reducing uncertainty for shrub biomass estimates.</p>