Tall shrub biomass estimates

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.