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

Joseph Mascaro; undefined undefined; Flint F. Hughes; Amanda L. Uowolo; Stefan A. Schnitzer

doi:10.1111/j.1744-7429.2011.00798.x

Treesearch

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

Formally Refereed

Authors:

Joseph Mascaro, undefined undefined, Flint F. Hughes, Amanda L. Uowolo, Stefan A. Schnitzer

Year:

2011

Type:

Scientific Journal

Station:

Pacific Southwest Research Station

DOI:

https://doi.org/10.1111/j.1744-7429.2011.00798.x

Source:

Biotropica (online)

Download (PDF 170 KB)

Download (PDF 170 KB): https://research.fs.usda.gov/download/treesearch/38956.pdf

Abstract

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

Citation

Mascaro, Joseph; Litton, Creighton M.; Hughes, R. Flint; Uowolo, Amanda; Schnitzer, Stefan A. 2011. Minimizing bias in biomass allometry: Model selection and log transformation of data. Biotropica.