Minimizing Bias in Biomass Allometry: Model Selection and Log‐Transformation of Data

Authors

Joseph Mascaro, University of Wisconsin - MilwaukeeFollow
Creighton M. Litton, University of Hawaii, Manoa
R. Flint Hughes, Institute for Pacific Islands ForestryFollow
Amanda Uowolo, Institute of Pacific Islands Forestry
Stefan A. Schnitzer, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

11-2011

Publisher

Association for Tropical Biology and Conservation

Source Publication

Biotropica

Source ISSN

0006-3606

Abstract

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the traditional 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.

Comments

Accepted version. Biotropica, Vol. 43, No. 6 (November 2011) : 649-653. DOI. © 2011 The Association for Tropical Biology and Conservation. Used with permission.

Stefan A. Schnitzer was affiliated with Smithsonian Tropical Research Institute and University of Wisconsin, Milwaukee at the time of publication.

Recommended Citation

Mascaro, Joseph; Litton, Creighton M.; Hughes, R. Flint; Uowolo, Amanda; and Schnitzer, Stefan A., "Minimizing Bias in Biomass Allometry: Model Selection and Log‐Transformation of Data" (2011). Biological Sciences Faculty Research and Publications. 746.
https://epublications.marquette.edu/bio_fac/746