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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)

Publication Series: Scientific Journal (JRNL)

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

Publication Notes:

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.



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.


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