Skip to main content
Dryad

Tall shrub biomass estimates

Data files

Nov 30, 2021 version files 52.82 KB

Abstract

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.

Given diameter D, single-component allometric equations describe woody biomass M as a power function M = aDp, with p >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 >Dmax) shrubs, where diameter Dmax offers the greatest acceptable uncertainty for M(D) = aDp. Step-1: Identify root collar. Step-2: Record diameter D<sub>1</sub> there. Step-3: If D1≤ Dmax, stop; aerial tips with D≤ Dmax have acceptably low uncertainty. If D1> Dmax, identify stem internode above D1 as a conic frustrum. Record its length L and end diameters D1> Dmax 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 D1. 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 >Dmax. 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.