Pacific Southwest

Title: Minimizing bias in biomass allometry: Model selection and log transformation of data

Author: Mascaro, Joseph; undefined, undefined; Hughes, Flint; Uowolo, Amanda; Schnitzer, Stefan A.

Date: 2011

Source: Biotropica (online)

Description: Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the raditional approach of log-transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models. Here, we show that such models may bias stand-level biomass estimates by up to 100 percent in young forests, and we present an alternative nonlinear fitting approach that conforms with allometric theory.

Keywords: allometry; Hawai‘i; heteroscedasticity; linear regression; nonlinear regression analysis; Psidium cattleianum

View and Print this Publication (165.56 KB)

Publication Notes: 

• We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
• This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

 Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

Citation

Mascaro, Joseph; undefined, undefined; Hughes, Flint; Uowolo, Amanda; Schnitzer, Stefan A.  2011.  Minimizing bias in biomass allometry: Model selection and log transformation of data.   Biotropica (online).